Power-Efficient Provisioning for Online Virtual Network ... - IEEE Xplore

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Power-Efficient Provisioning for Online Virtual Network Requests in Cloud-Based Data Centers Gang Sun, Vishal Anand, Dan Liao, Chuan Lu, Xiaoning Zhang, and Ning-Hai Bao

Abstract—A cloud computing paradigm enables users to access services, applications, and infrastructure resources by using thin clients anywhere and at any time. In this paradigm, multiple users can share cloud infrastructure resources. The application or service requests from a user can be abstracted as a virtual network (VN) request and can be submitted to the cloud-based data centers. How to map a VN onto the cloud infrastructure network is a challenging issue in cloud resource provisioning. Thus, efficient mapping techniques that intelligently use the resources of cloud infrastructure are important and necessary. Current research on VN mapping and design focuses on resource-efficient VN mapping or cost-efficient VN mapping. However, there is another important issue in cloud-based data centers that we must pay attention to, i.e., the amount of power or energy that is consumed by a data center. The power consumption in data centers can be a significant percentage of the total power consumption, and it not only leads to a higher data center operating cost but also contributes to carbon emissions and the greenhouse effect. In this paper, we propose a power-efficient resource provisioning technique in cloud-based

Manuscript received January 19, 2013; revised September 19, 2013; accepted October 30, 2013. Date of publication November 25, 2013; date of current version May 22, 2015. This work was supported in part by the National Grand Fundamental Research Program of China (973 Program) under Grant 2013CB329103; by the Natural Science Foundation of China under Grant 61271171, Grant 61001084, and Grant 61201129; by the Sichuan Youth Science and Technology Fund under Grant 2012JQ0020; by the Program for New Century Excellent Talents (NCET) in University under Grant NCET-110058; by the Fundamental Research Funds for the Central Universities under Grant ZYGX2010J002, Grant ZYGX2012J004, and Grant ZYGX2010J009; by the Guangdong Science and Technology Project under Grant 2012B090500003, Grant 2012B091000163, and Grant 2012556031; and by the Chongqing Municipal Education Commission through their Science and Technology Research Projects under Grant KJ120523. The work of V. Anand was supported in part by the Provost Fellowship and in part by the Scholarly Incentive Grant of The College at Brockport, State University of New York. G. Sun and D. Liao are with the Key Laboratory of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu 611731, China, and also with the Institute of Electronic and Information Engineering in Dongguan, University of Electronic Science and Technology of China, Dongguan 523000, China. V. Anand is with the Department of Computer Science, School of Science and Mathematics, The College at Brockport, State University of New York, Brockport, NY 14420 USA. C. Lu is with the Institute of Electronic and Information Engineering in Dongguan, University of Electronic Science and Technology of China, Dongguan 523000, China. X. Zhang is with the Key Laboratory of Optical Fiber Sensing and Communications (Ministry of Education), School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. N.-H. Bao is with the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Digital Object Identifier 10.1109/JSYST.2013.2289584

data centers while complying with service level agreements. We first model a power-efficient VN provisioning problem as a mathematical optimization problem, with the objective of minimizing the power consumption by employing mixed-integer programming. We then propose a heuristic algorithm to efficiently solve this model since this optimization problem is NP-hard. We validate and evaluate our framework and algorithm by conducting extensive simulations on different cloud infrastructure networks under various scenarios. The simulation results show that our approach performs well. Index Terms—Cloud computing, data centers, embedding, power efficiency, provisioning, virtual network (VN) requests.

I. I NTRODUCTION

C

LOUD computing is a new paradigm that enables transparent resource sharing over multiple state-of-the-art data centers for the on-demand provisioning of various application requests based on the “pay-as-you-go” model. In cloud computing, Infrastructure as a Service (IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS) are the three main categories of cloud computing services. Commercial cloud computing providers, such as Amazon [1], Microsoft [2], Google [3], and Yahoo [4], deliver cloud computing service to customers all over the world. Virtualization or network virtualization particularly is a key enabler for cloud computing. The application requests of users can be instantiated as virtual machines (VMs), which allow the isolation of applications from the underlying hardware and other VMs, and the customization of the platform to suit the needs of the end-user, and can be hosted on hundreds of thousands of interconnected servers in multiple data centers. Most research on cloud computing focuses on resource efficiency and virtual network (VN) embedding without considering power consumption issues. However, the amount of energy that is consumed by an average data center is equivalent to that of 25 000 households [5]. According to Amazon’s estimation of its data centers, expenditures on the cost and operation of the servers measured up to 53% of the total budget, whereas the expenditures on energy consumption account for 42% of the total [6]. Moreover, a higher power consumption leads to some other critical problems, such as reducing the lifetime of devices, wasting energy, and emitting CO2 , resulting in global warming. Thus, it is necessary to pay attention to power-efficient provisioning in cloud computing while complying with service level agreements (SLAs). In cloud computing, multiple geographically separated servers or server clusters that are interconnected by a physical network constitute the cloud infrastructure (data centers). A

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wavelength-division multiplexing (WDM) network works as the best choice for the physical network because of its advantages of high speed, transparent transmission, and abundant bandwidth resources [7]. Cloud computing enables the distributed usage of resources and services to achieve resource usage efficiencies while providing a variety and high quality of services/applications. By using the underlying network, one can utilize the resources at different locations to achieve overall efficiency and to also increase fault tolerance by increasing redundancy and avoiding single-point points of failures. The power that is consumed by information technology (IT) equipment of data centers mainly consists of two parts, i.e., communication power consumption (the power that is consumed by networks) and processing power consumption (the power that is consumed by servers). The total power that is consumed by data centers in the U.S. was up to 61.4 billion kW per year, as of 2006. Even worse, the power consumption of data centers has significantly increased since 2006 [8]. Other power consumption sources in data centers come from the cooling and power distribution systems, which are beyond the scope of this paper and is hence not considered here. Due to the popularity of cloud computing, an increasing number of applications, such as web service, large-scale simulation, high-performance computing, and virtual laboratories have been deployed in private or public clouds [9]. However, these large-scale deployments have led to high power consumption by these data centers. Thus, power-efficient cloud-based data centers are necessary for both the lower power consumption and the sustainable environment. Green cloud computing aims at the efficient utilization of cloud infrastructure and at lowering power consumption [10], which is indispensable for paving the way for economical, environment-friendly, and development-sustainable cloud computing. To promote the development of green cloud computing, the physical resources of cloud infrastructure must be managed in a power-efficient manner. The customer’s service or application request that is submitted to a cloud-based data center can be abstracted as a VN request. In the cloud computing paradigm, multiple VN requests may be mapped or embedded onto the same cloud infrastructure and may share the underlying physical resources. In this paper, we study the power-efficient VN provisioning problem, and we propose a power-efficient provisioning scheme for VN requests that enables the intelligent use of the resources of the cloud infrastructure and that results in reducing the total power consumption of data centers. The issue of provisioning VN requests that is researched in this paper can be formulated as a 2-D bin-packing problem, which states that the VN nodes (application or task requests) of all VN requests must be instantiated on certain physical servers and that the VN edges need to be packed into certain physical links (i.e., the links onto which the VN edges are mapped), such that all of the resource requests are satisfied with minimum power consumption. In our research, we consolidate the VN nodes into fewer servers and turn off some unnecessary servers, and by using traffic grooming techniques, we groom the WDM network traffic into fewer wavelengths and shut down some

IEEE SYSTEMS JOURNAL, VOL. 9, NO. 2, JUNE 2015

unnecessary wavelengths on the fiber links to reduce power consumption. In this paper, we study intelligent and power-efficient resource management in cloud-based data centers, and we propose an efficient scheme and algorithm for VN provisioning to reduce power consumption while complying with SLAs. To the best of our knowledge, this is the first paper that develops a framework and efficient algorithms for power-aware1 mapping of VN requests that minimizes total power consumption. We validate and evaluate our approach by conducting extensive simulations on realistic networks. Simulation results show that our approach can significantly reduce the total power consumption. The rest of this paper is organized as follows. In Section II, we discuss the related works. In Section III, we give the detailed description of the problem of power-efficient VN provisioning. In Section IV, we give the optimization model of the problem by employing mixed-integer programming (MIP). Section V proposes a heuristic algorithm to efficiently solve our model. The simulation results for evaluating the performance of our algorithm are given in Section VI. Finally, in Section VII, we conclude this paper. II. R ELATED W ORKS A. VN Embedding Since cloud computing is becoming more and more popular, there has been a significant amount of research on network virtualization and techniques for VN embedding [11]–[20]. These works aim at addressing the issue of resource-efficient embedding of VN requests onto physical resources. Most of the recent studies model the efficient VN embedding problem as a mathematical optimization problem, with the objective of minimizing the resource cost while implementing the VN embedding. Mosharaf et al. [11] have studied the online VN embedding problem and proposed two algorithms, i.e., the D-ViNE and the R-ViNE, which have coordination between the VN node mapping and VN edge mapping processes, to increase the acceptance ratio and the revenue while decreasing the resource cost of VN embedding. Survivable VN provisioning is a challenging issue in VN embedding research. To provide resilient services for a VN request, survivable VN embedding approaches have been designed in [12]–[14] against the physical failure of cloud infrastructure. The VN embedding strategies with topology-aware node ranking have been proposed in [15] and [16], where the topology-aware node resource ranking are computed based on Markov random walks before mapping the VN components to improve the long-term average revenue and the VN acceptance ratio. For some special purposes, it is possible that VNs need to be provisioned across multiple domains owned by different infrastructure providers (InPs). A policy-based framework, i.e., the PolyViNE, for solving the interdomain VN embedding problem has been proposed in [17], in which the end-to-end VN embedding is implemented in 1 We interchangeably use the terms power-efficient and power-aware throughout this paper.

SUN et al.: PROVISIONING FOR VN REQUESTS IN CLOUD-BASED DATA CENTERS

a decentralized manner. In practice, the users’ demands and the corresponding VN requests often change dynamically. In our previous work [18], we have studied the issue of how to optimally reconfigure and embed an existing VN while this VN dynamically changes. Zhang et al. [19] have proposed the MIP models for VN embedding, with the objective of increasing the acceptance ratio and the revenue of InPs; they have also devised a unified enhanced particle-swarm-optimization-based VN embedding algorithm to solve these models. Different from most of the existing VN embedding algorithms that employ simple cost functions, including load balancing and maximizing revenue, Kim and Lee [20] have proposed a more realistic function, i.e., the exponential cost function, to present the embedding cost, in which diverse aspects of the VN embedding cost are considered; they have also designed a suitable greedy embedding algorithm for this cost function. All of these aforementioned studies on VN request embedding and VN designing can be classified as resource-efficient VN embedding. None of them consider power consumption while implementing VN embedding. In this paper, we address the issue of power-efficient provisioning for VN requests, without violating the negotiated SLAs. B. Power-Efficient Resource Provisioning Due to the exponentially growing demand of information systems, power consumption has been becoming an important issue and topic, and it has attracted more attention from researchers. Zafer and Modiano [21] proposed a rate-control-based calculus approach for minimizing the power consumption of data transmission while satisfying the QoS requirements. Their work adopted a novel cumulative curve methodology-based formulation to obtain the optimal policy with minimum power consumption for data transmission. They have also developed an online transmission policy based on that optimal offline policy. The dynamic provisioning strategies for dedicated pathprotection demands in a power-efficient WDM network have been proposed in [22] and [23]. For power saving, all the unused resources that are allocated to the protection path can be switched into sleep mode (inactive) while the network is normal and can be activated by failure. A powerefficient scheme for connection requests in WDM networks is devised in [24], in which the significant reduction in the total power consumption without a noticeable loss in the acceptance ratio can be achieved by introducing an intelligent load control policy and auxiliary graph. Cavdar et al. [25] designed a power-efficient survivable WDM network, and they considered efficient resource consumption through sharing the backup resources. Yetginer and Rouskas [26] studied the power consumption of a WDM network from another perspective, i.e., they modeled the total power consumption in terms of the power that is consumed by all of the lightpaths. The proposed model is formulated by using integer linear programming (ILP), in which the benefits of traffic grooming in saving the power consumption had been explored. Power-efficient networking is essential for communication networks. Shen and Tucker [27] had studied the power-saving problems of integer

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programming over WDM networks, and they developed a mixed ILP optimization model to reduce power consumption and proposed a lightpath bypass-based heuristic strategy to solve that model. The study of power efficient provisioning in data centers had been conducted in [28], in which the proposed scheduling approach, i.e., the data center energy-efficient network-aware scheduling (DENS), can be employed to balance the power consumption and the performance of individual job and traffic demands, and to optimize the tradeoff between them to reduce power consumption and to avoid hotspots. Beloglazov et al. [29] have developed the framework and principles for powerefficient cloud computing. Based on the framework, they proposed power-efficient heuristics for allocating data center resources to application requests while ensuring the QoS requirements. Le et al. [30] studied the relationship between the load placement and the temperatures of a data center, and they proposed dynamic load distribution policies that consider the power consumption and the transient cooling effects to save the operation cost of a cloud service provider. An online adaptive reconfiguration approach for resizing the VMs in large-scale data centers had been proposed in [31], which can be used to accurately predict the future application workloads by using Brown’s quadratic exponential smoothing. Then, the optimal reconfiguration policy can be found by adopting a genetic algorithm based on such accurate prediction, for the powerefficient purpose. Rodero et al. [32] presented a power-aware online provisioning approach for high-performance computing application requests in cloud-based data centers, which fulfills the power consumption saving purpose through a workloadaware just-right provisioning scheme and through power-off subsystems that were unnecessary for hosting VMs. Goiri et al. [33] proposed a policy for dynamic job scheduling for the purpose of power-aware resource management in data centers, which tries to consolidate VMs onto as fewer servers as possible while satisfying the resource requirements of job requests. This allows powering off the idle servers to reduce the power consumption. An approach to dynamically consolidate VMs for the power consumption saving purpose based on adaptive utilization thresholds had been proposed in [34], in which the SLAs were also guaranteed. Botero et al. [35] studied the problem of energy-aware VN embedding and proposed an MIP model for this problem to achieve energy-efficient VN embedding. However, the objective of the MIP model that is proposed in [35] is to minimize the inactive substrate links and nodes that are activated after the mapping of one VN request is performed, e.g., they only considered the basic power (the workload-independent power) consumption of substrate nodes and links. This is a simple version of research for the poweraware (energy-aware) VN mapping problem. In this paper, we will address the issue of how to optimally implement power-efficient provisioning for a VN request in cloud-based data centers, in which the communication power consumption (workload-independent power and workloaddependent power) and the processing power consumption (workload-independent power and workload-dependent power) are jointly optimized. To the best of our knowledge, this is the first paper that addresses this issue.

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Fig. 1. Examples of the cloud infrastructure and the VN request. (a) Cloud infrastructure. (b) VN request.

Fig. 2.

Examples of VN embedding.

In this section, we present the detailed description of the problem that is researched in this paper, including the models of cloud infrastructure and VN request, and the power-efficient provisioning for a VN request.

node. For each virtual edge e, e ∈ E, we use b(e) to represent the bandwidth request. Fig. 1(b) presents an example the VN request, where the numbers in rectangles next to the virtual nodes represent the amount of server resources requested by the virtual nodes, and the numbers next to the virtual edges represent the bandwidth requirement of the virtual edges.

A. Cloud Infrastructure

C. VN Provisioning

A typical cloud infrastructure consists of servers or server clusters of cloud-based data centers spread across multiple geographical physical locations that are interconnected by a mesh WDM network. Similar to our previous works [12], [36] [37], we model the cloud infrastructure as a weighted graph GS = (N, L, AN , AL ), where N and L denote the set of sites and fiber links, respectively. Each site n, n ∈ N is composed of a server (a server cluster)2 and a WDM node3 that provide server resources (such as a CPU, memory, and storage resources) and communication resources (such as wavelength and switches). An example of cloud infrastructure is shown in Fig. 1(a). AN and AL denote the attributes of sites and fiber links, respectively. The typical attributes of a site includes server resource capacity and communication resource capacity. The typical attribute of a fiber link refers to the wavelength capacity.

To respond to the VN request, the cloud infrastructure must be able to employ a suitable VN provisioning scheme, i.e., embedding the VN onto the cloud infrastructure and allocating physical resources, i.e., server and bandwidth resources, to the VN nodes and edges such that the VN resource requirements are satisfied. The VN provisioning process includes two key parts: the VN node assignment and the VN edge assignment. In the VN node assignment, the VN nodes from the same VN are mapped on different sites of the cloud infrastructure, i.e., a VN node is hosted in a one-to-one manner such that the server resource request is satisfied. In the VN edge assignment, each VN edge is assigned to a loop-free path in the underlying WDM network that satisfies the bandwidth requirements. The allocated resources will be released when the VN request expires. Fig. 2 gives the examples of two different schemes for embedding the VN request onto the cloud infrastructure (the VN request and the cloud infrastructure are shown in Fig. 1).

III. P OWER -E FFICIENT VN P ROVISIONING

B. VN Request The tasks, or the application requests and communication demands among these tasks, which are submitted to cloudbased data centers for the purpose of data and information exchanging, can be abstracted as a VN request. That is, each task or application request represents a node, which is called the virtual node, and each communication demand represents an edge, which is called the virtual edge. Similar to the substrate network, we model the VN request as an undirected weighted graph GV = (V, E, RV , RE ), where V represents the set of virtual nodes, and E denotes the set of virtual edges. The virtual nodes and edges are associated with constraints on resource requests, which are denoted by RV and RE , respectively. For each virtual node v, v ∈ V , we use req(v) to denote the amount of server resources requested from a specific substrate 2 In

this paper, we assume that each site includes only one server. the rest of this paper, we will use N to refer to the set of servers or the set of WDM nodes. 3 In

D. Power-Efficient VN Provisioning For provisioning the VN request, the components of the cloud infrastructure (the physical servers and the WDM network equipment) need to consume a certain amount of power. This power consumption can be divided into two parts: the workload-dependent power and the workload-independent power. In this paper, we refer to the workload-independent power consumption as “idle power.” The idle power that is consumed by the cloud infrastructure can be reduced by a power-efficient VN provisioning scheme. The basic idea of power-efficient VN provisioning is to turn off the lightly loaded equipment of the cloud infrastructure by consolidating the VMs onto fewer physical servers and by routing the communication demands on fewer fiber links. Fig. 3(c) shows a power-unaware embedding solution for two VN requests [see Fig. 3(b)], where the VN nodes a, b, and c in the first VN request are hosted by physical servers S1, S2, and S3, respectively; and VN edges (a − b), (b − c), and

SUN et al.: PROVISIONING FOR VN REQUESTS IN CLOUD-BASED DATA CENTERS

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Fig. 3. VN requests provisioning based on power-unaware and powerefficient schemes. (a) Cloud infrastructure. (b) Two VN requests. (c) Powerunaware VN provisioning. (d) Power-efficient VN provisioning.

(c − a) are mapped onto physical paths (A − B), (B − C), and (C − A), respectively. The VN nodes d, e, and f in the second VN request are hosted on physical servers S4, S5, and S6, respectively, and VN edges (d − e), (e − f ), and (f − d) are mapped onto physical paths (D − E), (E − F ), and (F − D), respectively. The power-unaware approach that is shown in Fig. 3(c) aims at balancing the workload on the underlying infrastructure. As a result, the workloads have been evenly distributed in the underlying cloud infrastructure. Thus, as shown in Fig. 3(c), the total idle-power consumption is contributed by six physical servers, six WDM nodes, and six fiber links. In the power-efficient approach, as shown in Fig. 3(d), VN nodes a and d are both hosted on physical server S1, VN nodes b and e are consolidated onto physical server S2, and VN nodes e and f are both mapped onto physical server S3. VN edges (a − b) and (d − e) are packed in the same physical path (A − B), (b − c) and (e − f ) are both mapped into physical path (B − C), and (c − a) and (f − d) are packed into the physical path (C − A). As shown in Fig. 3(d), the objective of the power-efficient approach is to reduce the total idle-power consumption by turning off some unnecessary equipment of the cloud infrastructure, such as physical servers S4, S5, and S6; WDM nodes D, E, and F ; and fiber links (D − E), (E − F ), and (F − D). As a result, the total idle-power consumption is only contributed by three physical servers, three WDM nodes, and three fiber links, i.e., the power-efficient approach has a 50% gain from the idle-power consumption. However, in the power-efficient provisioning for the VN request, as much equipment as possible need to be turned off for higher power efficiency, which may lead to a heavy workload in some specific physical equipment of the cloud infrastructure, such as servers S1, S2, and S3, and fiber links (A − B), (B − C), and (C − A), as shown in Fig. 3(d). This will result in the emergence of a hotspot in the cloud infrastructure, and this will influence the acceptance ratio of the VN requests. In order to avoid the bottlenecks of the underlying infrastructure, in this paper, we introduce a workload threshold at each physical

Fig. 4. Key notations used in our model.

component of the cloud infrastructure to control its workload. In our approach, the distribution of workloads in the cloud infrastructure can be easily balanced by adjusting the parameter threshold. Thus, the two contradictory targets, i.e., the power efficiency and the workload balance, will be well traded off to enhance the acceptance ratio of the VN requests. IV. S YSTEM M ODEL In this section, we model the problem of power-efficient provisioning for the VN requests as a mathematical optimization problem by using MIP. Fig. 4 presents the key notations that are used in our system model. A. Problem Definition The problem of power-efficient provisioning for the VN request that is researched in this paper can be summarized as follows: Instance: Given the cloud infrastructure GS = (N, L, AN , AL ) that is composed of physical servers and a WDM network, and the VN request GV = (V, E, RV , RE ) that is submitted to the cloud-based data center.

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IEEE SYSTEMS JOURNAL, VOL. 9, NO. 2, JUNE 2015

Problem: How to implement the power-efficient provisioning for VN request GV , i.e., determine a mapping from GV to GS such that: 1) all of the resource requests of each VN request must be satisfied; 2) the total power consumption for provisioning the VN, i.e., the power that is consumed by the servers and the WDM network is minimized; and 3) the workload distribution in the cloud infrastructure must be balanced to improve the acceptance ratio.

The power that is consumed by the WDM network for the establishment of a lightpath can be defined as in     w,l al · βi,j + φ n εn Plightpath = l∈L i,j∈N : i=j w∈W

+





n∈N

(δ t + δ r ) · cli,j

l∈L i,j∈N : i=j

+





γ s · cli,j .

(4a)

l∈L i,j∈N : i=j,src(l)=i,j

B. Power Consumption In cloud-based data centers, the power consumption for the VN request provisioning is mainly composed of two parts: the network power consumption and the server power consumption. Other power consumption sources of data centers that are contributed by cooling and power distribution systems are beyond the scope of this paper and is not considered. When a server is turned on, it will consume a certain amount of basic power (workload-independent power) even when it has no workload running on it, which is called idle power. With some workload, additional power (workload-dependent power) is needed for the server. Thus, the total power consumption of a running server is composed of two parts: the idle power and the workload-dependent power. Fan et al. [38] had studied the relationship between the workload and the total power consumption of a server. They found that the power consumption of a server linearly increases with the increase in workload from the idle power up to busy power (the power that is consumed by a fully loaded server). This relationship is shown in the following: P (u) = Pidle + (Pbusy − Pidle ) · u

(1)

where Pidle represents the power consumption of an idle server, Pbusy represents the power consumption of a fully utilized server, and u is the server utilization. Fan et al. [38] also give an empirical nonlinear relationship between the workload and power consumption of a server, which can more accurately predict the power consumption of the server than (1) does. It can be formulated as in P (u) = Pidle + (Pbusy − Pidle ) · (2u − ur )

(2)

where r is the calibration parameter used to minimize the square error. In this paper, the total power consumption of all the servers in the cloud infrastructure can be computed as in     hvn Pnidle + Pnbusy − Pnidle Pserver = v∈V n∈N

 · (2un − (un )r ) .

(3)

The power that is consumed by the WDM network for routing the traffic can be contributed by two main parts, i.e., the traffic/workload-dependent power consumption and the traffic/ workload-independent power consumption [25]. In the WDM network, a lightpath should be created before routing the traffic.

In (4a), the first item defines the total power that is consumed by the in-line amplifier on all of the fiber links; the second item presents the total idle power that is consumed by all of the WDM nodes; the third item denotes the total power that is consumed for wavelength transmitting and receiving by all of the lightpaths; and the last item gives the total power that is consumed by devices, such as the microelectromechanical systems optical switch and the wavelength converter at the WDM node, for the wavelength switching of all of the lightpaths. For a fiber link l, parameter al can be calculated as in the work in [25] as follows   dl +2 ×9 (4b) al = 80 where dl denotes the physical length of fiber link l. Considering a multigranularity communication demand in the cloud-based data centers, there are some communication demands that request a bandwidth that is less than a wavelength. We must employ the traffic grooming strategy to pack the subwavelength traffic into a lightpath for efficient resource utilization. The power consumption for traffic grooming can be computed as in   ci,j · p0 + rs,d · p (5) Pgrooming = i,j∈N : i=j

s,d∈N : s=d

where p0 denotes the basic power consumption for a lightpath in traffic grooming, and p represents the power that is consumed per unit of the traffic in traffic grooming. Therefore, the total power consumption of the WDM network defined as in Pnetwork = Plightpath + Pgrooming .

(6)

C. Objective Function We formulate the VN provisioning problem in the cloudbased data center as a mathematical programming problem by using MIP to minimize the total power consumption. The objective function is shown in Minimize {η · Pnetwork + ξ · Pserver }.

(7)

Objective function (7) tries to minimize the total power consumption for provisioning a VN request, i.e., the sum of the network power consumption and the server power consumption. Where η + ξ = 1, they can be used to balance the weights of Pnetwork and Pserver .

SUN et al.: PROVISIONING FOR VN REQUESTS IN CLOUD-BASED DATA CENTERS

D. Constraints

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The provisioning for the VN request must satisfy a certain amount of constraints in terms of resource capacity and location. VN Node Provisioning:  hvn = 1 ∀v ∈ V (8) hvn ≤ 1

∀n ∈ N



rn(n) = req(v) · hvn ,

(10)

∀v ∈ V ; ∀n ∈ N. (11)

To satisfy the server resource requirement, VN node mapping is done in a one-to-one manner, with (8) guaranteeing that a VN node is only mapped onto one server node. Constraint (9) ensures that the number of VN nodes that are mapped onto a server node is no more than one. Equation (10) is the capacity constraint of each server node, which guarantees that the amount of resources that is provisioned by each server node must not exceed its available resource capacity. Constraint (11) ensures that the server resources that are requested by each VN node must be satisfied. VN Edge Provisioning: Lightpath routing constraints can be expressed as multicommodity flow equations, as in the following:   cli,j − cli,j = 0 ∀i, j ∈ N : i = j; ∀n ∈ N \{i, j} (12) l∈Lout n

ci,j =



cli,j

∀i, j ∈ N : i = j

(13)

∀i, j ∈ N : i = j

(14)

l∈Lout i



ci,j = 

cli,j

l∈Lin j

(15)

l∈Lin i



∀i, j ∈ N : i = j.

cli,j = 0

w,l βi,j

∀w ∈ W ; ∀i, j ∈ N : i = j

(21)

w,l βi,j = 0 ∀w ∈ W ; ∀i, j ∈ N : i = j

(22)



w,l βi,j = 0 ∀w ∈ W ; ∀i, j ∈ N : i = j.

(23)

l∈Lout j

Equation (17) guarantees that, on a link that is traversed by a lightpath, there is only one wavelength that is is assigned to this lightpath. Equation (18) ensures that each wavelength on a link is at most assigned to one lightpath. Constraints (19)–(23) ensure that the same wavelength is used on all the links that are traversed by the lightpath, i.e., the wavelength continuity constraint. Equation (19) ensures that, for each lightpath, the number of wavelengths used for the incoming and outgoing lightpaths are the same at each intermediate WDM node. For each lightpath, constraints (20)–(23) are the corresponding constraints that must be satisfied while implementing wavelength assignment at both the source and sink WDM nodes of this lightpath  s,d ri,j ≤ ci,j × B × T ∀i, j ∈ N : i = j (24) s,d∈N :s=d



s,d ri,j ≥ (ci,j −1)×B

s,d∈N :s=d

j∈N \{i}

r

s,d



s,d ri,j −

(25)

∀s, d ∈ N : s = d;

j∈N \{i}

∀i ∈ N \ {s, d} (26)



=

s,d rj,i =0

∀i, j ∈ N : i = j

s,d rs,i

∀s, d ∈ N : s = d

(27)

s,d ri,d

∀s, d ∈ N : s = d

(28)

s,d ri,s =0

∀s, d ∈ N : s = d

(29)

s,d rd,i =0

∀s, d ∈ N : s = d.

(30)

i∈N \{s}

∀i, j ∈ N : i = j

cli,j = 0

(20)

l∈Lout i



l∈Lin n

∀w ∈ W ; ∀i, j ∈ N : i = j

l∈Lin i

(9) ∀v ∈ V ; ∀n ∈ N



ci,j ≥

v∈V

rn(n) ≤ capa(n) · hvn · Tn

w,l βi,j

l∈Lin j

n∈N





ci,j ≥

(16)

l∈Lout j



rs,d = 

i∈N \{d}

i∈N \{s}

where each lightpath between a WDM node pair corresponds to a commodity. Constraint (12) ensures that the amount of outgoing lightpaths must be equal to the amount of incoming lightpaths at each intermediate WDM node. For each lightpath, constraints (13)–(16) must be satisfied at both of its source and sink WDM nodes. The following constraints must be satisfied while implementing the wavelength assignment for each lightpath:  w,l βi,j ∀l ∈ L; ∀i, j ∈ N : i = j (17) cli,j = 

w∈W w,l βi,j ≤ 1 ∀l ∈ L;

i,j∈N :i=j



l∈Lout n

w,l βi,j −



w,l βi,j =0

∀w ∈ W

(18)

∀i, j ∈ N : i = j

i∈N \{d}

Constraint (24) is the lightpath capacity constraint, which ensures that the total amount of traffic that is carried by a lightpath must not exceed its available bandwidth capacity. Constraint (25) ensures that each established lightpath is necessary and prevents to establish idle lightpaths, which do not carry any traffic load. Equation (26) is the flow conservation constraint at each intermediate WDM node. Equations (27)–(30) are the flow conservation constraints that must be satisfied at the source and sink nodes of each traffic demand. V. PEVNP A LGORITHM

∀n ∈ N \ {i, j};

l∈Lin n

∀w ∈ W ;



(19)

The problem of optimal VN mapping is NP-hard [11]; hence, finding an optimal power-efficient VN provisioning using the

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MIP model proposed in Section IV is computationally intractable. Thus, we propose a heuristic algorithm, i.e., the power-efficient VN provisioning (PEVNP) algorithm, to solve the power-efficient VN provisioning problem.

A. Objective of PEVNP We focus on designing a PEVNP algorithm for the online problem, where the VN requests arrive and depart over time. From the cloud infrastructure provider’s point of view, the PEVNP algorithm would minimize the power consumption and contribute to reducing the operating costs or operating expenditures. We introduce the notation POW(t) that corresponds to the power consumption for provisioning VN requests at time t. Thus, the objective of our algorithm to minimize long-term power consumption can be formulated as Minimize

T lim

T →∞

t=0

POW(t) T

 .

(31)

Based on the description in Section IV and the definition of (7), the total power consumption POW(t) can be calculated as follows: POW(t) = η · Pnetwork + ξ · Pserver

(32)

where η + ξ = 1. They can be used to balance the importance of Pnetwork and Pserver . B. VN Node Ranking Mapping a VN request onto a shared infrastructure network includes two key steps: the VN node mapping and the VN edge mapping. We study the power-efficient online VN provisioning problem in this paper. For efficient utilization of the server resources and the high acceptance ratio, we rank the VN nodes according to their resource requirement before implementing the VN mapping in our algorithm. We give priority to a VN node with high resource requirement, since it is more difficult to satisfy high resource requiring nodes. To calculate the resource requirement for a VN node v, we add the amount of the requested server resource by v and the amount of bandwidth that is requested by the adjacent edge of v, which together represent the resource requirement of VN node v. This is because for mapping a VN node, we must take into account the resource requirements of its adjacent edges. For a VN node v, v ∈ V , the resource requirement RES(v) is defined according to RES(v) = req(v) +



b(e)

(33)

e∈Adj(v)

where req(v) denotes the amount of server resources that is required by VN node v, b(e) denotes the amount of bandwidth resources that is requested by VN edge e, and Adj(v) is used to represent the set of adjacent edges of VN node v.

C. Differentiated Pricing Strategy for Routing In the VN edge mapping process, a VN edge is assigned to a path on the infrastructure network. We assume that VN nodes u and v are hosted on server nodes s1 and s2 , respectively. If there are multiple paths between s1 and s2 , we pick the path with the minimum power consumption as the corresponding physical path of VN edge (u, v). This is the Min-Power Path First (MPPF) principle. However, if we only adopt the MPPF principle for the VN edge assignment, the VN edges will be mapped onto as fewer infrastructure links as possible; then, bottleneck/hotspot links appear. Since most VN requests are rejected due to bottleneck infrastructure links [39], we consider the capacity threshold of the links of the infrastructure network for load balancing in our online VN mapping problem to improve the acceptance ratio and to enhance the revenue of InPs. We introduce a differentiated pricing strategy to set the link weight in the routing in our approach to enable a traffic that is evenly distributed throughout the infrastructure network. The main idea of the differentiated pricing strategy is that, when the link resource utilization (LRU) falls into a specific range, the weight (power consumption) of the link is normal; otherwise, the link weight will be enlarged. The differentiated pricing strategy will guide and implement the uniform mapping of VN edges over the infrastructure network. For link l, the differentiated pricing strategy that is used to set link weight Weight(l) can be formulated as follows: l r ∈ (0, σ] Pbasic , (34) Weight(l) = l , r∈ / (0, σ] θ · Pbasic l where Pbasic denotes the power consumption of link l, r ∈ (0, 1] is the resource utilization of link l, σ ∈ (0, 1] is the capacity threshold (the bound of resource utilization) of link l, and θ > 1 is the penalty factor. For link l, resource utilization r can be contributed by all of the corresponding paths of the VN edges that traverse it. Thus, the resource utilization of link l can be calculated as follows:

b(e)

r=

e:l∈Path(e)

capa(l)

(35)

where e ∈ E denotes a VN edge, b(e) is the amount of resource requirement of VN edge e, Path(e) denotes the corresponding path of VN edge e, i.e., it is a set of links, and capa(l) represents the resource capacity of link l. D. PEVNP Algorithm In the online VN mapping problem, we use Q to denote the set of arriving VN requests. Our main interest is to find a power-efficient mapping solution for each VN request. For a VN request that needs to be provisioned, we first compute the resource requirement RES(v) of each VN node according to (33). Then, we sort the VN nodes by RES(v), in a descending order. In the VN edge mapping process, we introduce the differentiated pricing strategy to set the link weight according to (34) while routing a path on the infrastructure network for

SUN et al.: PROVISIONING FOR VN REQUESTS IN CLOUD-BASED DATA CENTERS

Fig. 5.

435

Pseudocode of the PEVNP algorithm.

a VN edge. We focus on finding a path with minimum weight (i.e., minimum power consumption) for each VN edge. All of the allocated resources, including the server resource and the link resource, will be released when the VN request expires. The pseudocode of the PEVNP algorithm is shown in Fig. 5. In our proposed PEVNP algorithm, the complexity for computing the resource requirements RES(v) for a VN request is O(|V|), where |V| represents the VN node number, and the complexity for VN node sorting is O(|V|2 ). In a sparse network, the complexity of the Dijkstra algorithm is O(|N| · log |N|+|L|) [40], where |N| and |L| represent the total node and edge number, respectively; The complexity of Steps 8–14 of the PEVNP algorithm is O(|V| · |N|+|E| · (|N| · log |N|+|L|)), where |E| denotes the total edge number of a VN request. Thus, the complexity of the PEVNP algorithm is O(|Q| · (|V| + |V|2 + |V| · |N|+|E| · (|N| · log |N|+|L|))), where |Q| represents the number of VN requests. VI. P ERFORMANCE E VALUATION The cloud computing paradigm enables users to access services, applications, and infrastructure resources by using thin clients anywhere and at any time. In this paradigm, multiple users can share the cloud infrastructure resources. The application or service requests (such as virtual datacenter requests) from a user can be abstracted as a VN request and submitted to the cloud-based data centers.

Fig. 6. Infrastructure network topologies used in our simulations. (a) Net 1: CERNET. (b) Net 2: NSFNET. (c) Net 3: USNET.

To evaluate the effectiveness of our approach, we conduct simulation experiments for the power-efficient VN mapping problem on different infrastructure networks. A. Simulation Environment In our simulation, we use three real networks, namely, the China Education and Research Network (CERNET), the National Science Foundation Network (NSFNET), and the Universal Sharing Network (USNET) (see Fig. 6) as the cloud infrastructure network, and we assume the bandwidth as the

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TABLE I VALUES OF P OWER C ONSUMPTION PARAMETERS

at time t. In our simulations, we calculate the RtPC per 200 VN requests. RtPC Preal can be computed as follows:  P (GV ) (36) Preal = GV ∈Qacc

where Qacc denotes the queue of accepted VN requests at time t, and P (GV ) denotes the power that is consumed by VN request GV . 2) Average Power Consumption (APC). APC refers to the average power consumption of all accepted VN requests at time t. In our simulations, we calculate the APC per 200 VN requests. APC Paver can be calculated as follows:

P (GV ) Paver =

GV ∈Qacc

|Qacc |

(37)

where |Qacc | denotes the number of VN requests in the queue Qacc . 3) Blocking Ratio (BR). The BR is an important metric for evaluating the performance of the online VN mapping approach. It can be defined as follows: link resource. These networks vary in terms of the number of nodes, links, and connectivity, and thus provide a good basis for the evaluation of our approach. In these three substrate networks, each fiber link consists of eight wavelengths, and each wavelength has a bandwidth capacity of 40 G. All node resource capacities are assumed to be 400 units. We assume that the costs of a per-unit node resource and per-unit link resource are both equal to 1. The bound of LRU σ [see (34)] varies from 0.6 to 1.0 in a step of 0.2. The values of the parameters that are used to calculate the power consumption in our simulations are summarized in Table I, where the power consumption parameters of the WDM network are set according to the work in [41]. The VN requests are randomly generated, such that the number of VN nodes is equal to a given number N , and the average probability of connectivity between any VN node pair is about 0.3. The generated VN requests consist of four VN nodes for all three infrastructure networks. All of these generated VN requests arrive as a Poisson process, with the amount of node resource that is required by each VN node uniformly distributed between 10 and 30 CPU or memory units, and the link resource requirement of each VN edge is uniformly distributed between 10 and 20 bandwidth units. The resources that are allocated to the VN requests are released when the holding time of the VN request in the network expires. We have implemented the algorithms that are compared in our simulations by using Microsoft Visual Studio 2005 and the C++ programming language. All algorithms are run on a computer with a memory of 2 GB and 2.66-GHz CPU. B. Performance Metrics In our simulations, we use the following performance metrics to evaluate various algorithms. 1) Real-time Power Consumption (RtPC). RtPC refers to the total power that is consumed by the accepted VN requests

BR = 1 −

|Qacc | |Qarr |

(38)

where |Qarr | denotes the number of VN requests in the arrived VN queue Qarr . 4) LRU. LRU refers to the average resource utilization of all occupied fiber links at time t. The average resource utilization of the fiber links can be defined as follows:

r(l) l∈Lused (39) LRU = |Lused | where r(l) represents the resource utilization of link l, and Lused denotes the set of occupied fiber links at time t. VN Mapping Cost (VNMC). The VNMC refers to the average mapping cost of all accepted VN requests at time t. For provisioning a VN request GV , the cost of all consumed resources Cost(GV ) can be formulated as follows:   f lle p(l) + rnvn p(n) (40) Cost(GV ) = e∈E l∈L

v∈V n∈N

f lle

where represents the amount of bandwidth that is allocated to the virtual edge e on fiber link l, and rnvn denotes the amount of node resource that is allocated to virtual node v on server node n. The notations p(l) and p(n) denote the cost of the per-unit link resource and server resource, respectively. Thus, the VNMC, which is denoted as CST, can be defined as follows:

Cost(GV ) CST =

GV ∈Qacc

|Qacc |

(41)

where |Qacc | denotes the number of VN requests in the queue Qacc .

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437

TABLE II A LGORITHMS C OMPARED

C. Algorithms Compared Since most researches on the VN embedding problem mainly focus on resource efficiency, the algorithms that are proposed in these works are not comparable with our approach. Yu et al. [13] have researched the optimal VN mapping problem. For comparison purposes, we extend the algorithms that are proposed in [13] by modifying its outputs (e.g., the power consumption, the BR, and the LRU) and apply it into the online mapping problem that is studied in this paper. We use a different version of our approach that only considers the power consumption of servers while implementing VN provisioning to reveal the importance of jointly considering the power consumption of servers and networks in reducing the total power consumption. The algorithms that are compared in our simulations have been summarized in Table II. D. Simulation Results and Analysis In Fig. 7, we compare the APC of our approach and that of the approach that is proposed in [13]. It shows that our proposal, i.e., the PEVNP algorithm, significantly outperforms the other algorithms in terms of saving power consumption. We can see that our PEVNP algorithm can achieve about 15% gains compared with the power-unaware VN provisioning (PUVNP) algorithm and about 10% gains compared with the PEVNP_EPS algorithm. The improvement in the APC is still higher for larger infrastructure network sizes (Net 2 and Net 3). This is due to the fact that we consider the power consumption while implementing the VN node and edge assignments, and this reduces the power consumption for supporting the running VN. Introducing capacity threshold parameter σ [see (34)] while routing the paths for the VN edges will lead to higher power consumption. This is because capacity threshold parameter σ can be used to guide the workload from the heavy loaded substrate links to the light loaded substrate links to achieve load balancing. Thus, some VN edges may be assigned to the paths with more hops, which contributes to higher power consumption. Fig. 8 shows the RtPC of the different approaches. We can see that the power consumption fluctuates over time, corresponding to the dynamic VN requests that are arriving and expiring over time. In Fig. 9, we compare the average LRU of each approach. In this set of simulations, the average LRU has been calculated according to (39). The simulation results show that our

Fig. 7. APC. (a) Simulations for Net 1. (b) Simulations for Net 2. (c) Simulations for Net 3.

proposed approach has a higher average LRU than the resourceefficient approach that is proposed in [13]. For the purpose of reducing power consumption, we employ the traffic grooming strategy while implementing VN edge mapping to guide and consolidate as many paths of VN edges into a lightpath or fiber link as possible, resulting in higher LRU. The smaller value of parameter σ lowers the LRU, as the smaller value of σ helps lower the bound of available link resource capacity, leading to a lower LRU.

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Fig. 8. RtPC. (a) Simulations for Net 1. (b) Simulations for Net 2. (c) Simulations for Net 3.

Fig. 10 illustrates the BR (i.e., the reject rate) of the VN requests during our simulation. In this set of simulations, we calculate the BR of the VN requests according to (38). It shows that our approach performs well on the performance of the BR, i.e., the BR of our proposed approach is very close to that of the resource-efficient strategy that is proposed in [13]. This is due to the benefits of introducing VN node ranking and traffic grooming into our approach while implementing the mapping of VN nodes and edges. Fig. 10 also presents that relaxing

Fig. 9. LRU. (a) Simulations for Net 1. (b) Simulations for Net 2. (c) Simulations for Net 3.

capacity threshold parameter σ contributes to reducing the BR, since more link resource is allowed to be used in the provision of the VN requests. Furthermore, different infrastructure networks (i.e., Net 1, Net 2, and Net 3) show the different characteristics of the BR. Thus, we can conclude that the BR is related to the infrastructure network parameters, such as the network size and connectivity, since a larger infrastructure

SUN et al.: PROVISIONING FOR VN REQUESTS IN CLOUD-BASED DATA CENTERS

Fig. 10. BR comparison. (a) Simulations for Net 1. (b) Simulations for Net 2. (c) Simulations for Net 3.

network has more nodes and connectivity, with flexibility and chances for VN node mapping, load balancing, and traffic grooming while implementing the VN mapping. Fig. 11 shows the average VNMC. In this set of simulations, the bound of the LRU σ varies from 0.6 to 1 at a step of 0.2. We calculate the average network mapping cost according to (40) and (41). From Fig. 11, we can see that the average VNMC

439

Fig. 11. VNMC. (a) Simulations for Net 1. (b) Simulations for Net 2. (c) Simulations for Net 3.

of our approach is slightly higher compared with that of the resource-efficient approach that is proposed in [13], and the difference between them does not exceed the level of 5%. Due to our definition of the power consumption of communication networks, we succeed in making the assignment of a VN edge onto an underlying path with minimal power consumption (i.e., the path with minimal hops), leading to lesser resource

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utilization and a lower VNMC. However, parameter σ makes little impact on the average VNMC. VII. C ONCLUSION The power consumption of IT systems has enormously increased due to the exponential increase in users’ demand. Due to its advantages, such as powerful processing ability, high scalability, and rapid deployment, a large number of applications have been deployed in cloud-based data centers, which can consume large amounts of power. Thus, it is important to study the power-efficient provisioning in cloud-based data centers to reduce their power consumption. In this paper, we have researched the problem of powerefficient provisioning for VN requests in cloud-based data centers. We have developed an architectural framework and principle for this problem, in which we pack the VN edges and VN nodes into as few links and servers as possible to reduce the idle-power consumption, which leads to a lower total power consumption. However, simply focusing on reducing the power consumption can lead to hotspots in the cloud infrastructure, which in turn results in a high BR of VN requests. Accordingly, our framework makes a tradeoff between power consumption and the VN blocking ratio. We have formulated this problem as a mathematical programming problem by using MIP to minimize the total power consumption without violating the SLAs. Since the optimal VN provisioning problem is NP-hard, we have designed a heuristic algorithm to solve our problem efficiently. We validate and evaluate the correctness and effectiveness of our approach by conducting extensive simulation experiments on various networks. The simulation results show that our approach can reduce the power consumption and provide good power efficiency. R EFERENCES [1] Amazon Elastic Computing Cloud. [Online]. Available: http://aws. amazon.com/ec2/ [2] Windows Azure. [Online]. Available: http://www.microsoft.com/ windowsazure/ [3] Google App Engine. [Online]. Available: http://code.google.com/ appengine [4] Y. Chen, S. Jain, V. K. Adhikari, Z. L. Zhang, and K. Xu, “A first look at inter-data center traffic characteristics via Yahoo! datasets,” in Proc. IEEE INFOCOM, Shanghai, China, Apr. 10–15, 2011, pp. 1620–1628. [5] J. Kaplan, W. Forrest, and N. Kindler, “Revolutionizing data center energy efficiency,” McKinsey Co., New York, NY, USA, Tech. Rep., 2008. [6] A. Berl, E. Gelenbe, M. Girolamo and, and G. Giuliani, “Energyefficient cloud computing,” Comput. J., vol. 53, no. 7, pp. 1045–1051, Sep. 2010. [7] B. Mukherjee, Optical WDM Networks. New York, NY, USA: SpringerVerlag, 2006. [8] Í. Goiri, K. Le, M. E. Haque, R. Beauchea, T. D. Nguyen, J. Guitart, J. Torres, and R. Bianchini, “GreenSlot: Scheduling energy consumption in green datacenters,” in Proc. Int. Conf. High Perform. Comput., Netw., Storage Anal., Seattle, WA, USA, Nov. 12–18, 2011, pp. 1–11. [9] D. Villegas and S. M. Sadjadi, “DEVA: Distributed ensembles of virtual appliances in the cloud,” in Proc. LNCS, Euro-Par Parallel Process., 2011, vol. 6852, pp. 467–478. [10] R. Buyya, A. Beloglazov, and J. Abawajy, “Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges,” in Proc. PDPTA, Las Vegas, NV, USA, Jul. 12–15, 2010, pp. 1–12. [11] N. M. Mosharaf, M. R. Rahman, and R. Boutaba, “Virtual network embedding with coordinated node and link embedding,” in Proc. IEEE INFOCOM, Rio de Janeiro, Brazil, Apr. 19–25, 2009, pp. 783–791.

[12] H. Yu, C. Qiao, V. Anand, X. Liu, and G. Sun, “Survivable virtual infrastructure embedding in a federated computing and networking system under single regional failures,” in Proc. IEEE GLOBECOM, Miami, FL, USA, Dec. 6–10, 2010, pp. 1–6. [13] H. Yu, V. Anand, C. Qiao, and G. Sun, “Cost efficient design of survivable virtual infrastructure to recover from facility node failures,” in Proc. IEEE ICC, Kyoto, Japan, Jun. 5–9, 2011, pp. 1–6. [14] M. R. Rahman, I. Aib, and R. Boutaba, “Survivable virtual network embedding,” in Proc. 95th IFIP Int. Conf. Netw., 2010, pp. 40–52. [15] X. Cheng, S. Su, Z. Zhang, H. Wang, F. Yang, Y. Luo, and J. Wang, “Virtual network embedding through topology-aware node ranking,” Comput. Commun. Rev., vol. 41, no. 2, pp. 38–47, Apr. 2011. [16] X. Cheng, S. Su, Z. Zhang, K. Shuang, F. Yang, Y. Luo, and J. Wang, “Virtual network embedding through topology awareness and optimization,” Comput. Netw., vol. 56, no. 6, pp. 1797–1813, Apr. 2012. [17] M. Chowdhury, F. Samuel, and R. Boutaba, “PolyViNE: Policy-based virtual network embedding across multiple domains,” in Proc. ACM SIGCOMM Workshop VISA, New Delhi, India, Aug. 30/Sep. 3, 2010, pp. 49–56. [18] G. Sun, V. Anand, H. Yu, D. Liao, Y. Cai, and L. Li, “Adaptive provisioning for evolving virtual network request in cloud-based data centers,” in Proc. IEEE GLOBECOM, Anaheim, CA, USA, Dec. 3–7, 2012, pp. 1617–1622. [19] Z. Zhang, X. Cheng, S. Su, Y. Wang, K. Shuang, and Y. Luo, “A unified enhanced particle swarm optimization-based virtual network embedding algorithm,” Int. J. Commun. Syst., vol. 26, no. 8, pp. 1054–1073, Aug. 2013. [20] H. Kim and S. Lee, “Greedy virtual network embedding under an exponential cost function,” in Proc. ICOIN, Bali, Indonesia, Feb. 1–3, 2012, pp. 216–221. [21] M. A. Zafer and E. Modiano, “A calculus approach to energy-efficient data transmission with quality-of-service constraints,” IEEE/ACM Trans. Netw., vol. 17, no. 3, pp. 898–911, Jun. 2009. [22] A. Jirattigalachote, C. Cavdar, P. Monti, L. Wosinska, and A. Tzanakaki, “Dynamic provisioning strategies for energy efficient WDM networks with dedicated path protection,” Opt. Switching Netw., vol. 8, no. 3, pp. 201–213, Jul. 2011. [23] A. Muhammad, P. Monti, I. Cerutti, L. Wosinska, P. Castoldi, and A. Tzanakaki, “Energy efficient WDM network planning with dedicated protection resources in sleep mode,” in Proc. IEEE GLOBECOM, Miami, FL, USA, Dec. 6–10, 2010, pp. 1–5. [24] C. Cavdar, “Energy-efficient connection provisioning in WDM optical networks,” in Proc. OSA/OFC/NFOEC, Los Angeles, CA, USA, Mar. 6, 2011, pp. 1–3. [25] C. Cavdar, F. Buzluca, and L. Wosinska, “Energy-efficient design of survivable WDM networks with shared backup,” in Proc. IEEE GLOBECOM, Miami, FL, USA, Dec. 6–10, 2010, pp. 1–5. [26] E. Yetginer and G. N. Rouskas, “Power efficient traffic grooming in optical WDM networks,” in Proc. IEEE GLOBECOM, Honolulu, HI, USA, Nov. 30/Dec. 4, 2009, pp. 1–6. [27] G. Shen and R. S. Tucker, “Energy-minimized design for IP over WDM networks,” IEEE/OSA J. Opt. Commun. Netw., vol. 1, no. 1, pp. 176–186, Jun. 2009. [28] D. Kliazovich, P. Bouvry, and S. U. Khan, “DENS: Data center energy-efficient network-aware scheduling,” in Proc. IEEE/ACM Int. Conf. Cyber., Phys. Social Comput., Hangzhou, China, Dec. 18–20, 2010, pp. 69–75. [29] A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Future Gen. Comput. Syst., vol. 28, no. 5, pp. 755–768, May 2012. [30] K. Le, R. Bianchini, J. Zhang, Y. Jaluria, J. Meng, and T. D. Nguyen, “Reducing electricity cost through virtual machine placement in high performance computing clouds,” in Proc. Int. Conf. High Perform. Comput., Netw., Storage Anal., Seattle, WA, USA, Nov. 12–18, 2011, pp. 1–12. [31] H. Mi, H. Wang, G. Yin, Y. Zhou, D. Shi, and L. Yuan, “Online selfreconfiguration with performance guarantee for energy-efficient largescale cloud computing data centers,” in Proc. IEEE Int. Conf. Services Comput., Miami, FL, USA, Jul. 5–10, 2010, pp. 514–521. [32] I. Rodero, J. Jaramillo, A. Quiroz, M. Parashar, F. Guim, and S. Poole, “Energy-efficient application-aware online provisioning for virtualized clouds and data centers,” in Proc. Int. Green Comput. Conf., Chicago, IL, USA, Aug. 15–18, 2010, pp. 31–45. [33] I. Goiri, F. Julia, R. Nou, J. L. Berral, J. Guitart, and J. Torres, “Energyaware scheduling in virtualized data centers,” in Proc. IEEE Int. Conf. Cluster Comput., Heraklion, Crete, Sep. 20–24, 2010, pp. 58–67.

SUN et al.: PROVISIONING FOR VN REQUESTS IN CLOUD-BASED DATA CENTERS

441

[34] A. Beloglazov and R. Buyya, “Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers,” in Proc. MGC, Bangalore, India, Nov. 29/Dec. 3, 2010, pp. 1–6. [35] J. F. Botero, X. Hesselbach, M. Duelli, D. Schlosser, A. Fischer, and H. de Meer, “Energy efficient virtual network embedding,” IEEE Commun. Lett., vol. 16, no. 5, pp. 756–759, May 2012. [36] G. Sun, H. Yu, L. Li, V. Anand, Y. Cai, and H. Di, “Exploring online virtual networks mapping with stochastic bandwidth demand in multi-datacenter,” Photon. Netw. Commun., vol. 23, no. 2, pp. 109–122, Apr. 2012. [37] G. Sun, H. Yu, V. Anand, L. Li, and H. Di, “Optimal provisioning for virtual network request in cloud-based data centers,” Photon. Netw. Commun., vol. 24, no. 2, pp. 118–131, Oct. 2012. [38] X. Fan, W. D. Weber, and L. A. Barroso, “Power provisioning for a warehouse-sized computer,” in Proc. ISCA, San Diego, CA, USA, Jun. 9–11, 2007, pp. 13–23. [39] I. Fajjari, N. Aitsaadi, G. Pujolle, and H. Zimmermann, “VNR Algorithm: A greedy approach for virtual networks reconfigurations,” in Proc. IEEE GLOBECOM, Houston, TX, USA, Dec. 5–9, 2011, pp. 1–6. [40] M. L. Fredman and R. E. Tarjan, “Fibonacci heaps and their uses in improved network optimization algorithms,” J. Assoc. Comput. Mach., vol. 34, no. 3, pp. 596–615, Jul. 1987. [41] S. Aleksic, “Analysis of power consumption in future high-capacity network nodes,” IEEE/OSA J. Opt. Commun. Netw., vol. 1, no. 3, pp. 245– 258, Aug. 2009.

Dan Liao received the B.S. degree in electrical engineering and the Ph.D. degree in communication and information engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2001 and 2007, respectively. He is currently an Associate Professor with UESTC. His research interests include wired and wireless computer communication networks and protocols, and next-generation network.

Gang Sun received the M.S. degree in signal and information processing from Chengdu University of Technology, Chengdu, China, in 2009, and the Ph.D. degree in communication and information engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. He is currently with the Key Laboratory of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China. His research interests include network virtualization, cloud computing, and next-

Xiaoning Zhang received the B.S., M.S., and Ph.D. degrees in communication and information engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2002, 2005, and 2007, respectively. He is currently an Associate Professor with the Key Laboratory of Broadband Optical Fiber Transmission and Communication Networks, School of Communication and Information Engineering, University of Electronic Science and Technology of China. His research interests include network design, and optical and broadband networks.

generation Internet.

Vishal Anand received the B.S. degree in computer science and engineering from the University of Madras, Chennai, India, in 1996 and the M.S. and Ph.D. degrees in computer science and engineering from the University at Buffalo, State University of New York (SUNY), Buffalo, NY, USA, in 1999 and 2003, respectively. He is currently an Associate Professor with the Department of Computer Science, School of Science and Mathematics, The College at Brockport, SUNY, Brockport, NY, USA. His research interests include wired and wireless computer communication networks and protocols, and cloud and grid computing.

Chuan Lu received the B.S. degree in communication engineering and the M.S. degree in software engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2006 and 2011, respectively. He is currently with the Institute of Electronic and Information Engineering in Dongguan of the University of Electronic Science and Technology of China, Dongguan, China. His research interests include wireless communication networks and nextgeneration networks.

Ning-Hai Bao received the B.S. and M.S. degrees from the Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China, in 1997 and 2007, respectively, and the Ph.D. degree in communication and information systems from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. He is currently an Associate Professor with the School of Communication and Information Engineering, CQUPT. His research interests include network survivability and communication network technologies.