(MCC) systems, both the mobile access network and the cloud computing network are heterogeneous, implying the diverse configurations of hardware, software, ...
ICT MANAGEMENT
QoS-Aware Dynamic Resource Management in Heterogeneous Mobile Cloud Computing Networks SI Pengbo1, ZHANG Qian1, F. Richard YU1,2, ZHANG Yanhua1 1 2
College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, P.R. China Department of Systems and Computer Engineering, Carleton University, Ottawa K1S 5B6, Canada
Abstract: In mobile cloud computing (MCC) systems, both the mobile access network and the cloud computing network are heterogeneous, implying the diverse configurations of hardware, software, architec ture, resource, etc. In such heterogeneous mobile cloud (HMC) networks, both radio and cloud resources could become the system bottleneck, thus designing the schemes that separately and independently manage the resources may severely hinder the system performance. In this paper, we aim to design the network as the integration of the mobile access part and the cloud computing part, utilizing the inherent heterogeneity to meet the diverse quality of service (QoS) requirements of tenants. Furthermore, we propose a novel cross-network radio and cloud resource management scheme for HMC networks, which is QoS-aware, with the objective of maximizing the tenant revenue while satisfying the QoS requirements. The proposed scheme is formulated as a restless bandits problem, whose “indexability” feature guarantees the low complexity with scalable and distributed characteristics. Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared to the existing ones. Key words: service-aware approach; dynamic resource management; heterogeneous mobile China Communications • May 2014
cloud; restless bandits formulation
I. INTRODUCTION Nowadays, cloud computing is widely considered as one of the most promising paradigm to offer users on-demand computing and storage resources with a pay-as-you-go manner [1-3]. Future Internet applications including e-office, e-health, interactive gaming, scientific computing and virtual reality, are expected to be mainly supported by cloud computing services [2, 4]. Cloud computing is shifting computing-intensive services from personal devices to cloud data centers, which are composed of distributed powerful servers, with probably diverse hardware and software configurations [5, 6]. On the other hand, end-users, or tenants, may want to obtain access to a heterogeneous set of resources, such as different central processing unit (CPU)/graphic processing unit (GPU) types, memory/storage sizes and server bandwidth. Consequently, it becomes one of the key issues to manage the infrastructure and resources in such a heterogeneous cloud computing environment [7]. Besides, with the tremendous development of mobile networks and portable devices such as smart phones, tablets and wearable smart devices, the cloud services are delivered more and more via mobile connections, which forms mobile cloud computing (MCC) systems [8, 9]. Similarly,
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This paper, based on the intelligent mana g e m e n t m o d e l of EV’s batter y power, puts forward a battery transfer algorithm for the EV network which considers the traffic congestion that changes dynamically and uses improved Ant Colony Optimization.
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mobile access devices and networks are typically of different architectures and capacities, i.e. heterogeneous. Cloud computing technologies draw a lot of attention recently in both industry and academia. Several commercial cloud computing platforms, including Amazon EC2, Google App Engine and Microsoft Azure, have been already launched to provide services to public personal and enterprise users [10]. Besides, literatures on MCC mostly concentrate on applications/services, architectures, resource management, security, etc. In [11], the authors study the cloud mobile multimedia applications, and propose a rendering adaptation technique, which dynamically varies the richness and complexity of graphic rendering depending on the network and cloud computing constraints. To deal with the problems of varying bandwidth and available processing capacity, a cloud computing architecture that consists of a back-end cloud and a local cloud is proposed in [12]. In [13], a family of lightweight schemes is proposed to guarantee the privacy and file integrity during the period between uploading and downloading data. Considering the load balancing problem in MCC systems, the authors in [14] propose a service decision making system for inter-domain service transfer to maximize the rewards for both the cloud system and the users by minimizing the number of service rejections. In [15], the authors argue that in cloud-mobile converged networks, the screen rendering can also be moved to the cloud and the rendered screen can be delivered as part of the cloud services. Some work on heterogeneous clouds has been published recently as well. Mathematical models and analysis in provisioning a heterogeneous cloud computing environment are presented in [16], for both cases of one single heterogeneous datacenter and a larger computing service comprising many datacenters of varying ages. In [6], the traditional notions of cloud computing is extended to provide a cloud-based access model to clusters containing heterogeneous architectures and accelerators. To address the problems of multi-domain
heterogeneous cloud based applications integration and inter-provider and inter-platform interoperability, the inter-cloud architecture is presented in [7], which includes four inter-related components that address different issues. A next generation ubiquitous converged infrastructure to support cloud and mobile cloud computing services has been proposed in [17], which facilitates interconnection of fixed and mobile end users with data centers through a heterogeneous network integrating optical metro networks and wireless access networks. Although a lot of work has been done on mobile cloud and heterogeneous cloud networks, most of the literatures focus on network/service architecture, resource scheduling, energy efficiency and security, ignoring the joint consideration of the heterogeneity of user services, mobile networks and cloud servers. However, in the delivery of services from the heterogeneous clouds via heterogeneous mobile networks, both radio and cloud resources may become the bottleneck impairing the service quality and user experience, and designing resource management schemes separately and independently may severely hinder the performance improvement of MCC [18, 19]. Actually, in the heterogeneous mobile cloud (HMC) scenario, to satisfy the diverse user required quality of service (QoS), it becomes essential to manage the heterogeneous computing/communication resources in the heterogeneous clouds and the radio resources in the heterogeneous mobile access networks [5]. To deal with the problems addressed above, in this paper, we have a thorough study on the dynamic cross-network radio and cloud resource management in HMC networks, with the awareness of tenant required resource and QoS. The main contributions of this paper are as follows. T he idea of heterogeneous mobile cloud computing has been introduced. We aim to design the networks that treat the mobile access part and the cloud computing part as the integration, instead of separated ones. By dynamically exchanging information China Communications • May 2014
and jointly managing resources, the heterogeneity can be utilized to meet the diverse requirements of tenants. A novel cross-network radio and cloud resource management scheme has been proposed for the HMC networks. The heterogeneity of both the mobile network and the cloud computing sides, and the joint optimization of the scheduling of both radio and cloud resources are considered to improve the resource utilization efficiency with low costs. T he proposed resource management scheme is QoS-aware. Tenants are only concerned about service price, which is the summation of mobile access service price and cloud computing service price, and QoS performances, which include data rate, packet loss rate, delay, delay jitter, etc [20]. Thus the resource management scheme is designed to be QoS-aware in this paper, with the optimization objective of maximizing the tenant revenue while satisfying the QoS requirements. T he optimization of the resource management has been formulated as a restless bandits problem [21, 22]. As a stochastic decision optimization method, the restless bandits model has an “indexability” feature which dramatically reduces the optimization complexity. Besides, it is fully distributed, scalable and dynamic, thus meets the varying resource and network environments.
The rest of this paper is organized as follows. In Section II, a brief description on the resource management in heterogeneous mobile cloud networks is presented. The mobile networks, cloud networks and tenant tasks are modeled in Section III. We formulate the problem as a restless bandits model and solve it in Section IV and discuss the resource management process in Section V. Numerical simulation results are shown in Section VI and conclusions are drawn in Section VII.
II. RESOURCE MANAGEMENT IN HETEROGENEOUS MOBILE CLOUDS The description on the heterogeneous mobile cloud computing networks and the dynamic radio and computing resource management is presented in this section.
2.1 Heterogeneous mobile cloud computing networks In mobile communication systems, the complementary characteristics of different wireless networks make it attractive to integrate a wide range of radio access technology (RAT) standards, including wireless wide area networks (WWANs), wireless metropolitan area networks (WMANs) and wireless local area networks (WLANs) [23]. In heterogeneous mobile scenario, networks cooperate with each other for seamless user-accessing, with either tight coupling or loose coupling internetworking ways [24].
Fig.1 The typical architecture of a HMC network. China Communications • May 2014
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The cloud computing system may also be heterogeneous. It is known that data centers are often limited by power density and efficiency, as well as compute density [6]. Instead of general-purpose infrastructure, specialized devices can be optimized for specific kinds of computations, and this optimization can be performed for efficiency [6]. Cloud users could take advantage of the performance and efficiency advantages of heterogeneous computing, and the cloud infrastructure software must recognize and handle this heterogeneity. In this paper, we introduce the heterogeneous mobile cloud networks, in which end users, or tenants, access the heterogeneous clouds via heterogeneous mobile access networks. The network architecture is shown in Figure 1. Both the mobile network side and the cloud network side cooperate to provide the end-to-end QoS to the tenants, who specify not only the required computing resource in their cloud task requests, but also the communication QoS requirements. The heterogeneity of mobile network and cloud infrastructure can be investigated to satisfy the diverse requirements of the tenants, with high resource utilization efficiency.
paper to satisfy the diverse requirements of the tenants with high utilization efficiency of the heterogeneous resources. Since that either radio or cloud resources could be the bottleneck impairing the user experience on the cloud task, different resources need to be carefully scheduled by a scheme that optimizes the scheduling crossing the mobile access network and cloud computing network [25]. Besides, in this cross-network resource management scheme, both computing resource requirements and QoS requirements need to be considered in the design, which is different from traditional cloud computing systems that concern only computing resources [26]. And the dynamic nature of resource utilization and link bandwidth status also merit efforts on designing dynamic schemes to adapt to the system state changing [19]. Consequently, we focus on the QoS-aware dynamic joint resource management in HMC networks.
2.2 QoS-aware dynamic radio and computing resource management
3.1 Mobile access network model
The cloud computing tasks from the tenants can be scientific computing, multimedia file adaption, game data processing, massive data storage, etc, with very different computing and/or communication resource requirements. In this paper, in the cloud computing service requests from tenants, computing/ communication resources, quality of service and expected costs for the service are enclosed. We assume the tenant revenue to be the expected costs minus the real costs, thus due to the fact that the tenant expected costs are independent to the proposed scheme, our objective of maximizing the revenue is similar to minimizing the costs. Joint radio and computing resource management in HMC systems is adopted in this
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III. SYSTEM MODEL In this section, we have a discussion on the mobile access network model, cloud network abstraction and tenant task model, as the preliminary work for the system formulation.
The whole time line considered in this paper is divided into time slots. A time slot begins when a tenant sends a request to the mobile access and cloud computing network. At the beginning of each time slot, the decisions on choosing the most appropriate mobile access network and cloud server are made. This is called the decision time point, denoted by t, 1≤t≤T, where T is the total number of time slots considered. In the heterogeneous mobile access network scenario, multiple types of RATs are adopted for the mobile user access services. Different RATs provide varying QoS, including data transmission rate, delay, delay jitter, packet loss rate, power consumption and access price. For simplicity, we only adopt data transmission rate σtr,l,t and radio access price per time China Communications • May 2014
unit σpr,l,t at time point t to describe the service characteristics of mobile access network l, l∈... , where ... is the set of all the mobile access networks.
3.2 Abstraction of the cloud network We adopt the virtual cluster cloud network model presented in [27], and our scheme can be easily extended to other cloud network abstraction models (e.g., the virtual oversubscribed cluster model [27], Virtually Clustered Open Router (VICTOR) model [28], etc.). In the abstracted model of the cloud network, for server c, c∈∐, where ∐ is the set of all the cloud servers, we use to represent the hardware and software configurations, i.e. computing resources. For simplicity, we adopt CPU type , number of CPU slots , memory size , storage size , operating system , as the computing resources, thus we have (1) For computing resources virtualization, CPU slots, memory and storage are split into slices, each of which becomes the resource of one virtual machine on server c. Differently, and cannot be split. Thus we use (2) to denote the configurations (computing resources) of virtual machine (VM) vc at time point t on server c , vc∈Δc, where Δc is the set of VMs on server c, , and are the CPU slots, memory size and storage size allocated to VMvc at t, respectively. Let |Δc| denote the size of Δc. Here we redefine the operator to represent the summation of computing resources. Definition 1. The summation of the computing resources is the summation of splittable factors while keeping others unchanged, i.e.,
Note that, different from the widely adopted summation operator Σ, only calculates the summation of the splittable elements in , such as CPU slot, memory and storage size, while keeping others, such as CPU and China Communications • May 2014
operating system type, unchanged. Thus we have , where
denotes the spare computing resources after setting up the VMs on server c, and
(3)
to represent the total bandBesides, we use width (data transmission rate) of server c, and to represent the allocated bandwidth of VM
vc on at t. Similarly,
,
(4)
where is the spare bandwidth after allocating the bandwidth to the VMs on server c. We also define as the price charged by the cloud server c for the cloud computing service per time unit.
3.3 Tenant task model To initiate the cloud computing task, a tenant sends a request to the cloud via the mobile access networks, with the information of its requirements of the computing resources and expectation of the price for the services. We use to represent the required computing resource at t, (5) where are the required CPU type, CPU slots, memory size, storage size and operating system type by the tenant at t, respectively. Two relation operators are introduced in this paper to describe the relationship between the required resource and the available resource at the servers. , if the computing Definition 2. of the tenant can resource requirements be satisfied by the spare computing resource of server c at t, i.e. is compatible with is compatible with , , and (6) Definition 3.
, if any element in
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of the the computing resource requirements tenant cannot be satisfied by the spare computing resource of server c at t, i.e. either is not compatible with , is not compatible with , , or . (7) Besides, we use and to describe the data transmission rate requirement and the price expectation of the tenant at time point t, respectively.
IV. RESTLESS BANDITS FORMULATION In this section, we formulate the joint cloud and radio resource management for video transmissions problem as a restless bandits system, which is an extension of the classical multiarmed bandit problems [29]. It can be solved according to the indices of the projects, which are calculated by the linear programming (LP) relaxation [22]. Recent advances in solving the stochastic restless bandits problem make it a powerful modeling framework.
4.1 System states In the HMC networks, a tenant initiates the computing task by sending a request to the cloud via mobile access networks. The task request encapsulates the specifications on the required computing (hardware and software) resources , the data transmission resources and the price expectation . Denote by ≮tc the set of all possible specifications on the required computing resource,≮tr the set of all possible specifications on the required data transmission rate, and≮tp the set of all possible specifications on the price expectation per time slot. Then we can use to denote the tenant requirement state at time point . Besides, we use≮t to represent the set of all possible states of , and |≮t| to represent the size of the set ≮t. Taking heterogeneous cloud into account, cloud servers have various hardware, software, connection and price configurations. As depicted in the system model section, the cloud infrastructure including servers and network connections can be abstracted as a non-over-
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subscript model. We represent the state of cloud server at time point t as
, which
represent the residual computing resource, communication bandwidth and cloud service price. To reduce the computational complexity of the dynamic resource mancan be simplified as agement scheme, , this is because the states are constant during the whole time line considered, thus for a dynamic optimization problem, they could be excluded from the stochastic system states. Then,
(8)
Besides, we use≮c to represent the set of all possible states of , and |≮c| to represent the size of the set ≮t. The state of the heterogeneous mobile access network is also a stochastic and dynamic one. We use σl,t to represent the state of mobile access network at l time point t, . Besides, we use ≮m to represent the set of all possible states of σl,t, and |≮m| to represent the size of the set ≮m. In our proposed dynamic resource management scheme, on receiving the cloud computing task requirement from the tenant, the mobile access networks cooperate with each other to make the optimal decision on which mobile network is selected to provide the access service for the tenant, which will be discussed in Section 5.1. When the optimal mobile access network l* is selected, the price for wireless access service is determined, which is repreat t. sented by To formulate the cloud server scheduling as a restless bandits problem, we need to clarify the system state of each cloud server. The system state of server c at time point t is
≮
(9)
w h e r e ≮i s t h e s e t o f a l l p o s s i b l e , . As a part of , denotes the tenant’s expected price to pay for the cloud service. Let ≮t' be the set of all possible values of , and |≮t'| be the size of ≮t'.
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4.2 One-step state transition probabilities
ability of state from tion can be written as
Due to the heterogeneity of the cloud and the mobile access networks, assuming the tenant tasks as a simple Poisson process may not be proper. Consequently, in this paper we consider the tenant arriving at and departing from one cloud server as a Poisson process, taking into account the resource heterogeneity and task requirement diversity. Assume that the tenant leaving process is Poisson distributed, of which the tenant departure rate isν t , thus the probability of n tenants leaving the cloud in a time unit is
(10)
We also assume the tenant service arriving process to be Poisson distributed with the arriving rate μt, then the expected time length between two adjacent decision time points is 1/μt. Let Uq represent the tenant, where Q is the number of tenants. Define the departing tenant set and arriving tenant set to be the sets of the tenants that depart from and arrive at the cloud service, respectively. Then we can write the one-step transition probability of state from to under action a as
to
under ac-
(13)
Besides, the ones-step state transition probability of the cloud bandwidth state from to under action can be represented as , where specific states of
and
are two
. Mathematically,
(14)
Similar to
,
is
also a conditional probability, with the condiand. Let , 1≤ q ≤ Q, tion represent the requested bandwidth of tenant U q . Denote the set of tenants that satisfy to be and the set of all tenant sets
(15)
to be (16),
(11) where is the size of the set
. With these
definitions, the one-step state transition probability of the cloud server bandwidth where isfying
denotes the set of
and
sat
(12)
For simplicity, we consider only the case that all VMs on a server are allocated with the same CPU slots, memory size and storage size. Then we can use the number of available VMs that can be setup using the residual cloud server computing resource, , to represent , where . Consequently, the one-step transition probChina Communications • May 2014
(17) where is the number of tenants served by the server c at time t, and is the probability that tenant departs from the cloud server during the time period. The derivation of means that is the sum-
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mation of probabilities of all possible tenant leaving events pd, which makes the bandwidth of the server becomes . Obviously, this probability depends on and , thus instead of , we should use representing . With , the joint probability of be calculated as
and
and
(22)
where β is a discount factor. The actions in this formulation are the cloud server selection decisions. Server c’s action is denoted by ac,tat t
can
(18)
Since that the one-step state transition probabilities of other parts of system states are all independent to each other, the state transition probabilities of the system state under action a is , (19) where and are the one-step transition probabilities of δ' and , respectively. Note ' that both and are |≮ |×|≮' | matrices, with their elements on the i th row and j the column and respectively. Mathematically, t
t
(23)
ac,t =1 denotes that the server c is selected to be active to serve the tenant, and ac,t=0 denotes that the server c is passive and not selected to serve the tenant. ac,t satisfies
, for
∀1≤t≤T. is a matrix of opThe optimal policy timal actions maximizing the discounted total reward, where ≜ is the set of all admissible policies A. a(i,j) is the element of A* in row i and column j, representing the action of the th server in ∐ at the jth decision time point j. Then the optimization of the cloud server scheduling can be written as
(24)
subject to 1≤t≤T As an optimization objective function, Equation (22).
(20)
4.3 System reward and user policies The optimization of the dynamic resource management is to maximize the tenant revenue over the whole time line, while guaranteeing the required quality of services. Define the reward as
(21)
With this definition, if the residual resources of a server can satisfy the requirements of the tenant, i.e. and the price required is lower than the tenant exby the server pected price a positive reward (or tenant revenue) can be collected. Otherwise, a zero or negative reward is imposed. The optimization goal is to maximize the total discounted reward which is defined as
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4.4 Solving the restless bandits problem The standard restless bandits problem allows M out of Nobjects to be active at time t. A reward is gained for each object c, with its state changing according to the one-step transition probability matrices . The total reward is time-discounted by the discount factor β. The aim is to find the optimal policy to maximize the expected total discounted reward. The restless bandits approach has an indexable rule that reduces the computational complexity significantly. For object c in state , we denote by the index . Then the optimal actions can be written as
(25) where and are the indices of object c and c' respectively. To solve the restless bandits problem formulated in the previous subsections, a hierarChina Communications • May 2014
chy of increasingly stronger linear programming (LP) relaxations is developed based on the result of LP formulations of Markov decision chains (MDCs) [22], the last of which is exact. To reduce the computational complexity, a heuristic algorithm for the stochastic restless bandits problem can be used, utilizing the information contained in optimal primal and dual solutions to the first-order relaxation [22].
V. DYNAMIC RESOURCE MANAGEMENT PROCESS AND COMPLEXITY ISSUES In the proposed optimal dynamic resource management process, at each decision time point t, 1≤t≤T, a request from the tenant is sent to the cloud computing networks via the mobile access networks, after which the radio resource and cloud computing resource management decisions are made.
5.1 Mobile network resource management process The process of mobile network resource management is as follows. 1. At decision time point t, 1≤t≤T, on receiving the tenant’s request, each mobile network l, l∈⋯, calculates its immediate reward R l,t if it provides the access service to the tenant.
(26)
which implies that the reward of mobile network l is non-negative if and only if the service with relatively good quality and low price can be provided to the tenant. 2. With Rl,t for each l∈⋯, the optimal decision on the selection of mobile access network at t is made.
(27)
i.e. if the requirements of the requesting tenant cannot be satisfied by any mobile network, then no mobile network is selected, and no request is forwarded to the cloud networks. China Communications • May 2014
3. The tenant acceptance/rejection step is processed as follows. a) If , mobile access network l* allocates the radio resources (to satisfy the tenant requirement ) to the communication between its BS/AP and the requesting tenant, and forward the tenant’s request to the cloud. Simultaneous, a price is charged. b) Otherwise, inform the tenant that the requirement cannot be satisfied and reject the request. 4. Repeat the above steps.
5.2 Cloud resource management process The cloud servers cooperate with each other to management the cloud computing and communication resources at decision time points t, 1≤t≤T. The spectrum allocation is based on the indices of the servers, which are computed off-line for each available state of each server, and stored in index tables. In the on-line stage, it is only needed to look up from the tables to decide the current index according to the current state. Off-line Computing: In the cloud computing initialization procedure, each server c computes the indices , according to the algorithm in Section 4.4, based on the possible states sc∈≮, the transition probabilities , the reward Rc,t, and the discount factor β. Store , {psa(i,j)} and {Rc,t} in a table. On-line Allocating: 1. At each decision time point t, 1≤t≤T, on receiving the request forwarded by the mobile access network l*, each server c, t, 1≤t≤T, c∈∐, updates its system state sc,t. 2. Each server decides whether the requirements of the tenant can be satisfied a) If so, server looks up from its index table to find the index corresponding to the current state sc,t. b) Otherwise, server c sets its index , representing the state that the requirements cannot be satisfied.
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3. Server c shares its index with other servers. 4. After receiving all the indices from other servers, c sorts the list of the indices of all servers from the lowest to the highest. A server is set active if its index is the lowest item of the sorted list. Other servers are passive. Assume that server c' is the active one. a) If , c' accepts the request from the tenant, and allocates the computing and communication resources according to the tenant’s requirements and . Simultaneously, a price is charged. b) Otherwise, inform the tenant that the requirement cannot be satisfied and reject the request. 5. Repeat the above steps.
consider three RATs: 3G-LTE, WiMAX and WLAN. Since that we mainly focus two QoS parameters (QoS satisfactory probability and the gap between the tenant expected costs and the actual costs), admission sets and access prices of the mobile access networks are considered in the simulations of this paper. Besides, the bandwidth and price parameters of the mobile networks are adopted from [23] and [30], which are also shown in Table I. In the heterogeneous cloud computing networks, we assume three types of servers, each of which is configured with the following parameters.
5.3 Computing complexity analysis In heterogeneous mobile cloud computing networks, computing complexity issue, which is key to real-time joint resource management, needs to be taken into consideration. In our proposed scheme, all the indices are computed and stored off-line into the index table in the off-line stage. What the cloud servers need to do on-line is simply looking up from the index table according to several parameters, sharing the indices and comparing them, as described in Section 5.2. By the proposed algorithm, the original NP-hard problem can be reduced to simply selecting the server with the smallest index. Thus the computational complexity in the proposed scheme is significantly reduced. Moreover, since the index is computed for each server separately and there is no need for a centralized control point, the resource management scheme is easily scalable.
VI. SIMULATION RESULTS AND DISCUSSIONS In this section, based on our simulation platform for heterogeneous with cloud computing module, the performance of the proposed dynamic radio and cloud resource management scheme is illustrated by computer simulations. In heterogeneous mobile access networks, we
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where ,
, ,
, ,
, ,
,
, , , and . Assume the set of all possible tenant expected cost to be {$ 0.5 per hour, $ 0.9 per hour, $ 1.3 per hour, $ 1.7 per hour, $ 2.1 per hour} with the distribution probabilities {0.2,0.2,0.2,0.2,0.2} [31]. The tenants also randomly request other resource parameters, i.e., , ={4 GB,5 GB,6 GB}, ={20GB,40GB,60GB} and . In our simulations, the off-line computation takes about 15 seconds using a regular PC with Intel(R) Core(TM) i3-2120 CPU @3.30GHz and 4.00 GB RAM. To illustrate the convergence characteristic of our proposed scheme, in Figure 2, the system reward and tenant costs performance is presented. Three types of servers and three types of radio access networks are assumed in this simulation. Clearly, the system reward and tenant costs of our proposed dynamic resource management scheme converge within 10 steps and become stable afterward. In our simulations, two existing schemes
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Table I Simulation Parameters of Radio Access Networks Parameter
Value
Parameter
Value
Spectrum band
2.66 GHz
UE bandwidth
10 MHz
Antennas
2×2
HARQ
3 retransmissions
Link adaptation
Enabled
Code rate
From 0.1 to 0.93
Modulation
QPSK/16QAM/64QAM
3G-LTE
WiMAX System Bandwidth
7 MHz
Number of carriers
256
Number of data carriers
192
Sampling factor
8/7
Guard period ratio
1/4
Average SNR
15 dB
Average channel bit rate
11 Mbps
Slot time
11 s
Propagation delay
1s
Time to transmit a PHY header
48 s
Time to transmit a MAC header
25 s
Time to transmit a RTS
15 s
Time to transmit a CTS
10 s
Time to transmit an ACK
10 s
AIFSN
1
Maximum contention window
32
WLAN
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1 System reward Tenant costs
0.9
System reward / tenant costs
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0
10
20
30 40 Decision time point t
50
60
Fig.2 System reward and tenant costs for each decision time point.
0.8 0.7 0.6 0.5 System reward
are considered, both of which ignore the optimal joint radio and computing resource scheduling to maximize the tenants’ revenue in HMC networks, as presented in [17]. Existing scheme I separately optimize the radio and computing resources for better QoS, and existing scheme II schedule the resources without QoS-aware optimization. We plot the system reward performance in Figure 3 with different numbers of servers of each type. As defined previously, three types of servers are considered in the simulation, the number of which is shown on the x-axis. For example, the x-axis being 2 denotes that there are 6 servers, 2 of which is of each type. From the figure we can see that the proposed scheme significantly improves the system reward for number of servers from 3 to 18, compared to the existing ones that ignore the joint mobile and cloud computing resource management. In Figure 4, we use different mobile access network price to compare the system reward performance. According to [23] and [30], the price of WLAN access is about $ 0.05 per hour, and the prices of WiMAX and LTE are approximately 2 and 4 times of WLAN access, respectively. Thus in Figure 4, we change the price of WLAN, keeping the prices of WiMAX and LTE 2 and 4 times of that. Similar
0.4 0.3 0.2 0.1 Proposed Scheme Existing Scheme I Existing Scheme II
0 -0.1
1
2
3 4 Number of servers of each type
5
6
Fig.3 System reward comparison with different numbers of servers of each type.
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to Figure 3, our proposed scheme also outperforms the existing ones. Besides, the system reward of all the schemes decreases as the prices goes up, this is because higher costs induce lower reward as shown in Equation (20). Since that the optimization objective in this paper is to maximize the tenant revenue, which is equivalent to minimizing the tenant costs, we compare this performance of the proposed scheme with the existing ones in Figure 5 and 6. In Figure 5, with the increasing of the number of servers of each type, the costs rise for all the three schemes. This is because that the more the tenant requests can be satisfied, the higher the costs are needed for the services. No matter how many servers are considered in the cloud network, the tenants’ costs are always the lowest.
0.7 0.6
System reward
0.5 0.4 0.3 0.2 0.1 0 -0.1 0.01
Proposed Scheme Existing Scheme I Existing Scheme II 0.03
0.05 Price of WLAN access
0.09
0.07
Fig.4 System reward comparison with different prices of mobile access networks.
0.9 Proposed Scheme Existing Scheme I Existing Scheme II
0.8
Tenant costs
0.7 0.6 0.5 0.4 0.3 0.2
1
2
3 4 Number of servers of each type
5
6
Fig.5 Tenant cost comparison with different numbers of servers of each type.
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Similar conclusion that our proposed scheme provides the lowest costs compared to existing ones can be drawn from Figure 6. We use the simulation parameters in Figure 4. The tenants’ costs increase linearly to the mobile access network price for the existing schemes, but the increase of our proposed scheme is not quite dramatic, thanks to the algorithm trying to minimize the tenant costs by dynamically schedule the computing and radio resources in the heterogeneous mobile cloud networks. In Figure 7, 8 and 9, we demonstrate the QoS satisfaction probability with different simulation parameters. QoS satisfaction probability is defined as the probability that the tenant task request is accepted and the requirements are satisfied. From Figure 7 we can see that, with our proposed scheme that jointly optimize the cloud and radio resource, the QoS satisfaction probability is almost 90% with adequate number of cloud servers, but existing schemes performs much worse. This is because that the scheme proposed in this paper jointly and optimally allocates the most proper radio and computing resources to the heterogeneous tenant tasks. As the mobile access network prices increase, the QoS satisfaction probability performance, which is shown in Figure 8, does not dramatically varies for all the three schemes, due to the fact that QoS satisfaction probability is affected not by the prices but by tenant requests and available resources. For all the difference prices of mobile access networks, our proposed scheme always outperforms the existing ones significantly. We compare the QoS satisfaction probability for different cloud server configurations in Figure 9 as well. Configuration I provides the strongest server capacity and Configuration IV has the lowest. Due to the space limitation, we omit the detailed description of the configurations of the servers. As the computing and communication capabilities of the servers increase, the performance can be improved. Our proposed provides the highest QoS satisfaction probability, and the random scheme has the lowest. For Configuration III and IV, China Communications • May 2014
In mobile cloud computing systems, the heterogeneity of both the mobile access networks and the cloud computing networks can be investigated for improving the service quality. In this paper, with the introduction of heterogeneous mobile clouds, a novel cross-network radio and cloud resource management scheme for HMC networks was proposed to jointly schedule the resources on both sides, with the optimization objective of maximizing the tenant’s revenue while satisfying the tenant’s various requirements on both communication QoS and computing resources. Furthermore, taking the tenants’ dynamic requirements and the system’s dynamic status into consideration, the proposed scheme has been formulated as a restless bandits problem to dynamically optimize the radio and cloud resource allocation. The “indexability” feature of the formulation guaranteed low computational complexity with scalable and distributed characteristics. Extensive simulation results were also presented to demonstrate the significant China Communications • May 2014
Proposed Scheme Existing Scheme I Existing Scheme II
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Fig.6 Tenant cost comparison with different prices of mobile access networks.
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VII. CONCLUSIONS
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Fig.7 QoS satisfaction probability comparison with different numbers of servers of each type.
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the performance doesn’t change significantly because the servers are over-qualified for the tenant services. Resource utilization efficiency is another key performance for HMC networks. We compare the cloud computing resource utilization efficiency in Figure 10 and 11. Since that the proposed scheme aims to optimally manage the radio resource by scheduling the cloud servers, it improves the computing resource performance significantly compared to the existing ones, as demonstrated in Figure 10. Furthermore, in Figure 11, the computing resource efficiency performance with different mobile access network prices is compared. Similar to the results shown in Figure 8, the computing resource efficiency performance does not quite depend on the prices for all the three schemes. We can also find out from this figure that our proposed scheme is still much better than the existing ones, for price of WLAN access from $0.01 per hour to $0.09
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Fig.8 QoS satisfaction probability comparison with different prices of mobile access networks.
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performance improvement compared with the existing schemes.
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ACKNOWLEDGEMENTS
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This work was supported in part by the National Natural Science Foundation of China under Grant 61101113, 61372089 and 61201198, the Beijing Natural Science Foundation under Grant 4132007, 4132015 and 4132019, and the Research Fund for the Doctoral Program of Higher Education of China under Grant 20111103120017.
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Fig.9 QoS satisfaction probability comparison with different cloud server configurations.
References [1]
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[6]
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[7]
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[8]
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Fig.11 Computing resource efficiency comparison with different prices of mobile access networks.
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Biographies SI Pengbo, received the B.E. and Ph.D. degree in communications engineering and communication and information system both from Beijing University of Posts and Telecommunications, Beijing, China, in 2004 and 2009, respectively. He joined Beijing University of Technology, Beijing, China, in 2009, where he is currently an Associate Professor. During November 2007 and November 2008, he was a visiting PhD student at Carleton University, Ottawa, Canada. His research interests include cognitive radio system, heterogeneous wireless networks, distributed wireless networks. He served as the Technical Program Committee (TPC) Co-Chair of the IEEE ICCC-GMCN’2013, Program Vice-Chair of IEEE GreenCom’2013, and TPC member of numerous conferences including IEEE GLOBECOM’2013/2011, ISCI’2013, ICUMT’2013/2012/2011, CHINACOM’2012. He was the principle investigator of the National Natural Science Foundation of China, Beijing Natural Science Foundation, etc. He is a member of the IEEE. *The corresponding author. Email: sipengbo@bjut. edu.cn
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ZHANG Qian, received the B.E. degree in communications engineering from Beijing University of Technology, Beijing, China, in 2013. She is currently a M.S. candidate at Beijing University of Technology. Her research interests include mobile cloud computing and cognitive radio networks. She is a student member of the IEEE. F. Richard Yu, received the PhD degree in electrical engineering from the University of British Columbia (UBC), Vancouver, Canada, in 2003. From 2002 to 2004, he was with Ericsson (in Lund, Sweden), where he worked on the research and development of dual mode UMTS/GPRS handsets. He held a position as a Postdoctoral Research Fellow with UBC in 2005 and 2006. He joined Carleton School of Information Technology and the Department of Systems and Computer Engineering (cross-appointment) at Carleton University, Ottawa, Canada, in 2007, where he is currently an Associate Professor. He received the “Thousand Talents Program” award and joined Beijing University of Technology, Beijing, China, in 2012 as a professor. His research interests include cross-layer design, security and QoS provisioning in wireless networks. He serves on the editorial boards of several journals, including IEEE Transactions on Vehicular Technology, IEEE Communications Surveys & Tutorials, Springer/ACM Wireless Networks, EURASIP Journal on Wireless Communications Networking, Ad Hoc & Sensor Wireless Networks, Wiley Journal on Security and Communication Networks, and International Journal of Wireless Communications and Network-
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ing, and a Guest Editor for IEEE Systems Journal for the special issue on Smart Grid Communications Systems. He has served on the Technical Program Committee (TPC) of numerous conferences and as the Publication Chair of ICST QShine 2010, Co-Chair of ICUMT-CWCN’2009, TPC Co-Chair of IEEE Globecom’2013 – Cognitive Radio and Networks Symp., CCNC’2013, INFOCOM-CCSES’2012, ICC-GCN’2012, IEEE VTC’2012S – Wireless Networks Track, Globecom’2011 – Cognitive Radio Network Symp., INFOCOM-GCN’2011, CNSR’2011, INFOCOM-CWCN’2010, IEEE IWCMC’2009, VTC’2008F Track 4, WiN-ITS’2007. He is a senior member of the IEEE. ZHANG Yanhua, received the B.E. degree from Xi’an University of Technology, Xi’an, China, in 1982, and the M.S. degree from Lanzhou University, Lanzhou, China, in 1988. From 1982 to 1990, he was with Jiuquan Satellite Launch Center (JSLC), Jiuquan, China. During the 1990s, he is a visiting professor at Concordia University, Montreal, Canada. He joined Beijing University of Technology, Beijing, China, in 1997, where he is currently a professor. His research interests include QoS-aware networking, channel estimation, radio resource management in wireless communications systems. He served as the Technical Program Committee (TPC) Co-Chair of the IEEE ICCC-GMCN’2013, and TPC member of numerous conferences. He was also the principle investigator of projects of the National High-Tech R&D Program of China (863 Program), National Natural Science Foundation of China, etc.
China Communications • May 2014
