challenge of embedding the various VDCs. Situations like host resource is abundant while bandwidth or switch capacity is used out, happen in data center.
ICT MANAGEMENT
Traffic-Aware VDC Embedding in Data Center: A Case Study of FatTree LUO Shouxi1, YU Hongfang1, LI Lemin1, LIAO Dan1,2, SUN Gang1,2 Key Laboratory of Optical Fiber Sensing and Communications, Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China 2 Institute of Electronic and Information Engineering in Dongguan, UESTC, Dongguan 523808, China 1
Abstract: Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands (modeled as virtual data center, VDC) to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding problem is under the assumption that switch resource is the bottleneck of data center networks (DCNs). To this end, we not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided source-destination based on the property of fat-tree, but also design the traffic aware embedding algorithm (TAE) and first fit virtual link mapping (FFLM) to map virtual data center requests to a physical data center. Simulation results show that TAE+FFLM could increase acceptance rate and reduce network cost (about 49% in the case) at the same time. The traffic aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation. Keywords: virtual data center; embedding; switch capacity; fat-tree
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I. INTRODUCTION Virtualization technology has been widely used in data center to achieve efficient and flexible use of resources. Customers employ clouds for various kinds of services hosting, like scientific computing, network processing, and MapReduce jobs. These resource requests can be regarded as the demands of virtual data centers (VDCs), where a VDC is defined as a set of VMs with an associated service level agreement (SLA), specifying the needs of CPU, memory, storage and inter-bandwidth. In practice, multiple VDCs are hosted in a shared infrastructure, various technologies are used to realize access isolation, performance guarantee and resource sharing. Effective VDC embedding techniques are one of the keys to making efficient use of data center resources. Embedding a VDC consisting of placing the VMs into suitable physical hosts and allocating routing paths for virtual links. However, such a problem is extremely challenging for several practical reasons. Firstly, since hardware, software and configuration faults are common in modern day data centers [1], VMs from a same VDC should be placed on different hosts to reduce the damage caused by machine halts. Secondly, VDC requests have multidimensional resources constraints; resources allocation in
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We not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided s o u rc e - d e s t i n a t i o n based on the property of fat-tree, but also design the traffic aware embedding algorithm (TAE) and first fit virtual link mapping (FFLM) to map virtual data center requests to a physical data center.
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data center is complex as networking sharing involved in [2]. For instance, the network allocation for a virtual link from vmA to vmB depends on any VM pairs whose traffic shares a physical link with it. Thirdly, data center networks (DCNs) suffer from insufficient switch/ router capacity. For example, commodity switch/router uses the ternary content addressable memories (TCAMs) to store forwarding rules for fast matching. However TCAMs are expensive and power-hungry, rule spaces are insufficient in switches/routers. It is particularly problematic for OpenFlow switches since its match fields explode [3]. What’s more, the traffic in data center is high activity, they can eat up rules TCAMs space easily. Fourthly, the fixed DCN structures introduce an additional challenge of embedding the various VDCs. Situations like host resource is abundant while bandwidth or switch capacity is used out, happen in data center. The shortage of host resources can be overcome easily by deploying more servers, but the shortage of network resources is even thornier. Because modern DCNs follow specific well-designed structures (e.g. Dcell[4], BCube[5], Fat-Tree[6]), adding components may break the presumed characteristics and principles. These four properties make the VDC embedding problem very difficult. In fact, the problem is computationally intractable, even if some of these properties are ignored. For example, if we regard switch resource constraints as a part of bandwidth constraints, VDC embedding problem can be reduced to the single-source unsplittable flow problem, which is NP-hard proved by [7]. Previous research has addressed the problem of virtual network (VN) embedding [8] [9]. VN embedding is mapping multiple virtual networks, constraints on the virtual nodes and links, to a shared infrastructure to make efficient use of the underlying network resources. Two aspects make the VN embedding different from VDC embedding. First, VN embedding generally treats all the substrate links equally without discrimination, while the significance of links varies in VDC embedding.
For instance, the core links are more important than edge links in data center since a core link may be used by more VMs than an edge link ordinarily. Furthermore, the VDC embedding has more constraints, as VM placement, virtual link allocation, switch resource consumption and special topologies are involved in simultaneously. Recently, Boroff et al. [10] has explored how to allocate VMs to physical servers dynamically, aiming at improving the utilization ratio of server resource. But it does not consider the network resource sharing in data center. The work of TVMPP [11] employs a clustering method to place VMs with large mutual bandwidth usage to hosts in close proximity, but it ignores the differences between VMs, and it does not provide network resource guarantee. In [7], a data center network virtualization architecture called SecondNet is designed to provide bandwidth guarantee for VDCs. SecondNet demonstrates that VDC isolation and bandwidth guarantee can be implemented with commodity devices, but it cares less about the general resource allocation problem of VDC. Unfortunately, all these efforts have given little attention to the shortage of switch capacity, which is serious, especially for further OpenFlow-based DCNs. In this paper, we study the problem of VDC embedding with bandwidth and switch capacity guaranteed in data center. Our study of VDC embedding problem is under the assumption that switching resource is the bottleneck of DCNs. We aim at making efficient use of data center resources. Our main contributions are summarized as follows: We model the general VDC embedding l problem in data center with switch capacity consumption considered. We propose relative cost to embody the cost of bandwidth and switch capacity, and to evaluate embedding strategies. Motivated by the shortage of switch capacity in enterprise data centers (OpenFlow-based especially), we believe these problem to be worth studying. Taking the example of popular fat-tree like l China Communications • July 2014
data centers as a case, thus, we focus on the problem of VDC embedding in fat-tree like data center in this paper. We decouple VDC embedding into two independent steps: VM placement with resource allocation in racks (hosts, access bandwidth and flow table of ToR switch) and virtual link mapping with decided source-destination, based on the property of fat-tree. Such a decoupling simplifies the primal problem greatly, and give a more flexible way for algorithm designs. • We propose a novel VDC embedding strategy made up of traffic aware VM placement(named TAE) and energy conservation virtual link mapping(named FFLM), to make efficient use of data center resources. Simulation results (with the scale of 100,000 VDC requests) show that our approach yielded 0.034 acceptance rate improvement and save about 49% of the network cost in the case. It reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation. The remainder of this paper is organized as follows: Section 2 defines the general VDC embedding problem and formalizes its model and objectives. Section 3 firstly gives a glance of fat-tree, then decouples VDC embedding into two independent sub-problems, and design heuristics. We evaluate these algorithms with simulations in Section 4, and conclude the paper in Section 5.
II. VIRTUAL DATA CENTER EMBEDDING PROBLEM In this section, we first describe a general model of the virtual data center embedding problem, then present its objectives and constraints.
2.1 Formal model Current data centers follow to common treelike network architectures, known as the three-tier architecture, named core, aggregation and edge respectively, as Fig.1(b) shows. China Communications • July 2014
We study the problem of placing VMs on a set of physical hosts and allotting paths for VM-to-VM links across data center switches (hereinafter referred to as hosts and switches respectively). In this paper, single-path mapping are used to avoid the out-of-order arrival problem of multi-path with assumptions. A physical data center can be described as a weighted undirected graph denoted by Gs, where V(Gs) is the set of hosts and switches, labeled Nsrv(Gs) and Nnet(Gs) respectively. E(Gs) is the set of substrate physical links. Each host or switch node v∈V(Gs) is associated with a positive integer weight c(v), Such a weight denotes the amount of available resources if the node is a host, or the number of bidirectional flow pairs that switch can accommodate if it is a switch. To specific the discussion, we use the size of rule space as an instantiation of switch capacity in this paper, and there is a specific term named flow table for the rule space in OpenFlow switches. Similarly, Each substrate link e(i,j)∈E(Gs) connecting node i and node j is associated with the bandwidth capacity weight value bw(e), denoting the bandwidth capability. In most data centers, each host connects to an access switch for networking, and the hosts connected with the same ToR (Top of Rack) switch form a rack (Fig.1(b)). So the hosts N srv(G s) in data center are those leaf nodes with 1-value degree in graph Gs. In some particular data center network architectures, such as Dcell[4], BCube[5] and MDCube[12] , each host connects to multiple switches, we can easily regard each host as a normal host binding to an ideal switch to model hosts as leaves. Similar to PDC, a VDC request is a weighted undirected graph denoted by G v, where Req.1
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each weighted node v∈V(G v) represents a virtual machine (VM) request with resource w(v), and each edge e(u,v)∈E(Gv) represents the bidirectional bandwidth demand between VM u and VM v with value bw(e), as Fig.1(a) shows. Embedding a VDC to PDC includes two mappings: for nodes, for links, where P is the set of loop-free paths between hosts, consisting of switches and links (to simplify the expression, a path does not contain two endpoints here).
2.2 Constraints & objectives For an embedding scheme π, suppose VM u and VM v are placed on host πN(u) and πN(v) , the related virtual link demand e(u,v) is mapped to path πL(e) with endpoints πN(u) and πN(v). So V(πL(e)) and E(πL(e)) represents the employed switch nodes and physical links respectively. The resource consumption of link bandwidth, switch capacity (the rule space is considered and hereinafter referred to as flow table) is determined by the total amount of accommodated traffic. We use the per-VM Pipe model [2] [7] to describe the traffic demands of VDC, and hypothesize that each VM-pair occupies a bidirectional flow entry pair in flow table for fine grained traffic control. In addition, a VDC request Gv could be accepted by data center Gs with scheme π, if and only if the capacity limitations of each host, flow table and link bandwidth are obeyed. For any certain VDC request, as the payment is fixed, the only way to maximize the revenue of accepting it is to minimize the embedding cost. It could be the weighted sum of the cost of host, flow table and bandwidth. Similar to the work in VN embedding [8], we introduce the tunable weight α and β that allows the hypervisor to strike a balance between the relative costs of these three classes of resources. So, the embedding cost can be estimated as:
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where |S| is the number of members in set S, hops(p) is the number of hops in path p, ∑e∈E(Gv)| V(πL(e))| is the depletion of flow entry pairs with no flow merging strategy employed, and α · γ = β. We define: ,
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then the cost of embedding Gv with scheme π can be formulated as NaCost(Gv) +αReCost(Gv,π), where NaCost(·) is the nature cost determined by Gv and ReCost(·) is the relative cost influenced by mapping scheme. So we use ReCost(·) to measure the quality of embedding scheme π. By the way, acceptance ratio is another evaluation index which reflects the performance of embedding schemes, more details follows Section 4.
III. EMBEDDING DESIGN: A CASE STUDY OF FAT-TREE To get a clear understanding of VDC embedding, we take the well-known fat-tree topology data center [6] as a case study in the paper. Firstly, we give a brief introduction of fat-tree, and then analysis the characteristics of fat-tree architecture. After that, we decouple the VDC embedding into VM placement with resource allocation in racks (hosts, access bandwidth and flow table of ToR switch) and virtual links mapping with decided source-destination, to simplify the problem, based on the property of fat-tree. Since both problem is hard, we design heuristics for VM placement and virtual link mapping at the end.
3.1 Fat-tree at a glance Fat-tree is a clos-like topology, Fig.2 depicts a 4-ary fat-tree built as a multi-stage topology from constituent 4-port switches. We split
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the fat-tree into three layers, labeled edge, aggregation and core as Fig.2 shows. There are k pods, each containing two layers (edge and aggregation respectively) of k/2 switches. A k-port switch in the lower layer is directly connected to k/2 hosts, its remaining k/2 ports are connected to k/2 aggregation switches in the same pod. There are (k/2)2 k-port core switches. Each core switch has one port connected to each of k pods. The ith port of any core switch is connected to pod i such that consecutive ports in the aggregation layer of each pod switch are connected to core switches on (k/2) strides. In general, a k-ary three-stage fat-tree built from k-port switches can support non-blocking communication among k3/4 end hosts using 5k2/4 individual k-port switches.
3.2 Observations and embedding strategy design Fat-tree is rearrangeable non-blocking, meaning that for arbitrary communication patterns, there is some set of paths that will saturate all the bandwidth available to the end hosts in the topology. For VDCs, if their demands are met at racks (including hosts and access switches), there will always be an allocation(s) that make all traffic demands met. So, VDC embedding in fat-tree data center can be decoupled into two steps: (1) VM placement with resource guaranteed in racks, (2) and virtual link mapping with decided source-destination. However, this is by no means that the link mapping is easy to realize and always successful. In fact, bad VM placement schemes would make link mapping hard and failure-prone, which will be confirmed in simulations. As equation (3) in Section 2.2 shows, less hops a mapping scheme uses, the better performance it achieves. This is expected since the less network resources an allocation uses, the more VDCs it can accept. From Fig.2, it is obviously that, for VM pairs, the cost for intra-rack, inter-rack but intra-pod and inter-pod traffic are constant in the ratio of 1:3:5, according to equation(3). This gives us the insight of placing VMs with heavy traffic China Communications • July 2014
in a same rack or pod to reduce the total cost. To achieve this, we design heuristics to place VMs, as it is extremely likely NP-complete [13]. After the hosts of all VMs were determined, virtual link mapping and path allocation follows. Obviously, there may be multiple equivalent paths for each link. Since VDC embedding aims at reducing the network traffic cost, resulting in low load of core link traffic, aggregate them and shut the idle switches and links down can save energy [14]. We map virtual links in an energy conservation way.
3.3 VM placement Aiming at placing VMs with heavy traffic close with each other to reduce the embedding cost, we can greedy place each VM to a host that cased the least communication cost addition repeatedly. A description of such a procedure is shown in Fig.3, named Cost-aware Embedding (CAE). Core
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A brief explanation is following: suppose a new VDC request Gv arrives and the data center with residual resources Gs needs to allocate resources to host it. Embedding blue print is stored in π (π consists of πN and πL). CAE places VMs to the candidate hosts in turn using greedy-based strategies. Each round, CAE chooses an un-embedded VM u, which holds the heaviest link with embedded VMs, places it on a host causing the lowest relative cost increasing with all embedded VMs, called opt in Fig.3. The increased cost of embedding u to host s is formulated by ∑e∈ed hops(es,πN(v)) (1+γ·bw(ev,u)), where ed is set of all embedded VMs, and e s,πN (v) may be any path between host s and πN (v). Even though the path is not chosen yet, but hops(es,πN(v)) is determined by the relative locations of two end-points. Suppose the number of VMs and candidate hosts is n and m respectively, since embedding a VM needs to check every unused candidate with all the embedded VMs, the computational complexity of this algorithm will not exceed O(n2m). Actually, we can pre-partition available hosts into candidate-clusters and do VDC embedding parallelly in practice. The computational complexity could approximate to O(n3), if effective partition were made. Cost-aware Embedding proposes an economical way to place VMs. It greedily chooses the local optimum host each step, this may miss some important opportunities. Inspired by TVMPP [11], we propose a traffic aware embedding algorithm (TAE) to place VMs more economically and wisely. TVMPP employs a method adapted from minimum k-cut algorithm to partition VMs into VM-clusters according to inter-VM traffic and data center network characteristics, then embeds VM-clusters to pre-partitioned slots(similar to hosts) clusters to reduce the networking cost. For a request of n VMs, the complexity and approximation ratio for grouping algorithm used in TVMPP is O(n4) and (k-1)n/k. Unfortunately, because of the methods in TVMPP promise nothing to VMs, they cannot be employed for VDC embedding as resource
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guarantee wanted. In particular, VM grouping and embedding are independent in TVMPP, thus the size of a VM-cluster is fixed once partitioned. There is no guarantee that all pre-grouped VM-clusters can meet suitable hosts-clusters. Unlike TVMPP, TAE adjusts the scales of VM-clusters dynamically, rather than fixed size pre-partition. This avoids the failure of resources allocation for VM-clusters. Fat-tree like data centers consist of pods, racks and hosts recursively. TAE first finds the idlest pod (the one has the maximum available hosts, denoted by pi), and idlest rack (denoted by rj), then places the VM with the heaviest total bandwidth demands to the first available host in rack rj. After that, TAE iteratively finds the heaviest link that connects an un-embedded vm (denoted as u) with an embedded (denoted as v), and try to place u into the same rack or pod with v in turn. In this way, VMs are partitioned into nondeterministic VM-clusters and their scales increase during embedding. The first step in TAE is very important as it increases the possibility of embedding most of heavy traffic VMs into a single rack or pod. Actually, all the VMs in some thin VDC could be embedded in a rack or pod. The procedure of TAE is described in Fig.4, we implement TAE in the iteration style and its computational complexity is similar to CAE. We also implement another common baseline embedding algorithm (BLE) regardless of VM-pair traffic and inter-hosts cost. In BLE, VM placement is carried out by retrying to place VMs into candidate hosts in turn, both VMs and candidates are in ascending order according to bandwidth demands/volume. The comparisons between BLE, CAE and TAE are shown in Section 4.
3.4 Virtual link mapping Virtual link mapping is more restrained in a fat tree topology, after all the end-points were determined. There may be multiple candidate paths for each virtual link. In practical terms, when the two end-points of a virtual link are China Communications • July 2014
in the same rack, there is only one path for it. But, there are k/2 paths for each intra-pod virtual link and k2/4 for each inter-pod virtual link. In consideration of turning off switches is more effective than ports [14], we focus on use the fewest switches to carry all inter-hosts traffic. To achieve that, we design a First-Fit way called First Fit Link Mapping (FFLM) to map virtual links, as it is similar to the problem of Bin Packing [15] here. A brief explanation is following: FFLM firstly sorts all virtual links E(Gv) in descending order and allocates path for each in turn. If there are multiple candidate paths for a link, FFLM greedily chooses the one with the minimum residual resources, where the available residual resources of path p is defined as mine∈E(p)w(e), as Fig.5 shows. The computational complexity of mapping m links into a k-ary fat-tree data center with FFLM will not exceed mk2/4. In fact, it is easier to map the links of a TAE-placed VDC, as most of its links may are intra-rack, or intra-pod. We apply FFLM to map virtual links for BLE, CAE and TAE, simulations results follow in Section 4.
4.1 Simulation environment We implement a discrete event VDC embedding simulator using Python to evaluate the performance of our algorithms. The substrate data center is a k-ary fat-tree topology built
IV. SIMULATIONS In this section, we first describe the simulation environment and parameters definition, then present our main simulation results. The simulation results show that, combining with the same virtual link mapping algorithm (First Fit Link Mapping, FFLM), both cost aware embedding (CAE) and traffic aware embedding (TAE) increase the acceptance ratio of VDC requests with less network resource consuming compared with ordinary baseline embedding (BLE). In general, those VM placement schemes that are prone to make heavy traffic locally, can reduce the amount of cross domain traffic and make link mapping more easily. Moreover, the fact of switch capacity shortage dragging the whole resource utilization down is also shown in comparison.
Fig.4 Traffic Aware Embedding (TAE)
Fig.5 First Fit Link Mapping (FFLM) China Communications • July 2014
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up with homogeneous commodity servers and switches. We assume the VDC requests arrive in a Poisson process and have exponentially distributed. We measure both the acceptance ratio, ReCost of VDC requests and the variation of edge resources utilization ratio of the substrate data center. Edge resources include host capability, access link bandwidth and the flow table of edge switches (named as ToR FlowTable in figures). We also count both the idle aggregation and core switches obtained by CAE+FFLM and TAE+FFLM. We do lots of experiments and find different values achieved but the same law observed, when VDC/PDC parameter varies. This is expected since the compute-intensive VDC is much different from the network-intensive. The diagrams we present in the paper is a result of network intensive VDC requests and the parameters are defined as following: the PDC is a 16-ary fat-tree, each host possesses 128 units of resources, while each switch can manage 1,500 flow pairs concurrently and the bandwidth capacity of each link is 4,000 units. For each VDC request, the number of VM is randomly determined by a uniform distribution between 3 and 40, the host resource demands for each VM are chosen uniformly from 1, 2, 4, 8 or 16 units. Each pair of VMs are randomly connected with probability 0.6, and the corresponding bandwidth requirement follows a uniform distribution between 1 and 40 units. This means that for a n-node virtual data center, we have 0.3∗n(n − 1) links on average. The arrivals of VDC requests are
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4.2 Simulation results Fig.6 shows the acceptance ratio of these algorithms. After about 45,000 time windows, the experiment arrives a steady state. Both acceptance ratio and edge resource utilization ratio vary in intervals after that. The acceptance ratio(s) of them are about 0.743(BLE+FFLM), 0.775(CAE+FFLM) and 0.777(TAE+FFLM) respectively. Traffic aware embedding can accept more VDC requests than BLE and CAE, the gap of revenue in the long term would be huge, the PDC employing TAE accepts more about 3,399 VDC requests than BLE after about 100,000 time windows in the case. As mentioned before, ReCost is influenced by mapping schemes. We use Relative Cost Ratio to evaluate the quality of embedding algorithms. Relative Cost Ratio is defined as the ratio of reality ReCost to ideal ReCost, where ideal ReCost achieved if all links were mapped to one-hop paths. In fat-tree data center, it is obvious that Relative Cost Ratio belongs to [1, 5], and 1, 3, 5 is the ratio of pure intra-rack, intra-pod, inter-pod embedding respectively. Fig.7 shows the cumulative distribution function (CDF) of Relative Cost Ratio for different algorithms on the same VDC requests set. For each algorithm, all those VDCs rejected by the algorithm are ignored when counting. The statistical result shows about 30% and 69% of VDCs are intra-Rack and intra-Pod embedding in TAE+FFLM, while almost all the VDCs are cross-Pod embedding in BLE+FFLM. We also calculate the total relative cost saving of the VDCs accepted by both BLE+FFLM, CAE+FFLM and TAE+FFLM. The result shows CAE+FFLM and TAE+FFLM achieves about 20% and 49% cost saving respectively, compared with BLE+FFLM in the case. The Relative Cost Ratio of TAE is better than CAE, we think this is because CAE only considers the China Communications • July 2014
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increased cost with all embedded VMs when embedding a new one, which may separate heavy traffic VM pairs into different racks or pods with unreasonable embedding order. Fig.8 shows the utilization ratio of edge resources. The utilization ratios of host resource and edge bandwidth of TAE+FFLM and CAE+FFLM are very close with each other, and both better than those of BLE+FFLM. It is reasonable as the VDC acceptance ratio of TAE+FFLM and CAE+FFLM are very close, and both better than BLE+FFLM. On the other hand, the utilization ratios of ToR flow table of BLE+FFLM are higher than CAE+FFLM, and that of CAE+FFLM are higher than TAE+FFLM alike. This is because if two linked VMs are hosted in the same rack, the link only costs one flow table entry pair in the ToR switch, instead of two. TAE+FFLM produces more intra-rack embedding. Such a result is in conformity with the result shown in Fig.7. The VDC embedding methods here are made up of VM placement and virtual link mapping, we find that link mapping is not always successful. Fig.9 shows the failure ratio of link mapping varies with time. In the case, no virtual link mapping failure occurs in TAE. TAE makes link mapping much more easily since most of its virtual links are intra-rack or intra-pod. We also evaluate the amount of idle switches got by BLE+FFLM, CAE+FFLM and TAE+FFLM in steady state, results are shown in Fig.10. The VDCs shown in the case are network-intensive, as the utilization ratio of ToR flow table and edge bandwidth are high (more than 90%), while the utilization ratio of host resource is less than 70%. Simulations in our previous work [13] have shown that reducing the size of flow table would make acceptance ratio and utilization ratio of host resource and edge bandwidth decrease. This is because the state of data center could be steady only if any kind of resources is nearly used out so that no more VDC requests can be accepted, or resources are abundant all the time and the acceptance ratio is 100%. Simulation results show that TAE achieves
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an efficient use of resources. TAE reduces the load of aggregation and core switches, and make link mapping much more easily. It uses less aggregation and core switches without damaging the quality of service, and gives the opportunity for network energy conservation in data centers.
V. CONCLUSION We have proposed virtual data center (VDC) as customer resource demands unit in cloud. Then we studied the problem of VDC embed-
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(named FFLM) to achieve efficient link embedding. Our large-scale simulation results show that TAE+FFLM yielded 0.034 acceptance rate improvement and saved 49% of the network cost simultaneously, compared with ordinary embedding algorithm regardless of inter-VM traffic and inter-hosts cost in the case. Traffic aware embedding reduces the load of core link traffic and brings the optimization opportunity for data center network energy conservation.
ACKNOWLEDGEMENT This research was partially supported by the National Grand Fundamental Research 973 Program of China under Grant (No. 2013CB329103), Natural Science Foundation of China grant (No. 61271171), the Fundamental Research Funds for the Central Universities (ZYGX2013J002, ZYGX2012J004, ZYGX2010J002, ZYGX2010J009), Guangdong Science and Technology Project (2012B090500003, 2012B091000163, 2012556031).
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Fig.10 The amount of idle switches
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Biographies LUO Shouxi, is currently a Ph.D. student in University of Electronic Science and Technology of China, Chengdu, China. His research interests include data center networks and software-defined networking. YU Hongfang, received her B.S. degree in Electrical Engineering in 1996 from Xidian University, her M.S. degree and Ph.D. degree in Communication and Information Engineering in 1999 and 2006 from University of Electronic Science and Technology of China, respectively. From 2009 to 2010, she was a Visiting Scholar t the Department of Computer Science and Engineering, University at Buffalo (SUNY). Her research interests include network survivability and next generation Internet, cloud computing etc. LI Lemin, graduated from Jiaotong University, Shanghai, China in 1952, majoring in electrical engineering. From 1952 to 1956 he was with the Department of Electrical Communications at Jiaotong University. Since 1956 he has been with Chengdu Institute of Radio Engineering (now the University of Electronic Science and Technology of China). From August 1980 to Aug. 1982, he was a Visiting Scholar in the Dept. of Electrical Engineering and Computer Science at the University of California at San Diego, USA, doing research on digital and spread spectrum communications. His present research work is in the area of communication networks including broadband networks and wireless networks. LIAO Dan, is an associate professor at University of Electronic Science and Technology of China (UESTC). He received his B.S. degree in Electrical Engineering in 2001 from UESTC, and his Ph.D. degree in Communication and Information Engineering in 2007 from University of Electronic Science and Technology of China, respectively. His research interests are in the area of wired and wireless computer communication networks and protocols, next generation network. SUN Gang, received his M.S. degree in Signal and Information Processing from Chengdu University of Technology, China in 2009, and the Ph.D. degree in Communication and Information Engineering in 2012 from University of Electronic Science and Technology of China. His research interests are in the area of network virtualization, cloud computing and next generation Internet.
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