Minimizing Resource Cost for Camera Stream Scheduling in Video Data Center

Transcription

1 Gao YH, Ma HD, Liu W. Minimizing resource cost for camera stream scheduing in video data center. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 32(3): May DOI /s x Minimizing Resource Cost for Camera Stream Scheduing in Video Data Center Yi-Hong Gao, Hua-Dong Ma, Feow, CCF, and Wu Liu, Member, CCF Beijing Key Laboratory of Inteigent Teecommunications Software and Mutimedia, Beijing University of Posts and Teecommunications, Beijing , China E-mai: hii {mhd, Received May 20, 2016; revised December 3, Abstract Video surveiance service, which receives ive streams from IP cameras and forwards the streams to end users, has become one of the most popuar services of video data center. The video data center focuses on minimizing the resource cost during resource provisioning for the service. However, itte of the previous work comprehensivey considers the bandwidth cost optimization of both upoad and forwarding streams, and the capacity of the media server. In this paper, we propose an efficient resource scheduing approach for onine muti-camera video forwarding, which tries to optimize the resource sharing of media servers and the networks together. Firsty, we not ony provide a fine-grained resource usage mode for media servers, but aso evauate the bandwidth cost of both upoad and forwarding streams. Without oss of generaity, we utiize two resource pricing modes with different resource cost functions to evauate the resource cost: the inear cost function and the non-inear cost functions. Then, we formuate the cost minimization probem as a constrained integer programming probem. For the inear resource cost function, the drift-pus-penaty optimization method is expoited in our approach. For non-inear resource cost functions, the approach empoys a heuristic method to reduce both media server cost and bandwidth cost. The experimenta resuts demonstrate that our approach obviousy reduces the tota resource costs on both media servers and networks simutaneousy. Keywords video data center, resource scheduing, video surveiance as a service, muti-camera networking 1 Introduction IP video deivery is a hot research topic of distributed mutimedia systems [1]. Many video data center (VDC) techniques are proposed to anayze and deiver arge-scae video streams [2-8]. Recenty, video stream forwarding from IP cameras is widey empoyed by video data center to provide video surveiance as a service (VSaaS) 1. Different from other video deivery services, VSaaS requires IP cameras to execute anaysis tasks. The media server of the video data center receives the anayzed video streams from IP cameras, and then forwards them to end users [4-6]. Usuay, one media server simutaneousy serves more than one end user. Ony one network connection is buit up between the media server and each IP camera to receive the ive video stream, which incudes origina video and its anaytica resuts for different end users. The media server must provide the origina video with the corresponding anaytica resut for each end user. Thus, the media server must provide the resource sot and buids up the network connection for each end user. Since one resource sot of a media server ony answers one arriva request each time, the service capacity of the media server wi be fast occupied by the arriva requests. The service capacity denotes the maximum Reguar Paper The research is supported by the Nationa Natura Science Foundation of China-Guangdong Joint Fund under Grant No. U , the Nationa Natura Science Foundation of China under Grant No , the Funds for Creative Research Groups of China under Grant No , the Beijing Training Project for the Leading Taents in Science and Technoogy under Grant No. jrc , and the Cosponsored Project of Beijing Committee of Education. 1 VSaaS appications have aready been empoyed by peope in the rea word. For exampe, IP cameras on the roads can simutaneousy give massive traveers rea-time videos of traffic to determine their trip routes. Dec Springer Science + Business Media, LLC & Science Press, China

2 556 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 number of end users that one media server can simutaneousy serve. As the resut, the video data center must provide the resource scaabiity. When the video data center provides resources for the end users, it not ony optimizes the distribution of the end user requests on media servers to reduce the upoad bandwidth cost, but aso maximizes the number of the end user requests on each media server to reduce the media server cost. Therefore, the objective of the camera stream scheduing is to provide scaabiity with the minimum tota resource cost incuding the media server cost as we as the bandwidth cost on both upoad and forwarding streams. Video data centers usuay provide two types of video deivery services: the video on demand (VoD) service and the IPTV service. However, neither of the two services utiize upoad video stream scheduing approach to optimize the upoad bandwidth cost. For the VoD services in [7-15], videos are stored and deivered by the media servers of video data center, and the VoD services mainy aim at minimizing the media server cost and the forwarding bandwidth cost on the networks. However, the VoD services evauate the media server cost mainy as the storage cost, which makes the resource scheduing approaches of the VoD service ignore the service capacity of each media server. For the IPTV services in [16-21], media servers are empoyed to forward the ive video streams. The resource scheduing approaches of the IPTV service ony focus on minimizing the media server cost and optimizing the forwarding bandwidth usage. Moreover, the service capacity of each media server is not propery discussed either. For exampe, the authors of [21] presented a resource sharing approach between VoD and IPTV service to improve the resource utiization. However, it is not reasonabe to assume that one request wi just take up one media server. In concusion, the upoad bandwidth cost and the capacity of each media server are mentioned in none of the above studies. Nonetheess, it is very important to schedue the upoad video streams among media servers to reduce the tota resource cost. When the resource cost function is noninear (shown in [11-12, 21]), the probem is more compex and needs ong deay to gain an optima soution. Therefore, the existing resource scheduing approaches for the video deivery services obviousy cannot optimize the tota resource cost to support the scaabiity on the VSaaS. Ceary, acking the evauation of the media server s resource utiization and the upoad bandwidth cost, the resource scheduing approach of VSaaS can provide neither high scaabiity nor ow tota resource cost. Since different video data centers utiize different server and bandwidth charges, the approach must adapt to different resource pricing modes. Finay, the optima scheduing soutions for VSaaS need to be gained within an acceptabe deay to search the arge-scae candidates. Therefore, we can concude that VSaaS imposes the great chaenges to the resource provisioning of the video data center. In this paper, to sove the chaenges, we firsty introduce a genera system framework of video data center to provide VSaaS, which is iustrated in Fig.1. The framework comprises of a scheduer, media servers, and IP cameras. In the framework, the scheduer assigns the end user requests to the media servers with ide resources. Then, the media servers receive the streams from the IP cameras to serve the arriva end users. Based on this framework, we propose an efficient and scaabe muti-camera stream scheduing approach to minimize the tota resource cost in the video data center. Compared with the previous work, our approach takes media server capacity and upoad bandwidth cost into consideration. Moreover, the approach utiizes different resource cost modes, which empoy different resource cost functions to comprehensivey evauate the cost of the resource usage. The media server and the bandwidth cost functions can be either inear functions or non-inear functions. Furthermore, we formuate our resource cost minimization probem as a constrained integer programming probem to minimize the tota resource cost. Utiizing the drift-pus-penaty method [22], the programming probem can be handed under the inear cost function. However, the scheduing probem with the non-inear cost functions usuay cannot gain an optima resoution within acceptabe time for the surveiance video service. To sove this probem, we further propose a heuristic scheduing approach in the resource cost modes with the non-inear cost functions. Compared with the state-of-the-art approaches under different situations, our approach spends the minimum resource cost on the upoad and forwarding networks and the media servers. The main contributions of our work are as foows. We empoy comprehensive modes to evauate the resource utiization of the upoad and forwarding bandwidth as we as the media server in the resource scheduing probem, which can be formuated as a constrained optimization probem. To minimize the tota resource cost in different resource cost mode, we propose an approach based on

3 Yi-Hong Gao et a.: Minimizing VDC Resource Cost for Camera Stream Scheduing 557 a drift-pus-penaty method under the inear cost function, and a heuristic approach under the non-inear cost function. User Group for Camera 1 Forwarding Stage Upoad Stage User Group for Camera 2 Scheduer User Group for Camera n IP Camera 1 IP Camera 2 IP Camera n Fig.1. Genera VSaaS framework. Video Data Center Media Servers The comprehensive evauations compared with the state-of-the-art approaches on a buit rea-word scenario demonstrate the effectiveness and efficiency of our approach. The rest of this paper is organized as foows. In Section 2, the system framework is designed to provide VSaaS. In Section 3, we provide the system modes, and further formuate the scheduing probem. Then, we give our resource scheduing approaches in Section 4. In Section 5, the evauations of our approaches are presented. Furthermore, we give reated work in Section 6. Finay, we concude our work in Section 7. 2 System Framework The components of the video data center, which incude a scheduer and a media server custer, are given in Fig.2 to provide VSaaS. Moreover, the scheduer incudes the request monitor and the server scheduer to communicate with end users and media servers, respectivey. We aso provide the user API to hep the end users to get their required video streams on the web. Finay, the IP cameras, which are set up in the surveiance environments, dynamicay execute the video anaysis tasks based on the end user requirements, andsendtheivevideostreamstothevideodatacenter. 2.1 User API The end users access the video data center to get their required ive videos and the attached anaytica resuts, which we totay caed the ive video streams, by the user API. The user API is web-based to communicate with the request monitor, get the media server address, and provide video streams from the chosen media server. Users User API Forwarding Address Media Server Custer Scheduer Request Monitor Server Scheduer Server Server Server Server Server New Server Message Exchange Fig.2. Components of the video data center. 2.2 Request Monitor VDC When the end users require the video streams from the video data center, they firsty access the request monitor. The request monitor is a sub-component of the scheduer, which takes charge of deivering the end user requests to the server scheduer and gets the scheduing resuts, i.e., the media server addresses, from the server scheduer. Then, the scheduing resuts are directy sent to the end users. A ight-weight web server architecture is utiized in the server, which runs the request monitor to dea with the high accessing concurrency of the end users. 2.3 Server Scheduer To provide cost-efficient resource usage on the video data center, a server scheduer is designed to manage both the network and the server resources of the video data center, and evauate the resource cost. The server scheduer periodicay communicates with current media servers to receive their status messages, and records the resourceusageofthe videodatacenterateachtime. The status message consists of the heartbeat to show it is sti aive, the number of the network connections to the IP cameras, and the remaining capacities to serve the end users. When requests arrive at the scheduer, the scheduer decides which media server to serve the arriva requests, sends the decisions to the chosen media servers, and waits for the responses. If new resources are needed to serve the end user requests, the server

4 558 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 scheduer immediatey opens new media servers, and buids up new network connections. Any new resource optima approach can be added into the scheduer by using the designed API. The depoyed approach must consider the resource cost on both the networks and the media servers during resource provisioning. 2.4 Media Server Custer Virtua machines (VMs) are empoyed as the media serversinthevideodatacenter. Abrokerisdepoyedon each media server, which reports the status messages of the media server to the scheduer and gets the messages from the scheduer. The media server connects to the IP cameras to receive the ive video streams, and generates the accessing addresses of these streams. When it successfuy receives the ive video streams, the media server combines the origina videos with the anaytica resuts for each end user. Then, the accessing addresses of the streams are sent to the server scheduer as responses to the arriva requests. Based on the system framework above, the stream scheduing process works as foows. The scheduer maintains the resource usage information and dispatches the end user requests to the proper media servers to get the ive video streams. When the chosen media server firsty connects to the required IP camera to receive the ive video stream, the upoad bandwidth cost is charged. Then, the media servers forward the ivevideostreamsasthe outputtothe end users. When a media server has aready connected with the IP camera, it can forward the ive video streams of this IP camera directy to any other end users. We ca the surveiance video of an IP camera as a channe in this paper. Each channe has its own media server resources to deiver its ive video stream. To fuy specify the resource capacity of the media server, we utiize a finegrained server resource usage mode where each media server consists of resource sots. The resource sot is the smaest resource unit to forward the ive video stream. The end user arrives at the video data center to occupy the ide resource sot, and eaves the video data center to reease the occupied resource sot. When media server custer has no ide resource sots, new media server is added with a media server cost. 3 Probem Formuation In this section, three modes are empoyed to evauate the resource utiization of the service at first. Then, based on these modes, we further iustrate the resource scheduing probem and formuate this probem as a constrained programming probem. The important parameters utiized in this paper are shown in Tabe System Modes We set up system modes to describe the resource utiization, the arriva request and the resource cost. The modes are based on the video channes to describe the resource usage of each IP camera s requests, which arepresentedinfig.3. Theendusersarriveatthechannes to require corresponding video streams. Therefore, we firsty empoy the resource utiization mode to denote the resource usage on each video channe which has media servers to forward ive video streams. The arriva requests take up the resource sots of the media servers Tabe 1. Definitions of Important Parameters Parameter m j (t) m K j α j (t) A(t) Q j (t) C ( ) Cb out ( ) Cb in( ) C b (j,t) C(t) s j (t) r j (t) x j j y j (t) Definition Number of channe j s media servers at time t Tota number of the media servers in the video data center Number of the ide resource sots in the -th media server of channe j Number of the channe j s arriva requests at time t Tota number of the arriva requests of a channes Number of the end users of channe j on the media servers at time t Resource cost function of the media servers Resource cost function of the forwarding bandwidth Resource cost function of the upoad bandwidth Tota bandwidth cost of channe j at time t Tota media server and network bandwidth cost at time t Number of the added media servers of channe j at time t Number of the new upoad connections of channe j at time t Denoting whether the scheduer chooses the -th media server of channe j s to serve channe j s requests Number of the exchanged resource sots at time t

5 Yi-Hong Gao et a.: Minimizing VDC Resource Cost for Camera Stream Scheduing 559 (the shaded parts after the media servers in Fig.3) to serve the end users. The rest of the ide resource sots wait for the arriva end users (the bank parts after the media servers in Fig.3). Note that the media server can share its resource sots to the end users requiring different IP cameras for cost savings. Additionay, we buid up the arriva request mode to describe the resource requirements. Finay, the resource cost mode is utiized to evauate the resource cost of both media servers and bandwidths. In this mode, the ive video transmission from an IP camera to the media server eads to additiona upoad bandwidth cost. Thus, the mapping between the end user requests and the media servers deepy infuences the fina resource cost on both the media servers and the networks in video data center. Requests A t Scheduer α 1 t α 2 t α n t Channes Q 1 t Q 2 t Q n t Fig.3. Muti-channe system modes Resource Utiization Mode Media Servers m 1 t m 2 t Resource Sot m n t Assuming that n channes manage the resource sots ofmmediaserverstoforwardipcamerastreamswithin time period T. For any IP camera j (j {1,2,...,n}), there is a video channe to deiver j s ive video stream. Video channe j hods m j (t) media servers at time t T, and n j=1 m j(t) = m. When there are no requests to occupy the resource sots or some end users eave the channes, the resource sots are ide and wait for new arriva requests. Furthermore, we denote the number of ide resources as m j(t) =1 k j for channe j. k j ( {1,2,...,m j (t)})is the number ofthe ide resource sots in the -th media server of channe j. The ide resource sots can be shared with other channes to save the media server cost. If there are no ide resource sots in the media server, k j = 0. If the ide resource sots in these media servers are insufficient to serve the arriva requests, the ide resource sots beonging to other channes are utiized, or new media servers are added. For the network usage, when the required IP camera is not yet connected to the chosen media server, new network connection between the media server and the IP camera is buit up. The ive video stream of this connection can serve a the foowing requests for the same IP camera on the media server unti the connection is disconnected. Since the bandwidth utiization of the networks is considered on the infrastructure of the video data center [23], we mainy focus on the resource utiization of the media servers Arriva Request Mode Assuming that A(t) end user requests arrive at the video data center at time t. Then, we denote the number of channe j s arriva requests as α j (t) at time t, and n j=1 α j(t) = A(t). For a cost saving scheme, one media server, which forwards channe j s video stream, can share its ide resource sots with the requests of other channes. In this situation, the request arriva rate of channe j on the media server i is denoted as α ij (t) at time t. We have α j (t) = m i=1 α ij(t), for any i {1,2,...,m}. We denote m = n j=1 m j as the number of media servers to serve the arriva end users. m j is the number of media servers in channe j. We denote β j (t) = m i=1 β ij(t) as the number of the end users eaving from channe j, and β ij (t) is the number of the end users eaving from media server i. The tota number of the end users of channe j is denoted as Q j (t) at time t. At time t+1, the tota number of the end users of channe j can be denoted as Q j (t+1) = max(q j (t) β j (t),0)+α j (t) Resource Cost Mode We empoy three different resource cost modes to formuate the resource costs, which empoy inear, convex and concave resource cost functions, respectivey. The convex and the concave modes are non-inear modes. A the resource cost modes are widey empoyed in the video data centers shown in [11, 21]. The inear cost function depicts that the resource cost equas that a fixed price mutipies the amount of the resources. The convex cost function defines the case where the resource cost smoothy grows initiay, and then quicky increases. The concave cost function describes the tendency that the resource cost quicky increases at the beginning, and fattens out finay. For the media server cost formuation, we denote C (s j (t)) as the rents to ease s j (t) media servers for channe j. s j (t) is the number of the channe j s media servers added at time t. C ( ) is the cost function of the media servers. We utiize the upoad and forwarding bandwidth cost to evauate the bandwidth cost. To evauate bandwidth

6 560 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 cost to forward video streams to the end users, we denote the forwarding bandwidth cost of channe j as Cb out (Q j (t)). Cb out ( ) is the forwarding bandwidth cost function. Furthermore, the bandwidth cost from the IP cameras to the video data center is aso charged by the VSaaS providers. Simiary, the upoad bandwidth cost from IP camera j is denoted as Cb in(r j(t)). Cb in ( ) is the upoad bandwidth cost function. r j (t) is the tota number of the connections to the IP camera j at time t. If the requests of certain channe take up too many sharing media servers, the upoad bandwidth cost is obviousy increased for adding additiona IP camera connections. Then, the tota bandwidth cost is denoted as C b (j,t), which equas Cb out (Q j (t))+cb in(r j(t)) at time t. Without oss of generaity, C ( ), Cb out ( ) and Cb in( ) can be inear or non-inear cost functions. 3.2 Resource Scheduing Probem We try to formuate the cost optimization probem. The resource cost of the service is caused by the resource scaabiity and the ide resource sharing for ive video forwarding. On one hand, new media server is required when current ide resource sots run out. Adding new media server needs to pay server rent, and buiding up a network connection to connect an IP camera needs to pay upoad bandwidth cost. On the other hand, the channe commony shares its ide resource sots with other channes to save the media server cost. But sharing the ide resource sots increases more additiona connections to the required IP cameras. Making fuuseoftheideresourcesotstominimizethenumber of media servers does not aways save the tota resource cost. Thus, it is a resource scheduing probem on how to optimay empoy new resources and ide resources to cut the tota resource cost. To iustrate the scheduing probem, we give an exampe in Fig.4. Assuming that four end users simutaneousy require the ive video streams from channe j. Suppose channe j just has one media server, and a its resource sots are occupied. At the same time, there is a neighbour channe j 1 which has four ide resource sots distributed in two media servers. The scheduer has two choices here. One is to utiize the ide resource sots of the neighbour channe. The other one is to add a media server to serve a the arriva end user requests of channe j. If the scheduer chooses the first method, it saves the media server cost but spends more bandwidth cost to connect to the camera j twice (the soid ines in Fig.4). If the scheduer adds a new media server with four resource sots to serve the requests, it just needs one IP camera connection (the dash ines in Fig.4). Athough the second method reduces the bandwidth cost, it spends morerentsto adda new media server. To compare the resource cost of the two choices under different cost functions, the scheduer can take optima scheduing pan with the minimum resource cost. Therefore, we formuate the scheduing probem as a constrained integer optimization probem. We define the tota resource cost C(t) = n j=1 (C (s j (t)) + C b (j,t)). For a the arriva end user requests for the video streams of the cameras during time T, the resource scheduing probem can be formuated as foows: 4 Stream Requests Video Data Center Scheduer Channe j 1 Channe j Media Server 1 Media Server 2 Media Server 3 New Media Server Resource Sot 1 Resource Sot 1 Resource Sot 1 Resource Sot 1 Resource Sot 2 Resource Sot 2 Resource Sot 2 Resource Sot 2 Resource Sot 3 Resource Sot 3 Resource Sot 3 Resource Sot 3 Resource Sot 4 Resource Sot 4 Resource Sot 4 Resource Sot 4 Channe j Fig.4. Exampe of the resource scheduing probem.

7 Yi-Hong Gao et a.: Minimizing VDC Resource Cost for Camera Stream Scheduing 561 subject to j =1 =1 min 1 T T 1 t=0 C(t) m n j x j j k j (t)+s j(t)k α j (t). (1) Constraint (1) means that the provided resource sots to the requests of channe j are no ower than the tota requests of channe j at each time t. The provided resource sots consist of the ide resource sots n j =1 mj =1 xj j k j (t), and the new added resource sots s j (t)k. k is the maximum resource sots of each media server to serve the arriva requests. We denote x j j = 1, if the scheduer chooses the -th media server of channe j to serve channe j s requests; otherwise, x j j = 0. Note that n n j =1 j=1 is no higher than xjj the number of the media servers m j, which have ide resource sots of channe j. Furthermore, the assigned requests to each media server must be ess than its ide resource sots. The ide resource sots are exchanged from the surpus channe (k j α j (t)) to the overoad channe (k j > α j (t)). The number of the exchanged resource sots can be denoted as y j (t), and it equas: y j (t) m n j j = =1 =1 m n j j =1 =1 x j j k j (t)+s j(t)k, if m j(t) =1 x jj k j (t) b j(t)k, otherwise, k j α j(t), in which the surpus channe denotes as the channe with the ide resource sots, and overoad channe means the channe does not have enough resource sots to serve the arriva requests. b j (t) is the number of the media servers that are without any requests, and not assigned to any other channes at time t. Channe j wi reease these b j (t) media servers for cost savings. 4 Resource Scheduing Approach In this section, we give the detais of our resource scheduing approach. The approach aims at minimizing the tota resource cost and stabiizes the resource usage of the media server custer. 4.1 Approach for Linear Cost Function We first anayze the resource usage during the resource scheduing, and then we provide the resource scheduing approach for the inear cost function. To depict the resource sot migration on the media servers, we define the resource sot usage of each channe as a virtua queue. We denote Z j (t) as the backog of the virtua queue of channe j at time t, and Z j (0) = 0 at the initia time. Z j (t) evoves as: Z j (t+1) = max(z j (t)+y j (t),0), and we denote that Z j (t) Q j (t). Therefore, the resource scheduing approach can empoy the Lyapunov optimization to stabiize the queue size and minimize the tota resource cost. Let L(t) be the Lyapunov function to measure the tota queue backog at time t. L(t) = 1 2 n Z j (t) 2. j=1 Then, we denote the Lyapunov drift as (t) = L(t + 1) L(t): (t) 1 2 n y j (t) 2 + j=1 n Z j (t)y j (t). j=1 Since 1 2 n j=1 y j(t) 2 has the positive constant upper bound B, then: (t) B + n Z j (t)y j (t). j=1 According to constraint (1), for a t and a possibe Z(t), we have: B E{ 1 n y 2 j(t) 2 Z(t)}, j=1 { yj(t) 2 (α j (t)+k 1) 2, if k j α j(t), ( K j (t)) 2, otherwise, where K j (t) is the tota ide resource sots of channe j, and we can concude that E{yj (t)2 Z(t)} is no more than max((α j + k 1) 2,( max[k j ])2 ) in channe j. K j is the number of the resource sots which need to be shared with other channes. According to the driftpus-penaty expression, the tota resource cost of the scheduing pan is utiized as the penaty, and we set the weight of the penaty to V. To this end, we can get the foowing inequaity: (t)+vc(t) B +VC(t)+ n Z j (t)y j (t). j=1 The scheduing approach chooses the contro action X j (t) = {X 1j (t),x 2j (t),...,x nj (t)} for channe j.

8 562 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 Let X j j(t) be the vector of the chosen ide resource sots on each media server of the surpus channe j {1,2,...,n}, which are schedued to the overoad channe j. According to constraint (1), the minimum number of the new added media serverss min (t) equas (α j (t) n j =1 mj =1 xj j i k j (t))/k. Therefore, the resource cost of the new media servers is C s min j (t)+c in j (t). j b smin For sharing the ide resource sots with any other channes, the upoad bandwidth cost to buid up new IP camera connections is Cb inf(x j j(t)). The ide resource sharing makes the media server connect with more than one camera. Therefore, we denote f( ) as the function to cacuate the number of the new connections of certain IP camera. When new connections of the required IP cameras are buit up, the foowing requests of the same IP cameras do not need to spend any additiona upoad bandwidth cost. The bandwidth cost to deiver the ive video streams to the end users is Cb out, which is ony reated to the amount of the requests Q j (t). Then, we have (t)+vc(t) n B + Z j (t)y j (t)+ V j=1 n (C (s min j (t))+cbj out +C b (s min j (t))+ j=1 C b (f(x j j(t)))). (2) To minimize n j=1 Z j(t)y j (t) + V n j=1 (C (s min j (t)) + C b (s min j (t))+c b (f(x j j(t)))) in inequaity (2) at each time t, the scheduer takes the optima resource scheduing pan with the minimum tota resource cost. When the cost function is inear, the probem can be formuated as a 0-1 integer programming probem, and the approximate optima soution can be directy got by the method shown in Chapter 3 of [22]. 4.2 Genera Approach for Non-Linear Cost Function When the cost functions are non-inear (e.g., convex and concave functions), the functions are too compicated to sove the cost optima probem within acceptabe time [22] (in some situations, it cannot even take an optima soution within a poynomia time). The above approach cannot be utiized in this situation. Therefore, we introduce a heuristic scheduing approach to minimize the right hand function of inequaity (2). Our scheduing approach periodicay coects the resource usage information from each media server, and tries to expore the resource sots at a ower cost to serve the arriva end users. For this purpose, the scheduing approach is divided into severa rounds. In each round, we find out the cheapest scheduing pan for the current arriva end users. We provide the sefish scheduing method and the shared scheduing method in our scheduing approach to schedue ide resource sots to the end user requests. Then, new media servers are added to serve the surpus arriva requests. The sefish scheduing means that each channe ony accepts its own requests to occupy its ide resource sots. The sefish scheduing method has the owest resource cost on both the media server and the network, and does not take up any ide resource sot of other channes. Because the resource sots are neither shared nor added, we have y j (t) = 0 and V n j=1 (C (s min j (t))+c b (s min j (t))+c b (f(x j j(t)))) = 0 in this scheduing method. The shared scheduing means that the channe accepts the requests from any other channes to occupy its ide resource sots. The shared scheduing method consists of the oca shared scheduing and the goba shared scheduing. For the oca shared scheduing, the channe ony shares its ide resource sots with the sma group of the channes requests, which makes y j (t) and V n j=1 (C (s min j (t)) + (t))+c b (f(x j j(t)))) equa 0. The sma group of the requests covers the channes that have aready occupied the media servers resource sots of the current channe. Thus, the oca shared scheduing does not generate any additiona upoad bandwidth cost. Moreover, the goba shared scheduing aows the channes to sharetheir ide resourcesots with a the other channes, and it aows the media servers to buid up new connections to get the ive video streams. Therefore, the goba shared scheduing tries to minimize the vaue C b (s min j of n j=1 Z j(t)y j (t)+v n j=1 (C b(f(x j j(t)))). Finay, the rest of the end user requests are assigned to new media servers, which aim at minimizing the vaue of n j=1 Z j(t)y j (t)+v n j=1 (C (s min j (t))+c b (s min j (t)). The heuristic scheduing approach works as foows. At first, each channe utiizes the sefish scheduing to dispatch the requests to their own ide resource sots shown in Agorithm 1. If there are remaining requests after the sefish scheduing, the scheduer searches for the ide resource sots among channes. Then, the shared scheduing is empoyed in the scheduer shown in Agorithm 2 to share the ide resource sots with the requests of the overoad channes. For the media servers aready share their ide resource sots with a group of

9 Yi-Hong Gao et a.: Minimizing VDC Resource Cost for Camera Stream Scheduing 563 channes, the scheduer utiizes the oca shared scheduing to dispatch the remaining requests of the group to occupy the current ide resource sots. We denote w j as the weight of overoad channe j to occupy the ide resource sots. Let w j equa α j / K jj. We denote α j as the remaining requests of channe j, and K jj is the number of the ide resource sots of the media serversin channe j that aready shares resource sots with channe j. w j evauates the capacity that the remaining requests of channe j can be consumed by the current ide resource sots without additiona upoad bandwidth cost. The higher vaue of w j means the ess bandwidth cost. Therefore, the highest w j aways has the priority to occupy the ide resource sots of the shared channes. Agorithm 1. Sefish Scheduing 1: for a j {1,2,...,n} do 2: if α j > 0 and K j > 0 then 3: Dispatch the maximum {α 1j,α 2j,...,α mj } to the media servers in channe j, so that m i=1 α ij(t) α j and m i=1 α ij(t) K j 4: end if 5: end for Agorithm 2. Shared Scheduing 1: Start the oca shared scheduing: 2: for a α j > 0 and K j > 0 do 3: Get the sorted w j ist of the channes 4: for j {1,2,...,n} do 5: Dispatch channe j s requests with the maximum w j to the ide resource sots that have aready connected to channe j 6: Adjust α j and K j 7: end for 8: end for 9: Start the goba shared scheduing: 10: for a α j > 0 and K j > 0 do 11: Get the minimum number of the new media servers s new to contain the current requests {α 1,α 2,...,α n} 12: for i {1,2,...,s new} do 13: Dispatch the requests of the server i with the argest ki new to utiize the current ide resource sots 14: if C ide C new then 15: Dispatch the requests to the ide resource sots 16: Remove the media server i 17: end if 18: end for 19: end for If there are sti requests and ide resource sots after the oca shared scheduing, the scheduer further utiizes the goba shared scheduing method to decide whether to dispatch the current requests to the remaining ide resource sots. The main idea of the goba shared scheduing is to find an equiibrium point, which makes the tota resource cost to empoy the ide resource sots no higher than that to add new media servers. We first schedue a the rest of the requests to the new media servers. The number of the new media servers is denoted as s new. The ide resource sots in new servers i are denoted as ki new. Furthermore, we choose the argest ki new in the new media server ist each time to put its requests into current ide sots to cacuate the tota resource cost Cide i. Suppose the tota resource cost of adding s new media servers is C new. Cide i and C new are cacuated by the non-inear cost functions. If Cide i C new, the scheduer removes media server i from the new media server ist, assigns these requests to occupy the ide resource sots, and adjusts the remaining requests and the vaue of s new. Then, the scheduer sti chooses the argest ki new for the next comparison. We repeat this operation unti a the new media servers are compared. The goba shared scheduing method can support that the ide resource sots are fuy utiized, and the tota resource cost is aso minimized. After the shared scheduing, the remaining new media servers are added to the custer. Finay, we denote the dominant channe as the channe with the highest number of end user requests on the media server. To stabiize the media server usage in each channe, the scheduer migrates the media server of channe j {1,2,...,n} to its dominant channe, if the current media server does not have channe j s requests any more. When there are ide media servers without any end user requests, the scheduer wi reease the media servers for cost savings. The onine heuristic scheduing approach is given by Agorithm 3, which can aso be utiized under the inear cost function. The time compexity of our approach is O(n 2 ) in the worst case. Agorithm 3. Muti-Camera Resource Scheduing 1: Get the arriva requests {α 1,α 2,...,α n} 2: for a i {1,2,...,m} and j {1,2,...,n} do 3: Find out the media server i of channe j that does not have j s requests 4: Move media server i to its dominant channe 5: end for 6: Start the sefish scheduing in Agorithm 1 7: if A(t) 0 and {K 1,K 2,...,K n} {0} then 8: Start the shared scheduing in Agorithm 2 9: end if 10: if there are sti requests then 11: Add media servers to serve the rest of the requests in each channe 12: end if 13: if there are media servers without any requests then 14: Reease the ide media servers 15: end if 16: Adjust the resource usage of each channe

10 564 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 5 Evauation In this section, we first introduce the detais of the experimenta setting. Then, we compare our approach with the state-of-the-art approaches. The resource usage and the tota resource cost of the approaches are given to evauate the performance. 5.1 Experimenta Setting Patform Impementation We evauate the performance of our scheduing approach by setting up a patform to provide VSaaS on the video data center. The patform is impemented as foows. We empoy Live555 2 to buid up the media server to forward the ive IP camera streams. A scheduer is running to manage the resources of the media server custer. Netty 3 is empoyed to impement the distributed communication among the end users, the scheduer and the media servers. A the end user requests arrive at the scheduer at first. The scheduer redirects the requests to the proper media servers to get the ive video streams. RTSP protoco is utiized on the connections between the media servers and the IP cameras. The resource configuration of each media server is 2 CPU cores and 4 GB Memory, which takes a reference to the t2.medium VM in the EC2. The patform manages 80 IP cameras, which are depoyed by China Teecom. to serve the pubic in Fuzhou, China. Each IP camera is assigned to a media server at the initia time. According to our experiments, the bitrates of HD video stream of the IP camera are around 500 KB/s. To support the high quaity of the video stream forwarding, one media server consists of k = 50 resource sots in our media server custer. The scheduer counts the number of the media servers and the upoad network connections to the IP cameras to evauate current resource usage. Based on the resource usage, tota resource cost is cacuated each time to compare the three resource scheduing approaches Request Arriva Setup We empoy two different end user arriva modes to evauate our approach performance: the crowded arriva mode and the ordinary arriva mode, which are aso shown in the performance evauation of [12]. More end users arriveat the video data center in the crowded arriva mode. There is more than one end user to require each IP camera each time in this mode. For the ordinary arriva mode, there are ess end users accessing the video data center. There is no more than one end user to require each IP camera each time. We suppose that the end user arriva process is a poisson process, which is aso empoyed in the experiments of [12-16]. End user interests to the IP cameras foow a power aw distribution. The end users randomy eave the channes, and reease the occupied resource sots at each time. We define t as the minimum time unit. During t, a random number of end user requests arrive at and eave the media servers. In our experiments, t asts for a few seconds, which is no higher than the required response time of the service. Therefore, the timeperiod tissmaenoughtoadapttotheonineresource scheduing. Our experiments run t time periods. The number of the media servers, the network connections and the tota resource cost are recorded by every 10 t time period. We run each approach 20 times, and get the average experimenta resuts Scheduing Approach Setup We compare our approach with the other two approaches: the sefish scheduing approach (SA) and the open scheduing approach (OA). For the sefish scheduing approach, the media server of each channe ony serves its own end user requests. It means that the media server does not share its ide resource sots with other channes. The sefish scheduing approach is shown in [16], which just determines the minimum number of media servers in each channe. For the open scheduing approach, the media servers not ony serve their own end user requests, but aso share the ide resource sots with other channes. The media servers of each channe firsty accept their own end user requests. If there are sti ide resource sots, the media servers accept the requests of other channes. The opening scheduing approach is very common in the traditiona VoD service, and is aso empoyed in the scheduing method in [21]. Finay, our new scheduing approaches (NA) with three different cost functions are denoted as NA L for the inear cost function, NA X for the convex cost function, and NA C for the concave cost function, respectivey. 2 Live555 is a very popuar media server appication, which can forward iver video streams using RTSP protoco. Dec Netty is an open source communication framework, which is mainy written by Java. Dec. 2015

11 Yi-Hong Gao et a.: Minimizing VDC Resource Cost for Camera Stream Scheduing Crowded Arriva Mode Evauation We firsty observe that our approach spends more time than the other two resource scheduing approaches. Since the three approaches are rea-time, a the approaches provide arriva end users the onine resource scheduing service. The tota average number of the arriva and eaving requests under the crowded arriva mode is shown in Fig.5. Number of Requests Τ SA OA Arriva NA L NA X NA C Approaches Fig.5. Tota end user requests in each approach Resource Usage Leaving We compare the resource usage resuts of NA L, NA X and NA C with the other two approaches, and the experimenta resuts are shown in Fig.6. It shows that NA can obviousy reduce both media server resource usage and network usage among the three approaches. At the beginning of the resource scheduing, both the media servers and the network connections are sharpy increased. The increasing process is not stabe, and the added and removed requests are itte different among the approaches. After running neary t, the resource usage is stabe. The resuts show that SA has the highest media server usage, because it does not share the ide resource sots with any other channes shown in Fig.6(a). On the contrary, OA has the maximum number of IP camera connections, because it tries to dispatch the arriva requests to a the media servers shown in Fig.6(b). Athough the resuts of our approach are different under three different cost functions, a the resuts of NA approach neither empoy the most number of the media servers, nor buid up the most number of the upoad network connections from the IP cameras. Tota Servers at Each Tota Connections at Each SA OA NAL NAX NAC (Τ10 4 ) (a) (b) SA OA NAL NAX NAC Fig.6. Tota resource usage in each approach. (a) Tota media servers. (b) Tota buit-up connections Tota Resource Cost To evauate the tota resource cost of the three approaches, the expressions of the three cost functions are given. Firsty, the inear cost function is denoted as C(x) = αx + β, in which x is the number of the media servers or connections. α and β are two constants. Since the Amazon Web Services provide the pay-as-yougo pricing mode which utiizes a inear cost function, we choose the Amazon EC2 pricing mode to describe media server cost 4, and the Amazon Coudfront pricing mode to describe the bandwidth cost of IP camera connections 5. Note that the bandwidth cost is much ower than the media server cost. The tota resource cost under the inear cost function is shown in Fig.7(a). Furthermore, the convex cost function is denoted as C(x) = αe βx, where C(x) is the convex function, which is given by the work of [21]. On the contrary, the concave cost function is denoted as C(x) = αnβx. The tota resource costs under these two cost functions are shown in Fig.7(b) and Fig.7(c), respectivey. 4 The Amazon EC2 can dynamicay provide new media servers. Dec The Amazon Coudfront is a nove content deivery patform on coud. Dec

12 566 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 Tota Cost (Linear Function) Tota Cost (Convex Function) Tota Cost (Concave Function) SA OA NA L (a) (b) SA OA NA X SA OA NA C Fig.7. Tota resource cost comparison under three different cost functions. (a) Tota resource cost under inear cost function. (b) Tota resource cost under convex cost function. (c) Tota resource cost under concave cost function. The experimenta resuts show that our approach hastheowesttotaresourcecostatthemostoftimeunder a these three cost functions, because our approach decreasesthe number of both the media serversand the network connections. At the initia time, the tota resource cost is not stabe because of the unstabe changes of the requests. The unstabe changes are due to the random functions in the simuation of the request arriva and eaving. In Fig.7, there are a few connections buit up initiay because of the sma number of the arriva requests. Thus, the server cost is higher at first, and SA has the highest resource cost in the three approaches. As the requests are accumuated, the upoad (c) network connections are increased. The tota resource cost of OA is obviousy increased, which becomes into the most expensive scheduing approach. The cost of our approach grows smoothy compared with the other two approaches. Most importanty, our approach aways maintains ower resource cost due to a comprehensive consideration on both media server cost and the upoad and forwarding bandwidth cost. 5.3 Ordinary Arriva Mode Evauation The ordinary arriva mode is a typica onine mode, which has ess requests. This mode supposes that there is no more than one request arriving at each channe at each time. We denote such three approaches as OSA, OOA and ONA, which represent the ordinary SA, the ordinary OA and the ordinary NA, respectivey. In the ordinary arriva mode, the tota average number of the arriva requests and the number of the eaving requests are shown in Fig.8. Number of the Requests (Τ10 3 ) OSA Arriva OOA Approaches Leaving ONA L ONA X ONA C Fig.8. Tota end user requests of ordinary arriva mode Resource Usage The tota arriva and eaving end users of the ordinary arriva mode are ess than those of the crowded arriva mode. According to the resuts of our experiments, no more than six end users arrive at the video data center to require different IP cameras each time. We aso observe that most resource sots are assigned by the sefish scheduing and the oca shared scheduing in the proposed approach ONA. The goba shared scheduing is ess utiized in this mode compared with the crowded arriva mode. Therefore, the resource usage of ONA under the three different cost functions is neary the same. We utiize an average vaue of the three experimenta resuts under different cost functions in Fig.9 to represent the resource usage of ONA. The number of the media servers of ONA is higher than the

13 Yi-Hong Gao et a.: Minimizing VDC Resource Cost for Camera Stream Scheduing 567 number of OOA, but much ess than that of OSA. Furthermore, the number of the IP camera connections of ONA is higher than the number of OSA, but much ess than that of OOA. It means that ONA can aso reduce the media server usage and network usage among the three approaches ony by the sefish scheduing and the oca shared scheduing. Tota Servers at Each Tota Connections at Each (a) OSA OOA ONA OSA OOA ONA (b) Fig.9. Tota resource usage of ordinary arriva mode in each approach. (a) Tota media servers. (b) Tota buit-up connections Tota Resource Cost The experimenta resuts of the ordinary arriva mode are shown in Fig.10. The tota resource costs of Figs.10(a) 10(c) are cacuated by the inear, convex and concave cost functions, respectivey. The resource cost functions of the experiments are shown in Subsection We can find that the resource usage contradiction between the media server cost and the network connection cost sti exists in this mode. Our approach handes this probem better in the onine mode, which has the owest tota resource cost under the three cost functions. The changes of the resource costofonaaresimiartothoseofooa,but the vaues aremuchowerthanthoseofosa.thatisbecauseboth ONA and OOA share their ide resource sots among channes. Nevertheess, the saved cost of goba sharing scheduing in ONA is not obvious during the resource scheduing for the ordinary arriva mode. However, our approach has a more optima connection management strategy than OOA. There are fewer upoad network connections buit up in ONA, which means ess bandwidth cost. Tota Cost (Linear Function) Tota Cost (Convex Function) Tota Cost (Concave Function) OSA OOA ONA L (a) (b) OSA OOA ONA X (c) OSA OOA ONA C Fig.10. Tota resource cost comparison of ordinary arriva mode under three different cost functions. (a) Tota resource cost under inear cost function. (b) Tota resource cost under convex cost function. (c) Tota resource cost under concave cost function. As the experimenta resuts shown in Fig.7 and Fig.10, our approach aways maintains ower cost due to a comprehensive consideration on both the media server cost and the upoad and forwarding bandwidth cost. Therefore, we can concude that our heuristic resource scheduing approach can optimize the resource

14 568 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 usage on both the media servers and the upoad and forwarding networks in the most of the end user arriva mode and the resource pricing mode. 6 Reated Work Recenty, coud computing techniques are widey empoyed to improve the scaabiity of the video deivery service on the video data center. There are two types of video deivery services: the VoD services and the ive video services. Most of the VoD services on the video data center aim at deivering video fies to the end users. The resource scheduing approaches of the service mainy reduce the number of the new media servers to minimize the tota resource cost in [10-15]. For the ive video service (e.g., IPTV, video conference and video surveiance), the resource scheduing approaches in [16-21] deiver the ive video streams to the end users. However, the existing work did not optimize the upoad bandwidth cost from the video sources to the video data center. The CoudMedia in [10] is a nove VoD service on private coud. End user access reguarity is empoyed in the CoudMedia to predict the resource demands in future. Based on the expected number of arriva requests, the scheduer of the video data center determines the number of the media servers to deiver video fies. In [11], the resource prices are evauated during the resource scheduing. The authors of [11] supposed that the media content providers (MCP) eased media serversfrom thepubic coud todeivervideofiesto the end users. However, the media server price usuay foows a non-ineary discount based on the number of the media servers and the easing duration. Therefore, an innovative scheme was proposed to hep MCP make resource easing decision at each time to save the media server cost. Another resource charging mode was presented in [12], and the resource price was charged based on the number of the arriva end users. Tang et a. [12] proposed two end user arriva modes: the norma mode and the fash crowd mode. The two modes have different resource cost, which incudes the media server cost and the video output cost. The scheduer predicts the type of the end user arriva mode at each time to minimize the tota resource cost. A the above work utiized the sefish scheduing approach to schedue the media servers for the end users. The ide resource sharing probem for different video sources was proposed in [13]. The authors of [13] supposed that the end users were divided into severa communities by their geographica ocations, the end users usuay recommended the interested videos in their communities and video data centers shared their resources to each community to store and deiver the videos. Different video data centers had different video deivery deay and media server cost. The authors of [13] aimed at achieving the tradeoff between the media server cost and the video deivery deay of each community. Finay, an optima video fie depoyment pan was given in [14]. The optima depoyment pan determines how many fie copies are needed to save the fie storage cost of media servers. Furthermore, the work of [15] not ony optimizes the video forwarding QoE for end users, but aso reduces the energy usage and emissions of the video data center. For the ive video service, the media servers of the video data center must get the ive video streams from the video sources for ive stream forwarding. The resource scheduer in [16] takes the time/region diversities of the end user requests into consideration. That is to say, the users access and eave from the media servers at any pace and any time. In this system, there is ony one proxy in the video data center to manage a the videos. The proxy sends the ive video streams to each media server of the video data center. The proposed resource scheduing approach is a sefish scheduing approach, and ony aims at minimizing the number of media servers which forward ive stream to the end users. Simiar to the work of [16], the approach proposed in [17] improves the traditiona CDN system to forward the ive video streams. But the proposed approach ony focuses on improving the network performance between the media servers to reduce the forwarding cost and deay. An interactive IPTV service was proposed in [18] for onine video conference on coud. This system mode extends the singe video source to mutipe video sources, which are the cameras on the termina equipments of the end users. Nevertheess, the interactive IPTV service did not introduce any optima resource scheduing approach. The authors of [19] and [20] just provided the soutions to optimize the forwarding network performance across inter-connected video data centers. Finay, the authors of[21] provided an ide resource sharing method among different media servers during the ive video deivery. An ide resource sharing approach was given to share resources between the VoD service and the IPTV service. The server pricing modes utiize three different server cost functions: maximum, convex and concave functions. Then, the approximate optima agorithms were proposed under

15 Yi-Hong Gao et a.: Minimizing VDC Resource Cost for Camera Stream Scheduing 569 each cost function to share the ide resources with the minimum media server cost. Bandwidth cost on both upoad and forwarding streams was not mentioned in this work. Bandwidth cost from the video source to the media server is not the main consideration in the above agorithms. However, the bandwidth cost of the video stream deivery of the IP camera incudes the upoad and forwarding bandwidth cost. The upoad bandwidth cost is the cost for the ive video deivery from the IP camera to the media server. The forwarding bandwidth cost is the cost for ive video forwarding from the media server to the end user. Athough the upoad stream scheduing is not mentioned in the above work, it must be we controed to reduce the tota resource cost. Furthermore, most of the work did not mention the capacity of the media server. Usuay, the media server capacity represents that the media server can simutaneousy serve more than one end user, and it aso deepy affects the tota resource cost. Therefore, the current work obviousy cannot we optimize both the upoad and forwarding bandwidth cost and the media server cost of the VSaaS on the video data center. 7 Concusions In this paper, we presented the resource cost minimization approaches on the video data center to provide VSaaS. We gave a comprehensive consideration on both the upoad and the forwarding bandwidth cost in resource scheduing process, and provided a fine-grained resource usage mode for media servers. The proposed approach aims at minimizing the tota resource cost on both media servers and networks. To we evauate the resource cost of the media servers and the networks, we empoyed different pricing modes which contain inear and non-inear cost functions. We formuated the cost minimization probem as a constrained integer programming probem under the inear cost function. For the noninear cost functions which mainy are convex and concave cost functions, the proposed programming approach cannot obtain the optima resuts within acceptabe time. Therefore, we further gave a heuristic scheduing approach to minimize the tota resource cost in the video data center. The experimenta resuts showed that our approach coud optimize the resource usage on both media servers and networks. Due to the resource usage optimization, the scheduer coud get the minimum tota resource cost under different resource cost functions and request arriva modes compared with the state-of-the-art approaches. References [1] Passarea A. A survey on content-centric technoogies for the current Internet: CDN and P2P soutions. Computer Communications, 2012, 35(1): [2] Zhu W, Luo C, Wang J et a. Mutimedia coud computing. 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16 570 J. Comput. Sci. & Techno., May 2017, Vo.32, No.3 [16] Wang F, Liu J, Chen M. CALMS: Coud-assisted ive media streaming for gobaized demands with time/region diversities. In Proc. the 31st IEEE Internationa Conference on Computer Communications (INFOCOM), March 2012, pp [17] Mukerjee M K, Nayor D, Jiang J et a. Practica, reatime, centraized contro for CDN-based ive video deivery. In Proc. the ACM Conference on Specia Interest Group on Data Communication (SIGCOMM), August 2015, pp [18] Nam Y, Park H J, Cho C H et a. An interactive IPTV system with community participation in coud computing environments. IEEE Systems Journa, 2014, 8(1): [19] Feng Y, Li B, Li B. Airift: Video conferencing as a coud service using inter-datacenter networks. In Proc. the 20th IEEE Internationa Conference on Network Protocos (ICNP), Oct. 30-Nov. 2, [20] Feng Y, Li B, Li B. Jetway: Minimizing costs on interdatacenter video traffic. In Proc. the 20th ACM Internationa Conference on Mutimedia (MM), October 2012, pp [21] Aggarwa V, Gopaakrishnan V, Jana R et a. Optimizing coud resources for deivering IPTV servers through virtuaization. IEEE Trans. Mutimedia, 2014, 15(4): [22] Neey M. Stochastic network optimization with appication to communication and queueing system. Synthesis Lectures on Communication Networks, 2010, 3(1): [23] Lee J, Turner Y, Lee M et a. Appication-driven bandwidth guarantees in datacenters. In Proc. the ACM Conference on Specia Interest Group on Data Communication (SIGCOMM), August 2014, pp Hua-Dong Ma is a Changjiang Schoar, professor and director of Beijing Key Laboratory of Inteigent Teecommunications Software and Mutimedia, and the executive dean of the Schoo of Computer Science, Beijing University of Posts and Teecommunications, Beijing. He received his Ph.D. degree in computer science from Institute of Computing Technoogy, Chinese Academy of Sciences, Beijing, in He was awarded Nationa Funds for Distinguished Young Scientists in His current research focuses on mutimedia system and networking, sensor networks and Internet of Things. Wu Liu is currenty a ecturer in Beijing Key Laboratory of Inteigent Teecommunications Software and Mutimedia, Beijing University of Post and Teecommunication, Beijing. He received his B.E. degree from Shandong University, Jinan, in 2009, and Ph.D. degree in computer appication technoogy at the Institute of Computing Technoogy, Chinese Academy of Sciences, Beijing, in His research interests incude mutimedia information retrieva and computer vision. Yi-Hong Gao is now a Ph.D. candidate of Beijing Key Laboratory of Inteigent Teecommunications Software and Mutimedia and the Schoo of Computer Science, Beijing University of Posts and Teecommunications, Beijing. His current research mainy focuses on resource scheduing approach, video data center, coud computing. text text text text text text text text text text