Adaptive Load Sharing in Video-On-Demand Distributed Multimedia System Using Parallel Servers

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1 Adaptive Load Sharing in Video-On-Demand Distributed Multimedia System Using Parallel Servers Mohammad Riaz Moghal, Member IEEE a nd Mohammad Saleem Mian Department of Electrical Engineering University of Engineering Lahore Communication Systems Lab., Research Center, UET, Lahore PAKISTAN Abstract: - This Video data is stored in a video server for delivery to multiple receivers over a communications network in a traditional video-on-demand system. The hardware in this video server has the following limitations: 1) the maximum storage capacity, and 2) the maximum number of video sessions that can simultaneously be delivered. We propose a novel Adaptive-Multiple-VIdeo-Server -Architecture (AMVISA) that manipulates parallelism to achieve incremental scalability. This AMVISA architecture 1) exploits data striping in stead of data partition and replication, at the server level to achieve fine-grain load sharing across multiple servers, 2) employs a client-pull service model to eliminate the need for inter -server synchronization, 3) an admission-scheduling algorithm is proposed to further control the dynamic load at each server so that linear scalability can be accomplished. The results in our proposed AMVISA architecture show that the architecture can be linearly scaled up to more concurrent users simply by adding more video-servers and sharing the video data among the servers. The results also support that our new architecture for VoD system is more reliable than single-server VoD system. Key-Words:- Video-On-Demand, Video-Server, Distributed Multimedia Systems, Client-Server Architecture, Load Balancing, Load Sharing, Adaptive Resource Management. 1 Introduction The available video-on-demand (VoD) system uses single-server as a video-server. T he video server can range from a simple PC to highly-parallel supercomputers system [1-4]. The price/performance ratio tends to increase swiftly for high-end video-servers, due to lack of economy of scale. This causes the single-server approach costly for large-scale Distributed Multimedia VoD applications. Furthermore, the power or capacity of a single server is still eventually limited and, hence, replication is often required to develop system with adequate capacity [5-6]. This inspires us to investigate the design of AMVSA on-demand Systems architecture using multiple-servers. The integration of the capacity and bandwidth of the multiple-servers enables us to exceed the limit of a single-server VoD system. This new AMVISA architecture permits us to incremental scale up the capacity of the system by simply adding the servers to the system and then sharing the video data among the video-servers. Video allows us to incrementally scale up the system capacity by adding (rather than replacing) servers to the system and then redistributing (rather than replicating) the video data among the servers. In this work: 1) we propose a system design based on AMVISA architecture to build incrementally scalable VoD systems, 2) we discuss the use of a client-pull service model which completely prevented the need for inter-server synchronization, 3) we study the dynamic load imbalance problem caused by the parallel AMVISA architecture and propose a staggering -based admission scheduler that enables the system to be linearly scalable, and 4) we assess the performance of the proposed AMVISA architecture using results and confirm that the architecture is indeed linearly scalable. The rest of paper is organized as follows: Section 2 presents the background and AMVISA system architecture, section 3 addresses the load imbalance issue and presents the admission scheduler, section 4 discusses the Heuristic solutions. Section 5 addresses the results and simulation. Section 6 presents related work. Finally, section 7 concludes the paper and discusses some future works. 2 AMVSA Video-On-Demand System A multiple-video-server is a collection of multiple autonom ous servers connected to the client by an interconnection network as shown in Fig.1. Each

2 server has a separate set of resources such as CPU, memory, disk storage, and network interface. Preferably, each server should be scheduled / configured so that all resources are fully utilized. For example, one would use a disk array with sufficient disks to fully utilize the disk controller I/O capacity, install multiple CPUs to fully utilize the system bus if it is cost effective. The server-level parallelism would provide the path for capacities beyond what can be accomplished from a single-server. In this model, the video title is divided into fixed-size blocks and shared among all video-server nodes in the system. Whereas in previous approaches one would use ser ver replication or partition and a video title is stored in one of the video-server nodes or replicated over multiple video-server nodes. In present paper, we assume that the size of video title is much larger than the size of a video block so that load inequality due to uneven allocation between severs can be ignored. This assumption is generally true for movies application where the size of a video block is only in the range of tens to hundred extremely short video objects are striped in systems with a very large number of servers. In the light of the placement policy of the video block, a client will send Quality of Service (QoS) requests to each server to retrieve the required video blocks. The servers upon receiving client requests will retrieve and transmit the requested data back to the client. The client will resequence video packets from multiple servers for decode and playback. Let S be the number of servers and C be the number of clients (or number of sessions, or chains of task, and each task requires some resource for computation) in the system. The objective of the proposed AMVISA architecture is to achieve linear scalability, i.e. invariant client-server ratio when more servers are added to the system. Here the storage requirement reveals the same regardless of the scale of the system, the-per-client actually decreases when scaling up the system. 2.1 AMVISA Video-Server Segmentation The organization of stripe / segment units among servers in the system is shown in Fig 2. The storage space in each video-server is divided into fix-size stripe units of B bytes each. The stripe units under the same column are stored at the same video-server. A chain of stripe units is called a stripe and collectively all stripe units make up the entire storage. The stripe units are ordered in a round-robin approach in which each unit in a stripe belongs to a different video-server. In this way, the server uniformly share client loads irrelevant to the skewness of the video titles. Under this segmentation / striping approach, C l i e n t1 C l i e nt2 C l i e n t3 C l i e n t4 V i d e o S e r ve r s D i s k I n t e r Co n n e c ti on N e t W o r k A r r a y F i g 1. A D A P T I V E --- M U L T I P L E --- V I D E O S E R V E R A R C H I T E C T U R (A M V I S A) S t r i pe S t r i pe U n i t B - B yt e ) V S 1 V S 2 V S 3 V S 4 V 1 V 2 V 5 V 9 V 1 3 V 6 V 1 0 V 1 4 V 3 V 7 V 1 1 e. g. V S 1 = V i d e o S e r v e r 1 Fi g 2. S E R V E R S T R I P I N G F O R F I V E S E R V E R S M U L T I P L E V I D E O S E R V E R O F ( A M V I S A V 4 V 8 V 1 2 V 1 5 V 1 6 the allocation policy can be completely described by three parameters: 1) the address of the video-server that stores the first video block, 2) the number and address of all video-servers, and 3) the size of a stripe unit. The client can obtain threes parameters easily by consulting a database or other application-dependent approaches. 2.2 Service Model for AMVISA In single-server based video server designs the server-push service model is commonly used. In this model, the server schedules the periodic retrieval and transmission of video data once a video session is in progress. This model permits one to design resource-schedulers to optimize disk and network utilization. However, extending this server-push service model for use in multiple-video-server needs synchronizing transmissions for multiple-servers intended to the same client, which is as significant problem in its own right. Unlike, to most studies we assume that the client-pull service model (SM) for AMVISA in the present paper where a client periodically sends requests to the servers to retrieve blocks of video data. The present approach does not need explicit inter-server synchronization because each request is served at the server independently of all other requests. We will show that this approach can be linearly scalable even though the video-servers are completely asynchronous. 2.3 Server QoS-Request Processing

3 As shown in Fig. 3, at each of the server node, there are one request queue(r Q ), one SH Q shared queue, and send queues (S Q ). Dynamically arriving requests are first stored into the request queue. The disk retrieval process then serves requests in batches by allocating free buffers and reading video blocks from the disk into the buffer units. Requests within a batch may be served out-of-sequence to optimize disk efficiency. However, requests are grouped according to their arrival time so that no request is starved of service. After retrieval, these buffers are then moved into the shared queue to wait for transmission at the traffic shaper / smoother. The traffic smoother is used to manage the transmission rate at the video-server to decrease outgoing traffic burstiness. Particularly, a video block inside a send queue is packetized into smaller packets of Y B bytes each for transmission. The nonempty send queues are served in a round-robin approach with one packet transmitted from each send queue in a service round. The number of send queues S Q are chosen such that the transmission rate is high enough to maintain the video playback rate but not too high to avoid network overloading. For example, suppose the network throughput is 40 Mbps and the video bit-rate is 1.2 Mbps. Then setting S Q = 10 will result in a transmission rate of 4 Mbps, which is higher than the video bit-rate but still substantially lower than the maximum transmission rate that can cause network congestion. (e.g., the client may be connected via a 4 Mbps link)[7]. The matchless property avoids the server complexity from going up when scaling up the system for more servers and clients. R e q u e s t s R e q u e s t Q u e u e D i s k R e t r i e v a l P r o c e s s S h a r e d Q u e u e H a r d D i s k T R A F F I C S M O O T H E R S Q S e n d Q u e u e s F i g 3. R E Q U E S T P R O C E S S I N G A T E A C H I N D I V I D U A L R V E R O F ( M V S E A S A ) 3 Admission Scheduling Server striping guarantees that loads from each and every client will be evenly shared across all servers on the average. However if we allow clients to start video sessions dynamically, the system may encounter dynamic load imbalance if many clients happen to start video sessions synchronously. The dynamic arrival of QoS-session-requests can cause N e t w o r k certain machines overloaded (called Bottleneck resource utilization (BRU)), while others become under-loaded or idle. In that case the objective is to find dynamic load balancing through minimal-bru approach that will improve the efficiency of the system by evenly distributing load on each machines of the AMVSA system. We present an algorithm (REMS+VoD) for load balancing/ resource scheduling in AMVSA system. If in the worst case, all clients will send requests to the same server at the same time, possibly temporary overloading that server. Still our approach with dynamic algorithm will rebalance load on the following events. To avoid dynamic load imbalance, we can explicitly schedule the start times for new video sessions to prevent synchrony, To obtain this, an admission scheduler to manage when a client can start a new video session, This admission scheduler is a software process running on a computer host connected to the same network as the clients, The scheduling algorithm used in this admission scheduler is explained below. 3.1 Start-Time Staggering The staggering scheme with period length T round and N slots shown in Fig 4(a) for the admission scheduler. The period length is calculated according to the number of servers in the system as well as the aver-age video playback rate. Each time slot has two states: available (Ava) or Allocated (All). When a client dynamically wants to start a new video session i, it will first send a request to the scheduler. Ignoring network and processing delays and, assuming the request arrives at the scheduler at time t, the scheduler will admit the new session if and only if the time slot n = [mod(t, T round ) / (T round / N)] (1) is available Fig. 4(b). After accepting the new session i (by sending respons e back to the client), the time slot will be marked as Allocated until the session ter- minates. On the other hand, if the requested time slot is allocated, the resource scheduler will wait (effectively increasing t) until an available / free time slot is available Fig. 4(c). Hence, the session start time will be staggered in time and, hence, client synchrony decreased. Note that a client only links the, admission scheduler once at the start and once at the end of a video session. In the remaining time duration, the client sends requests directly to the server nodes.

4 T r o u n d S l l o t t 0 S l l o t t 1 A l l o c at a t e e d S S l l o o t t,, A v a i l a b l e S l o ot t N n W k = Pr{(k+1)th slot available Pk } Pk = N k k 1 n i ( ) 1 k n. (6) i= 0 N N e w R e q u e s t A d m i s s i o n G r a n t e d Hence, given n and N the average and worst-case scheduling delay can be found accordingly. N e w R e q u e s t D E L A Y A d m i s s i o n G r a n t e d F i g 4 ( a ) A d m i s s i o n S l o t, ( b ) N o D e l a y, ( c ) D e l a y 3.2 Scheduling Delay in AMVSA The scheduler presented in the above section may gain extra scheduling delay to the client. We define: scheduling delay = (the time when the request is granted by the admission scheduler) - (the time when the client requests a new video session). Assume that n out of the N slots are allocated. Then for a client requesting a new dynamic video session, the probability of having an available / free slot immediately is given by formula: V 0 = (N n ) / N (2) In this way, P 1 = ( 1 V 0 ) will be the probability of the requested slot being allocated. Now, provided that the requested slot is allocated, the probability that the next slot is available is V k =Pr{next slot available P1}=(N n)/(n -1) (3) This is also the probability for a client having to wait one time slot provided that the requested slot is already allocated. It can be shown that the probability for a client to wait k time slots provided that the first k slots are all allocated is V k =Pr((k +1)th slot available P1}=(N n )/(N -1) (4) To achieve Pk, we note that the probability that the first slot is allocated is given by n /N and the probability that the second slot also allocated is given by ( n-1 ) / (N - 1), and for the third one is by ( n-2 ) / (N - 2), etc. Hence, the probability for the first k slots all allocated is k 1 n i P k = ( ) i= 0 N 1 k n. (5) In this way, we can calculate for the unconditional probability of a client having to wait k time slots, denoted by Wk, from 4 Heuristic Solutions and Algorithm An AMVSA shown in fig 1 can be represented as shown in Fig 5. There are n video requests (or chains of tasks) created by clients that share m resources [32-37]. If there are sufficient resources including CPU cycles, system Memory, and network bandwidth available then the load balancer should provide each request the desired resources. When the system s available resources cannot maintain the desired one, the load balancer must negotiate the operating QoS of a set of requests[39-42]. By reducing resolution may produce such results in video application. This may result in degraded-levels of information details. The adaptation of QoS in AMVSA may be initiated by many different events, such as start of a request, drop of an existing request, change of system load, change of network load and dynamic changes of user s choices/requirements. The QoS for a multiple concurrent video requests is dynamically adapted to the available resources/execute time user choices in an AMVSA. Such models and algorithms for load balancing for AMVSA have been discussed in [32-37]. It presents a unified and feasible way to solve the admission problem for newly distributed multimedia video request, and the dynamic QoS adaptation and integrated resource allocation problems for existing requests [38-40]. Assume a user creates a request i which has an operating quality q i in an AMVSA environment. We assume one-to-one mapping from an operating quality to the resources required for a particular quality. The resources required by the request are represented by R(q i ) where R(q) denotes the quality to resource mapping (f: Q? R) function. Consider, a system has three resources i.e., CPU cycles, system memory, and network bandwidth. In fact this particular problem is of type, multiple-choice multiple-dimension knapsack (MMKP) problem, which is, in general, an NP-complete problem. For simplicity, consider video application, and map it onto a (single-server -client) [37,44] and is solved in [34] using SQ algorithm.

5 Client Client Client Client Cut Tasks Chains of n- resquests Video Server Video Server Video Server Video Server Fig. 5. Dynamic task chains cut/partitioning and mapping onto Clie nts and Server machines. Left - subset - side of the partitioned task chain is mapped onto the attached Client and the Right subset - side onto the Video- Server. We map it onto our double-server -client distributed architecture and solve it by our better heuristic RED [28, 34]. The Service Model can be used for video-request admission, dynamic quality adaptation, integrated resource management and priority-based resource allocation in distributed real-time multimedia systems. The terms request acceptance, QoS adaptation, integrated resource management and priority based resource allocations are discussed in [42]. For this partitioning problem for chain-structured video-applications, we need to partition each chain of tasks into two sets, one for the attached client to the chain and the other for server, as shown in fig 5. A cut-in-chains or a path-in-chains, corresponds to a task-allocation which is subject to multiple constraints is an NP-complete problem. Such a NPcomplete problem can be handled using some heuristics [36-37]. An algorithm is developed to find all the multiple-sum paths in a layered graph of chains for such NP-complete problem. The load is dynamically shared among clients and server(s) in such a fashion that minimum BRU is encountered using our algorithm. For multiple -video-server -client architecture a new algorithm (REMS+VoD) for distributed multimedia video applications. The main aims of our algorithm are (1) to make the load balance as good as possible, (2) to respond to a new QoS -video-request from application(s) as soon as possible, 3) improves video-request-acceptance-ratio, and 4) improves overall systems-utilization/system-benefit. 4.1 The (REMS +VOD) Algorithm Our proposed algorithm REMS + VoD calculates initial chain-cuts using an efficient greedy approach. It gradually improves the chain cuts and eliminates bad alternatives until each chain cut is left with only one alternative. This yields the lowest BRU. The lowest BRU corresponds to a high acceptance ratio to new web-request with more responsiveness of the system. The next part of REMS.+ VoD algorithm considers several iterations. Both the best and the worst cuts in terms of BRU are derived for each chain in iteration. The worst case is disabled and is not further considered during next iteration. One cut is disabled for each chain during each iteration. The m cuts involves m-1 iterations. There are at most cm cuts, whereas each cut involves k number of constraints for additions and comparisons. Hence the complexity of the algorithm is O ( kcm 2 ). 5 Results and Simulation We have implemented and evaluated our proposed algorithm for various problem sizes. Results are shown and compared on the basis of Average Relative BRU in Tables 2, 3, and 4 for the different problem sizes. For these problems we have varied the number of servers 1, 2, 3, and 4. In al theses problems our algorithm find the best solutions in terms of minimum BRU. In all these cases the results show our (REMS + VoD) algorithms outperform to SQ and RED algorithms. Evaluation of our (REMS+VoD) algorithm have been performed by varying both the number of Servers and the Random load of each task in a chain. This shows that our algorithm is more dynamic to handle the dynamic situations of the video-requests of distributed multimedia applications in terms of lower BRU. Exhaustive simulation results show that our algorithm with three-video-servers outperforms with 9%, 10% and 11.31% than RED and 30.30%, 30.62% and 31.29% than SQ algorithms with different

6 0 (REMS+VoD) Algorithm: Input: c = # of chains of tasks or # of video-requests ; m = # of cuts with 2k-dimensional load-vectors; Left-side-of-chain-cut for a client-machine and Right-side-of-chain-cut for a Video-Server machine k = # of constraints L = normalization value (Between 0 to 1) ClientId = Cp, where C = # of Clients; 1 p c n Video-Servers = VS1-VideServer1, VS2-VideoServer2, VSn-VideoServern Step (1): The Client and VS1 or VS2 or VSn, loadinitialization for all Cp, set used load -vector to 0 for VS1 or VS2,,VSn set used load-vector to 0 Step (2): Calculate initial cuts for chains for (i=1; i <= c; i++){ for (j=1; j <= m; j++){ vector-add load Vector of chain i at cut j to used - -load of Cp and VS1 or VS2, VSn get the current BRU if current BRU lower than best BRU so far store j as best cut so far and update best BRU remove load of chain i from Cp and VS1 or VS2 or VSn } vector-add load of chain i at the best cut found to used load of Cp and VS1 or VS2 VSn } Step (3): improve iteratively the selected cuts for( iteration=1; iteration=m-1; iteration ++){ for (i=1; i <= c; i++) { remove load of chain i from Cp and VS1or VS2 or, VSn as implied by best cut so far find best cut & worst cuts from chain i as in Step (1) } mark worst cut as disabled vector-load of chain i at the best cut found to used load of C and VS1 or VS2,,VSn. } Step (4): compare result For (i=1; i c; i++) C = best cut for Best-Load -Balance after last iteration of Step (3) BRU = bottleneck resource usage after last iteration of step (3) Output: Set of Cuts { } Bottleneck resource usage BRU Bottleneck resource id BR = Best Cut SQ RED R e l a t i v e B R U ( % ) A p p r o x. T i m e C o m p l e x i t y Fig. 6. C o m p a r i s o n o f A v e r a g e R e l a t i v e B R U f o r D i f f e r e n t A l g o r i t h m s f o r a p r o b l e m w i t h i t e r a t i o n s. SQ RED R e l a t i v e B R U ( % ) A p p r o x. T i m e C o m p l e x i t y F i g 7 C o m p a r i s o n o f A v e r a g e R e l a t i v e B R U f o r D i f f e r e n t A l g o r i t h m s f o r a p r o b l e m w i t h i t e r a t i o n s SQ RED R e l a t i v e B R U ( % ) A p p r o x. T i m e C o m p l e x i t y Fig. 8 Comparison of Average Relative BRU for Different Algorithms for a problem with 1000 iterations. Figure 9: Servers Linear Scalability Figure10:Servers Linear Scalability and Fault-Tolerance problem sizes given in table 2, 3, and 4 respectively. Our algorithm with four-video-servers outperforms with 27%, 26% and 28.80% than RED and 44.93%, 43.55% and 44.45% than SQ algorithms with different problem sizes given in table 2, 3, and 4 respectively. Further, simulation results are also presented in Fig 6-10 that also support performance of our algorithm. We have implemented our algorithm using computer simulation. We have performed these simulations on Pentium-II machine, 128-MB RAM using C++. The main aims of our algorithm are (1) to make the load balance as good as possible, and (2) to respond a QoS-request from application(s) as soon as possible. 6 Related Work The principle of using parallel devices to achieve scalability has been studied in various contexts. For example, parallelism has been proposed for disk arrays, tape arrays, and even network transmissions [15-18]. In particular, the use of parallel disk systems have been studied extensively in VoD systems literatures[11-14]. Recently, there is an increasing interest in exploiting server-level parallelism for designing scalable VoD systems. For example, studies have been conducted by Biersack et al. [19-20], Bolosky et al., Buddhikot, and Parulkar, Freedman and DeWitt, Ghandeharizadeh and Ramos

7 , Lee and Wong, Lougher et al. Reddy, Tewari et al., and Wu and Shu [19-28]. A comprehensive study of architectural alternatives and the approaches employed by existing systems can be found in [29]. Below, we highlight and compare the key differences between the mentioned studies and the architecture studied in this paper. The pioneering study by Ghandeharizadeh and Ramos [11-17,24] considered the retrieval of multimedia data from a parallel multimedia information system. They proposed striping multimedia data over multiple disks located in storage nodes connected by a high-speed network. Their study focused on disk I/O bottleneck and stripping policy designed to support multimedia data objects with different playback bit-rates. Their striping algorithm also requires the scheduling of arriving requests to prevent disk load imbalance. This paper differs from their studies in two major ways: (a) our study incorporated issues in network transmissiol1, bottleneck in CPU processing, and synchronization among servers, which are not considered in, and (b) we develop our admission scheduler for client-pull service model rather than server-push service model[11,24] The task allocation problem in distributed multimedia system is similar to the problem of partitioning and mapping parallel programs onto multiprocessor architectures [12,15,40]. The general representation of this problem is that of static program graph consisting of tasks interconnected via links, where tasks serve to perform various processing tasks and links indicate required Inter-Module Communication (IMC). Both modules and links are weighted with costs indicating load or cost properties. A distributed system graph is used to describe possible computation sites for program tasks. Nodes and links of the graph are labeled with resource constraints limiting node processing or communication capabilities. The objective is to find a program partitioning and mapping onto the system graph without violating any of the given constraints and minimizing overall cost. The problem formulation seems similar to the one considered in this paper. The mapping problem has been taken into account, among other factors, CPU load, memory load, and communication bandwidth. The existing approaches imply a higher computation complexity than the ones described in this paper because most of these approaches were designed for somewhat general mapping problems, e.g., for arbitrary program graphs. Our proposed approach directly addresses the requirements of the distributed multimedia Video-on-Demand application mapping onto multiple-video-server -client distributed architecture. 7 Conclusion Video data is stored in a video server for delivery to multiple receivers over a communications network in a traditional video-on-demand system. The hardware in this video server has the following limitations: 1) the maximum storage capacity, and 2) the maximum number of video sessions that can simultaneously be delivered. These limits are exceeding by growing requirement for better video quality and larger user population. We propose a novel Adaptive AMVSA architecture that manipulates parallelism to achieve incremental scalability. This architecture 1) exploits data striping in stead of data partition and replication, at the server level to achieve fine-grain load sharing across multiple servers, 2) employs a client-pull service model to eliminate the need for inter -server synchronization, 3) an admission-scheduling algorithm is proposed to further control the dynamic load at each server so that linear scalability can be accomplished. The results in our proposed AMVSA architecture show that the architecture can be linearly scaled up to more concurrent users simply by adding more video-servers and sharing the video data among the servers. For multiple-video-server-client architecture a new algorithm (REMS+VoD) for distributed multimedia video applications. The main aims of our algorithm are (1) to make the load balance as good as possible, (2) to respond to a new QoS -video-request from application(s) as soon as possible, 3) improves video-request-acceptance-ratio, 4) improves overall systems-utilization/system-benefit, and 5) provides fault tolerance. We propose adaptive task allocation using load balancing to improve the performance of parallel computations by automatically shifting workloads among video servers during video request execution. Load balancing can be achieved by minimizing load on heavily loaded/bottleneck machines, i.e. minimizing Bottleneck Resource Utilization (BRU). The policy in each video request in this strategy is adapted to dynamic changes of available resources based on distributed workloads and users choices. Our model supports two types of adaptation: 1) where only a subset of chains-of-tasks follows the adaptation model, 2) where all the chains-of-tasks follow the adaptation model. In this paper we present a novel algorithm, (REMS + VoD) for multiple-video-server-client architecture for distributed multimedia VoD applications.

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