The behavior of Large - scale Distributed VoD utilizing th e VODSim

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1 The behavior of Large - scale Distributed VoD utilizing th e VODSim 1Leandro Souza, 2 Christianne Dalforno, 2 Marcelo Soares Souza, 2 Saulo Andrade, 3 Fernando Cores, 1 Ana Ripoll. 1Computer Science Department, University Autonoma of Barcelona (UAB) - Spain 3Computer Science & Industrial Engineering Department, University of Lleida Spain 2Centro Baiano de Computação de Alto Desempenho (CEBACAD), Universida de Católica d o Salvador (UCSal) Brasil sa d.net, Abstract. Video on de mand (VoD) services for a large nu mber of users, in a scalable way, is a challenging problem. Distributed designs for VoD services are a solution for the high costs involved in m aintaining a centralized server to provide this service. This paper presents a use case of Large - scale Distributed Video on Demand (LDVoD) syste m on a simulated high - speed network, based on the topology of the dedicated research network called Rede Metropolitana de Alta Velocidade (REMAV) Using simulation we analyze some important facts, such as scalability, perfor mance, user - refusing and storage efficiency in our a pproach face to independent - servers. 1.Introduction The increasing interest in Video - on - Demand, explained by the emergent m ultimedia applications such as digital libraries, interactive digital TV and others, has originated many researches in this topic. Many solutions has been suggested and tested in an attempt to better attend the clients and reduce the costs of implantation and maintenance of this kind of syste m. The requirements involved in VoD services like storage capacity, huge network bandwidth, real time constraint and large disk bandwidth, limit the VoD server that can only support a limited number of users, depending on its capacity. This led to a new design of large - scale VoD architectures to support a large nu mber of users. One of the most com monly used approaches are based on employing only one large centralized continuous server (figure 1) to implement a VoD system. These solution has demonstrated to be a good solution for low - scale systems, but it does not accomplish all the

2 requirements for large - scale VoD due to its non - scalability, higher costs to build and to maintain, and poor efficient resource allocations. Figure 1: Centralized VoD Server Researchers have proposed distribution of the service in order to manage client dispersal. One of these architectures, called Independent - servers, is a set of servers that are completely autonomous [3]. This architecture involves higher storage requirements, because all servers have a full contents catalogue copy. By this way, all requests are attended locally, minimizing the aggregate bandwidth required, but results in a more inefficient system due to servers that can not share service resources, like storage capacity and network bandwidth among several requests. One of the alternatives for this solution is Distributed VoD Servers (DVS) [1], where interconnected servers, via a high - speed network, reduce the huge aggregated storage capacity, improving sharing capabilities. Systems based on this approach demonstrated the ability to provide minimu m communication - storage cost for distributed continuous media streaming applications [2]. The DVS architecture is compounded by a set of servers interconnected by a deter mined topology. Servers or, as also they are called, service nodes, are able to store a limited nu mber of videos and concurrent streams. When a video is requested to a server and it is available in its local storage this is served immediately to the client. If the video is not found in local storage, the request will be forwarded to another server that has the video. In this paper, we focus on the analysis the behaviour of a specific DVS, based in the REMAV network structure. Our objective is to compare and evaluate this DVS architecture perfor mance, measured as the nu mber of concurrent streams supported by the system and to analyse some factors of distributed video servers.

3 This paper is organized in the following way. In section 2, we will first undertake an overview of the solutions proposed in the literature for the construction of efficient LVoD systems. Following this, in section 3, we will describe the distributed architectures issues. In section 4, we will describe our simulation environment and in section 5 we show its evaluation. Finally, in the last section, we will indicate the main conclusions to be drawn from our analysis, and will suggest future lines of research. 2.Distributed Video - on - De mand architectures The main objectives of DVoD architecture are to reduce the system costs and improve the system capacity in a scalable way. Two solutions are com monly used to build a DVoD system: independent - servers and proxy - based architecture. 2.1 Independent - s ervers Independent - servers (figure 2) solution works grouping users in a network segment, known as local network, closer to a VoD Server. Thus each server does not need to attend all the clients of the system; they work as a group of distributed centralized server. Figure 2: Independent Servers This solution has an unlimited scalability, but with high costs of storage capacity, once that all VoD servers need to have all the m ultimedia contents. 2.2 Proxy - based architecture Once that independent server s strategy involves considerable high costs, certain proposals have opted for reducing the size of local servers in such a way that they do not store all the movies available within the system, but instead, only those with a higher access frequency. These servers are managed as main server caches and are called proxies, just as

4 their Internet counterparts. Figure 3 shows the general topology of a system based on proxies. It consists of a main server, which is connected, through the main network, to a group of independent networks with their proxy. Figure 3: Proxy based architecture In this architecture the main server is responsible for attending the requests that cannot be served by a local proxy. The major problem with this solution is that the main server can become a growth - limiting factor and a bottleneck to the system due the network bandwidth limits from proxies. 3.Distributed VoD Architecture s is sue s VoD server has some particular properties that m ust be analysed when a system will be drawn. In this section we will present the characteristics of a server system for VoD inherent to our use case. 3.1 System topology To evaluate distributed server architecture for VoD we have to take into account some characteristics like the connectivity, storage capacity and network bandwidth. The connectivity is the quantity of links that have each server to com municate with others different servers in topology, in other words the connectivity may be understood by the nu mber of neighbours that a specific server have. This parameter is m uch important because the fact of having more connectivity imply in increase the storage capacity in distributed mirror (explained in next subsection) and so increases the possibilities to hit a video in case of local failure. The storage sub - system is one of crucial part of the system. It requires great bandwidth resources to accomplish the real time characteristics and secure the quality of service (QoS) to send streams.

5 The storage capacity of each server may influence the system in two ways: first, small storage capacity may turn the system limited and force a high consu me in the network to serve remote requests. Second, a large capacity can turn the system implementation unavailable if we take into account the cost. The network bandwidth is other important parameter since that it is the main way to transfer the data from server to client. In our experiments we assu me two kinds of network: a local one that connects the client and another one that connects the distributed servers throw the topology, which we call, topologic network. 3.2 Videos distribution scheme Many researches have been done to propose techniques for manage storage capacity with proxies in DVS systems [4][5][6]. There are a variety of policies for managing proxy content, depending on whether they store complete movie or only certain parts: run - length caching, prefix caching, and segment caching [9]. For the system analysed in this study, the movies are supposed to be stored entirely on a particular server and the movies may be replicated and stored on many VoD servers on the network by a storage scheme. In our case, two different schemes are used: caching and mirroring. Caching scheme allows high locality, using only a s mall fraction of storage resources. For in such a way it stores most popular contents locally, without requiring access to remote servers. One of the problems in caching scheme is that this involves high replication, becoming practically impossible to collaborate among server caches in order to solve cache - misses. Mirroring scheme works storing a subset of system videos without verifying its popularity, creating a partial mirror on every server. Thus, this scheme provides a distributed management mechanism for cache - misses, through the creation of a distributed mirror by the aggregation of several partial mirrors. Figure 4 illustrates the distributed mirror scheme. Each server has a partial storage and shares it from a n - distance.

6 D i s t a n c e - 2 d i s t r i b u t e d m i r r o r D i s t a n c e - 1 d i s t r i b u t e d m i r r o r L o c a l s o t o r a g e Figure 4: Distributed Mirror Scheme. Mirrors videos are not nor mally changed for a long period of time; the mirror is statically assigned without take in account caching storage. Caching videos are mapped dynamically, based on their access frequency. Distributed mirroring and caching scheme, combined, allows to store locally the most frequently accessed contents, improving scalability and fault - tolerance. 3.3 Mapping police When a DVS solution is chosen to a VoD system there are some implications to be worried about on video ma pping. First of all the full distribution of the catalogue over all servers have to be guarantied. The form of how the system videos are distributed into server mirrors will still affect the system perfor mance. Because of that some mapping policies were proposed and analysed to attend this kind of requirements of DVS systems. The main goal in mapping policies is focused to minimize trans mission costs [10] [11]. Analysing system functionality, local requests consume the server local - network resources, while remote requests require additional network resources because it needs to be redirect to others servers, consuming bandwidth from these new networks. The average nu mber of networks that a stream has to cross from the server to the final client could be defined by mean service distance (msd). To reduce the msd, and so, improve the system performance, we utilize the ByDistance Gradual policy [14] that take into account videos that have already been mapped. Its work processing each server iteratively, calculating for every catalogued video a mapping priority (indicating is suitability for mapping on this server), and assigning selected videos (which one with the biggest mapping priority) into its partial - mirror. After all videos are distributed into mirrors, the caches are filled

7 with the most popular video from the catalogue. If the video is already mapped into local server, the immediate most popular video that follows is assigned. 4. Simulation Environm ent The simulation environment used is based on a dedicated network of Research and Educational propose at Salvador, Bahia, Brazil called REMAV-SSA (Rede Metropolitana de Alta Velocidade Salvador). Eight researchers institutes are interconnected by a high perfor mance com munication infrastructure based on the ATM (Assychronous Transfer Mode) standard technology (figure 5). In that way, people from the institutions interconnected by REMAV-SSA can take hand of this infrastructure to use basics and advanced applications and to do experimentations involved for research projects. The availability of this network facilitates the development of research, once where we can appropriate the network infrastructure. This study simulates the architecture of the REMAV-SSA to make an analysis about an LDVoD system that can, in the future, be developed as a prototype and use to prove the results founded here. Figure 5: REMAV- SSA ATM is a technology based on the segmentation and trans mission (com mutation) of the information in s mall packages called cells. This technology has, as main characteristics, fast com mutation of packages and QoS (Quality of Service) that they can inside improve the guarantee and the efficiency of packages trans mission. [12] ATM presents forms to adapt the already de facto standard technology for networking, the Ethernet technology. The ATM Forum and the IETF had considered one form for implementation the Ethernet on a ATM network known as LAN Emulation (LANE). [13] All REMAV-SSA environments are interconnected by an ATM optical fiber switch, at speed up to 155Mbps, centralized at the Backbone

8 of the project at Universidade Federal da Bahia (UFBa). [12] Internally each locality uses two ways for connection (figure 6) to main switch: One based in ATM, with a switch of 25Mbps using copper cables (RJ45). Another form its through one Ethernet switch of 10 / 100 Mbps, with a optical fiber UpLink to ATM switch at 155Mbps. Once the environment was defined, the simulation was available and the tool used to do that it was the VoDSim, created by Fernando Cores [14]. The main advantages of this tool, to simulate Large - scale Video - on - Demand systems, are: Allows analysis of a great variety of LVoD architectures with huge functional parameters inherent of VoD. The simulation allows us to use different levels of abstraction of the components of the physical system model and the flexibility found to apply in our use case. Some experimental results of this tool might be founded in previous work in [3]. Figure 6: Details of a Locality in the REMAV-SSA 5. Experimental Results In t his section, we p resen t res ults of perfor m a nce evaluation, u s er - refusing, and storage capacity of our DVS architecture. The main parameters of our simulation environment are showed in table 1. It is important to record that this study intends to compare two architectures (Independent Server and DVS based). In that way, it is necessary to simulate both architectures. The parameters are all identical with some restrictions, like the DVS Capacity that is of twenty (20) videos to the DVS-based architecture, and does not exists to the independent - server architecture, once all catalogues have to be in each one of the servers.

9 Table 1: Main parameters of simulation environment. Parameter Value Parameter Value Number of videos 100 Proxy capacity 20 videos Local networks 8 Video length 90 minutes Network bandwidth 155 Mbps ATM /100 Mbps Ethernet Multicast technique Patching Server bandwidth 400 Mbps Client buffer size 5 minutes Number of Clients 1,000,000 Request rate (λ) 10 req/min by net Poisson distribution k λi λi pi = e Zipf distribution k! i = N z The request inter - arrival time is generated by the simulation of a Poisson distribution with a mean of 1 / l, where l is the request - arrival rate in every local network in the VoD system. The selection of the video is modeled with a Zipf distribution, having a skew factor of 0.7 (z) and the request playback rate is equal to 1,5 Mbps and a 90 minutes video needs 8.1 GB of storage in a server. For our DVS-based architecture, the mapping policy utilized was the By Distance Gradual, explained in section 3 and the percentage of each server cache was equal to 30%. In other words, we utilized 113,4 GB of our storage capacity to map the videos in the distributed mirror. To accomplish our initial propose in this paper, we evaluated the following metrics: 1. System Service Bandwidth: The number of concurrent clients supported by the system with the available resources (total system ban dwidt h). 2. User Refusing: When a server cannot immediately attend a request with the available resources, the system them refuse it. Using this metric, we evaluated the architecture behavior when the system is saturated (ie, it does not have sufficient resources to server all concurrent requests). 3. Server - storage capacity: With the server storage capacity increases, more number of contents can be stored locally and consequently, more requests can be attended locally, increasing system performance. We first start to evaluate the system service bandwidth. This metric allows us to observe the system behavior in its first approximation. Figure 7 shows its status for two situations: First, utilizing twenty videos in the server storage system figure 7 a), and second, utilizing thirty videos for storage system figure 7 b). p i 1 j= 1 1 j z

10 Sytem Service Bandwidth (Mb/s) REMAV DVS Architecture Independent Servers 0 Unicast Service Policy Multicast Figure 7a: System effective bandwidth with 20 videos Sytem Service Bandwidth (Mb/s) REMAV DVS Architecture Independent Servers 0 Unicast Service Policy Multicast Figure 7b: System effective bandwidth with 30 videos. Figure 7, shows the system performance with the unicast and patching service policy. The unicast service policy is utilized to measure the system based capacity and the m ulticast policy is utilized to improve the system performance. In this case, with the m ulticast service policy, the DVS architecture improves the system effective bandwidth in 10% (Figure 7 b.) but is overcome in 25% by Independent server (figure 7 a). ). In this way it is easy to see that independent - server architecture prove the better perfor mance (Figure 7 a).). This occurs because, in DVSbased architecture, the user requests that can't be attended locally, have to be answered by other service node, in this case one client consuming additional bandwidth, crossing more networks. In case of independent - server architecture, it does not happen because all requests are attended locally. On the other hand, DVS-based architectures can minimize this issue, only increasing the server storage capacity, and then getting more efficient (Figure 7 b).).

11 In figure 8, we show the evaluation of REMAV architecture for storage capacity. The figure shows the system effective bandwidth for distinct storage capacity in DVS servers. As we can see, a modest percentage of data storage imply in the worst perfor mance. Nevertheless, right storage percentage dos not secure that the system will going to have the best perfor mance. The figure shows that the DVoD architecture overcomes the independent - server utilizing less storage (28%). After 45% of storage data, our architecture starts to lose perfor mance, having a similar behavior to the independent - servers architecture. This fact can be explained by the way the system starts to lose system sharing, powered by m ulticast technique, and starts to serve more local petitions, so the topology network stars to be unused. System Effective Bandwidth (Mb/s) REMAV-DVS Architecture Independent-Servers Server storage capacity Figure 8: Storage capacity Finally we see in figure 9 the behaviour of user - refusing parameter. In this figure, the behaviour of the lines shows the evaluation of refusing event during simulation time. The difference between the amount of refusing between our DVS architecture and Independent - server architecture is not so large as might be thought. On the other hand, in DVS systems, when a server is saturated its requests can be redirected to adjacent servers, reducing refusing. So with system scalability, this refusing will be reduced. Independent server cannot perfor m this load balancing due to every servers being isolated. Figure 9 a), illustrates the users - refusing when we utilize a DVSbased architecture with a storage capacity about twenty videos, while in figure 9 b) we present the same graph but now with thirty videos on server storage. As we can see in minute 90 the behaviour of the curve is distinct. This fact happen because the duration of a video is 90 minutes.

12 Figure 9a: behaviour of user - refusing parameter with 20 videos. Figure 9b: behaviour of user - refusing parameter with 30 videos. 6. Conclusion In this paper we have simulated two Large Distributed Video on Demand architectures. Then we made some analysis of our proposed solution; a DVS-based architecture, face to a com mon independent - server solution. The simulation was based on the network topology of the REMAV-SSA, a dedicated high speed network based on the ATM standard, using the simulator VODSim, developed by Fernando Cores [14]. The metrics used for these analysis were: System effective bandwidth, server - storage capacity and user - refusing. Whith respect to the system effective bandwidth, we demostrate that the nu mber of concurrent clients supported by Independent - server, in some cases, can be higher than DVoD archicture, but the huge cost of storage and management of a centralized server are important factors in our study.

13 On the other hand, with the evaluation of distinct percentages of server - storage capacity we show that the DVoD architecture, using less storage than the Independent - server, only 35% of catalogue videos. This means that the DVoD costs are lower, and also point to the fact that its scalability has a limit. After 45% of storage data the system shows a loss of performance. The Independent - server architecture keep the effective bandwidth constant once this architecture store all of videos catalogues. When a server dos not have enough resources to serve a user req ue st, t he sys te m generates t he u ser - refusing event. Also t he DVSbased and Independent - server architectures has de monstrated a similar behaviour when this metric is applied and the results shows that the difference between the amount of refusing between DVS and Independent - server architectures are not so great. We conclude that the analysed DVS-based architecture for LDVoD presents, as advantage over the traditional Indepedent - Server solution, the low capacity storage needs with a considerable gain. 7. Reference s [1]. C.Shahabi, F.Banei - Kashani, Decentralized Resource Management for a Distributed Continuous Media Server, IEEE Transaction on Parallel and Distributed Systems, vol 13 (5), Julio [2]. S.A. Barnett and G. J. Anido, "A cost comparison of distributed and centralized approaches to video - on - demand," IEEE Journal on Selected Areas in Com munications, vol. 14, pp , August [3]. D. Sitaram, A. Dan, "Multimedia Servers: applications, environments, and design", Morgan Kaufmann Publishers, ISBN , [4]. F. Cores, A. Ripoll, E. Luque, Double P-Tree: A Distributed Architecture for Large - Scale Video - on - Demand, Euro - Par 2002, LNCS 2400, pp , Aug [5]. H. Fabmi, M. Latif, S. Sedigh - Ali, A. Ghafoor, P. Liu, L.H. Hsu, Proxy servers for scalable interactive video support, IEEE Computer, Vol. 34 Iss. 9, pp Sept [6]. S.-H. G. Chan and F. Tobagi, Distributed Servers Architecture for Networked Video Services, in IEEE/ACM Transactions on Networking, Vol. 9, No. 2 April [7]. Tobagi, F. A., Distance learning with digital video, IEEE Multimedia Magazine, pp , Spring [8]. W-F. Poon, K-T. Lo, J. Feng, Hierarchical Network Architecture for Layered Video Streaming, 24th International Conference on Distributed Computing Systems Workshops - W1: MNSA (ICDCSW'04), March 23-24, 2004, Tokyo, Japan

14 [9]. Alan T.S. Ip, J.C Liu, John C.S. Lui, A Cooperative Proxy - Client Caching System for On - Demand Media Streaming, Networking, Waterloo, Canada, May 2-6, [10]. X. Zhou, R. Luling, Li Xie, Solving a Media Mapping Problem in a Hierarchical Server Network with Parallel Simulated Annealing, Procs Int. Conference on Parallel Processing, pp , [11]. X. Zhou and C. Xu, Optimal video replication and placement on a cluster of video - on - demand servers, Procs. International Conference on Parallel Processing, p p , Aug [12]. Souza, Marcelo Soares; Souza, Josemar Rodrigues de. Micheli, Milena. Influência da comunicação no rendimento de u ma máquina paralela virtual baseada em Redes Atm. In: I Workshop de Porto Alegre Internet 2 e Redes de Alta Velocidade. Divulgação CD - ROM - ST6: Máquina Paralela Virtual baseada em redes ATM - UCSAL (REMAV-Salvador). Pontifícia Universidade Católica do Rio Grande do Sul PUCRS, Porto Alegre, RS, Brasil [13]. Dória, Helton; Micheli, Milena Pessoa; Santos, Willian; Figueredo, Mércia. IP Multicast Sobre ATM. II Workshop RNP [14]. F. Cores, Distribute d Architectures for Large - Scale Video - on - Demand Systems, PhD thesis, University Autonoma of Barcelona, March 2004.

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