Massively Distributed Video File Server Simulation: Investigating Intelligent Caching Schemes

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1 Massively Distributed Video File Server Simulation: Investigating Intelligent Caching Schemes Alexander Castro, C. Edward Lazzerini, Vivekananda Kolla Department of Computer Science Cornell University Ithaca, NY {alcastro, lazz, 1.0 Introduction The emergence of low-cost, high performance personal computers and storage systems has fueled anticipation for commercial quality continuous media applications. One such application, video-on-demand (VOD), has generated a great deal of excitement due to the great number of applications. These include video collections of course lectures and seminars for interactive learning, library video archives of non-text media, product demonstrations for on-line shopping, and on-demand feature movie rentals. Various VOD system design issues have been investigated. T.D.C. Little and D. Venkatesh, have studied probabilistic popularity-based assignment of video to storage devices for effecient bandwidth utilization. Ramakrishnan, Vaitzblit, et al [6] provide a study of scheduling and admission control algorithms for both processor and storage resources of a video file server. Reddy and Wyllie [7] study disk scheduling, and the relationship between buffer space and the number of supported video streams. Experimental implementations are described by Rowe et al. [2][8], and Little et al. [5]. The architectures of current VOD implementations have focused on the central archive approach, which uses central archive servers (CAS) as tertiary storage devices to act as a central repository for video. Video file servers (VFS), local to clients, cache video for playback[2]. In order for VOD technology to gain widespread acceptance, scalable, reliable video service must be available. We believe the central archive architecture s dependency on a central repository for video storage fails to sufficiently meet these demands. Previous experience with VOD systems indicate that client video requests possess a high locality of reference. A particular video is popular, and therefore requested often. It is also possible to predict future access patterns that may signal an imminent rise in a video s popularity. An effective caching scheme that is successful in predicting future video requests can significantly improve the performance of a VFS, along with the overall VOD system. Typically, distributed video servers are not concerned with cache consistency, because video file servers are read only. In addition, video file servers deal with a smaller number of larger data objects. These properties suggest that traditional caching schemes (SCAN, FIFO, etc), which are required to be simple and fast, are no longer the only option. Intelligent caching schemes refer to more complex schemes that can efficiently predict user access patterns and that offer pre-fetching based on user directives. Such techniques can offer a great deal of performance advantages[8]. Intelligent

2 caching schemes also play an important role in providing scalable, reliable, and highly available services in addition to gains in throughput. In this paper we present an overview of a massively distributed video file server based VOD system architecture. We also propose several intelligent caching schemes, document experimental performance, and discuss how these intelligent caching schemes provide for scalabe, fault-tolerant, and reliable service. The remainder of this paper is orgainized as follows. In section 2, we provide an overview of the architecture of a massively distributed video file server based VOD system. In section 3, we introduce our intelligent caching schemes. In section 4, we present our approach for testing the merits of the proposed algorithms (simulation study). In section 5, we discuss future extensions to this work. 2.0 System Archetecture The current VOD system implementations fail to offer sufficient support for fault-tolerance and reliability. For example, if a VFS fails while currently serving a client video request, the client will lose its video feed and suffer an interruption in service. If the failed VFS server contains the only copy of that video (or part of the video), then the central archive server will have to retrieve the video in order to resume service to the client. The central archive server can take 30 minutes or more to retrieve a video 90 minutes in length. The latency associated with video retrieval from the central archive server results in a significant disruption of service. Such an interruption is clearly unacceptable for widespread use. Current commercial VOD systems also lack sufficient load balancing. The only instance of a video may reside on a particular VFS. If that video becomes popular, the VFS will receive multiple requests to service that video, causing that VFS to become heavily loaded. A possible solution to these problems is a VOD system based on massively distributed video file servers. In this scheme, machines are both clients and servers. Clients requests are handled by a coordinator, whose function resembles that of a location name server, except that it also devises the caching strategy that local client-servers will use.

3 Massively Distributed Video File Server System Machine B wants to play a video Contacts coordinator with client video request Coordinator locates remote VFS A B caches all (or part) of video frames from A C wants to play the same video Contacts coordinator with client video request Coordinator locates remote VFS A and local VFS B C caches all (or part) of video frames from A and B A C B Coordinator 2.1 Fault Tolerance and Reliability A massively distributed VFS approach to building video file augments both system reliability and faulttolerance. In this architecture, video is stripped and scattered across multiple video file servers. A group of VFSs will each serve a set of different video frames to the client. In the event of a VFS failure, the other group members will continue to service the client. Because a video stream must drop approximately 10 frames for noticable degradation of video quality to occur, this scheme will ensure acceptable service to the client. 2.2 Scalability The massively distributed VFS approach provides a greater degree of scalability. There are two ways in which this architecture provides for scalability. The use of intelligent caching techniques can guarantee that popular videos are more readily available. This allows the coordinator to locate multiple locally available servers for a video request as specified by quality of service requirements. The massively distributed architecture also scales gracefully by virtue of striping video across multiple servers. 3.0 Intelligent Cache Management The system we are investigating is massively distributed and is not plagued by cache consistency problems. As a result, we can implement video caching strategies that are more complex than traditional schemes like FIFO and LRU. LRU tends to perform well when there are peaks in the video request pool [9]. An example of this is a situation in which a particular VFS has a very popular movie in its cache. This VFS would receive a high concentration of hits. However, during a fairly smooth request period (one in which clients request a more diverse selection of videos), a VFS using LRU could easily remove a video fragment that some client might request soon after, causing an increase in client-request latency. FIFO also has a drawback in that it will handle multiple requests on the same video poorly, since it blindly eliminates the video segment that has been cached the longest.

4 To provide smarter solutions to reducing client-request latency and improving network utilization, we have developed the Least Popular and Most Locally Abundant caching algorithms. The Least Popular (LP) strategy employs a popularity measure that a given coordinator assigns to every video fragment that is cached within its local group. In this scheme, a host removes the video segment that is least popular from its cache when necessary. The popularity measure is defined by P = α(ltvr) + (1- α)(ttvr) where α is in [0,1], LTVR is the last time the video fragment was requested, and TTVR is the total number of times the video fragment has been requested locally. Note that α 0 LP LRU α 1 LP LFU ie, when α = 0, LP becomes LFU (least frequently used), and for α = 1, LP reduces to LRU. Another caching scheme that we devised is Most Locally Abundant. This strategy performs cache replacement by removing the video fragment that is found across the most servers within a given coordinator group. Because popular video sequences might be highly replicated, taking up a large amount of available cache, a cooordinator could use MLA to purge many of these copies from local caches once their demand diminishes. In this manner, MLA can act as a garbage collector for a DVFS system that previously used the LP caching strategy in tandem with some sort of replicate-when-popular scheme. 4.0 Simulation In order to quantify the effectiveness of the Intelligent Caching Algorithms, we constructed a simulation of the Distributed VFS System. This section describes the experimental network topology, and an overview of the simulation methodology. 4.1 Experimental Network Topology A challenge in providing video-on-demand service is the high-bandwidth requirements of video transmission. High quality, full motion video playback requires 2Mbps of bandwidth. Most currently installed local area network (LAN) technology can only offer maximum bandwidth of 10Mbps. So a LAN could support at most 5 simultaneous video streams before suffering a significant degragation of video quality. More advanced LAN technology, ATM and FDDI, provide a great deal more bandwidth for continuous media applications. For example, ATM switches can handle 100Mbps to 155Mbps. FDDI is a fiber based token ring technology capable of providing 100Mbps of bandwidth[1]. High bandwidth requirements are an even gerater challenge for wide area networks (WAN), whose bandwidth can range from 56bps to 155Mbps. The initial NFSNET could provide a peak bandwidth of

5 1.54Mbps. This was later replaced with the ANSNET backbone capable of providing 45Mbps. There is a great deal of optimism surrounding the vbns; its expected available bandwidth of 155Mbps will present excellent opportunities for continuous media applications over a general use WAN[1]. After investigating actual internet topologies and dedicated networks like the Seqouia 2000 network, we decided on an experimental network topology. This topology could use a variety of links (T1, T3, FDDI, etc.), between 1.5 Mbps providing 45Mbps. We have choosen to ignore LAN traffic and variable network congestion for the time being. Because we are modeling the effectiveness of these caching algorithms, we felt it was unecessary to provide a finer granularity simulation of the network. 4.2 Simulation Methodology The simulator adheres to the discrete event simulation paradigm. This approach utilizes models of different entities within a system, and how these entities interact. A client entity, for instance, contacts the local coordinator with a client video request. The coordinator will then contact the client with the location of the appropriate VFS. After the video transmission is complete the coordinator will provide a strategy for replacing cached video frames, and so on. The model components are the client-server, the strategist, and the network. The client-server component makes the initial request for a video, and caches the received video frames on the client s peer server. These cached video frames can then be used to service future client video requests. The strategist is encapsulated as part of the coordinator, and controls the caching algorithm that the simulator uses in a given session. 4.3 Results We tested the MLA and LP algorithms using our simulator, and compared the results obtained with those for LRU. Our objective was to discover which of these schemes would produce the greatest locality of reference, and also which scheme would do this the fastest. Figure 1 shows that both LP (α = 0.35) and MLA greatly outperform LRU, and that LP performs the best overall. These results are encouraging, since they suggest that intelligent caching algorithms can increase the local availability of videos, thereby cutting down on request latency.

6 Performance of Caching Algorithms LRU MLA 450 LP Number of Local Cache Hits LP MLA LRU Number of Movie Request (10x) 5.0 Extensions 5.1 Pre-scheduling of Video Requests Time did not permit us to implement any schemes based on pre-scheduling of video requests. Prescheduling would guarantee the availability of a user s request. In addition, if a client could inform the system ahead of time that there would be many requests for a particular video at a certain time, then the system could automatically replicate the requested video fragments and stripe them across several servers to increase fault-tolerance and improve load balance. 5.2 Best Path Selection Other schemes might extend the above idea by using some concept of best path to determine where highly popular videos should be cached. Note that best path does not necessarily imply the shortest geographical distance, but might mean the largest capacity/fastest link between the client and a group of possible servers. By retrieving the video from a best path source, we can effectively reduce client-request latency. 5.3 Adaptive Caching Algorithms A coordinator could monitor its local network (a LAN or MAN) and have servers use different caching methods depending upon network traffic conditions and server load. For example, a coordinator might order certain hosts in its group to use the MLA scheme for a time to remove over-replicated video segments from their caches.

7 In addition to the above suggestions, it will be important to analyze the performance of all caching algorithms under failures. A more sophisticated network simulator and topology would facilitate such an investigation. Our research has only scratched the surface of intelligent cache management for massively distributed video file server systems. Rowe et al have done related work in the DVFS area, but their system is distributed with respect to a group of video file servers. The design we support extends the distributed paradigm and allows all hosts to act as servers. We set out to gain insight into the intelligent caching algorithms that such a massively distributed system could employ, and have produced results that suggest these algorithms, and others like them, are promising. References [1] Douglas Comer, Internetworking with TCP/IP Volume I: Principles, Protocols, and Architecture, 3rd ed., ISBN [2] Craig Federighi and Lawrence A. Rowe, A Distributed Hierarchical Storage Manager for a Video- on-demand System, Storage and Retrieval for Image and Video Databases II, IS&T/SPIE Symp. on Elec. imaging Schi & Tech., San Jose, CA, February [3] D. James Gemmell, Harrick M. Vin, Dilip D. Kandlur, P. Venkat Rangan, Lawrence A. Rowe, Multimedia Storage Servers: A Tutorial, IEEE Computer, May 1995, pp [4] T.D.C. Little and D. Venkatesh, Probabilistic Assignment of Movies to Storage Devices in a Videoon-Demand System, Proc. 4th Intl, Workshop on Network and Operating System Support for Digital Audio and Video, Lancaster UK, November 1992, pp [5] T.D.C. Little and D. Venkatesh, Prospects for Interactice Video-on-Demand, IEEE Multimedia, Vol1, No 3, Fall 1994, pp [6] K. K. Ramakrishnan, et al., Operating System Support for a Video-on-Demand File Service, Proc. 4th Intl, Workshop on Network and Operating System Support for Digital Audio and Video, Lancaster UK, November [7] A. L. Narasimha Reddy, and Jim Wyllie, Disk Scheduling in a multimedia I/O system, Proc. ACM Multimedia 93, Anaheim, CA, August 1993, pp [8] Lawrence A. Rowe, John S. Boreczky, and Charles A. Eads, Indexes for User Access to Large Video Databases, Storage and Retrieval for Image and Video Databases II, IS&T/SPIE Symp. on Elec. imaging Schi & Tech., San Jose, CA, February [9] Song Bac Toh, Simulation of a Video-on-Demand System, PCS-TR95-260, Department of Computer Science, Dartmouth College, June 6, 1995.