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Load Management in Distributed Video Servers

Load Management in Distributed Video Servers. Chaitanya Chemudugunta chandra@ics.uci.edu. Load Management in Distributed Video Servers. By Nalini Venkatasubramanian Srinivas Ramanadhan. International Conference on Distributed Computing Systems (ICDCS 97) , May 1997. Overview. Architecture

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Load Management in Distributed Video Servers

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  1. Load Management in Distributed Video Servers Chaitanya Chemudugunta chandra@ics.uci.edu

  2. Load Management in Distributed Video Servers By Nalini Venkatasubramanian Srinivas Ramanadhan International Conference on Distributed Computing Systems (ICDCS 97), May 1997

  3. Overview • Architecture • Load Management Mechanisms • Characterizing Server Resource Usage • Adaptive Scheduling of Video Objects • Predictive Placement of Video Objects • Optimization Methods • Performance Evaluation Load Management in Distributed Video Servers

  4. A Scalable Video Server Architecture Distribution Network requests data Distribution Controller Data Source Data Source Data Source Tertiary Storage ... control Load Management in Distributed Video Servers

  5. Resources in a Video Server Client Client Network Processing Module Communication Modules Data Manipulation Modules Storage Modules Load Management in Distributed Video Servers

  6. Load Management Mechanisms • Replication • When existing copies cannot serve a new request • Request Migration • Unsuitable for distributed video servers – explicit tear-down and reestablishment of network connection. • Dereplication • Important – Storage space is premium Load Management in Distributed Video Servers

  7. Load Placement Scenario Data Source S2 Data Source S1 Storage: 2 objects Bandwidth: 8 requests Storage: 8 objects Bandwidth: 3 requests Access Network ... Clients Load Management in Distributed Video Servers

  8. Characterizing Server Resource Usage • Ability to service a request on a server depends on: • resource available • characteristics of a request • Load factor(LF) for a request: • represents how far a server is from request admission threshold. LF (Ri, Sj) = max (DBi/DBj , Mi/Mj , CPUi/CPUj , Xi/Xj) Ri – Request for Video Object Vi, Sj – Data Source j DB – Disk Bandwidth, M – Memory Buffer, CPU – CPU cycles, X – Network Transfer Bandwidth Load Management in Distributed Video Servers

  9. Adaptive Scheduling • When the distribution controller receives a request Ri for a video object Vi : • Consider only data sources that have a copy of Vi. • Consider only data sources that have sufficient resources to support Ri. • Chooser server for which LF (Ri, Sj) is a minimum. • If no such server exists • Reject request. • Perform replication-on-demand. • Perform request migration. Load Management in Distributed Video Servers

  10. Predictive Placement of Video Objects • Determines when, where and how many replicas of a video object. • Initiated periodically. • Results in an assignment of replicas to data sources. • Formulated as an optimization problem – metric to be optimized is the total revenue. Load Management in Distributed Video Servers

  11. Predictive Placement of Video Objects … Continued • Each request Ri is associated with a revenue ri. • ri is dependent on • Resource required for Ri • Characteristics of Vi • Popularity of Vi • Greedy approach to solve the optimization problem. Load Management in Distributed Video Servers

  12. The Greedy Cost Placement Matrix PM(Vi, Sj) is the maximum revenue that can accrue from allocating Vi to Sj. Greedy heuristic: Map(Vi,Sj) = 1 if PM(Vi,Sj) = a b max(PM(Va,Sb)) Load Management in Distributed Video Servers

  13. Optimizations • To minimize the overhead of replication • Eager replication • Replication of video object in anticipation • Performed when server resources are free • Lazy Dereplication • Critical nature of storage resources • Mark reusable resources, reclaim disk space later • If disk blocks are not overwritten, can be reclaimed Load Management in Distributed Video Servers

  14. Life of a video object Load Management in Distributed Video Servers

  15. Performance Evaluation Policies Load Management in Distributed Video Servers

  16. Performance Evaluation - Startup Latencies Load Management in Distributed Video Servers

  17. Performance of the basic configuration p1 p2 p3 p4 Load Management in Distributed Video Servers

  18. Performance Evaluation - Varying Replication BW Load Management in Distributed Video Servers

  19. Performance Evaluation Summary • P1: entails high startup latency, requires high storage and replication bandwidth. • P2: Unacceptably poor performance. • P3: Similar performance to P4 in many cases. • At low transfer bandwidths, P4 outperforms P3. • P4: Performs well in all cases. Load Management in Distributed Video Servers

  20. Thank You

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