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QoS Aware Scheduling in a Cluster-Based Web Server

This paper examines a novel approach to Quality of Service (QoS) aware scheduling in cluster-based web servers. It details how resources can be allocated proportionally among service classes to ensure performance guarantees, even under varying load conditions. The proposed scheduling framework utilizes techniques like weighted round robin and least load first for effective resource distribution among server nodes. Emphasizing performance isolation, it also offers insights into managing multimedia job scheduling, ensuring that QoS requirements are consistently met across different service demands.

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QoS Aware Scheduling in a Cluster-Based Web Server

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  1. QoS Aware Scheduling in a Cluster-Based Web Server Jiani Guo Architecture Lab Department of Computer Science and Engineering University of California, Riverside

  2. Reference • Performance Guarantees for Cluster-Based Internet Services, ICDCS 2003 Chang Li, Gang Peng, Kartik Gopalan Tzi-cker Chiuh State University of New York at Stony Brook

  3. Web Services Courtesy: Performance Guarantees for Cluster-Based Internet Services, Chang Li.

  4. Differentiated Service • A system is said to be capable of affording differentiated service among service classes if • The system permits its resources to be proportioned among the service classes • Given sufficient request load, a service class receives at least as much resources as were assigned to it irrespective of the load on other service classes • Resources not used by some service class may be distributed among other service classes. • QoS Metrics • The number of generic URL requests per second • A generic URL request represents an average web site access which is assumed to take 10 msec of CPU time, 10 msec of disk channel usage time and 2000-bytes of network bandwidth • For example: QoS requirement is 50 GRPS, which means 500 msec of CPU time, 500 msec of disk access time and 100 Kbytes of the network bandwidth

  5. Scheduling Framework

  6. Request scheduling • Request selection • Weighted round robin (WRR) • No idea about the resource a request will consume on dispatching it • Predict per-request resource usage using history • Feedback to correct the prediction • Server node selection • Load balancing among server nodes (Least Load First) • Select a node based on resource usage accounting • What to account • CPU, disk and network bandwidth • Accounting granularity • Per-request • Per-server • Per process-set Courtesy: Performance Guarantees for Cluster-Based Internet Services, Chang Li.

  7. Performance Isolation

  8. Performance deviation from ideal reservation Averaging Interval (secs)

  9. My Previous Work on Scheduling:Scheduling Multimedia Jobs among Servers

  10. Transcoding Workload A media unit is a Group Of Pictures(GOP) of MPEG stream • A media unit can be transcoded independently by any Worker in the cluster. Transcoding one media unit is considered an independent job. • No communication is required among jobs. • Each job consumes similar amount of processing time. • Consecutive media units in a stream are preferred to be processed in order.

  11. Find an available Computing Server fetch a unit Send the unit Load Balancing Schemes Computing Server • How to take QoS into consideration? • Streams make reservations • Received service is proportional to the reservations Computing Server Media Server Unit Buffer . . . Retriever Scheduler Computing Server

  12. Framework of Fair Scheduling

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