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Sizing the Streaming Media Cluster Solution for a Given Workload

Sizing the Streaming Media Cluster Solution for a Given Workload . Lucy Cherkasova and Wenting Tang HPLabs. Capacity Planning Scenarios. Service provider needs to migrate his media site to a new infrastructure.

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Sizing the Streaming Media Cluster Solution for a Given Workload

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  1. Sizing the Streaming Media Cluster Solution for a Given Workload Lucy Cherkasova and Wenting Tang HPLabs

  2. Capacity Planning Scenarios • Service provider needs to migrate his media site to a new infrastructure. • While he has information about the site workload (the media the server logs reflecting the accesses to the media site in the past), it is a problem to map workload requirements in the resource requirements • Can we design a tool helping to accomplish the capacity planning tasks? • The goal of the proposed capacity planning tool is to provide the best cost/performance configuration for support of a known media service workload.

  3. Capacity Planning Framework

  4. Main Components • Two main components: • A media workload profiler MediaProf that extracts a set of quantitative and qualitative parameters characterizing the service demand • The capacity measurements of h/w and s/w solutions using a specially designed set of media benchmarks; • The capacity planning tool matches the requirements of the media service workload profile, SLAs and configuration constraints to produce the best available cost/performance solution.

  5. Basic Benchmarks: • Single File Benchmark: all clients are accessing the same file (encoded at different bit rates) • Unique Files Benchmark: all clients are accessing different (unique) files(encoded at different bit rates) • In our tests, we use the sets of files encoded at different bit rates: • 28 Kb/s (analog modem users) • 56 Kb/s (analog modem and ISDN users) • 112Kb/s (dualISDN users) • 256Kb/s (cable modem users) • 350Kb/s (DSL/cable users) • 500Kb/s (high-bandwidth users)

  6. Workload-Aware Performance Model of Streaming Media Server Capacity • How to compute the expected media server capacity for realistic workload if the measured capacities under the basic benchmarks are given. • We introduce cost function which defines a fraction of system resources needed to support a particular stream depending on • file encoding bit rate and • file access type (streamed from memory or disk) . • Introduced cost function uses a single value to reflect the combined resource requirements such as CPU, disk, memory and server bandwidth necessary to support a particular media request.

  7. Computing Required System Capacity Capacity equation: Example: Computed Load of 4.5 indicates that considered media workload requires 5 nodes for its support.

  8. Workload Profiler MediaProf • MediaProf reflects the access traffic profile for capacity planning goals: • Evaluates the number of simultaneous (concurrent) connections over time; • Classifies the simultaneous connections into the encoding bit rate bins; • Classifies the simultaneous connections by the file access type: disk vs memory

  9. Segment-based Memory Model To stream the file from memory, it is not necessary to have the whole file in memory!

  10. Media Workload Characterization Example: analysis of the HP Corporate Media Site over a period of 1 year duration: Number of concurrent connections Peak Bandwidth requirements Number of requests served from memory Number of requests served from disk

  11. Overall Capacity Planning Process There are several logical steps in the capacity planning procedure: • Computing the media site workload profiles for different memory sizes of interest. During the initial step, we assume a “single node” cluster: N=1 • Computing the service demand profile. The service demand profile is the ordered list of pairs: (time duration, service demand). For example: (300, 4.5 ) (600, 4 ) (2000, 3.8) (1000, 3.5) …

  12. Overall Capacity Planning Process (cont.) • Combining the service demand requirements, the SLAs, and the configuration constraints: • SLAs: Based on the past workload history, find the configuration that 99% of the time is capable of processing the load; • Constraints: Based on the past workload history, find the configuration that 90% of the time is utilized under 70% of its capacity.

  13. Overall Capacity Planning Additionally, we need to do a cluster sizing with an appropriate load balancing strategy

  14. Evaluating Load Balancing Solutions • For an accurate cluster sizing we need to take into account both: increased processing power (N nodes) and increased memory size (N times M) • In our capacity planning tool, we implemented 2 strategies: • Round Robin • LARD (locality-aware ): first access goes to random node in the cluster, but subsequest requests to the same file are send to the same node. • If the outcome of the first iteration is k nodes then • Partition original workload in k sub-trace W1 , W2 , …,Wk where Dispatcher employs the corresponding load balancing strategy; • Compute media workload profile for each W1 , W2 , …,Wk using MediaProf • Merging the computed sub-workload profiles and computing overall service demand profile

  15. Performance Results • For workload generation, we used MediSyn: publicly available synthetic workload generator. • WSYN (with parameters that are typical for enterprise media workloads) • 20% of videos are 0-2 min long • 10% of videos are 2-5 min long • 13% of videos are 5-10 min long Video Duration • 23% of videos are 10-30 min long • 21% of videos are 30-60 min long • 13% of videos are longer than 60 min • 5% of videos encoded at 56 Kb/s • 20% of videos encoded at 112Kb/s • 50% of videos encoded at 256Kb/s File Encoding Bit Rate • 25% of videos encoded at 500Kb/s

  16. Simulation Environment (cont.) • The file popularity inWSYN is defined by Zipf-like distribution with alpha = 1.34. • Overall, WSYN has 800 files (with 41GB storage footprint), and 90% of requests target 10% of the files (with 3.8GB storage footprint) • Media server capacity : Let the server memory size of interest is: 0.5GB and 1Gb, and the cost of disk access is 5 times the cost of memory access.

  17. Capacity Planning (first iteration) Considered workload requires 5nodes with memory of 1GB, or 6 nodes with memory of 0.5GB

  18. Round Robin Strategy Since the RR strategy distributes the requests uniformly to all the machines, this prohibits efficient memory usage increased cluster memory does not provide additional performance benefits

  19. LARD Load Balancing Locality aware load balancing strategy provides significant performance benefits due to efficient memory usage.

  20. Cluster sizing • Cluster sizing results for a given synthetic workload are summarized in the following Table: Locality aware load balancing strategy utilizes the increased cluster memory more efficiently, and requires less nodes to Support the same traffic.

  21. Conclusion • We proposed a new unified benchmarking and capacity planning framework: • Measure media server via a set of basic benchmarks; • Derives the resource requirements using a single value cost function; • Estimate the service capacity requirements from the proposed media workload profile. • In future work, we would like to incorporate the availability requirements into the proposed capacity planning framework.

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