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Sizing the Streaming Media Cluster Solution for a Given Workload

Lucy Cherkasova and Wenting Tang

HPLabs

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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.

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Capacity Planning Framework

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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.

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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 (dual ISDN users)

• 256Kb/s (cable modem users)

• 350Kb/s (DSL/cable users)

• 500Kb/s (high-bandwidth users)

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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.

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Computing Required System Capacity

Example: Computed Load of 4.5 indicates that considered media workload requires 5 nodes for its support.

Capacity equation:

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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

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Segment-based Memory Model

To stream the file from memory, it is not necessary to havethe whole file in memory!

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Media Workload CharacterizationExample: analysis of the HP Corporate Media Site over a periodof 1 year duration:

Number of concurrent connections Peak Bandwidth requirements

Number of requests served from memory Number of requests served from disk

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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)

…

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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.

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Overall Capacity Planning

Additionally, we need to do a cluster sizing with an appropriate load balancing strategy

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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

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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

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Simulation Environment (cont.)

• The file popularity in WSYN 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.

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Capacity Planning (first iteration)

Considered workload requires 5nodes with memory of 1GB,or 6 nodes with memory of 0.5GB

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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

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LARD Load Balancing

Locality aware load balancing strategy provides significant performance benefits due to efficient memory usage.

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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 toSupport the same traffic.

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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.