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An SLA-Oriented Capacity Planning Tool for

Streaming Media Services

Lucy Cherkasova, Wenting Tang, and Sharad Singhal

HPLabs,USA

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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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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 and SLAs 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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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 three phases in the capacity planning procedure:

– Basic capacity planning:• Statistical Demand Guarantees;• Utilization Constraints.

– Performability planning:• Regular-mode Overload Constraints;• Node-Failure-mode Overload Constraints.

– Cluster size validation.

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Capacity Planning Framework

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Basic Capacity Planning• Compute the media site workload profile.

During the initial step, we assume a cluster of consisting of a “single node” with memory size of interest.

• Compute 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)

…

• Combine the service demand profile and the basic capacity requirements– Statistical demand guarantees: Based on the past workload

history, find the configuration that 95% of the time is capable of processing the load;

– Utilization 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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Performability Planning

• Basic capacity planning derives desirable configuration by sizing the system according to main requirements for the compliant time

• Performability planning refines the configuration in order to limit the amount possible overload during regular processing (in non-compliant time) and during periods of node failures.

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Example

The “aggregate” amountof overload is the same

A significant difference in the amount of “continuous” overload:

- no more than 10 min of 10% overload per node in “Thin Spikes Workload”

- 1.5 hour interval of “continuous” 10% overload per node in “Fat Spikes Workload”

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Building Interval Overload Profile

• Let the N-node cluster be considered• Let I be a duration of time interval of interest

(in min). • We use a moving window technique:

• For each I-interval, any service demand above N nodes is aggregated and averaged over NxI

• CDF of aggregate I-interval overload normalized over the number of I-intervals.

• Performability requirement (example): Based on the past workload history, find an appropriate

performance solution such that the amount of average overload is limited by 2% in any 60 min interval.

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Example

For the “Thin Spikes” workload, the 3-node cluster meets the performability requirements.

However, for the “Fat Spikes” workload 3-node cluster does not satisfy the desirable performability requirements, and the capacity planner will propose 4-node cluster as the minimal solution.

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Capacity Planning: a Case Study

• We used publicly available workload generator MediSyn to generate two synthetic media workloads W1 and W2 that closely imitate parameters of real enterprise media workloads.

• 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).

• Main difference in W1 and W2 is a diurnal access pattern:– W1 access pattern is defined using 1-hour-long bins,

– W2 access pattern is defined using 15-min-long bins.

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Simulation Environment• Media server capacity :

Let the server memory size of interest be 0.5GB,and the cost of disk access is 5 times the cost of memory access.

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Capacity Planning Requirements

• Find the appropriate systems for W1 and W2:– Statistical Demand Guarantees: for 95% of the time, the

system is capable of processing the given workload without overload;

– Utilization Constraints: for 90% of the time, the system is utilized under 70% of its capacity;

– Regular-mode Overload Constraints: during any 60 min. interval, the average overload per node is under 5%;

– Node-Failure-mode Overload Constraints: in case of 1-node failure, with 95% probability the average overload in the remaining system is under 10% during any 60 min. interval.

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Basic Capacity Planning Analysis

W1: D 95% = 4.1 Dutil = 3.3/0.7 =4.7 => Dbasic=5

W2: D 95% = 4 Dutil = 3.2/0.7 = 4.6 => Dbasic=5

First of all, taking into account the performance impact of memory when delivering streaming media workload results in significant h/w savings due to locality available in the media workloads (8 nodes vs 11 nodes)

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Performability Capacity Planning Analysis

W1: only 7-node cluster satisfies the desirable performability requirements.

W2: 5-node cluster satisfies both of the performability requirements.

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Conclusion• We proposed a new unified benchmarking and

capacity planning framework that:– Measures media server via a set of basic benchmarks;– Derives the resource requirements using a single value

cost function;– Estimates the service capacity requirements from the

proposed media workload profile;– Incorporates the requirements for desirable system

performance.

• In the future, we intend to use our capacity planning tool as a core of adaptive management system in the streaming media utility for deploying/releasing additional server resources.