1. 1 An SLA-Oriented Capacity Planning Tool for Streaming Media Services Lucy Cherkasova, Wenting Tang, and Sharad Singhal HPLabs,USA

2. 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. 3 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.

4. 4 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)

5. 5 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.

6. 6 Computing Required System Capacity Example: Computed Load of 4.5 indicates that considered media workload requires 5 nodes for its support. Capacity equation:

7. 7 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

8. 8 Segment-based Memory Model To stream the file from memory, it is not necessary to have the whole file in memory!

9. 9 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 memoryNumber of requests served from disk

10. 10 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.

11. 11 Capacity Planning Framework

12. 12 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.

13. 13 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.

14. 14 Example The “aggregate” amount of 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”

15. 15 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.

16. 16 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.

17. 17 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 W SYN is defined by Zipf-like distribution with alpha = 1.34. Overall, W SYN 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.

18. 18 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.

19. 19 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.

20. 20 Basic Capacity Planning Analysis W1: D 95% = 4.1 D util = 3.3/0.7 =4.7 => D basic =5 W2: D 95% = 4 D util = 3.2/0.7 = 4.6 => D basic =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)

21. 21 Performability Capacity Planning Analysis W1: only 7-node cluster satisfies the desirable performability requirements. W2: 5-node cluster satisfies both of the performability requirements.

22. 22 Load Balancing Strategy The final stage is the cluster size validation for evaluating the impact of the Round-Robin load balancing solution. The increased memory (due to combined memory of multiple nodes) does not provide additional performance benefits.

23. 23 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.