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Virtualization and Utilization
Ideas International/Performance Comparison
Total Cost of Ownership (TCO)
Consolidation: Case Study
It is the nature of computers that any computer can be programmed to accomplish any task.
Conversely, functionality is not enough.
Rational Platform Selection is based on the solution’s “non functional requirements”
Simplifying the Client Server Build Out Synchronization Time Bulk Data Traffic Shared Nothing High Latency Blades, Clusters,Squadrons HV Read Only Webserving , some DSS Shared Memory Low Medium Latency F6800,rx8400,rp8400 P670, Squadrons ML OLTP, Legacy SMP Shared Memory High Medium Latency F12000,F15000, SuperDome , P690 Data Warehouse, some DSS From: In Search of Clusters, The ongoing battle in lowly parallel comp uting by Greg Pfister , p461 Shared Everything Low Latency zSeries, Squadrons HE OLTP, Mixed Workload Price/Performance Total Capacity WLM & BI Function Virtualization Archit e ctural Divide Blades Midrange Client Server Mainframes Archit e ctural Divide High End UNIX Severs
Workload Characterization 2. I/O Bound – e.g. high I/O content applications 9. Protocol Serving – e.g. static HTTP, firewall, etc. 3. Mixed Low – e.g. multiple, data-intense applications or skewed OLTP, MQ 1 . Data Intensive – large working set and/or high I/O content applications 4. Mixed High – e.g. multiple, cpu-intense simple applications 8. Skewless OTLP – e.g. simple and predictable transaction processing 7. Java Heavy – e.g. cpu intensive java applications 6. Java Light – e.g. data intensive java applications 5. Database – e.g. Oracle DBMS or dynamic HTTP server 10. CPU Intensive – e.g. numerically intensive, etc. I/O Driven CPU Driven
Industry Benchmarks TPC-C, TPCE?? Parallel Hell positioning is empirical and folklore driven
Workloads may benefit from being physically close to the data
Most servers run in low to medium utilization
High I/O workloads Vs. High CPU workloads
Workloads may require high availability
Mission critical applications
Comparing servers using relative capacity : Given system B with capacity C B processing a workload at utilization U B capacity C A needed by system A to process the same workload is given by: where WLF is the Workload Factor. With WLF we try to compensate for all the architectural differences between system A and system B. It is simplified: Actually WLF = f(U B )
Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Global Interconnect Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Global Interconnect Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Global Interconnect Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Memory Memory I/O CPU Cache CPU Cache I/O Local Interconnect Global Interconnect
System Design and Benchmarks
Industry standard benchmarks are used by vendors to establish performance or price/performance leadership.
Benchmarks are chosen to show off a platform not to allow comparisons
Benchmarks frequently do not match real customer workloads
Small – very limited stress on data delivery infrastructure
Throughput oriented with highly paralyzed processing – scales with quantity of processors for all vendors
Real workloads (including virtualization and mixed workloads) place tremendous stress on cache and system interconnect.
Workload / Server Size Data Sharing / Workload Complexity On-Chip Cache Qty of Threads (1-2 Sockets) Any Benchmark Quantity of Cores / threads Parallelization Throughput Results TPC-H SAP SD 2-Tier SPECJBB2005 SPECintRate SPECfpRate SPECweb System Interconnect Cache Architecture Schedulers # of Processors TPC-C SAP SD 3-Tier Interconnect Cache Schedulers # Processors Virtualization Mixed Workload
'White space' = wasted capacity Shared Systems Separate Dedicated Systems
Peak and Average
The desired peak utilization is the “Utilization at Saturation design point”, Usd
Average Utilization is:
Where: P is the Peak Load A is the Average Load s is the number of servers used to implement the capacity
Virtualization enables higher CPU Utilization
Single workload model assumptions:
Average Utilization: 20.7%
As more copies of this workload are added, average utilization approaches peak
8:1 39% Average, peak 76%
16:1 48% Average, peak 78%
64:1 61% Average, peak 78%
As workload is added the number of CPUs required for the work grows at a much lower rate.
Why Larger Servers for Virtualization?
Higher utilization due to shared headroom.
More internal bandwidth to improve performance
Fewer disk & network adapters and ports
Able to share memory more effectively
More fault tolerant features
Fewer servers to order, install, track, maintain, and retire
Fewer Hypervisor instances to manage
Fewer firmware patches to apply
Data Center Advantages
Better power utilization
Reduced floor space
Hardware Capacity Usable VM Capacity Smaller Servers Medium Peak to Avg. Utilization Gap Larger Servers Small to Very Small Peak to Avg. Utilization Gap
Relative Capacity Criteria
What is the number and utilization of servers?
How big is this? What is the potential for virtualization or workload management? Need to profile utilization by intervals.
How Parallel is the work?
Read only partitioned data, mostly read minimum sharing, mostly read shared data, read/write partitioned data, read/write shared data, etc. (Leverage of shared v. partitioned resources)
How large are the working sets for the DB, Memory, and Cache?
What is the "Workload Factor?" Need throughput v. utilization by intervals
What are the testing and QA practices?
How much nonproduction hardware is there as a result?