05. performance-concepts-26-slides
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The document discusses key concepts for designing IT infrastructure to ensure high performance. It covers perceived performance from a user perspective, benchmarking systems, profiling users to predict load, identifying and managing bottlenecks, scaling systems horizontally and vertically, load balancing, caching frequently used data, and designing systems based on their intended use to optimize performance. The overall goal is to design infrastructure that can meet performance requirements under all conditions, both currently and as load increases over time.
05. performance-concepts-26-slides
- 1. IT Infrastructure Architecture Performance Concepts (chapter 5) Infrastructure Building Blocks and Concepts
- 2. Introduction • Performance is a typical hygiene factor. • Nobody notices a highly performing system. • But when a system is not performing well enough, users quickly start complaining.
- 3. Perceived performance • Perceived performance refers to how quickly a system appears to perform its task. • In general, people tend to overestimate their own patience. • People tend to value predictability in performance – When the performance of a system is fluctuating, they remember a bad experience, even if the fluctuation is relatively rare.
- 4. Perceived performance • Inform the user about how long a task will take – Progress bars – Splash screens
- 6. Performance during infrastructure design • Designing for performance ensures that a solution is designed, implemented, and supported to meet the performance requirements, even under increasing load. • When designing a system, performance must be considered not only when the system works as expected, but also when the system is in a special state. – Failing parts – Maintenance state – Performing backup – Running batch jobs • Calculating performance of a system in the design phase is extremely difficult and very unreliable.
- 7. Benchmarking • A benchmark uses a specific test program to assess the relative performance of an infrastructure component • Benchmarks provide a method of comparing the performance of various subsystems across different system architectures. • Often used for computer hardware – Floating Point Operations Per Second – FLOPS – Million Instructions Per Second – MIPS • Only useful for comparing the raw speed of parts of an infrastructure – Like the speed difference between processors or between disk drives
- 8. Vendor experience • The best way to determine the performance of a system in the design phase is to use the experience of vendors • They have a lot of experience running their products in various infrastructure configurations
- 9. Prototyping • Also known as proof of concept (PoC) • To measure the performance of a system at an early stage – Hiring equipment from suppliers – Using datacenter capacity at a vendor’s premise – Using cloud computing resources • Focus on those parts of the system that pose the highest risk, early in the design process
- 10. User profiling • Predict the load a new software system will pose on the infrastructure before the software is actually built • It is important to have a good indication of the expected usage of the system – Defining a number of typical user groups of the new system (personas) – Creating a list of tasks personas will perform on the new system.
- 11. User profiling personas/tasks Persona Number of users per persona System task Infrastructure load as a result of the system task Frequency Data entry officer 100 Start application Read 100 MB data from SAN Once a day Data entry officer 100 Start application Transport 100 MB data to workstation Once a day Data entry officer 100 Enter new data Transport 50 KB data from workstation to server 40 per hour Data entry officer 100 Enter new data Store 50 KB data to SAN 40 per hour Data entry officer 100 Change existing data Read 50 KB data from SAN 10 per hour
- 12. User profiling Infrastructure load Infrastructure load Per day Per second Data transport from server to workstation (KB) 10,400,000 361.1 Data transport from workstation to server (KB) 2,050,000 71.2 Data read from SAN (KB) 10,400,000 361.1 Data written to SAN (KB) 2,050,000 71.2
- 13. Performance of a running system
- 14. Managing bottlenecks • The performance of a system is based on the performance of all its components, and the interoperability of various components • Every system, regardless of how well it works, has at least one constraint (a bottleneck) that limits its performance (Bottleneck law) • A component causing the system to reach some limit is referred to as the bottleneck of the system • If the bottleneck does not negatively influence performance of the complete system under the highest expected load, it is OK
- 15. Performance testing • Load testing - This test shows how a system performs under the expected load • Stress testing - This test shows how a system reacts when it is under extreme load • Endurance testing - This test shows how a system behaves when it is used at the expected load for a long period of time
- 16. Performance testing - Breakpoint
- 18. Increasing performance on upper layers • 80% of the performance issues are due to badly behaving applications • Database and application tuning typically provides much more opportunity for performance increase than installing more computing power • Application performance can benefit from: – Prioritizing tasks – Working from memory as much as possible (as opposed to working with data on disk) – Making good use of queues and schedulers
- 19. Caching • Caching improves performance by retaining frequently used data in high speed memory, reducing access times to data. – Disk caching – Web proxies – Operational Data Store – Front-end servers – In-memory databases Component Time it takes to fetch 1 MB of data (ms) Network, 1 Gbit/s 675 Hard disk, 15k rpm, 4 KB disk blocks 105 Main memory DDR3 RAM 0.2 CPU L1 cache 0.016
- 20. Scalability • Scalability indicates the ease in with which a system can be modified, or components can be added, to handle increasing load • Two ways to increase the scalability of a system: – Vertical scaling (scale up) - adding resources to a single component – Horizontal scaling (scale out) - adding more components to the infrastructure
- 21. Scalability – Horizontal scaling
- 22. Load balancing • Load balancing spreads the load over various machines • It checks the current load on each server in the farm and sends incoming requests to the least busy server.
- 23. High performance clusters • High performance clusters provide a vast amount of computing power by combining many computer systems. • Usually a large number of cheap off the-shelf servers are used • A combination of relatively small computers can create one large supercomputer • Used for calculation-intensive systems – Weather forecasts – Geological research – Nuclear research – Pharmaceutical research • TOP500.ORG
- 24. Grid Computing • A computer grid is a high performance cluster that consists of systems that are spread geographically • The limited bandwidth is the bottleneck • Examples: – SETI@HOME – CERN LHC Computing Grid (140 computing centers in 35 countries) • Broker firms exist for commercial exploitation of grids • Security is a concern!
- 25. Design for use • Performance critical applications should be designed as such • Tips: – Know what the system will be used for. A large data warehouse needs a different infrastructure design than an online transaction processing system or a web application – In some cases, special products must be used for certain systems (real- time operating systems, in-memory databases, specially designed file systems) – Use standard implementation plans that are proven in practice – Have the vendors check the design you created. – When possible, try to spread the load of the system over the available time – Move rarely used data from the main systems to other systems
- 26. Capacity management • Capacity management guarantees high performance of a system in the long term • Performance of the system is monitored on a continuous base, to ensure performance stays within acceptable limits • Trend analyses can be used to predict performance degradation • Anticipate on business changes (like forthcoming marketing campaigns)