05. performance-concepts
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This document discusses various concepts related to ensuring adequate performance of IT infrastructure. It covers perceived performance from an end user perspective and how to account for performance during infrastructure design. Methods discussed for evaluating performance during the design phase include benchmarking, leveraging vendor experience, prototyping, and user profiling. The document also addresses managing performance of running systems through techniques like performance testing, identifying and addressing bottlenecks, leveraging caching, and scaling infrastructure through vertical and horizontal expansion approaches like load balancing.
05. performance-concepts
- 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, users 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 • A solution must be designed, implemented, and supported to meet the performance requirements – Even under increasing load • Calculating performance of a system in the design phase is: – Extremely difficult – Very unreliable
- 7. Performance during infrastructure design • Performance must be considered: – When the system works as expected – When the system is in a special state, like: • Failing parts • Maintenance state • Performing backup • Running batch jobs • Some ways to do this are: – Benchmarking – Using vendor experience – Prototyping – User Profiling
- 8. Benchmarking • A benchmark uses a specific test program to assess the relative performance of an infrastructure component • Benchmarks compare: – Performance of various subsystems – Across different system architectures
- 9. Benchmarking • Benchmarks comparing the raw speed of parts of an infrastructure – Like the speed difference between processors or between disk drives – Not taking into account the typical usage of such components – Examples: • Floating Point Operations Per Second – FLOPS • Million Instructions Per Second – MIPS
- 10. Vendor experience • The best way to determine the performance of a system in the design phase: use the experience of vendors • They have a lot of experience running their products in various infrastructure configurations • Vendors can provide: – Tools – Figures – Best practices
- 11. Prototyping • Also known as proof of concept (PoC) • Prototypes measure the performance of a system at an early stage • Building prototypes: – 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, as early as possible in the design process
- 12. User profiling • Predict the load a new software system will pose on the infrastructure before the software is actually built • Get a good indication of the expected usage of the system • Steps: – Define a number of typical user groups (personas) – Create a list of tasks personas will perform on the new system – Decompose tasks to infrastructure actions – Estimate the load per infrastructure action – Calculate the total load
- 13. 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
- 14. 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
- 15. Performance of a running system
- 16. Managing bottlenecks • The performance of a system is based on: – The performance of all its components – The interoperability of various components • A component causing the system to reach some limit is referred to as the bottleneck of the system • Every system has at least one bottleneck that limits its performance • If the bottleneck does not negatively influence performance of the complete system under the highest expected load, it is OK
- 17. Performance testing • Load testing - shows how a system performs under the expected load • Stress testing - shows how a system reacts when it is under extreme load • Endurance testing - shows how a system behaves when it is used at the expected load for a long period of time
- 18. Performance testing - Breakpoint • Ramp up the load – Start with a small number of virtual users – Increase the number over a period of time • The test result shows how the performance varies with the load, given as number of users versus response time.
- 19. Performance testing • Performance testing software typically uses: – One or more servers to act as injectors • Each emulating a number of users • Each running a sequence of interactions – A test conductor • Coordinating tasks • Gathering metrics from each of the injectors • Collecting performance data for reporting purposes
- 20. Performance testing • Performance testing should be done in a production-like environment – Performance tests in a development environment usually lead to results that are highly unreliable – Even when underpowered test systems perform well enough to get good test results, the faster production system could show performance issues that did not occur in the tests • To reduce cost: – Use a temporary (hired) test environment
- 22. Increasing performance on upper layers • 80% of the performance issues are due to badly behaving applications • Application performance can benefit from: – Database and application tuning – Prioritizing tasks – Working from memory as much as possible (as opposed to working with data on disk) – Making good use of queues and schedulers • Typically more effective than adding compute power
- 23. Disk caching • Disks are mechanical devices that are slow by nature • Caching can be implemented i: – Disks – Disk controllers – Operating system • All non-used memory in operating systems is used for disk cache • Over time, all memory gets filled with previously stored disk requests and prefetched disk blocks, speeding up applications. • Cache memory: – Stores all data recently read from disk – Stores some of the disk blocks following the recently read disk blocks
- 24. Caching 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
- 25. Web proxies • When users browse the internet, data can be cached in a web proxy server – A web proxy server is a type of cache – Earlier accessed data can be fetched from cache, instead of from the internet • Benefits: – Users get their data faster – All other users are provided more bandwidth to the internet, as the data does not have to be downloaded again
- 26. Operational data store • An Operational Data Store (ODS) is a read-only replica of a part of a database, for a specific use • Frequently used information is retrieved from a small ODS database – The main database is used less for retrieving information – The performance of the main database is not degraded
- 27. Front-end servers • Front-end servers serve data to end users – Typically web servers • To increase performance, store static data on the front-end servers – Pictures are a good candidate – Significantly lowers the amount of traffic to back- end systems • In addition, a reverse proxy can be used – Automatically cache most requested data
- 28. In-memory databases • In special circumstances, entire databases can be run from memory instead of from disk • In-memory databases are used in situations where performance is crucial – Real-time SCADA systems – High performance online transaction processing (OLTP) systems • As an example, in 2011 SAP AG introduced HANA, an in- memory database for SAP systems • Special arrangements must be made to ensure data is not lost when a power failure occurs
- 29. 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 scale a system: – Vertical scaling (scale up) - adding resources to a single component – Horizontal scaling (scale out) - adding more components to the infrastructure
- 30. Scalability – Vertical scaling • Adding more resources, for example: – Server: more memory, CPU’s – Network switch: adding more ports – Storage: Replace small disks by larger disks • Vertical scaling is easy to do • It quickly reaches a limit – The infrastructure component is “full”
- 31. Scalability – Horizontal scaling • Adding more components to the infrastructure, for example: – Adding servers to a web server farm – Adding disk cabinets to a storage system • In theory, horizontal scaling scales much better – Be aware of bottlenecks • Doubling the number of components does not necessarily double the performance • Horizontal scaling is the basis for cloud computing • Applications must be aware of scaling infrastructure components
- 32. Scalability – Horizontal scaling
- 33. Load balancing • Load balancing uses multiple servers that perform identical tasks – Examples: • Web server farm • Mail server farm • FTP (File Transfer Protocol) server farm • A load balancer spreads the load over the available machines – Checks the current load on each server in the farm – Sends incoming requests to the least busy server •
- 34. Load balancing
- 35. Load balancing • Advanced load balancers can spread the load based on: – The number of connections a server has – The measured response time of a server • The application running on a load balanced system must be able to cope with the fact that each request can be handled by a different server – The load balancer should contain the states of the application – The load balancing mechanism can arrange that a user’s session is always connected to the same server – If a server in the server farm goes down, its session information becomes inaccessible and sessions are lost
- 36. Load balancing • A load balancer increases availability – When a server in the server farm is unavailable, the load balancer notices this and ensures no requests are sent to the unavailable server until it is back online again • The availability of the load balancer itself is very important – Load balancers are typically setup in a failover configuration
- 37. Load balancing • Network load balancing: – Spread network load over multiple network connections – Most network switches support port trunking • Multiple Ethernet connections are combined to get a virtual Ethernet connection providing higher throughput • The load is balanced over the connections by the network switch • Storage load balancing: – Using multiple disks to spread the load of reads and writes – Use multiple connections between servers and storage systems
- 38. High performance clusters • High performance clusters provide a vast amount of computing power by combining many computer systems • A large number of cheap off the-shelf servers can create one large supercomputer • Used for calculation-intensive systems – Weather forecasts – Geological research – Nuclear research – Pharmaceutical research • TOP500.org
- 39. 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 when computers in the grid are not under control
- 40. 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 • Interactive systems are different than batch oriented systems – When possible, try to spread the load of the system over the available time
- 41. Design for use – 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 • Follow the vendor's recommended implementation • Have the vendors check the design you created – Move rarely used data from the main systems to other systems • Moving old data to a large historical database can speed up a smaller sized database
- 42. Capacity management • Capacity management guarantees high performance of a system in the long term • To ensure performance stays within acceptable limits, performance must be monitored • Trend analyses can be used to predict performance degradation • Anticipate on business changes (like forthcoming marketing campaigns)