1 / 30

Resource Sharing Across Users in Server Clusters

Resource Sharing Across Users in Server Clusters. Optimizing and Scaling Enterprise Applications. Krithi Ramamritham IIT Bombay krithi@iitb.ac.in. Enterprise (Information) Systems. Any kind of computing system that is of "enterprise class" offering high quality of service

archie
Download Presentation

Resource Sharing Across Users in Server Clusters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Resource Sharing Across Users in Server Clusters Optimizing and ScalingEnterprise Applications Krithi Ramamritham IIT Bombay krithi@iitb.ac.in

  2. Enterprise (Information) Systems • Any kind of computing system that is of "enterprise class" • offering high quality of service • dealing with large volumes of data • capable of supporting a large organization -- “an enterprise“ • Enterprise Information Systems • provide a technology platform that enables organisations to integrate and coordinate their business processes. • provide a single system that is central to the organisation. • ensure that information can be shared across all functional levels and management hierarchies. • help eliminate the problem of information fragmentation caused by multiple information systems in an organisation.

  3. Request Application Server Layer Content Web Server Layer Database Optimizing and Scaling Enterprise Applications… • Enterprise applications are constructed using a multi-tier architecture for simplified development and maintenance • Considerable time and money is invested in the server infrastructure • A significant amount of developer time is being spent to optimize Web applications

  4. WS vs. AS • Web servers • Do well defined and quantifiable local work • e.g., processing HTTP headers, serving static content • Application servers • Run multi-layer programs • e.g., scripts involving calls to backends

  5. Inside the Application Layer3-tier model HTML • JSP • ASP PRESENTATION Objects • Servlets • COM+ • EJB ADDT’L SERVICES BUSINESS LOGIC Row Set • Commerce • Content Mgt. • Personalization DATA CONNECTOR • JDBC • ODBC Legacy Systems Databases

  6. 1. JSP invokes a Servlet 2. Servlet contacts CMS 3. CMS requests data Inside the Application Layer… Code Block(s) PRESENTATION . . . ADDT’L SERVICES Code Block(s) BUSINESS LOGIC . . . • Commerce • Content Mgt. • Personalization DATA CONNECTOR • JDBC • ODBC 4. DBMS calls storage system Legacy Systems Databases

  7. Performance and Scalability Issues • Computationally-intensive logic executed atmultiple tiers • Cross-tier communication • Object instantiation and cleanup processing • External I/O calls • Database connection pool latencies • Content conversion and formatting

  8. JSP • ASP PRESENTATION ADDT’L SERVICES • Servlets • COM+ • EJB BUSINESS LOGIC DATA CONNECTOR • JDBC • ODBC Optimizing the Application LayerTraditional Means • Optimize each tier independently: • Presentation-level caches built inside application server processes • Main memory database employed over persistent DBMS • Persistent object storage techniques employed inside content management systems … and so on Local cache and optimization code

  9. Query result caching • Many application server products offer this feature -- mitigates only local database access latency -- only a subset of query results may be reused in page generation -- page fragments may not all be from databases

  10. Middle tier database caching • Caching database tables in main memory Oracle 9i Cache Main-memory databases, e.g., TimesTen -- mitigates only database access latency -- caching at table granularity results in poor cache utilization -- main-memory databases are difficult to integrate and maintain and can be expensive

  11. Page Level Caching • Dynamically generated HTML pages are cached + Can completely offload work from web/app server • Low reusability for highly personalized web pages • URL may not uniquely identify a page -- increasing the risk of delivering incorrect pages • Often introduces excessive invalidations -- e.g., even if a single element on the page changes

  12. Optimizing the Application LayerIssues • Traditional techniques impact specific components within the application, but not the entire application • No mitigation of component-to-component interaction latencies • Different synchronization and invalidation policies risk data integrity • Each optimization scheme consumes programmer timefor development and maintenance

  13. Key ideas • Re-use program results to eliminate redundant work • Facilitate single-point, architecture-wide optimization • Apply to both • programmatic objects and result fragments

  14. cache Optimizing the Application Layer • JSP • ASP PRESENTATION • Servlets • COM+ • EJB ADDT’L SERVICES BUSINESS LOGIC Enables the results of programs to be re-used. • Commerce • Content Mgt. • Personalization DATA CONNECTOR • JDBC • ODBC Legacy Systems Databases

  15. Code Block(s) PRESENTATION . . . 1. JSP invokes a Servlet 2. Servlet contacts CMS ADDT’L SERVICES Code Block(s) BUSINESS LOGIC . . . • Commerce • Content Mgt. • Personalization 3. CMS requests data DATA CONNECTOR • JDBC • ODBC 4. DBMS calls storage system Databases Usually…. Legacy Systems Plus, at each step there are communication delays and logic processing delays

  16. With Our Solution… Can store any program output, but is most commonly an HTML fragment or a Programmatic Object. Appl. Programming Interface Chutney tags Real-time storage engine Code Block(s) PRESENTATION . . . Result Parameter(s) Function Code Block(s) BUSINESS LOGIC . . . Tags trigger calls to the storage engine. When the Result of a Function with a specific Parameter set is already known (and up-to-date), the work normally necessary to produce that Result is bypassed. DATA CONNECTOR • JDBC • ODBC

  17. Cache Management • A critical aspect of any caching solution • Support novel cache management strategies: • Prediction-based cache replacement • Observation-based cache invalidation

  18. Site Graph News Sports Hockey Schedules Scores Players Teams Cache Replacement • Prediction-based replacement • fragments having lowest probability of access replaced • Least-Likely-to-be-Used (LLU) • Access probabilities based on: • Current user navigational patterns over site graph • (in the form of clickstreams) • Historical user navigational patterns over site graph • (in the form of association rules) (News, Sports, Hockey)  Schedules = 20% (News, Sports, Hockey)  Players = 15% LLU (News, Sports, Hockey)  Teams = 10% (News, Sports, Hockey)  Scores = 55%

  19. Cache Invalidation Need to support common cache invalidation techniques: • Time-based: Each cache element assigned a TTL • Event-based: Updates to the database send an invalidation message to the cache • On demand: Manual invalidation of selected elements • ….

  20. Cache Invalidation… • Other invalidation techniques supported: • Observation-based • User-initiated updates are observed in scripts; each such update sends an invalidation message to the cache • Most appropriate for auction sites, online trading sites • Invalidation does not require communication with the databases • Keyword-based: • Elements can be associated with keywords; e.g., a retailer may wish to invalidate all “seasonal” items • Regular expression-based: • Elements can be invalidated based on regular expression matching

  21. Other Fragment Level Caching… • app servers (e.g., BEA’s WebLogic, IBM’s WebSphere) cache fragments produced by JSP scripts • + can offload presentation layer tasks • runs in the application server process space • => competes for server resources • application server cluster • => multiple cache instances, • duplication of content, • additional synchronization overhead Application Server Cluster

  22. Performance Study… Test Site • Fictitious online retail site, allows browsing of product catalog • Pages generated using JSP scripts • Site content stored in Oracle database • Database schema based on Dublin Core Metadata Open Standard • Contains 200,000 products and 44,000 categories • Each page consists of 3 components, each involving a database call

  23. Performance Study… Test Setup • Content Database Server: Oracle 8.1.6 • Web/Application Server: WebLogic 6.0 running on cluster of 2 machines • Server machines: have 1 GB RAM, dual P III-933 Mhz processors run Windows 2K Advanced Server

  24. Testing Methodology... • Baseline Parameters: • Cache Size, i.e., percentage of fragments that fit into cache: 75% • Cache replacement policy: LLU • User load is varied by sending requests from client machines running Radview’s WebLoad • Simulated users navigate site according to Zipf 80-20 distribution (i.e., 80% of users follow 20% of navigation links)

  25. Performance Impact 80% faster response times through existing application infrastructure Source: Fortune 100 client results

  26. Chutney Throughput Impact 250% increase in transaction rates Source: Fortune 100 client results

  27. Presentation Business Logic Data Presentation Business Logic Data Presentation Business Logic Data Broad Interoperability Java-based JSP, Servlets, EJB, BEA WebLogic, IBM WebSphere, iPlanet, Broadvision, etc. Chutney cache Microsoft-based ASP, COM, IIS, MS TransactionServer, etc. Other ColdFusion, Perl, etc. Multi-server, heterogeneous environments can interface with a single storage engine.

  28. Sources Repositories Clients Alternative: CDNs Content Distribution Networks e.g., Akamai Push BasedCore Infrastructure

  29. Request Distribution within Clusters Maximizing affinity Exploit application characteristics

  30. Summary • Bottlenecks persist throughout multi-tier architectures • Traditional optimization approaches focus on individual components, not the entire application • Need a solution which optimizes every tier of a web application, globally

More Related