Mingyi Zhang
ABSTRACT
Resource allocation in database management systems is a performance management process in which an autonomic DBMS makes resource allocation decisions based on properties like workload business importance. We propose the use of economic models to guide the resource allocation decisions. An economic model is described in terms of business concepts and has been successfully applied in computer system resource allocation problems. In this paper, we present an approach that uses economic models to allocate multiple resources, such as main memory buffer space and CPU shares, to workloads running concurrently on a DBMS. The economic model enables workloads to meet their service level objectives by allocating resources through partitioning the individual DBMS resources and making system-level resource allocation plans for the workloads. The resource allocation plans can be dynamically changed to respond to changes in workload performance requirements. Experiments are conducted on IBM® DB2® databases to verify the effectiveness of our approach.
- D. F. Ferguson, C. Nikolaou, J. Sairamesh, Y. Yemini. "Economic Models for Allocating Resources in Computer Systems". In Scott Clearwater, Editor, Market-Based Control: A Paradigm for Distributed Resource Allocation, Scott Clearwater. World Scientific, Hong Kong, 1996. Google Scholar
Digital Library
- J. O. Kephart, D. M. Chess. "The Vision of Autonomic Computing". IEEE Computer, Volume 36, Issue 1, pages 41--50, 2003. Google ScholarDigital Library
- D. L. Davison, G. Graefe. "Dynamic Resource Brokering for Multi-User Query Execution". In Proc. of the ACM SIGMOD Intl. Conf. on Management of Data pages 281--292, 1995. Google ScholarDigital Library
- H. Boughton, P. Martin, W. Powley, and R. Horman. "Workload Class Importance Policy in Autonomic Database Management Systems", Seventh IEEE Intl. Workshop on Policies for Distributed Systems and Networks, pages 13--22, London, Canada, June 5--7, 2006. Google ScholarDigital Library
- K. P. Brown, M. Mehta, M. J. Carey, and M. Livny. "Towards Automated Performance Tuning for Complex Workloads", Proc. of the 20th Intl. Conf. of Very Large Data Bases, pages 72--84, Santiago, Chile, Sept. 1994. Google ScholarDigital Library
- B. Niu, P. Martin, W. Powley, R. Horman, and P. Bird. "Workload Adaptation in Autonomic DBMSs", Proc. of the 2006 Conf. of the Centre for Advanced Studies on Collaborative Research, Article No. 13, Toronto, Canada, Oct. 2006. Google ScholarDigital Library
- B. Schroeder, M. Harchol-Balter, A. Iyengar, and E. Nahum. "Achieving Class-Based QoS for Transactional Workloads", Proc. of the 22nd Intl. Conf. on Data Engineering (ICDE'06), page 153, Apr. 03--07, 2006. Google ScholarDigital Library
- M. Zhang. "Using Economic Models to Tune Resource Allocations in Database Management Systems". M.Sc. Thesis, School of Computing, Queen's University, 2008.Google Scholar
- E. Lazowska, J. Zahorjan, G. S. Graham and K. C. Sevcik "Quantitative System Performance: Computer System Analysis Using Queueing Network Models". Prentice-Hall Inc., Englewood Cliffs, New Jersey, 1984. Google ScholarDigital Library
- IBM DB2 Universal Database Version 8.1 Administrative Guide: Performance, 2003.Google Scholar
- H. Zawawy, P. Martin, H. Hassanein. "Supporting Capacity Planning for DB2 UDB", Proc., of the 2002 Conf. of the Center for Advanced Studies on Collaborative Research, Toronto, Canada, 2002. Google ScholarDigital Library
- Transaction Processing Performance Council. http://www.tpc.org/tpcc/Google Scholar
- W. E. Walsh, G. Tesauro, J. O. Kephart, R. Das. "Utility Functions in Autonomic Systems". In Proc. of Intl. Conf. on Autonomic Computing (ICAC'04), pages 70--77, New York, USA, May 17--18, 2004. Google ScholarDigital Library
- G. Pacifici, M. Spreitzer, A. Tantawi, and A. Youssef. "Performance Management for Cluster Based Web Services", IEEE Journal on Selected Areas in Communications, Volume 23, Issue 12, page 2333--2343, Dec. 2005. Google ScholarDigital Library
- IBM DB2 Query Patroller Guide: Installation, Administration, and Usage, 2003.Google Scholar
Index Terms
- Using economic models to allocate resources in database management systems
Recommendations
Fair access to scarce resources in ad-hoc grids using an economic-based approach
MGC '07: Proceedings of the 5th international workshop on Middleware for grid computing: held at the ACM/IFIP/USENIX 8th International Middleware ConferenceIn ad-hoc Grids where the availability of resources and tasks changes over the time, distributing the tasks among the scarce resources in a balanced way is a challenging task. In this paper, we present how using an economic-based approach, the tasks can ...
Intelligent management of virtualized resources for database systems in cloud environment
ICDE '11: Proceedings of the 2011 IEEE 27th International Conference on Data EngineeringIn a cloud computing environment, resources are shared among different clients. Intelligently managing and allocating resources among various clients is important for system providers, whose business model relies on managing the infrastructure resources ...
Fuzzy Modeling Based Resource Management for Virtualized Database Systems
MASCOTS '11: Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication SystemsThe hosting of databases on virtual machines (VMs) has great potential to improve the efficiency of resource utilization and the ease of deployment of database systems. This paper considers the problem of on-demand allocation of resources to a VM ...
Comments