Analysis of Affinity Based Routing in Multi-System Data Sharing

Abstract The rapid growth in the transaction rate requirement of high volume database systems has forced users and vendors to (a) couple multiple database systems to run against a common database, and (b) to implement each single system with faster processors. Multiple system coupling incurs performance degradation due to inter-system interference: inter-system (global) lock contention and database buffer invalidation. At high transaction rates, the level of inter-system interference can have a severe impact on performance. In this paper, we exploit transaction routing as a means of reducing inter-system interference and quantify its effect. A methodology, employing an integer linear programming technique, is developed to classify incoming transactions into affinity groups based on their database call reference pattern. The key idea of affinity based routing is to determine affinity groups and route transactions in the same affinity group to the same system. Based on traces from two of IBM's high volume single system customers, we find that, at high transaction rates, affinity based routing significantly reduces lock contention probability and leads to a substantial reduction in transaction response time. Improvement in hierarchical locking by taking advantage of affinity based routing is demonstrated. Further, the reduction in inter-system data contention produces a large impact on the performance of an optimistic type concurrency control strategy.

[1]  Irving L. Traiger,et al.  The notions of consistency and predicate locks in a database system , 1976, CACM.

[2]  Wesley W. Chu,et al.  Task Allocation in Distributed Data Processing , 1980, Computer.

[3]  Philip S. Yu,et al.  Distributed Concurrency Control Analysis for Data Sharing , 1985, Int. CMG Conference.

[4]  Shahid H. Bokhari,et al.  Control of Distributed Processes , 1978, Computer.

[5]  Peter P. Uhrowczik,et al.  IMS/VS: An Evolving System , 1982, IBM Syst. J..

[6]  Stephen S. Lavenberg A simple analysis of exclusive and shared lock contention in a database system , 1984, SIGMETRICS '84.

[7]  Debasis Mitra,et al.  Probabilistic Models of Database Locking: Solutions, Computational Algorithms, and Asymptotics , 1984, JACM.

[8]  J. T. Robinson,et al.  On optimistic methods for concurrency control , 1979, TODS.

[9]  Y. C. Tay,et al.  A mean value performance model for locking in databases: the waiting case , 1984, PODS '84.

[10]  Lawrence J. Watters Letter to the Editor - Reduction of Integer Polynomial Programming Problems to Zero-One Linear Programming Problems , 1967, Oper. Res..

[11]  Philip S. Yu,et al.  Modelling of centralized concurrency control in a multi-system environment , 1985, SIGMETRICS '85.

[12]  Asser N. Tantawi,et al.  Optimal static load balancing in distributed computer systems , 1985, JACM.

[13]  Kazuo Goto,et al.  The DCS: a new approach to multisystem data-sharing , 1984, AFIPS '84.

[14]  Philip S. Yu,et al.  On multisystem coupling through function request shipping , 1986, IEEE Transactions on Software Engineering.

[15]  Y. C. Tay,et al.  A mean value performance model for locking in databases: the no-waiting case , 1985, JACM.

[16]  Jim Gray,et al.  Notes on Data Base Operating Systems , 1978, Advanced Course: Operating Systems.