FLEXIBLE AND ADAPTABLE BUFFER MANAGEMENT-TECHNIQUES FOR DATABASE-MANAGEMENT SYSTEMS

FALOUTSOS, C; NG, R; SELLIS, T

dc.contributor.author	FALOUTSOS, C	en
dc.contributor.author	NG, R	en
dc.contributor.author	SELLIS, T	en
dc.date.accessioned	2014-03-01T01:43:51Z
dc.date.available	2014-03-01T01:43:51Z
dc.date.issued	1995	en
dc.identifier.issn	0018-9340	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/24229
dc.subject	BUFFER MANAGEMENT	en
dc.subject	PERFORMANCE ANALYSIS	en
dc.subject	RELATIONAL DATABASES	en
dc.subject.classification	Computer Science, Hardware & Architecture	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.title	FLEXIBLE AND ADAPTABLE BUFFER MANAGEMENT-TECHNIQUES FOR DATABASE-MANAGEMENT SYSTEMS	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	1995	en
heal.abstract	The problem of buffer management in database management systems is concerned with the efficient main memory allocation and management for answering database queries. Previous works on buffer allocation are based either exclusively on the availability of buffers at runtime or on the access patterns of queries. In this paper, we first propose a unified approach for buffer allocation in which both of these considerations are taken into account. Our approach is based on the notion of marginal gains which specify the expected reduction in page faults by allocating extra buffers to a query. Then, we extend this approach to support adaptable buffer allocation, An adaptable buffer allocation algorithm automatically optimizes itself for the specific query workload. To achieve this adaptability, we propose using runtime information, such as the load of the system, in buffer allocation decisions, Our approach is to use a simple queuing model to predict whether a buffer allocation will improve the performance of the system. Thus, this paper provides a more theoretical basis for buffer allocation. Simulation results show that our methods based on marginal gains and our predictive methods consistently outperform existing allocation strategies. In addition, the predictive methods have the added advantage of adjusting their allocation to changing workloads.	en
heal.publisher	IEEE COMPUTER SOC	en
heal.journalName	IEEE TRANSACTIONS ON COMPUTERS	en
dc.identifier.isi	ISI:A1995QT09700006	en
dc.identifier.volume	44	en
dc.identifier.issue	4	en
dc.identifier.spage	546	en
dc.identifier.epage	560	en