Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Performance analysis of affinity clustering on transaction processing coupling architecture

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yu, P.S. ; IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA ; Dan, A.

Coupling multiple computing nodes for transaction processing has become increasingly attractive for reasons of capacity, cost, and availability. This paper presents a comparison of robustness (in terms of performance) of three different architectures for transaction processing. In the shared nothing (SN) architecture, neither disks nor memories are shared. In the shared disk (SD) architecture, all disks are accessible from all nodes, whereas in the shared intermediate memory (SIM) architecture, a shared intermediate level of memory is introduced. Coupling multiple nodes inevitably introduces certain interferences and overheads, which take on different forms and magnitudes under the different architectures. Affinity clustering, which attempts to partition the transactions into affinity clusters according to their database reference patterns, can be employed to reduce the coupling degradation under the different architectures, though in different ways. However, the workload may not be partitionable into N affinity clusters of equal size, where N is the number of nodes in the coupled system, so that the load can be evenly spread over all nodes. In addition to balancing the load, we need to maintain a large fraction of data references within the database affiliated with the affinity cluster. These become increasingly harder to achieve for large values of N. In this paper, we examine the impact of affinity on the performance of these three different coupling architectures

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:6 ,  Issue: 5 )