Skip to main content
SpringerLink
Log in

Dynamic Affinity Cluster Allocation in a Shared Disks Cluster

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

A shared disks (SD) cluster couples multiple computing nodes for high performance transaction processing, and all nodes share a common database at the disk level. In the SD cluster, a front-end router selects a node for an incoming transaction to be executed. An affinity-based routing can increase the buffer hit ratio of each node by clustering transactions referencing similar data to be executed on the same node. However, the affinity-based routing is non-adaptive to the changes of the system load. This means that a specific node would be overloaded if corresponding transactions rush into the system. In this paper, we propose a new transaction routing algorithm, named Dynamic Affinity Cluster Allocation (DACA). DACA can make an optimal balance between the affinity-based routing and indiscriminate sharing of load in the SD cluster. As a result, DACA can increase the buffer hit ratio and reduce the frequency of inter-node buffer invalidations while achieving the dynamic load balancing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Carey, S. Krishnamurthi, and M. Livny. Load control for locking: the ‘half-and-half’ approach. Proc. 9th ACM PODS, 72–84, (1990).

  2. H. Cho. Database recovery using incomplete page versions in a multisystem data sharing environment. Information Processing Letters, 83(1):49–55, (2002).

    Google Scholar 

  3. H. Cho and J. Park. Maintaining cache coherency in a multisystem data sharing environment. J. Syst. Architecture, 45(4):285–303, (1998).

    Article  MathSciNet  Google Scholar 

  4. A. Dan and P. Yu. Performance analysis of buffer coherency policies in a multisystem data sharing environment. IEEE Trans. Parallel and Distributed Systems, 4(3):(1993), 289–305.

    Article  Google Scholar 

  5. DB2 Universal Database for OS/390 and z/OS—Data Sharing: Planning and Administration, IBM SC26-9935–01, (2001).

  6. D. DeWitt and J. Gray. Parallel database systems: the future of high performance database systems. Comm. ACM, 35(6):85–98, (1992).

    Article  Google Scholar 

  7. S. Haldar and D. K. Subramanian. An affinity-based dynamic load balancing protocol for distributed transaction processing systems. Performance Evaluation, 17(1):53–71, (1993).

    Google Scholar 

  8. B. Hamidzadeh, L. Y. Kit, D. J. Lilja. Dynamic task scheduling using online optimization. IEEE Trans. Parallel and Distributed Syst. 11(11):1151–1163, (2000).

    Article  Google Scholar 

  9. V. Kanitkar and A. Delis. Site selection for real-time client request handling. In Proc. 19th Int. Conf. on Distributed Comp. Syst., 298–305, (1999).

  10. T. Kunz. The influence of different workload descriptions on a heuristic load balancing scheme. IEEE Trans. Software Eng., 17(7):725–730, (1991).

    Article  Google Scholar 

  11. C. Mohan and I. Narang. Recovery and coherency control protocols for fast intersystem page transfer and fine-granularity locking in a shared disks transaction environment. In Proc. 17th Int. Conf. on VLDB, 193–207, (1991).

  12. C. N. Nikolaou, M. Marazakis, and G. Georgiannakis. Transaction routing for distributed OLTP systems: survey and recent results. Information Sciences, 97(1–2):45–82, (1997).

    Article  Google Scholar 

  13. E. Rahm. A framework for workload allocation in distributed transaction processing systems. J. Syst. Software, 18(3):171–190, (1992).

    Google Scholar 

  14. H. Schwetmann. User’s Guide of CSIM18 Simulation Engine. Mesquite Software, Inc., (1996).

  15. B. A. Shirazi, A. R. Hurson, and K. M. Kavi. Scheduling and Load Balancing in Parallel and Distributed Systems. IEEE Computer Society Press, (1995).

  16. M. Vallath. Oracle Real Application Clusters. Elsevier Digital Press, (2004).

  17. M. Yousif, Shared-storage clusters, Cluster Comp., 2(4):249–257, (1999).

    Article  MathSciNet  Google Scholar 

  18. P. Yu and A. Dan. Performance analysis of affinity clustering on transaction processing coupling architecture. IEEE Trans. Knowledge and Data Eng., 6(5):764–786, (1994).

    Google Scholar 

  19. P. Yu and A. Dan. Performance evaluation of transaction processing coupling architectures for handling system dynamics. IEEE Trans. Parallel and Distributed Syst., 5(2):139–153, (1994).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Gyungsan, Gyungbuk, 712–749, Korea

    Kyungoh Ohn & Haengrae Cho

Authors
  1. Kyungoh Ohn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haengrae Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ohn, K., Cho, H. Dynamic Affinity Cluster Allocation in a Shared Disks Cluster. J Supercomput 37, 47–69 (2006). https://doi.org/10.1007/s11227-006-4650-4

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-006-4650-4

Keywords

Search

Navigation