skip to main content
research-article

A request-routing framework for SOA-based enterprise computing

Published: 01 August 2008 Publication History

Abstract

Enterprises may use a service-oriented architecture (SOA) to provide a streamlined interface to their business processes. To scale up the system, each tier in a composite service usually deploys multiple servers for load distribution and fault tolerance. Such load distribution across multiple servers within the same tier can be viewed as horizontal load distribution. One limitation of this approach is that load cannot be further distributed when all servers in the same tier are fully loaded. In complex multi-tiered systems, a single business process may actually be implemented by multiple different computation pathways among the tiers, each with different components, in order to provide resiliency and scalability. Such SOA-based enterprise computing with multiple implementation options gives opportunities for vertical load distribution across tiers. In this paper, we propose a requestrouting framework for SOA-based enterprise computing that takes into consideration both horizontal and vertical load distribution. Through experimentation we show that our algorithm and methodology scale well up to a large system configuration comprising up to 1000 workflow requests to a complex composite service with multiple implementations. We also show that a combination of both horizontal and vertical load distributions gives the maximum flexibility to improve performance and fault tolerance.

References

[1]
F. Casati, S. Ilnicki, L. Jin, V. Krishnamoorthy, and M.-C. Shan. Adaptive and Dynamic Service Composition in eFlow. In Proceedings of CAISE, 2000.
[2]
Cisco. Ace application-level load balancer.
[3]
Cisco. Scalable content switch.
[4]
L. Davis. Job Shop Scheduling with Genetic Algorithms,. In Proceedings of the International Conference on Genetic Algorithms, 1985.
[5]
G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S Sivasubramanian, P. Vosshall, and W. Vogels. Dynamo: Amazon's highly available key-value store. In SOSP, 2007.
[6]
R. Dewri, I. Ray, I. Ray, and D. Whitley. Optimizing on-demand data broadcast scheduling in pervasive environments. In EDBT, 2008.
[7]
A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Springer, 1998.
[8]
F5 Networks. Big-ip application-level load balancer.
[9]
D. Goldberg. Genetic Algorithms in Searth, Optimization, and Machine Learning. Kluwer Academic, 1989.
[10]
J. Holland. Adaptation in Natural and Artificial Systems. MIT Press, 1992.
[11]
IBM. Business process execution language for web services, v 1.1, 2005. www-128.ibm.com/developerworks/library/ws-bpel/.
[12]
J. Josephraj. Web Services Choreography in Practice. In www-128. ibm. com/developerworks/library/wschoreography.
[13]
T. Phan and W.-S. Li. Dynamic Materialization of Query Views for Data Warehouse Workloads. In ICDE, 2008.
[14]
T. Phan and W.-S. Li. Load Distribution of Analytical Query Workloads for Database Cluster Architectures. In EDBT, 2008.
[15]
S. Ponnekanti and A. Fox. Interoperability among Independently Evolving Web Services. In Proceedings of Middleware, 2004.
[16]
M. Shankar, M. De Miguel, and J. W.-S. Liu. An end-to-end qos management architecture. In Proceedings of the Fifth IEEE Real Time Technology and Applications Symposium.
[17]
G. Soundararajan, K. Manassiev, J. Chen, A. Goel, and C. Amza. Back-end Databases in Shared Dynamic Content Server Clusters. In ICAC, 2005.
[18]
B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal. Dynamic Provisioning of Multi-Tier Internet Applications. In Proceedings of ICAC, 2005.
[19]
P. Van Hentenryck and R. Bent. Online Stochastic Combinatorial Optimization. MIT Press, 2006.
[20]
T. Yu and K.-J. Lin. Adaptive algorithms for finding replacement services in autonomic distributed business processes. In Proc. of the 7th International Symposium on Autonomous Decentralized Systems, Chengdu, China, 2005.
[21]
T. Yu and K.-J. Lin. Service selection algorithms for web services with end-to-end qos constraints. Inf. Syst. E-Business Management, 3(2):103--126, 2005.
[22]
T. Yu and K.-J. Lin. Qcws: An implementation of qos-capable multimedia web services. Multimedia Tools and Applications, 30(2):165--187, 2006.
[23]
T. Yu, Y. Zhang, and K.-J. Lin. Efficient algorithms for web services selection with end-to-end qos constraints. ACM Transactions on the Web (TWEB), 1(1), 2007.
[24]
L. Zeng, B. Benatallah, M. Dumas, J. Kalagnanam, and Q. Sheng. Quality Driven Web Services Composition. In Proceedings of WWW, 2003.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment  Volume 1, Issue 1
August 2008
1216 pages

Publisher

VLDB Endowment

Publication History

Published: 01 August 2008
Published in PVLDB Volume 1, Issue 1

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 300
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 09 Feb 2025

Other Metrics

Citations

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media