A Component Assignment Framework for Improved Capacity and Assured Performance in Web Portals

Authors:
Nilabja Roy;Yuan Xue;Aniruddha Gokhale;Larry Dowdy;Douglas C. Schmidt
Affiliations:
Electrical and Computer Science Department, Vanderbilt University,;Electrical and Computer Science Department, Vanderbilt University,;Electrical and Computer Science Department, Vanderbilt University,;Electrical and Computer Science Department, Vanderbilt University,;Electrical and Computer Science Department, Vanderbilt University,
Venue:
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
Year:
2009

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Abstract

Web portals hosting large-scale internet applications have become popular due to the variety of services they provide to their users. These portals are developed using component technologies. Important design challenges for developers of web portals involve (1) determining the component placement that maximizes the number of users/requests (capacity) without increasing hardware resources and (2) maintaining the performance within certain bounds given by service level agreements (SLAs). The multitude of behavioral patterns presented by users makes it hard to identify the incoming workloads. This paper makes three contributions to the design and evaluation of web portals that address these design challenges. First it introduces an algorithmic framework that combines bin-packing and modeling-based queuing theory to place components onto hardware nodes. This capability is realized by the Component Assignment Framework for multi-tiered internet applications (CAFe). Second, it develops a component-aware queuing model to predict web portal performance. Third, it provides extensive experimental evaluation using the Rice University Bidding System (RUBiS). The results indicate that CAFe can identify opportunities to increase web portal capacity by 25% for a constant amount of hardware resources and typical web application and user workloads.