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RESEARCH FOCUS
PAPERS
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Although the field of autonomic computing is in its formative state, IBM Research has already published a number of research papers in this area. Following is a growing repository of papers from IBM Research on autonomic computing. A list of other IBM papers on autonomic computing is available here.
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The Vision of Autonomic Computing
J. Kephart and D. Chess, Computer Magazine, IEEE, 2003
Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems. |
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Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation
C. Boutilier, R. Das, J. Kephart, G. Tesauro, W. Walsh, Uncertainty in Artificial Intelligence, 2003
Decentralized resource allocation is a key problem for large-scale autonomic (or self-managing) computing systems. Motivated by a data center scenario, we explore efficient techniques for resolving resource conflicts via cooperative negotiation. Rather than computing in advance the functional dependence of each elements utility upon the amount of resource it receives, which could be prohibitively expensive, each elements utility is elicited incrementally. Such incremental utility elicitation strategies require the evaluation of only a small set of sampled utility function points, yet they find near-optimal allocations with respect to a minimax regret criterion. We describe preliminary computational experiments that illustrate the benefit of our approach. |
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Performance Management for Cluster Based Web Services
G. Pacifici, M. Spreitzer, A. Tantawi, and A. Youssef, IBM Technical Report, 2003
We present an architecture and prototype implementation of a performance management system for cluster-based web services. The system supports multiple classes of web services traffic and allocates server resources dynamically so to maximize the expected value of a given cluster utility function in the face of fluctuating loads. The cluster utility is a function of the performance delivered to the various classes, and this leads to differentiated service. In this paper we will use the average response time as the performance metric. The management system is transparent: it requires no changes in the client code, the server code, or the network interface between them. The system performs three performance management tasks: resource allocation, load balancing, and server overload protection. We use two nested levels of management mechanism. The inner level centers on queuing and scheduling of request messages. The outer level is a feedback control loop that periodically adjusts the scheduling weights and server allocations of the inner level. The feedback controller is based on an approximate first-principles model of the system, with parameters derived from continuous monitoring. We focus on SOAP-based web services. We report experimental results that show the dynamic behavior of the system. |
BUSINESS FOCUS 
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