Utility functions in autonomic systems

By William E. Walsh, G. Tesauro, Jeffrey O. Kephart, Rajarshi Das. International Conference on Autonomic Computing, 2004. Proceedings., 2004

490 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9f0ba484145cb1605bbebe229570f01bf74bc5c9

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Citations in the corpus

An architectural approach to autonomic computing (2004, background): “Specifically, it should authenticate all requests and requestors, and it should protect itself against inappropriate responses to its requests, e.g. by checking that each such response conforms to the pertinent agreement.”
Research challenges of autonomic computing (2005, background): “First, in section 2, I describe a framework that has proved to be useful in deﬁning and describing IBM’s autonomic computing research program.”
Transfer via inter-task mappings in policy search reinforcement learning (2007, background): “A utility function for each job type maps the job’s completion time to the utility derived by the user [16].”
Self-adaptive software: Landscape and research challenges (2009, background): “demonstrate how utility functions can enable autonomic elements to continually optimize the use of computational resources in a dynamic and heterogeneous environment (a data center prototype) [Walsh et al. 2004].”
A survey of autonomic computing—degrees, models, and applications (2008, methodology): “IBM s MAPE-K (Monitor, Analyse, Plan, Execute, Knowledge) reference model for autonomic control loops. then taking each of its components in turn, describes the work that focuses its research on that component.”
On the use of hybrid reinforcement learning for autonomic resource allocation (2007, methodology): “Specifically in the Data Center scenario, we plan to add several other state variables (e.g. mean response time, mean queue lengths, etc.) to the RL input representation in order to investigate the effect on training time and sample complexity, as well as whether further performance improvements can”
An artificial intelligence perspective on autonomic computing policies (2004, methodology): “Related work on using Utility Function policies for network and systems optimization includes [15], [16], [17], [18], [19], [20], [21].”
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation (2006, methodology): “Our prototype system has been described in detail in our prior work [11], [1], [2] and we have substantial”