Utility-Based Reinforcement Learning for Reactive Grids

13 years 9 months ago
Utility-Based Reinforcement Learning for Reactive Grids
—Large scale production grids are an important case for autonomic computing. They follow a mutualization paradigm: decision-making (human or automatic) is distributed and largely independent, and, at the same time, it must implement the highlevel goals of the grid management. This paper deals with the scheduling problem with two partially conflicting goals: fairshare and Quality of Service (QoS). Fair sharing is a wellknown issue motivated by return on investment for participating institutions. Differentiated QoS has emerged as an important and unexpected requirement in the current usage of production grids. In the framework of the EGEE grid (one of the largest existing grids), applications from diverse scientific communities require a pseudo-interactive response time. More generally, seamless integration of the grid power into everyday use calls for unplanned and interactive access to grid resources, which defines reactive grids. The major result of this paper is that the combina...
Julien Perez, Cécile Germain-Renaud, Bal&aa
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where ICAC
Authors Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
Comments (0)