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...

• Type: Conference
• Year: 2008
• Where: ICAC
• Authors: Julien Perez, Cécile Germain-Renaud, Balázs Kégl, Charles Loomis
• Keywords: Autonomic Computing, Grid, ICAC 2008, Production Grids, Utility Functions