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Abstract. Resource reservations in advance are a mature concept for the allocation of various resources, particularly in grid environments. Common grid toolkits such as Globus support advance reservations and assign jobs to resources at admission time. While the allocation mechanisms for advance reservations are available in current grid management systems, in case of failures the advance reservation perspective demands for strategies that support more than recovery of jobs or applications that are active at the time the resource failure occurs. Instead, also already admitted, but not yet started applications are affected by the failure and hence, need to be dealt with in an appropriate manner. In this paper, we discuss the properties of advance reservations with respect to failure recovery and outline a number of strategies applicable in such cases in order to reduce the impact of resource failures and outages. It can be shown that it pays to remap also affected but not yet started jobs to alternative resources if available. Alike reserving in advance, this can be considered as remapping in advance. In particular, a remapping strategy that prefers requests that were allocated a long time ago, provides a high fairness for clients as it implements similar functionality as advance reservations, while achieving the same performance as the other strategies.

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Abstract-Resource reservations in advance are a mature concept for the allocation of various resources, particularly in grid environments. Common grid toolkits support advance reservations and assign jobs to resources at admission time. In such a distributed environment, it is necessary to develop carefully tailored failure recovery mechanisms that provide seamless transparent migration of jobs from one resource to another. As the migration of running jobs is difficult, an important issue in advance reservation, i.e., planning based, management infrastructures is to determine the duration of a failure in order to remap jobs that are already allocated to a currently failed resource but not yet active. As shown in previous work, underestimations of the failure duration and as a consequence the remapping of too few jobs results in an increased amount of job terminations. In order to overcome this drawback, in this paper we propose a load-based computation of the jobs to be remapped. A centralized and a distributed version of the strategy are presented, showing it is not necessary to have knowledge beyond the local allocation on the failed resource. The loadbased strategy achieves to effectively remap jobs while avoiding -inevitably inaccurate -estimations of the failure duration.

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