International audienceWe propose in this paper a new algorithm for scheduling independent jobs in volunteer computing platforms. In such platforms, the resources are not continuously available over time. Moreover, the beginning and finishing times of the resource availability periods are subject to some uncertainties since the machines are directly administrated by the owners (and thus, there is no global centralized control). The performance of the applications is achieved by a suitable adaptation of the algorithms to the volunteer environments. We propose an efficient method that optimizes the performance and reduces the impact of disturbances by means of stability. Stability gives the guarantee that the disturbed solution does not differ too much from the initial solution. It is obtained on the basis of a reputation mechanism of the resources that makes the scheduling decisions more adequate. The quality of our algorithm is demonstrated by a campaign of experiments using simulations of actual traces of BOINC where the results are compared to other existing reference algorithms

In this work, we consider the execution of applications on desktop grids. Such parallel systems use idle computing resources of desktops distributed over the Internet for running massively parallel computations. The applications are composed of workflows of independent non-preemptive sequential jobs that are submitted by successive batches. Then, the corresponding jobs are executed on the distributed available resources according to some scheduling policy. However, most resources are not continuously available over time since the users give their idle CPU time only for some time when they are not using their desktops. Moreover, even if the dates of unavailability periods are estimated in advance, they are subject to uncertainties. This may drastically impact the global performances by delaying the completion time of the applications. The aim of this paper is to study how to schedule efficiently a set of jobs in the presence of unavailability periods on identical machines. In the same time, we are interested in reducing the impact of disturbances on the unavailability periods. This is achieved by maximizing the stability that measures the distance between the makespan of the disturbed instance over the initial one. Our main contribution is the design of a new parametrized algorithm and the analysis of its performance through structural properties. This algorithm reduces the impact of disturbances on availability periods without worsening too much the makespan. Its interest is assessed by running simulations based on realistic workflows. Moreover, theoretical results are obtained under the assumption that the size of every availability interval is at least twice the size of the largest job.

Uncertainties stemming from multiple sources affect distributed systems and jeopardize their efficient utilization. Desktop grids are especially concerned by this issue as volunteers lending their resources may have irregular and unpredictable behaviors. Efficiently exploiting the power of such systems raises theoretical issues that received little attention in the literature. In this paper, we assume that there exist predictions on the intervals during which machines are available. When these predictions have a limited error, it is possible to schedule a set of jobs such that the effective total execution time will not be higher than the predicted one. We formally prove it is the case when scheduling jobs only in large intervals and when provisioning sufficient slacks to absorb uncertainties. We present multiple heuristics with various efficiencies and costs that are empirically assessed through simulations.