Combinatorial Auctions for Temperature-Constrained Resource Management in Manycores

Although manycore processors have plenty of cores, not all of them may run simultaneously at full speed and even some of them might need to be power-gated in order to keep the chip within safe temperature limits. Hence, a resource management technique, that allocates cores to application aiming at maximizing the system performance, will not be able to achieve its goal without taking into account the on-chip temperature and its impact on the availability of the chip's resources. However, considering a temperature constraint by the resource management will further increase its complexity, especially in manycores, and thus implementing it in a centralized scheme might lead to a computation bottleneck and a single point of failure. To avoid such scenarios, it is inevitable to distribute the computation required by the resource management technique throughout the chip. In this article, we propose a distributed resource management technique that considers temperature as an essential factor in allocating cores to applications and determining the power states of these cores and their voltage/frequency levels, while taking into account the performance models of the applications in order to maximize the overall system performance under a temperature constraint. Our proposed technique employs, for the first time, combinatorial auctions within an agent system to achieve the targeted goal in a distributed manner. The experimental evaluations show that our proposed technique achieves significant performance improvements with an average of 41% compared to several distributed resource management techniques.