Self-Organizing maps for detecting abnormal thermal behavior in data centers

Abstract

The increasing success of Cloud Computing applications and online services has contributed to the unsustainability of data center facilities in terms of energy consumption. Higher resource demand has increased the electricity required by computation and cooling resources, leading to power shortages and outages, specially in urban infrastructures. Current energy reduction strategies for Cloud facilities usually disregard the data center topology, the contribution of cooling consumption and the scalability of optimization strategies. Our work tackles the energy challenge by proposing a temperature-aware {VM} allocation policy based on a {Trust-and-Reputation} System ({TRS}). A {TRS} meets the requirements for inherently distributed environments such as data centers, and allows the implementation of autonomous and scalable {VM} allocation techniques. For this purpose, we model the relationships between the different computational entities, synthesizing this information in one single metric. This metric, called reputation, would be used to optimize the allocation of {VMs} in order to reduce energy consumption. We validate our approach with a state-of-the-art Cloud simulator using real Cloud traces. Our results show considerable reduction in energy consumption, reaching up to 46.16\% savings in computing power and 17.38\% savings in cooling, without {QoS} degradation while keeping servers below thermal redlining. Moreover, our results show the limitations of the {PUE} ratio as a metric for energy efficiency. To the best of our knowledge, this paper is the first approach in combining {Trust-and-Reputation} systems with Cloud Computing {VM} allocation.