Reducing the Number of Comatose Servers: Automatic Tuning as an Opportunistic Cloud-Service

This paper deals with the reduction of the number of comatose servers. The characteristic of such a server is to consume electricity while not delivering useful information services. According to recent studies, up to 30% of the servers (including those in datacenters) are comatose. The existence of these servers lowers the interest in clouds for green computing. Our paper assumes a cloud provider whose services are operated on a minimal number of dedicated servers in a datacenter, we also assume that this provider delivers software as a service (SaaS). In order to reduce the number of dedicated servers that could be comatose, we propose to automatically generate and run auto-tuning tasks that, in servers idle time, will learn to calibrate the execution of the software delivered by the provider. Our proposition follows the goal of delivering auto-tuning as an opportunistic cloud-service. For this purpose we introduce an opportunistic auto-tuning service (QTuning) that exploits servers idle time. The service is based on a theoretical model that includes two computational problems: an exploration and exploitation problem. In the former problem, the goal is to evaluate the performance of a software in different configurations. In the latter, assuming a set of performance results for a given software, the goal is to compute the best configurations to run the software with. Our second contribution is to propose and evaluate solutions for the exploitation problem.