DVFS-Power Management and Performance Engineering of Data Center Server Clusters

Abstract

Dynamic Voltage and Frequency Scaling (DVFS) is a method to save energy consumption of electronic devices and to protect them against overheating by automatic sensing and adaptation of their energy consumption. This can be accomplished either on the program instruction level for electronic devices or on the task or job level for server clusters. This paper models DVFS on the job level and through which Service Levels Objectives can be guaranteed with respect to prescribed mean or quantiles of service delays according to given Service Level Agreements (SLA) between user and service provider. The two parameters V (voltage) and f (frequency) cannot be changed independently of each other; typically only several combinations of V and f values are implemented in hardware for several power states. In this paper a novel analysis of operating DVFS is suggested for Server Clusters of Cloud Data Centers (CDC) under prescribed bounds of service level objectives which are defined by SLAs. The method is based on the theory of queuing models of the type GI/G/n for a server cluster to establish a relationship between SLA parameters and the power consumption and is performed for the example of the Intel Pentium M Processor with Enhanced SpeedStep Power Management. As result of this method precise bounds are provided for the load ranges of service request rates $\lambda$ for each power mode which guarantee minimum power consumption dependent on given SLA values and job arrival and service statistics. As the instantaneous load in a CDC can be highly volatile the current load level is usually monitored by periodic sensing which may result in a rather high frequency of DVFS range changes and corresponding overhead. For that reason an automated smoothing method is suggested which reduces the frequency of DVFS range changes significantly. This method is based on a Finite State Machine (FSM) with hysteresis levels.