Co‐scheduling of data intensive jobs and processor redistribution under temperature constraints

Abstract

SummaryWith the congregation of more and more data intensive computational task in cloud environment, the distributive paradigm of data processing acquire the state of being more cost‐sensitive in commercial cloud computing environment. Also, the rise in power density from physical machines and memory systems has caused the development of mechanisms to cater efficient thermal management system. Owing to the reactive nature, these methods usually suffer from poor predictability; hence, there lies a dearth of availability of such mechanism to efficiently co‐schedule data intensive jobs and thermal management of physical computational cores in order to establish a workflow which guarantee in managing scheduling operations under time critical situations and thermal constraints. In this study, we present a reinforcement learning inspired heuristic reduction to co‐schedule such operations in near real‐time scenario. While this problem is, in general sense, corresponding to NP‐Hard problem, but the pluggable co‐scheduler so presented in this study can provide significant savings in affordable computational time under temperature constraints.