Power and performance management for parallel computations in clouds and data centers

• Address task scheduling in a data center as combinatorial optimization problems.
• Use level-by-level scheduling algorithms to deal with precedence constraints.
• Use a simple system partitioning and processor allocation scheme.
• Use two heuristic algorithms for scheduling independent tasks in the same level.
• Adopt a two-level energy/time/power allocation scheme.

We address scheduling independent and precedence constrained parallel tasks on multiple homogeneous processors in a data center with dynamically variable voltage and speed as combinatorial optimization problems. We consider the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint on multiple processors. Our approach is to use level-by-level scheduling algorithms to deal with precedence constraints. We use a simple system partitioning and processor allocation scheme, which always schedules as many parallel tasks as possible for simultaneous execution. We use two heuristic algorithms for scheduling independent parallel tasks in the same level, i.e., SIMPLE and GREEDY. We adopt a two-level energy/time/power allocation scheme, namely, optimal energy/time allocation among levels of tasks and equal power supply to tasks in the same level. Our approach results in significant performance improvement compared with previous algorithms in scheduling independent and precedence constrained parallel tasks.