This paper proposes a multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperature (T). Our algorithm includes problem-specific solution encoding, determining the initial population of the solution space, and the genetic operators that collectively work on generating efficient solutions in fast turnaround time. Multiple schedules offer a diverse range of values for makespan, energy consumed, and peak temperature and thus present an efficient way of identifying trade-offs among the desired objectives, for a given application and machine pair. We also present a methodology for selecting one solution from the Pareto front given the user's preference. The proposed algorithm for scheduling tasks to cores achieves three-way optimization with fast turnaround time. The proposed algorithm is advantageous because it reduces both energy and temperature together rather than in isolation. We evaluate the proposed algorithm using implementation and simulation, and compare it with integer linear programming as well as with other scheduling algorithms that are energy- or thermal-aware. The time complexity of the proposed scheme is considerably better than the compared algorithms.

Enabled by high-speed networking in commercial, scientific, and government settings, the realm of high performance is burgeoning with greater amounts of computational and storage resources. Large-scale systems such as computational grids consume a significant amount of energy due to their massive sizes. The energy and cooling costs of such systems are often comparable to the procurement costs over a year period. In this survey, we will discuss allocation and scheduling algorithms, systems, and software for reducing power and energy dissipation of workflows on the target platforms of single processors, multicore processors, and distributed systems. Furthermore, recent research achievements will be investigated that deal with power and energy efficiency via different power management techniques and application scheduling algorithms. The article provides a comprehensive presentation of the architectural, software, and algorithmic issues for energy-aware scheduling of workflows on single, multicore, and parallel architectures. It also includes a systematic taxonomy of the algorithms developed in the literature based on the overall optimization goals and characteristics of applications.