One of the major design constraints of a heterogeneous computing system is optimal scheduling, that is, mapping of tasks on the processing nodes in order to optimize the QoS parameters. Because of the huge energy consumption by computing resources, negative environmental effects and reduced system reliability, energy has unavoidably been added as a new parameter to the list of QoS parameters. Energy optimization in scheduling strategies along with makespan makes it an even more challenging combinatorial optimization problem. This work proposes two energy-aware scheduling algorithms G1 and G2 to schedule a batch-oftasks, made of a collection of independent tasks, on heterogeneous processors in order to minimize the makespan and the energy consumption. The proposed algorithms schedule tasks based on weighted aggregation cost function to the appropriate processors followed by task migration phase designed to further minimize the makespan and the energy consumption. The study evaluates the performance of the proposed algorithms with some of the peers, that is, MinMin, MINSuff on account of makespan, energy consumption, flowtime, and utilization. An experimental study reveals that the proposed algorithm (G2) consistently performs better under various test conditions. the reduced reliability of the system. The increased temperature leads to system failure resulting in high monetary cost as well as environmental concerns. Therefore, reducing energy consumption of computing systems has become a must with the appeal to develop energy-efficient computing units, storage, networking, and scheduling models that consume energy and other resources as minimum as possible [4][5][6][7].Scheduling is the indispensable process of resource management in computing paradigms and one of the potential components that can be very much helpful in order to optimize the energy consumption of resources. In general, optimal scheduling has been proven to be NP-complete, and it is intractable to realize an optimal scheduling model in the presence of heterogeneity of jobs as well as resources, limited memory bandwidth, and many other constraints [8]. Job scheduling on heterogeneous parallel processors in order to minimize the makespan as well as energy consumption is one of the classical combinatorial optimization problems [9,10]. The batch-of-tasks (BoT) consists of many independent tasks belonging to different applications and can be run simultaneously to utilize the resources as much as possible. The scheduling of BoT applications on HCS is very common as it has been used by many streams, namely, optimization, cryptographic analysis, high energy physics, engineering, biochemistry, drug discovery, weather forecasting, simulation applications to name a few. The wide applicability of BoT applications on HCS makes it an important area for research, and therefore, a large number of algorithms have been developed for single as well as multi-objective problems. In order to deal with a bi-objective problem, weighted aggregation is one of the widely ac...
