Abstract The load balancing (LB) may be used at different levels to reduce overhead for the decision‐making process. In the past decade, cloud computing has drawn a lot of attention from both the academic and commercial communities to get demanded resources (machines, platforms, data, storage, software, and so forth) as a service on rent economically. Generally, a situation may arise when requests are not meeting their deadlines and the cloud provider wants to finish the running application in minimum time. In this article, a receiver initiated deadline aware LB strategy (RDLBS2) has been proposed which attempts the migration of incoming cloudlets to appropriate virtual machines (VMs) where the deadlines of the cloudlets are met to optimize the turnaround time by exploiting the remaining processing capacities of VMs. A simulation study has been carried out by using Cloud‐Sim as a simulator. A sensitivity analysis has been presented to analyze the effects on performance parameters by varying the number of cloudlets and the number of VMs while keeping the remaining input parameters fixed. The experimental evaluation and analysis suggest that RDLBS2 performs significantly better than its peers on objective parameters almost in all cases under study.

Summary Scheduling on dynamic voltage and frequency scaling enabled processors to determine the Pareto‐optimal solutions with optimized makespan and energy consumption demands faster multi‐objective scheduling algorithms. In general, the problem of multi‐objective optimization, ie, finding the Pareto‐optimal solutions to optimize two or more QoS parameters, has been proven to be an NP‐complete problem. In this work, we propose a novel energy‐efficient quantum‐inspired stochastic Q‐HypE algorithm to schedule the batch‐of‐stochastic‐tasks (BoT) on DVFS‐enabled processors with the aim to optimize the makespan of BoT as well as the energy consumption of processors. The stochastic processing times of tasks are drawn from independent probability distributions. The proposed Q‐HypE algorithm evolves from combined characteristics of quantum computing and a hypervolume based multi‐objective optimization HypE algorithm. The proposed Q‐HypE algorithm simultaneously minimizes the makespan and energy consumption of the Pareto‐optimal solutions whereas the dynamics of quantum computing accelerate the process of HypE to further minimize the overheads of hypervolume estimation. Experimental results reveal the effectiveness of the proposed Q‐HypE algorithm both in terms of the number and quality of solutions offered.

Summary 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‐of‐tasks, 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. Copyright © 2015 John Wiley & Sons, Ltd.