Abstract

With the rapid increase in computation-intensive tasks, the current research task is to minimize energy consumption due to resource constraints and increased cost. For complex computations where multiple computer systems are required to execute a single task such as in a federated cloudlet environment, load balancing is the main challenge. Load balancing means dividing the total workload between all the present nodes to obtain the maximum benefits from the available resources and to minimize energy consumption. A cloudlet is a resourceful computer that is coupled to the Internet and is accessible for mobile devices in their vicinity. A Cloudlet Federation is the concept of a cooperative framework to share resources and load balancing among various cloudlets. Different tasks consume different amounts of energy for their execution, which results in a large amount of heat dissipation. Due to heat, the performance of the systems is decreased. The more heat is present, the more the performance degrades. To address this problem, this paper proposes a novel scheduling strategy that will assign incoming tasks to systems according to their energy consumption level. The proposed methodology is tested in a Cloudlet Federation environment and the results show improved load balancing in terms of energy consumption and heat dissipation.

Highlights

  • The core function of a computer system is to assist in meeting the end user’s requirements

  • All computer systems have a CPU as the key component for scheduling and management of different tasks on the basis of available resources [1,2]

  • Different task scheduling algorithms have been offered in previous years for scheduling the tasks to obtain the maximum output [3]

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Summary

Introduction

The core function of a computer system is to assist in meeting the end user’s requirements. These techniques do not consider the energy level of the system on which the tasks are running [4,5]. Some techniques balance the load on the basis of incoming tasks regardless of their power consumption [6,7,8]. The proposed technique observes how much energy a task will consume for its execution and considers the power state of the system and on the basis of this information selects the optimal system for task execution. The algorithm takes the systems list, their power state, energy requirements for the tasks as input values, checks the status of system and thengaf=tePr co*ntducts the comparison, finding the available optimal system

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Conclusions and Future Directions
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