Background:
Cloud computing is a multi-tenant model for computation that offers various features for
computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem
of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling
creates fundamental issue in cloud environment.
Introduction:
Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the
cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently
resolved through the incorporation of efficient load balancing and task scheduling strategies.
Method:
In this paper, we develop an efficient co-operative method to solve the most recent approaches against load
balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in
the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud
environment.
Results:
The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than
the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay.
Conclusion:
A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in
relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for
determining the applicant nodes is based on which the load balancing approach would essentially depend. By
incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can
be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant
cloud-based physical properties.
Applying the Intelligent Decision Heuristic to Solve Large Scale Technician and Task Scheduling Problems
