Abstract
Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.
Highlights
In this modern age, the use of the internet has increased, and demands of users are growing significantly
State-of-the-art heuristic and meta-heuristic task schedulers are analyzed in depth, and it is determined that scheduling results in inefficient load balancing of cloud resources
The need for multi-objective optimization is expressed as a motivation, where load balancing serves as a significant metric parallel to makespan for efficient task scheduling
Summary
The use of the internet has increased, and demands of users are growing significantly. Maximum resource utilization and high throughput are computational QoS parameters that can have a noteworthy impact on task scheduling. Improvement in these metrics helps to achieve optimal task scheduling. BGA receives a number of tasks in a batch and maps them to VMs. Recently there have been hybrid schedulers introduced in task scheduling to meet the objectives of better makespan, load balancing and throughput. Load balancing can benefit the cloud service provider, and a better makespan with high throughput can benefit both the cloud service provider and consumer These objectives need to be met through an optimization approach to solve. Both load balancing and better makespan cannot be achieved without multi-objective optimization.
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