Abstract
Despite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the Infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the backend where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or underloaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature's findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Makespan.
Highlights
As we shift more towards online storage and services, Cloud Computing technology becomes an essential part of the business
The proposed Load Balancing algorithm is developed mainly focusing on the Infrastructure as a Service (IaaS) model out of the three service models in the cloud where authors deal with the Cloud Computing technology's backend, such as server workload
The method consists of both processes: Task Scheduling process to assign deadline and completion time to cloudlets and secondly, Load Balancing process to perform migration of workload in case of Virtual Machines (VMs) violation to maintain a balanced load in the cloud environment
Summary
As we shift more towards online storage and services, Cloud Computing technology becomes an essential part of the business. Virtualization is the backbone and essential feature [2] of cloud-based applications This technique can significantly affect the performance of the scalable and on-demand services provided to clients if the migration process and allocation of virtual machine resources are handled inefficiently. These requests are stored in Virtual Machines (VMs), and CSP in every delivery model must maintain the QoS by ensuring the users' requests can be executed and completed within a specific deadline This process depends highly on the scheduling policy's efficiency (Data Broker) which should be programmed to result in a high technique for balancing workload among the machines and servers. Since virtualization plays an essential role in cloud technology, issues such as inappropriate scheduling techniques or efficient mapping of tasks [1] to correct Virtual Machines/resources can quickly degrade cloud-based applications' performance. This challenge exists in wireless communication systems [13] where priority among users should be applied and resources must be distributed and fairly
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