A novel multi-level hybrid load balancing and tasks scheduling algorithm for cloud computing environment

Abstract

Today cloud computing is at the heart of all information technologies. This prodigious technological paradigm relies on a very simple concept defined as the ability to deliver hardware and software resources as service directly over internet. A set of mechanisms cooperate to maintain the cloud reliability and to allow continuous delivery of these services while guaranteeing the same quality of service (QoS) and respecting the service-level agreement (SLA) for each client. Load balancing is one of those mechanisms and it ensures a crucial service, it can be defined as the ability of the system to ensure fairness in the distribution of workload over all servers. The most recent load balancing techniques are hybrid methods involving in major cases the combination between static and dynamic approaches, in other cases it can go further by integrating other mechanisms in order to improve the overall efficiency of the systems. The performance can be evaluated by parameters which generally refers to the degree of compliance with SLA and QoS. In order to enhance load balancing and tasks scheduling in cloud environment we propose in this paper a different hybrid approach which allows the decomposition of the problem and to operate on two levels by going through two stages : (i) first clusters are built for each datacenter grouping together sub-sets of servers that have close utilization rates, (ii) then tasks scheduling and load balancing operate at the datacenter level to deal with distribution over clusters and at the cluster level to ensure fairness between servers of the same cluster. Our method allows hot-deployment in already operating cloud environments and an excellent scalability. It also offers decoupling of missions and strong interoperability between the different mechanisms. To prove its validity, we implemented it on the standard cloudSim plus simulator, before carrying out a comparative study which shows better results than existing approaches in terms of makespan, reduces reaction time, number of migrations required and SLA violations.

Deep reinforcement learning task scheduling method based on server real-time performance

“…Considering the diversity and dynamics of task demands and the continuity of tasks, the subsequent arrival and execution of tasks are influenced by the impact of previously arrived tasks on the performance of virtual machines. To accurately capture the real server load dynamics, in this article, the virtual machine global load score in Elsakaan & Amroun (2024) is adopted as the load caused by task requirements on the virtual machine. Therefore, the load generated by the resource request of CPU, memory, and disk I/O by the -th task is…”