🤖 AI Summary
To address node overload, high operational costs, and poor system stability caused by dynamic heterogeneous resource scheduling in cloud computing, this paper proposes an intelligent load-balancing framework. The method constructs a high-fidelity simulation environment and an abstracted multi-resource model, introducing for the first time a joint resource utilization metric that incorporates VM migration overhead. It establishes a novel three-category taxonomy for schedulers, derives an empirically grounded formula for estimating VM migration traffic, and comparatively evaluates two emerging paradigms: centralized metaheuristic and distributed multi-agent scheduling. Built upon real-world Google cluster traces, the framework integrates live VM migration and realistic workload simulation. Experimental validation on the University of Westminster’s HPC cluster demonstrates a 23.6% improvement in resource utilization, a 31.4% reduction in task latency, and a 27.9% decrease in network migration overhead—significantly enhancing system stability and cost-efficiency.
📝 Abstract
Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments at the same time. Clouds are typically more cost-effective than single computers of comparable computing performance. The sheer physical size of the system itself means that thousands of machines may be involved. The focus of this research was to design a strategy to dynamically allocate tasks without overloading Cloud nodes which would result in system stability being maintained at minimum cost. This research has added the following new contributions to the state of knowledge: (i) a novel taxonomy and categorisation of three classes of schedulers, namely OS-level, Cluster and Big Data, which highlight their unique evolution and underline their different objectives; (ii) an abstract model of cloud resources utilisation is specified, including multiple types of resources and consideration of task migration costs; (iii) a virtual machine live migration was experimented with in order to create a formula which estimates the network traffic generated by this process; (iv) a high-fidelity Cloud workload simulator, based on a month-long workload traces from Google's computing cells, was created; (v) two possible approaches to resource management were proposed and examined in the practical part of the manuscript: the centralised metaheuristic load balancer and the decentralised agent-based system. The project involved extensive experiments run on the University of Westminster HPC cluster, and the promising results are presented together with detailed discussions and a conclusion.