Novel Load Balancing Optimization Algorithm to Improve Quality-of-Service in Cloud Environment

Scheduling cloud resources calls for allocating cloud assets to cloud tasks. It is possible to improve scheduling outcomes by treating Quality of Service (QoS) factors as essential constraints. However, efficient scheduling calls for improved optimization of QoS parameters, and only a few resource scheduling algorithms in the available literature do so. The primary objective of this paper is to provide an effective method for deploying workloads to cloud infrastructure. To ensure that workloads are executed efficiently on available resources, a resource scheduling method based on particle swarm optimization was developed. The proposed method's performance has been measured in the cloud. The experimental results prove the efficiency of the proposed approach in reducing the aforementioned QoS parameters. Several metrics of algorithm performance are used to gauge how well the algorithm performs

Intelligent Load Balancing in Cloud Computer Systems

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

An optimised dynamic resource allocation algorithm for Cloud's backbone network

International audienceSky computing is a promising concept enabling a flexible deployment of geographical distributed applications. Whereas, it is faced with a fundamental challenge which is: “efficient resource utilisation” within Cloud's infrastructure. Hence, a high flexible and intelligent resource allocation scheme is necessary to accommodate unpredictable and variable users demands. This paper tackles the fundamental challenge of efficient resource allocation within Cloud's backbone network. The ultimate goal is to satisfy the Cloud's user requirements while maximising Cloud provider's revenue. The problem consists in embedding virtual networks within substrate infrastructure. A new dynamic adaptive virtual network resource allocation strategy named Backtracking-VNE is investigated to deal with the complexity of resource provisioning within Cloud network. The proposal coordinates virtual nodes and virtual links mapping stages to optimise resources usage. Moreover, thanks to forecasting module, Backtracking-VNE guarantees an efficient resources share between embedded virtual links with respect to their occupancy. We demonstrate through extensive simulations that contrarily to static bandwidth allocation approaches, Backtracking-VNE enhances substrate bandwidth usage whilst minimising virtual links congestion. Acceptance rate of virtual networks and Cloud providers income are also improved compared with related strategies

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010