Optimization Strategies Of Data Management And Task Scheduling In Cloud Computing

Cloud computing has expanded the scope of the accessed resources. It supplies a solid base environment for multiple information applications and extensions by establishing an architecture in which information resources can be fetched elastically and on-demand in the form of services. Cloud computing is the reinforcement of the Service-orient Architecture (SOA). It processes various kinds of data that come from customers and aims at meeting their needs and increasing the resource utilization rate. To realize this goal, many cloud computing systems and their optimizing strategies have been proposed. However, considering the influences of network status, resources distribution, service forms and patterns, there are still a lot of challenges in the optimal management of data and efficient scheduling.Based on the existing work in the data management and optimal scheduling, this dissertation goes deeply into the optimization of operating costs. Synthesizing a variety of system and environmental factors, as well as the failures of physical node, a data deployment strategy is proposed according to block-popularity. Furthermore, a method of a new node performance measurement and a mechanism of failure node repair are proposed. The strategies are verified by simulation and experiments. The results show that they can help service providers to optimize their operating costs without reducing the Quality of Service (QoS). The study of these issues has important industrial and academic value.The main contributions of this dissertation are as follows.1) A data block storage strategy based on block-popularity is proposed, targeting at block deployment with the optimization of the service cost. In the proposed strategy, data blocks with different popularities will have different optimal replicas. The differences in the costs of service nodes are further considered and a minimum service cost strategy that meets consumers’needs is proposed. By introducing adjustment factor and considering system load, an adaptive strategy of minimum cost of storing blocks is provided.2) A measurement method of node performance is proposed, which can optimally deploy the data blocks with multiple parameters. By taking node business characterization into consideration, the synthesized utility values of the alternative node are considered regarding to the node cost, load rate, bandwidth, network delay and computation complexity of the tasks. Evaluation criterion of synthesized utility is given. Basing on it the task scheduling model and strategy with optimal service quality are proposed.3) To guarantee efficient data recovery and utility with minimum service cost, we proposed a data recovery method to deal with node failure. In this method, a selective data recovery algorithm and a date redeployment strategy are presented. Considering block-popularity, a cost matrix adjustment factor is also provided to recover lost data elastically and improve the service performance of content access without changing the minimum total service costs.