Resource Management And Job Scheduling In Cloud Environment:Research And Applications

The cloud computing technology developed in industry is introduced to service science and engineering as a new approach. The traditional cloud, which always refers to the data computing cloud, provides shared computing resources to public via internet according to their demand. This kind of cloud is characterized by its large-scale supply, highly scalable and on-demand services, which is similar to other common service systems.In order to represent a general framework for different service system, this study proposes a general service cloud framework as an extension of data computing cloud.In the cloud environment, the service resource management, especially the reservation scheduling is of great importance to the service efficiency and effectivity of service system. The traditional reservation system just supports one-to-one interaction between user and service provider, regardless of the association between users’jobs, or service resources, moreover, it defines the service ability just according to the time availability of single resource and disregards the complexity of resources. In order to deal with these problems, the system may be reconfigured and deployed backward in cloud environment, in addition, by means of service ability abstraction, resources virtualization and service registry, the efficiency and effectivity of the comprehensive service system should be improved and optimized accordingly, and thus, some widely used cloud computing methods can also be employed in system optimization. This thesis would be organized as following sections.Chapter2introduces the definition and features of cloud environment, scheduling related concepts, as well as the relationship between resource allocation and job scheduling. In concern of the insufficient reservation problem in traditional service environment, this study proposes a transferable scheme to represent the abstraction process of service resources and allocation of jobs in cloud environment.With respect to resources allocation, Chapter3develops a model to simplify the abstraction process for multidimensional resources. Several allocation algorithms for the multidimensional resources have been developed accordingly, and further compared using a simulation case with different model parameters. Consequently, alternative allocation strategies are recommended under different conditions.With respect to job scheduling, Chapter4focuses on the scheduling strategy across cloud platforms. A service ability scheduling model is proposed for registration center with constraints of time consumption. In the model, the cost of jobs is calculated by a combination of service price and transportation cost, which can thus be optimized globally to achieve better system benefit.Finally, Chapter5demonstrates the applications of proposed allocation and scheduling models in the "211" engineering project of China Equipment and Education Resource System (CERS). The prototype system for the scheduling, analysis and assessment of equipment reservation module is realized finally based on the proposed models and related algorithms, which can be further applied in the future project and other related service systems.