In recent years, several researchers have applied workflow technologies for service automation on ubiquitous compating environments. However, most context-aware workflows do not offer a method to compose several workf...In recent years, several researchers have applied workflow technologies for service automation on ubiquitous compating environments. However, most context-aware workflows do not offer a method to compose several workflows in order to get mare large-scale or complicated workflow. They only provide a simple workflow model, not a composite workflow model. In this paper, the autorhs propose a context-aware workflow model to support composite workflows by expanding the patterns of the existing context-aware wrY：flows, which support the basic woddlow patterns. The suggested workflow model of. fers composite workflow patterns for a context-aware workflow, which consists of various flow patterns, such as simple, split, parallel flows, and subflow. With the suggested model, the model can easily reuse few of existing workflows to make a new workflow. As a result, it can save the development efforts and time of context-aware workflows and increase the workflow reusability. Therefore, the suggested model is expected to make it easy to develop applications related to context-aware workflow services on ubiquitous computing environments.展开更多
With the development of cloud computing, more and more data-intensive workflows have been deployed on virtualized datacenters. As a result, the energy spent on massive data accessing grows rapidly. In this paper, an e...With the development of cloud computing, more and more data-intensive workflows have been deployed on virtualized datacenters. As a result, the energy spent on massive data accessing grows rapidly. In this paper, an energy-aware scheduling algorithm is proposed, which introduces a novel heuristic called Minimal Data-Accessing Energy Path for scheduling data-intensive workflows aiming to reduce the energy consumption of intensive data accessing. Extensive experiments based on both synthetical and real workloads are conducted to investigate the effectiveness and performance of the proposed scheduling approach. The experimental results show that the proposed heuristic scheduling can significantly reduce the energy consumption of storing/retrieving intermediate data generated during the execution of data-intensive workflow. In addition, it exhibits better robustness than existing algorithms when cloud systems are in presence of I/O- intensive workloads.展开更多
基金supported by the The Ministry of Knowledge Economy,Korea,the ITRC(Information Technology Research Center)support program(ⅡTA-2009-(C1090-0902-0007))
文摘In recent years, several researchers have applied workflow technologies for service automation on ubiquitous compating environments. However, most context-aware workflows do not offer a method to compose several workflows in order to get mare large-scale or complicated workflow. They only provide a simple workflow model, not a composite workflow model. In this paper, the autorhs propose a context-aware workflow model to support composite workflows by expanding the patterns of the existing context-aware wrY：flows, which support the basic woddlow patterns. The suggested workflow model of. fers composite workflow patterns for a context-aware workflow, which consists of various flow patterns, such as simple, split, parallel flows, and subflow. With the suggested model, the model can easily reuse few of existing workflows to make a new workflow. As a result, it can save the development efforts and time of context-aware workflows and increase the workflow reusability. Therefore, the suggested model is expected to make it easy to develop applications related to context-aware workflow services on ubiquitous computing environments.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60970038,61272148the Science and Technology Plan Project of Hunan Province of China under Grant No.2012GK3075the Scientific Research Fund of Hunan Provincial Education Department of China under Grant No.13B015
文摘With the development of cloud computing, more and more data-intensive workflows have been deployed on virtualized datacenters. As a result, the energy spent on massive data accessing grows rapidly. In this paper, an energy-aware scheduling algorithm is proposed, which introduces a novel heuristic called Minimal Data-Accessing Energy Path for scheduling data-intensive workflows aiming to reduce the energy consumption of intensive data accessing. Extensive experiments based on both synthetical and real workloads are conducted to investigate the effectiveness and performance of the proposed scheduling approach. The experimental results show that the proposed heuristic scheduling can significantly reduce the energy consumption of storing/retrieving intermediate data generated during the execution of data-intensive workflow. In addition, it exhibits better robustness than existing algorithms when cloud systems are in presence of I/O- intensive workloads.
