Task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consump...Task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consumption and Quality of Service(QoS) requirements under the changing environment and diverse tasks. Considering both processing time and transmission time, a PSO-based Adaptive Multi-objective Task Scheduling(AMTS) Strategy is proposed in this paper. First, the task scheduling problem is formulated. Then, a task scheduling policy is advanced to get the optimal resource utilization, task completion time, average cost and average energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.展开更多
为确保云平台上虚拟机系统用户信息的安全,提出了一种基于混合流策略的按需分布式云信息流控制模型(Mixed Flow Policy Based On-demand Distributed Cloud Information Flow Control Model,MDIFC)。该模型以分布式信息流控制模型为基础...为确保云平台上虚拟机系统用户信息的安全,提出了一种基于混合流策略的按需分布式云信息流控制模型(Mixed Flow Policy Based On-demand Distributed Cloud Information Flow Control Model,MDIFC)。该模型以分布式信息流控制模型为基础,结合中国墙策略形成混合流策略,通过引入污点传播思想跟踪来敏感数据以实现策略,为用户数据提供更好的安全保障。为提高模型的灵活性,考虑到虚拟域行为更具主动性的特征,提出了"按需受控"的概念及与之相适应的"输出型机密性"。同时,通过按需受控显著地降低了污点传播造成的开销。利用π演算对模型规格进行形式化描述,并借助PicNic工具证明模型的无干扰性。最后,通过一个应用示例说明了模型的实用性。展开更多
基金partially been sponsored by the National Science Foundation of China(No.61572355,61272093,610172063)Tianjin Research Program of Application Foundation and Advanced Technology under grant No.15JCYBJC15700
文摘Task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consumption and Quality of Service(QoS) requirements under the changing environment and diverse tasks. Considering both processing time and transmission time, a PSO-based Adaptive Multi-objective Task Scheduling(AMTS) Strategy is proposed in this paper. First, the task scheduling problem is formulated. Then, a task scheduling policy is advanced to get the optimal resource utilization, task completion time, average cost and average energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.
文摘为确保云平台上虚拟机系统用户信息的安全,提出了一种基于混合流策略的按需分布式云信息流控制模型(Mixed Flow Policy Based On-demand Distributed Cloud Information Flow Control Model,MDIFC)。该模型以分布式信息流控制模型为基础,结合中国墙策略形成混合流策略,通过引入污点传播思想跟踪来敏感数据以实现策略,为用户数据提供更好的安全保障。为提高模型的灵活性,考虑到虚拟域行为更具主动性的特征,提出了"按需受控"的概念及与之相适应的"输出型机密性"。同时,通过按需受控显著地降低了污点传播造成的开销。利用π演算对模型规格进行形式化描述,并借助PicNic工具证明模型的无干扰性。最后,通过一个应用示例说明了模型的实用性。
