As more and more large-scale scientific workflows are delivered to clouds,the business model of workflow-as-a-service is emerging.But there are many kinds of threats in the cloud environment,which can interrupt the ta...As more and more large-scale scientific workflows are delivered to clouds,the business model of workflow-as-a-service is emerging.But there are many kinds of threats in the cloud environment,which can interrupt the task execution and extend the workflow completion time.As an important QoS parameter,the workflow completion time is determined by the critical task path.Therefore,critical path redundancy method is proposed to create a redundant path having the interact parallel relationship with the critical path,which can provide the protection for the tasks in the critical path and reduce the probability of the critical path interruption.Computing instance allocation is an essential part of the cloud workflow execution,since only the tasks assigned the instance can begin execution.In order to further reduce the workflow completion time,computing instance allocation algorithm based on HEFT(heterogeneous earliest finish time)is proposed.The algorithm considers diverse task dependency relationships and takes full advantages of the critical path redundancy method,which can improve the efficiency of workflow execution.Experimental results demonstrate that the proposed method can effectively reduce the cloud workflow completion time under the task interruption.展开更多
Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. ...Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. As an effective security services aggregation methodology, Trust Work-flow Technology (TWT) has been used to construct composite services. However, in cloud environment, the existing closed network services are maintained and functioned by third-party organizations or enterprises. Therefore service-oriented trust strategies must be considered in workflow scheduling. TWFS related algorithms consist of trust policies and strategies to overcome the threats of the application with heuristic workflow scheduling. As a significance of this work, trust based Meta heuristic workflow scheduling (TMWS) is proposed. The TMWS algorithm will improve the efficiency and reliability of the operation in the cloud system and the results show that the TMWS approach is effective and feasible.展开更多
In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired ...In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.展开更多
为解决混合云环境下科学工作流数据布局问题,在考虑数据的安全需求的前提下,以优化跨数据中心传输时延为目标,提出了一种混合云环境下面向安全的科学工作流布局策略。分析数据集的安全需求以及数据中心所能提供的安全服务,提出安全等级...为解决混合云环境下科学工作流数据布局问题,在考虑数据的安全需求的前提下,以优化跨数据中心传输时延为目标,提出了一种混合云环境下面向安全的科学工作流布局策略。分析数据集的安全需求以及数据中心所能提供的安全服务,提出安全等级分级规则;设计并提出基于遗传算法和模拟退火算法的自适应粒子群优化算法(adaptive particle swarm optimization algorithm based on SA and GA,SAGA-PSO),避免算法陷入局部极值,有效提高种群多样性;与其它经典布局算法对比,基于SAGA-PSO的数据布局策略在满足数据安全需求的同时能够大大降低传输时延。展开更多
为了解决IaaS(Infrastructure as a Service)云的工作流调度优化问题,提出基于预算约束的工作流调度算法。以最小化工作流调度时长为目标,算法分调度任务选择和虚拟机实例选择两阶段进行。第一阶段将工作流任务依据依赖关系作层次划分,...为了解决IaaS(Infrastructure as a Service)云的工作流调度优化问题,提出基于预算约束的工作流调度算法。以最小化工作流调度时长为目标,算法分调度任务选择和虚拟机实例选择两阶段进行。第一阶段将工作流任务依据依赖关系作层次划分,同层次组成包任务,以Min-Max方法对层次任务估算时间作标准化处理,定义最迟完成时间与最早完成时间差值最大者为调度任务;第二阶段在期望预算下以最早完成时间最小为标准选择资源,实现任务与资源间的映射。利用算例阐述了算法实现过程,并通过仿真实验测试了算法性能。结果证实,改进算法执行效率与调度成功率优于同类算法。展开更多
基金The National Key R&D Program of China(2018YFB0804004)The Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61521003)。
文摘As more and more large-scale scientific workflows are delivered to clouds,the business model of workflow-as-a-service is emerging.But there are many kinds of threats in the cloud environment,which can interrupt the task execution and extend the workflow completion time.As an important QoS parameter,the workflow completion time is determined by the critical task path.Therefore,critical path redundancy method is proposed to create a redundant path having the interact parallel relationship with the critical path,which can provide the protection for the tasks in the critical path and reduce the probability of the critical path interruption.Computing instance allocation is an essential part of the cloud workflow execution,since only the tasks assigned the instance can begin execution.In order to further reduce the workflow completion time,computing instance allocation algorithm based on HEFT(heterogeneous earliest finish time)is proposed.The algorithm considers diverse task dependency relationships and takes full advantages of the critical path redundancy method,which can improve the efficiency of workflow execution.Experimental results demonstrate that the proposed method can effectively reduce the cloud workflow completion time under the task interruption.
文摘Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. As an effective security services aggregation methodology, Trust Work-flow Technology (TWT) has been used to construct composite services. However, in cloud environment, the existing closed network services are maintained and functioned by third-party organizations or enterprises. Therefore service-oriented trust strategies must be considered in workflow scheduling. TWFS related algorithms consist of trust policies and strategies to overcome the threats of the application with heuristic workflow scheduling. As a significance of this work, trust based Meta heuristic workflow scheduling (TMWS) is proposed. The TMWS algorithm will improve the efficiency and reliability of the operation in the cloud system and the results show that the TMWS approach is effective and feasible.
基金Project(61473078)supported by the National Natural Science Foundation of ChinaProject(2015-2019)supported by the Program for Changjiang Scholars from the Ministry of Education,China+1 种基金Project(16510711100)supported by International Collaborative Project of the Shanghai Committee of Science and Technology,ChinaProject(KJ2017A418)supported by Anhui University Science Research,China
文摘In order to improve the performance of multi-objective workflow scheduling in cloud system, a multi-swarm multiobjective optimization algorithm(MSMOOA) is proposed to satisfy multiple conflicting objectives. Inspired by division of the same species into multiple swarms for different objectives and information sharing among these swarms in nature, each physical machine in the data center is considered a swarm and employs improved multi-objective particle swarm optimization to find out non-dominated solutions with one objective in MSMOOA. The particles in each swarm are divided into two classes and adopt different strategies to evolve cooperatively. One class of particles can communicate with several swarms simultaneously to promote the information sharing among swarms and the other class of particles can only exchange information with the particles located in the same swarm. Furthermore, in order to avoid the influence by the elastic available resources, a manager server is adopted in the cloud data center to collect the available resources for scheduling. The quality of the proposed method with other related approaches is evaluated by using hybrid and parallel workflow applications. The experiment results highlight the better performance of the MSMOOA than that of compared algorithms.
文摘为解决混合云环境下科学工作流数据布局问题,在考虑数据的安全需求的前提下,以优化跨数据中心传输时延为目标,提出了一种混合云环境下面向安全的科学工作流布局策略。分析数据集的安全需求以及数据中心所能提供的安全服务,提出安全等级分级规则;设计并提出基于遗传算法和模拟退火算法的自适应粒子群优化算法(adaptive particle swarm optimization algorithm based on SA and GA,SAGA-PSO),避免算法陷入局部极值,有效提高种群多样性;与其它经典布局算法对比,基于SAGA-PSO的数据布局策略在满足数据安全需求的同时能够大大降低传输时延。
文摘为了解决IaaS(Infrastructure as a Service)云的工作流调度优化问题,提出基于预算约束的工作流调度算法。以最小化工作流调度时长为目标,算法分调度任务选择和虚拟机实例选择两阶段进行。第一阶段将工作流任务依据依赖关系作层次划分,同层次组成包任务,以Min-Max方法对层次任务估算时间作标准化处理,定义最迟完成时间与最早完成时间差值最大者为调度任务;第二阶段在期望预算下以最早完成时间最小为标准选择资源,实现任务与资源间的映射。利用算例阐述了算法实现过程,并通过仿真实验测试了算法性能。结果证实,改进算法执行效率与调度成功率优于同类算法。
