Cloud computing is an advance computing model using which several applications,data and countless IT services are provided over the Internet.Task scheduling plays a crucial role in cloud computing systems.The issue of...Cloud computing is an advance computing model using which several applications,data and countless IT services are provided over the Internet.Task scheduling plays a crucial role in cloud computing systems.The issue of task scheduling can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources so that we can achieve the desired goals for tasks.With the enlargement of users of cloud the tasks need to be scheduled.Cloud’s performance depends on the task scheduling algorithms used.Numerous algorithms have been submitted in the past to solve the task scheduling problem for heterogeneous network of computers.The existing research work proposes different methods for data intensive applications which are energy and deadline aware task scheduling method.As scientific workflow is combination of fine grain and coarse grain task.Every task scheduled to VM has system overhead.If multiple fine grain task are executing in scientific workflow,it increase the scheduling overhead.To overcome the scheduling overhead,multiple small tasks has been combined to large task,which decrease the scheduling overhead and improve the execution time of the workflow.Horizontal clustering has been used to cluster the fine grained task further replication technique has been combined.The proposed scheduling algorithm improves the performance metrics such as execution time and cost.Further this research can be extended with improved clustering technique and replication methods.展开更多
With more large-scale scientific computing tasks being delivered to cloud computing platforms, cloud workflow systems are designed for managing and arranging these complicated tasks. However, multi-tenant coexistence ...With more large-scale scientific computing tasks being delivered to cloud computing platforms, cloud workflow systems are designed for managing and arranging these complicated tasks. However, multi-tenant coexistence service mode of cloud computing brings serious security risks, which will threaten the normal execution of cloud workflows. To strengthen the security of cloud workflows, a mimic cloud computing task execution system for scientific workflows is proposed. The idea of mimic defense contains mainly three aspects: heterogeneity, redundancy, and dynamics. For heterogeneity, the diversities of physical servers, hypervisors, and operating systems are integrated to build a robust system framework. For redundancy, each sub-task of the workflow will be executed simultaneously by multiple executors. Considering efficiency and security, a delayed decision mechanism is proposed to check the results of task execution. For dynamics, a dynamic task scheduling mechanism is devised for switching workflow execution environment and shortening the life cycle of executors, which can confuse the adversaries and purify task executors. Experimental results show that the proposed system can effectively strengthen the security of cloud workflow execution.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
文摘Cloud computing is an advance computing model using which several applications,data and countless IT services are provided over the Internet.Task scheduling plays a crucial role in cloud computing systems.The issue of task scheduling can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources so that we can achieve the desired goals for tasks.With the enlargement of users of cloud the tasks need to be scheduled.Cloud’s performance depends on the task scheduling algorithms used.Numerous algorithms have been submitted in the past to solve the task scheduling problem for heterogeneous network of computers.The existing research work proposes different methods for data intensive applications which are energy and deadline aware task scheduling method.As scientific workflow is combination of fine grain and coarse grain task.Every task scheduled to VM has system overhead.If multiple fine grain task are executing in scientific workflow,it increase the scheduling overhead.To overcome the scheduling overhead,multiple small tasks has been combined to large task,which decrease the scheduling overhead and improve the execution time of the workflow.Horizontal clustering has been used to cluster the fine grained task further replication technique has been combined.The proposed scheduling algorithm improves the performance metrics such as execution time and cost.Further this research can be extended with improved clustering technique and replication methods.
基金Project supported by the National Natural Science Foundation of China(Nos.61521003 and 61602509)the National Key Technologies R&D Program of China(Nos.2016YFB0800100 and 2016YFB0800101)the Key Technologies R&D Program of Henan Province,China(No.172102210615)
文摘With more large-scale scientific computing tasks being delivered to cloud computing platforms, cloud workflow systems are designed for managing and arranging these complicated tasks. However, multi-tenant coexistence service mode of cloud computing brings serious security risks, which will threaten the normal execution of cloud workflows. To strengthen the security of cloud workflows, a mimic cloud computing task execution system for scientific workflows is proposed. The idea of mimic defense contains mainly three aspects: heterogeneity, redundancy, and dynamics. For heterogeneity, the diversities of physical servers, hypervisors, and operating systems are integrated to build a robust system framework. For redundancy, each sub-task of the workflow will be executed simultaneously by multiple executors. Considering efficiency and security, a delayed decision mechanism is proposed to check the results of task execution. For dynamics, a dynamic task scheduling mechanism is devised for switching workflow execution environment and shortening the life cycle of executors, which can confuse the adversaries and purify task executors. Experimental results show that the proposed system can effectively strengthen the security of cloud workflow execution.
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。
