Service composition based on discrete particle swarm optimization in military organization cloud cooperation

This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users（MSU）. A group of atom services, each of which has its level of quality of service（QoS）, can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users＇ will. In this paper a method based on discrete particle swarm optimization（DPSO） is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU＇s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization（PSO） and genetic algorithm（GA）.

考虑执行可靠性的云制造服务组合算法

由于制造资源的调用与执行比计算资源要复杂得多,使得在云制造环境下的服务组合执行可靠性成为必须考虑的问题.为此,提出一种基于离散粒子群智能优化的、考虑执行可靠性的云制造服务组合算法.以概率密度函数描述服务可靠性,基于服务模型库动态生成云制造服务组合的工作流模型,基于语义实现服务匹配,通过离散变量粒子群优化算法计算出执行可靠性最优的云制造服务组合方案.最后以结构设计工作流模型为例,在不同服务实例规模、不同恶意服务占比情况下验证了其有效性和可靠性.实例结果表明,在恶意服务占比较高时,该算法仍能保持较高的服务执行可靠性.

基于离散粒子群优化的云计算QoS调度算法

针对云计算环境下用户任务的多种服务质量(QoS)需求,综合考虑任务截止时间、调度预算和可靠性,提出一种多QoS约束离散粒子群优化(QoS-DPSO)的任务调度算法。对任务的QoS进行定义和数学建模,通过截止时间和调度预算约束DPSO的搜索空间,根据可靠性重新定义DPSO的适应度函数,由适应度值搜索最优的任务调度方案。实验结果表明,与PSO,DPSO,DBC和EDF算法相比,QoS-DPSO在满足调度截止期的情况下具有较高的可靠性,并且对Makespan性能的影响较小。