In a shared cluster,each application runs on a subset of nodes and these subsets can overlap with one another. Resource management in such a cluster should adaptively change the application placement and workload assi...In a shared cluster,each application runs on a subset of nodes and these subsets can overlap with one another. Resource management in such a cluster should adaptively change the application placement and workload assignment to satisfy the dynamic applications workloads and optimize the resource usage.This becomes a challenging problem with the cluster scale and application amount growing large.This paper proposes a novel self-adaptive resource management approach which is inspired from human market：the nodes trade their shares of applications＇ requests with others via auction and bidding to decide its own resource allocation and a global high-quality resource allocation is achieved as an emergent collective behavior of the market.Experimental results show that the proposed approach can ensure quick responsiveness, high scalability,and application prioritization in addition to managing the resources effectively.展开更多
Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dy...Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).展开更多
基金Supported by the National Basic Research 973 Program of China under Grant No.2009CB320700the National High Technology Research and Development 863 Program of China under Grant Nos.2007AA010301,2008AA01Z139,2009AA01Z1391the National Natural Science Foundation of China under Grant Nos.60603038,60773151.
文摘In a shared cluster,each application runs on a subset of nodes and these subsets can overlap with one another. Resource management in such a cluster should adaptively change the application placement and workload assignment to satisfy the dynamic applications workloads and optimize the resource usage.This becomes a challenging problem with the cluster scale and application amount growing large.This paper proposes a novel self-adaptive resource management approach which is inspired from human market：the nodes trade their shares of applications＇ requests with others via auction and bidding to decide its own resource allocation and a global high-quality resource allocation is achieved as an emergent collective behavior of the market.Experimental results show that the proposed approach can ensure quick responsiveness, high scalability,and application prioritization in addition to managing the resources effectively.
文摘Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).
