Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters.Meanwhile,datacenter infrastructure comprises hardw...Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters.Meanwhile,datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels.In cloud datacenter,Virtual Machines(VMs)need to be allocated on various Physical Machines(PMs)in order to minimize resource wastage and increase energy efficiency.Resource allocation problem is NP-hard.Hence finding an exact solution is complicated especially for large-scale datacenters.In this con text,this paper proposes an Energy-oriented Flower Pollination Algorithm(E-FPA)for VM allocation in cloud datacenter environments.A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM.The allocation uses a strategy called Dynamic Switching Probability(DSP).The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search.It considers a processor,storage,and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs.Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware(GAPA)by 21.8%,Order of Exchange Migration(OEM)ant colony system by 21.5%,and First Fit Decreasing(FFD)by 24.9%.Therefore,E-FPA significantly improves datacenter performance and thus,enhances environmental sustainability.展开更多
Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems.The study of bionics bridges the functions,biological structures and functions and organizat...Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems.The study of bionics bridges the functions,biological structures and functions and organizational principles found in nature with our modem technologies,numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies.Output of bionics study includes not only physical products,but also various optimization computation methods that can be applied in different areas.Related algorithms can broadly be divided into four groups:evolutionary based bio-inspired algorithms,swarm intelligence-based bio-inspired algorithms,ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms.Bio-inspired algorithms such as neural network,ant colony algorithms,particle swarm optimization and others have been applied in almost every area of science,engineering and business management with a dramatic increase of number of relevant publications.This paper provides a systematic,pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms,swarm intelligence based bio-inspired algorithms,ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms.展开更多
文摘Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters.Meanwhile,datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels.In cloud datacenter,Virtual Machines(VMs)need to be allocated on various Physical Machines(PMs)in order to minimize resource wastage and increase energy efficiency.Resource allocation problem is NP-hard.Hence finding an exact solution is complicated especially for large-scale datacenters.In this con text,this paper proposes an Energy-oriented Flower Pollination Algorithm(E-FPA)for VM allocation in cloud datacenter environments.A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM.The allocation uses a strategy called Dynamic Switching Probability(DSP).The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search.It considers a processor,storage,and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs.Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware(GAPA)by 21.8%,Order of Exchange Migration(OEM)ant colony system by 21.5%,and First Fit Decreasing(FFD)by 24.9%.Therefore,E-FPA significantly improves datacenter performance and thus,enhances environmental sustainability.
文摘Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems.The study of bionics bridges the functions,biological structures and functions and organizational principles found in nature with our modem technologies,numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies.Output of bionics study includes not only physical products,but also various optimization computation methods that can be applied in different areas.Related algorithms can broadly be divided into four groups:evolutionary based bio-inspired algorithms,swarm intelligence-based bio-inspired algorithms,ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms.Bio-inspired algorithms such as neural network,ant colony algorithms,particle swarm optimization and others have been applied in almost every area of science,engineering and business management with a dramatic increase of number of relevant publications.This paper provides a systematic,pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms,swarm intelligence based bio-inspired algorithms,ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms.
