Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying options.Users can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant dis...Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying options.Users can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant discount.However,users have to carefully weigh the low cost of spot instances against their poor availability.Spot instances will be revoked when the revocation event occurs.Thus,an important problem that an IaaS user faces now is how to use spot in-stances in a cost-effective and low-risk way.Based on the replication-based fault tolerance mechanism,we propose an on-line termination algorithm that optimizes the cost of using spot instances while ensuring operational stability.We prove that in most cases,the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal of-fline algorithm that knows the exact future a priori.Through a large number of experiments,we verify that our algorithm in most cases has a competitive ratio of no more than 2,and in other cases it can also reach the guaranteed competitive ratio.展开更多
In infrastructure as a service(IaaS)cloud mode equipment simulated training,to keep the resource utilization ratio in a rational high level,improve the training effect and reduce the system running cost,the problem of...In infrastructure as a service(IaaS)cloud mode equipment simulated training,to keep the resource utilization ratio in a rational high level,improve the training effect and reduce the system running cost,the problem of training virtual machine(TVM)placement needs to be resolved first.We make analysis to the problem and give the mathematical formulation to the problem.Then,we figure out the principle and target of the TVM placement.Based on above analysis,we propose a constrained immune memory and immunodominance clone(CIMIC)TVM placement optimization algorithm.By reverse optimization of the initial antibody population,the searching range is reduced.The common antibody population and the immunodominance antibody population evolve simultaneously,which realizes the simultaneous progressing of global searching and local searching of solutions.Further,local optimal is avoided by this means.Memory antibody makes ful use of the unfeasible solutions and the diversity of antibody population is maintained.The constraint information of the problem is utilized to improve the optimization effect.Experiment results show that the CIMIC algorithm improves the overall optimization effect of TVM placement,reduces the server number and improves the resource utilization and system stability.展开更多
In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of th...In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.展开更多
In the cloud data centers,how to map virtual machines(VMs) on physical machines(PMs) to reduce the energy consumption is becoming one of the major issues,and the existing VM scheduling schemes are mostly to reduce ene...In the cloud data centers,how to map virtual machines(VMs) on physical machines(PMs) to reduce the energy consumption is becoming one of the major issues,and the existing VM scheduling schemes are mostly to reduce energy consumption by optimizing the utilization of physical servers or network elements.However,the aggressive consolidation of these resources may lead to network performance degradation.In view of this,this paper proposes a two-stage VM scheduling scheme:(1) We propose a static VM placement scheme to minimize the number of activating PMs and network elements to reduce the energy consumption;(2) In the premise of minimizing the migration costs,we propose a dynamic VM migration scheme to minimize the maximum link utilization to improve the network performance.This scheme makes a tradeoff between energy efficiency and network performance.We design a new twostage heuristic algorithm for a solution,and the simulations show that our solution achieves good results.展开更多
基金This work was supported by the National Key Research and Development Program of China under Grant No.2018YFB14-04501。
文摘Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying options.Users can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant discount.However,users have to carefully weigh the low cost of spot instances against their poor availability.Spot instances will be revoked when the revocation event occurs.Thus,an important problem that an IaaS user faces now is how to use spot in-stances in a cost-effective and low-risk way.Based on the replication-based fault tolerance mechanism,we propose an on-line termination algorithm that optimizes the cost of using spot instances while ensuring operational stability.We prove that in most cases,the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal of-fline algorithm that knows the exact future a priori.Through a large number of experiments,we verify that our algorithm in most cases has a competitive ratio of no more than 2,and in other cases it can also reach the guaranteed competitive ratio.
基金Equipment Pre-research Fund of China under Grant No.9140A04030214JB34058.
文摘In infrastructure as a service(IaaS)cloud mode equipment simulated training,to keep the resource utilization ratio in a rational high level,improve the training effect and reduce the system running cost,the problem of training virtual machine(TVM)placement needs to be resolved first.We make analysis to the problem and give the mathematical formulation to the problem.Then,we figure out the principle and target of the TVM placement.Based on above analysis,we propose a constrained immune memory and immunodominance clone(CIMIC)TVM placement optimization algorithm.By reverse optimization of the initial antibody population,the searching range is reduced.The common antibody population and the immunodominance antibody population evolve simultaneously,which realizes the simultaneous progressing of global searching and local searching of solutions.Further,local optimal is avoided by this means.Memory antibody makes ful use of the unfeasible solutions and the diversity of antibody population is maintained.The constraint information of the problem is utilized to improve the optimization effect.Experiment results show that the CIMIC algorithm improves the overall optimization effect of TVM placement,reduces the server number and improves the resource utilization and system stability.
基金the National Natural Science Foundation of China，the National High Technology Research and Development Program of China (863 Program)，the Fundamental Research Funds for the Central Universities，the Natural Science Foundation of Gansu Province,China，the Open Fund of the State Key Laboratory of Software Development Environment
文摘In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.
基金supported by the National Natural Science Foundation of China(61002011)the National High Technology Research and Development Program of China(863 Program)(2013AA013303)+1 种基金the Fundamental Research Funds for the Central Universities(2013RC1104)the Open Fund of the State Key Laboratory of Software Development Environment(SKLSDE-2009KF-2-08)
文摘In the cloud data centers,how to map virtual machines(VMs) on physical machines(PMs) to reduce the energy consumption is becoming one of the major issues,and the existing VM scheduling schemes are mostly to reduce energy consumption by optimizing the utilization of physical servers or network elements.However,the aggressive consolidation of these resources may lead to network performance degradation.In view of this,this paper proposes a two-stage VM scheduling scheme:(1) We propose a static VM placement scheme to minimize the number of activating PMs and network elements to reduce the energy consumption;(2) In the premise of minimizing the migration costs,we propose a dynamic VM migration scheme to minimize the maximum link utilization to improve the network performance.This scheme makes a tradeoff between energy efficiency and network performance.We design a new twostage heuristic algorithm for a solution,and the simulations show that our solution achieves good results.
