基于云计算环境的资源提供优化方法研究
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摘要
作为一种新兴的信息处理模式,云计算(Cloud Computing)技术已经成为信息领域备受关注的研究热点。云计算以虚拟化(Virtualization)作为支撑技术,以按需方式向Internet用户提供动态可扩展的服务。然而,由于云计算环境规模大,资源管理与分配动态可伸缩的特点,导致云数据中心的能耗问题及其资源提供效率成为影响云计算性能的关键因素。本文以新的计算基础设施——云计算技术为背景,研究如何优化云计算数据中心的能耗及其资源的优化配置问题。到目前为止,云计算的能耗问题及其资源提供依然存在很多亟待解决的问题。本文重点从节能机制、负载均衡和市场经济模型等方面研究云计算环境中的高效资源提供优化方法,主要的研究工作包括以下几点：
1)系统研究了云计算环境中的节能机制及其资源提供优化方法。
首先,从云计算的基本概念入手,介绍了云计算的特点、服务类型及层次；其次,重点研究了云计算中的节能优化策略,分析比较了策略的应用环境及优缺点；然后,进一步研究了云计算中的资源提供技术,并对该领域目前的优化策略进行了分类比较；最后,对云实验环境CloudSim进行介绍并对其资源提供机制进行实验分析。
2)提出了基于能量与SLA均衡的虚拟机资源提供策略。
针对云计算环境中应用需求的动态变化特性,提出了基于强局部加权回归的虚拟机自适应部署算法RLWR, RLWR可以根据应用负载所体现的资源占用历史信息动态决策主机的超载时机。检测出超载主机后,提出了迁移周期最优的虚拟机迁移选择算法MPM和迁移量最小算法MNM进行迁移虚拟机的选择,然后提出以基于功耗的降序最佳适应启发式算法PBFDH对迁移虚拟机进行再次优化部署。该自适应部署策略比较静态阈值算法STH、MPA和DVFS,不仅可以动态地将虚拟机部署到更少物理主机上,从而关闭闲置主机,提高了能效,而且通过主机资源的负载预测实现了高可靠的QoS服务交付,避免了用户与资源提供者之间过多的SLA违例。实验结果表明,策略在保证能效的同时,在减少SLA违例确保QoS方面也具有明显的效果。
3)提出了基于多数据中心的绿色高能效资源提供策略。
数据中心的能效通常被多个动态因素影响,包括：能源成本、碳排放率、负载类型、CPU能效及冷却系统等,该策略将同时考虑以上因素研究跨越多个地理位置环境中的多数据中心的全局能效问题。首先建立了多数据中心的资源提供模型,将能耗制约的收益问题和碳排放(Carbon Footprint)问题形式化为QoS约束的收益函数和代价函数的多目标最优化模型,证明了该模型是NP-hard问题。针对该问题提出了绿色云优先的CMM、MCMP算法和收益优先的PMM、 MPMC算法,算法综合考虑了碳排放、能耗、收益和应用的QoS需求,目标是降低碳排放,增加收益,同时满足用户应用的QoS需求。执行应用阶段,在数据中心中利用提出的NDVS方法进一步优化能耗,求解了给定负载情况下单个数据中心功耗最小时CPU频率满足的条件,并求解了CPU的最优频率,证明了该频率下能耗达到局部极小。实验结果表明,策略不仅可以降低能耗成本,优化任务调度,而且还可以权衡碳足迹。
4)提出了基于遗传算法的虚拟机资源提供负载均衡策略。
应用需求的多样性和节点资源的异构性不可避免地会导致资源提供过程中云计算节点的负载失衡问题,这极大地降低了云计算的整体资源提供效率。如何通过高效的负载均衡机制协调主机负载以提高资源利用率和系统性能是目前丞待解决的问题。针对这一问题,提出了基于负载均衡的虚拟机资源提供遗传算法VMPGALB, VMPGALB舍弃了传统二进制编码方法,采用了更适宜体现虚拟机提供特点的树型编码方案。制定选择策略时,采用基于适应度的比例选择策略和最优保存策略,该方法使得具有较小适应度的个体也有被选择的机会并直接保留最优个体至后代中。设计杂交算子时,通过对两个父代个体的交叉操作,并利用生成树方法,使VMPGALB具有更好的杂交性能。同时,为避免求解过程陷入局部最优,VMPGALB还按一定比例对产生的个体进行了变异操作。实验结果表明,比较传统遗传算法BGA、MOGA、启发式算法BFH和WLC,VMPGALB不仅遗传性能更优,虚拟机迁移次数更少,而且能以较快的收敛速度求解虚拟机提供的负载均衡方案。
5)提出了基于市场经济学模型的资源提供博弈策略。
市场经济学模型可以通过均衡理论实现资源的优化配置,研究了以市场经济模型为基础的云计算资源提供机制,结合博弈论在资源管理领域的优势,首先,建立了非合作竞争市场的资源提供模型,提出了非合作博弈资源提供算法RPANCG,该算法以非合作博弈进行建模,RPANCG的目标是寻找使得各个资源提供者效用达到最优的Nash均衡解,证明了RPANCG算法可以产生唯一的Nash均衡。然后,在RPANCG算法满足效用相互最优的基础上,为了进一步增加集体收益,并满足效率与公平的约束,在非合作竞争市场的基础上提出了议价市场中的资源提供算法RPABG,该算法以议价博弈进行建模,RPABG的目标则是寻找Nash议价解。实验结果表明,RPANCG算法可以收敛到唯一的Nash均衡解,资源提供者的效用达到相互最优,整个资源提供趋于合理。而RPABG则在RPANCG算法的基础上进一步兼顾了资源分配的效率和公平性,并且能够提高资源提供者的整体效用,实现了Pareto改进,从而达到云资源的公平、合理和均衡的优化分配。
本文的研究得到了国家自然科学基金项目(批准号：60970064,61272116),新世纪优秀人才支持计划项目(批准号：NCET-08-0806),教育部博士点基金项目(批准号：20120143110014)及湖北省高端人才引领培养计划项目的资助。
As a kind of novel information processing model, cloud computing technology has become a focus in the field of information. With virtualization as a support technology, cloud computing provides a kind of dynamic and scalable service for Internet users on demand. However, as cloud computing environment is characterized by the large-scale, the dynamic and flexibility of resource management and allocation, the energy comsumption problem and resource provision efficiency are critical factors that impact on the performance of cloud computing. Based on the new computing infrastructure—cloud computing technology, this thesis focuses on how to optimize the energy comsumption and the resource allocation of cloud data center. So far, there are still many problems to be solved concerned the energy consumption problem and resource provision of cloud computing. This thesis focuses on the resource provision optimization methods of cloud computing environment from three aspects:energy-saving mechanism, load balance and market economy model. The main contributions of this thesis include:
1) Study systematically on energy-saving mechanism and optimization method of resource provision in cloud computing environment.
First, starting from the basic concept of cloud computing, the characteristic, the service type and layer of cloud computing are introduced respectively. Second, the energy-saving optimization strategies in cloud computing are stressed, the application environment and the advantages and disadvantages of them are analyzed and compared as well. Then, the resource provision technology is studied further and the optimization strategies of this field are classified and compared. At last, the experimental tool CloudSim is introduced and the mechanism of its resource provision is analyzed through several simulation experiments.
2) A virtual machine resource provision strategy based on the equilibrium between energy and SLA is proposed.
Aiming at dynamical changes of application workload requirements, a self-adaptive deployment strategy RLWR based on robust local weight regression is presented. The algorithm can decide the overload time of hosts dynamically according to the historical resource occupation information of application workload. After detecting overloaded hosts, two virtual machine migration selection algorithms, MPM and MNM are proposed. The objective of the former is to get optimal migration period, the later is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH based on power consumption aware. Contrasting to static threshold algorithm STH, MPA and DVFS, virtual machines are not only deployed on fewer physical hosts in the self-adaptive deployment strategy, which promotes energy efficiency through turning off unused hosts, but also the load prediction of resource can bring high-reliable QoS delivery and avoid overmuch SLA violations between users and resource providers. The experimental results show that the strategy has an obvious effect on decreasing SLA violation under ensuring energy-efficiency.
3) A green energy-efficient resource provision strategy based on multiple data centers is presented.
The energy efficiency of data center is usnally affected on many dynamic factors, including energy costs, carbon emission rate, load type, CPU energy-efficiency and cooling system and so on. The above factors are considered in the proposed strategy and the global energy-efficiency of data center across multiple geopraphically heterogeneous environments is studied. First, the resource provision model of multiple data centers is set up, the profit and carbon emission issure influenced by energy comsumption is formatted as a multi-objective optimization model of the profit function and the cost function with QoS constraints. This model is proved to be an NP-hard problem. Aiming at this issue, four algorithms are proposed, CMM, MCMP based on green cloud priority and PMM, MPMC based on profit priority. The carbon emission, energy cosumption, profit and QoS requirements are considered in algorithms synthetically. The objective is to reduce carbon emission and increase revenue with meeting QoS requirements of user applications. During implementing applications, for optimizing energy consumption further, the optimal frequency of CPU is sovled and we prove that the energy consumption will reach a local munimum at this frequency. The experimental results show that the strategies not only reduce the energy costs, otpimize the task scheduling and also balance the carbon footprint.
4) A virtual machine resource provision load balance strategy based on genetic algorithm is proposed.
The diversity of application demand and the heterogeneity of resources inevitably lead to load imbalance of cloud computing during the process of resource provision, which greatly reduces the global efficiency of resource provision. How to improve resource utilization and system performance through an efficient load balance mechanism is an urgent need to solve currently. Aiming at this problem, a virtual machine resource provision genetic algorithm based on load balance, VMPGALB is presented. Abandoning the traditional binary encoding, VMPGALB adopts a tree encoding scheme, which is more suitable to reflect the characteristics of virtual machine provision. During designing the selection strategy, the proportion selection based on fitness and the elite-preserving strategy are applied, which make individuals with smaller fitness to be selected possibly and retain the best individuals directly to offspring. Druing devising the crossover operator, VMPGALB has a better crossover performance through the crossover operation of two parent individuals and applying spanning tree method. Meanwhile, in order to avoid a local optimum, VMPGALB implements mutation operations on individuals according to a certain percentage. The experimental results show that, contrasting to BGA, MOGA, BFH and WLC, VMPGALB not noly can get better genetic performance and fewer migration numbers, but also gain the load balance scheme of virtual machine provision with a faster convergence speed.
5) A resource provision game strategy based on market economic model is presented.
Market economics model can achieve the optimal allocation of resource through the equilibrium theory, cloud computing reource provision mechanism based on the market economic model is researched. Combined with the advantages of game theory in resource management, first, the resource provision model in non-cooperative competitive market is set up and a non-cooperative game resource provision algorithm RPANCG is proposed, which is based on non-cooperative game. The objective of RPANCG is to find Nash equilibrium that makes the utility of all resource providers optimal. We prove that RPANCG can generate a unique Nash equilibrium. Then, based on RPANCG meeting mutual optimum of the revenue, for increasing collective revenue further and meeting the efficiency and fairness, a resource provision algorithm RPABG is proposed in bargaining market based on non-cooperative competitive market. RPABG builds its model with bargaining game theory and its objective is to find Nash bargaining solution. The experimental results show that RPANCG can converge to a unique Nash equilibrium, the utilities of resource providers reach mutual optimum and the whole resource provision tends to be more reasonable. Moreover, RPABG can further give consideration to the efficiency and fairness of resource allocation based on RPANCG, improve the overall utility of resource providers and realize the Pareto efficiency, which leads to an optimal allocation of cloud resource with fairness, rationality and equilibrium.
This thesis is supported by National Natural Science Foundation of China (No.60970064, No.61272116), New Century Excellent Talent Support Plan (No.NCET-08-0806), Specialized Research Fund for the Doctoral Program of Higher Educaion of China (No.20120143110014) and Hubei province high-end talent lead cultivation project.
1)系统研究了云计算环境中的节能机制及其资源提供优化方法。
首先,从云计算的基本概念入手,介绍了云计算的特点、服务类型及层次；其次,重点研究了云计算中的节能优化策略,分析比较了策略的应用环境及优缺点；然后,进一步研究了云计算中的资源提供技术,并对该领域目前的优化策略进行了分类比较；最后,对云实验环境CloudSim进行介绍并对其资源提供机制进行实验分析。
2)提出了基于能量与SLA均衡的虚拟机资源提供策略。
针对云计算环境中应用需求的动态变化特性,提出了基于强局部加权回归的虚拟机自适应部署算法RLWR, RLWR可以根据应用负载所体现的资源占用历史信息动态决策主机的超载时机。检测出超载主机后,提出了迁移周期最优的虚拟机迁移选择算法MPM和迁移量最小算法MNM进行迁移虚拟机的选择,然后提出以基于功耗的降序最佳适应启发式算法PBFDH对迁移虚拟机进行再次优化部署。该自适应部署策略比较静态阈值算法STH、MPA和DVFS,不仅可以动态地将虚拟机部署到更少物理主机上,从而关闭闲置主机,提高了能效,而且通过主机资源的负载预测实现了高可靠的QoS服务交付,避免了用户与资源提供者之间过多的SLA违例。实验结果表明,策略在保证能效的同时,在减少SLA违例确保QoS方面也具有明显的效果。
3)提出了基于多数据中心的绿色高能效资源提供策略。
数据中心的能效通常被多个动态因素影响,包括：能源成本、碳排放率、负载类型、CPU能效及冷却系统等,该策略将同时考虑以上因素研究跨越多个地理位置环境中的多数据中心的全局能效问题。首先建立了多数据中心的资源提供模型,将能耗制约的收益问题和碳排放(Carbon Footprint)问题形式化为QoS约束的收益函数和代价函数的多目标最优化模型,证明了该模型是NP-hard问题。针对该问题提出了绿色云优先的CMM、MCMP算法和收益优先的PMM、 MPMC算法,算法综合考虑了碳排放、能耗、收益和应用的QoS需求,目标是降低碳排放,增加收益,同时满足用户应用的QoS需求。执行应用阶段,在数据中心中利用提出的NDVS方法进一步优化能耗,求解了给定负载情况下单个数据中心功耗最小时CPU频率满足的条件,并求解了CPU的最优频率,证明了该频率下能耗达到局部极小。实验结果表明,策略不仅可以降低能耗成本,优化任务调度,而且还可以权衡碳足迹。
4)提出了基于遗传算法的虚拟机资源提供负载均衡策略。
应用需求的多样性和节点资源的异构性不可避免地会导致资源提供过程中云计算节点的负载失衡问题,这极大地降低了云计算的整体资源提供效率。如何通过高效的负载均衡机制协调主机负载以提高资源利用率和系统性能是目前丞待解决的问题。针对这一问题,提出了基于负载均衡的虚拟机资源提供遗传算法VMPGALB, VMPGALB舍弃了传统二进制编码方法,采用了更适宜体现虚拟机提供特点的树型编码方案。制定选择策略时,采用基于适应度的比例选择策略和最优保存策略,该方法使得具有较小适应度的个体也有被选择的机会并直接保留最优个体至后代中。设计杂交算子时,通过对两个父代个体的交叉操作,并利用生成树方法,使VMPGALB具有更好的杂交性能。同时,为避免求解过程陷入局部最优,VMPGALB还按一定比例对产生的个体进行了变异操作。实验结果表明,比较传统遗传算法BGA、MOGA、启发式算法BFH和WLC,VMPGALB不仅遗传性能更优,虚拟机迁移次数更少,而且能以较快的收敛速度求解虚拟机提供的负载均衡方案。
5)提出了基于市场经济学模型的资源提供博弈策略。
市场经济学模型可以通过均衡理论实现资源的优化配置,研究了以市场经济模型为基础的云计算资源提供机制,结合博弈论在资源管理领域的优势,首先,建立了非合作竞争市场的资源提供模型,提出了非合作博弈资源提供算法RPANCG,该算法以非合作博弈进行建模,RPANCG的目标是寻找使得各个资源提供者效用达到最优的Nash均衡解,证明了RPANCG算法可以产生唯一的Nash均衡。然后,在RPANCG算法满足效用相互最优的基础上,为了进一步增加集体收益,并满足效率与公平的约束,在非合作竞争市场的基础上提出了议价市场中的资源提供算法RPABG,该算法以议价博弈进行建模,RPABG的目标则是寻找Nash议价解。实验结果表明,RPANCG算法可以收敛到唯一的Nash均衡解,资源提供者的效用达到相互最优,整个资源提供趋于合理。而RPABG则在RPANCG算法的基础上进一步兼顾了资源分配的效率和公平性,并且能够提高资源提供者的整体效用,实现了Pareto改进,从而达到云资源的公平、合理和均衡的优化分配。
本文的研究得到了国家自然科学基金项目(批准号：60970064,61272116),新世纪优秀人才支持计划项目(批准号：NCET-08-0806),教育部博士点基金项目(批准号：20120143110014)及湖北省高端人才引领培养计划项目的资助。
As a kind of novel information processing model, cloud computing technology has become a focus in the field of information. With virtualization as a support technology, cloud computing provides a kind of dynamic and scalable service for Internet users on demand. However, as cloud computing environment is characterized by the large-scale, the dynamic and flexibility of resource management and allocation, the energy comsumption problem and resource provision efficiency are critical factors that impact on the performance of cloud computing. Based on the new computing infrastructure—cloud computing technology, this thesis focuses on how to optimize the energy comsumption and the resource allocation of cloud data center. So far, there are still many problems to be solved concerned the energy consumption problem and resource provision of cloud computing. This thesis focuses on the resource provision optimization methods of cloud computing environment from three aspects:energy-saving mechanism, load balance and market economy model. The main contributions of this thesis include:
1) Study systematically on energy-saving mechanism and optimization method of resource provision in cloud computing environment.
First, starting from the basic concept of cloud computing, the characteristic, the service type and layer of cloud computing are introduced respectively. Second, the energy-saving optimization strategies in cloud computing are stressed, the application environment and the advantages and disadvantages of them are analyzed and compared as well. Then, the resource provision technology is studied further and the optimization strategies of this field are classified and compared. At last, the experimental tool CloudSim is introduced and the mechanism of its resource provision is analyzed through several simulation experiments.
2) A virtual machine resource provision strategy based on the equilibrium between energy and SLA is proposed.
Aiming at dynamical changes of application workload requirements, a self-adaptive deployment strategy RLWR based on robust local weight regression is presented. The algorithm can decide the overload time of hosts dynamically according to the historical resource occupation information of application workload. After detecting overloaded hosts, two virtual machine migration selection algorithms, MPM and MNM are proposed. The objective of the former is to get optimal migration period, the later is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH based on power consumption aware. Contrasting to static threshold algorithm STH, MPA and DVFS, virtual machines are not only deployed on fewer physical hosts in the self-adaptive deployment strategy, which promotes energy efficiency through turning off unused hosts, but also the load prediction of resource can bring high-reliable QoS delivery and avoid overmuch SLA violations between users and resource providers. The experimental results show that the strategy has an obvious effect on decreasing SLA violation under ensuring energy-efficiency.
3) A green energy-efficient resource provision strategy based on multiple data centers is presented.
The energy efficiency of data center is usnally affected on many dynamic factors, including energy costs, carbon emission rate, load type, CPU energy-efficiency and cooling system and so on. The above factors are considered in the proposed strategy and the global energy-efficiency of data center across multiple geopraphically heterogeneous environments is studied. First, the resource provision model of multiple data centers is set up, the profit and carbon emission issure influenced by energy comsumption is formatted as a multi-objective optimization model of the profit function and the cost function with QoS constraints. This model is proved to be an NP-hard problem. Aiming at this issue, four algorithms are proposed, CMM, MCMP based on green cloud priority and PMM, MPMC based on profit priority. The carbon emission, energy cosumption, profit and QoS requirements are considered in algorithms synthetically. The objective is to reduce carbon emission and increase revenue with meeting QoS requirements of user applications. During implementing applications, for optimizing energy consumption further, the optimal frequency of CPU is sovled and we prove that the energy consumption will reach a local munimum at this frequency. The experimental results show that the strategies not only reduce the energy costs, otpimize the task scheduling and also balance the carbon footprint.
4) A virtual machine resource provision load balance strategy based on genetic algorithm is proposed.
The diversity of application demand and the heterogeneity of resources inevitably lead to load imbalance of cloud computing during the process of resource provision, which greatly reduces the global efficiency of resource provision. How to improve resource utilization and system performance through an efficient load balance mechanism is an urgent need to solve currently. Aiming at this problem, a virtual machine resource provision genetic algorithm based on load balance, VMPGALB is presented. Abandoning the traditional binary encoding, VMPGALB adopts a tree encoding scheme, which is more suitable to reflect the characteristics of virtual machine provision. During designing the selection strategy, the proportion selection based on fitness and the elite-preserving strategy are applied, which make individuals with smaller fitness to be selected possibly and retain the best individuals directly to offspring. Druing devising the crossover operator, VMPGALB has a better crossover performance through the crossover operation of two parent individuals and applying spanning tree method. Meanwhile, in order to avoid a local optimum, VMPGALB implements mutation operations on individuals according to a certain percentage. The experimental results show that, contrasting to BGA, MOGA, BFH and WLC, VMPGALB not noly can get better genetic performance and fewer migration numbers, but also gain the load balance scheme of virtual machine provision with a faster convergence speed.
5) A resource provision game strategy based on market economic model is presented.
Market economics model can achieve the optimal allocation of resource through the equilibrium theory, cloud computing reource provision mechanism based on the market economic model is researched. Combined with the advantages of game theory in resource management, first, the resource provision model in non-cooperative competitive market is set up and a non-cooperative game resource provision algorithm RPANCG is proposed, which is based on non-cooperative game. The objective of RPANCG is to find Nash equilibrium that makes the utility of all resource providers optimal. We prove that RPANCG can generate a unique Nash equilibrium. Then, based on RPANCG meeting mutual optimum of the revenue, for increasing collective revenue further and meeting the efficiency and fairness, a resource provision algorithm RPABG is proposed in bargaining market based on non-cooperative competitive market. RPABG builds its model with bargaining game theory and its objective is to find Nash bargaining solution. The experimental results show that RPANCG can converge to a unique Nash equilibrium, the utilities of resource providers reach mutual optimum and the whole resource provision tends to be more reasonable. Moreover, RPABG can further give consideration to the efficiency and fairness of resource allocation based on RPANCG, improve the overall utility of resource providers and realize the Pareto efficiency, which leads to an optimal allocation of cloud resource with fairness, rationality and equilibrium.
This thesis is supported by National Natural Science Foundation of China (No.60970064, No.61272116), New Century Excellent Talent Support Plan (No.NCET-08-0806), Specialized Research Fund for the Doctoral Program of Higher Educaion of China (No.20120143110014) and Hubei province high-end talent lead cultivation project.
引文
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