云计算环境下资源分配策略的研究
发布时间：2018-08-19 20:12
【摘要】：以2010年为界限，我们可以把过去分为个人电脑时代和互联网时代，，而未来就是云计算、物联网和三网融合的时代。云计算这种新的计算模式的诞生，使ICT界发生了革命性的变化。云计算一出现就成为了业界关注的热点，它的设计理念就是用户获得各种服务就像使用水电一样，按需使用，付费使用。这种计算模式让每一个人都能以极低的成本接触到顶尖的ICT技术。ICT界的商业巨头微软、谷歌、IBM、亚马逊、Salesforce等都纷纷推出了自己的云计算产品，并把云计算作为其未来发展的主要战略目标之一。 如何对资源进行有效的分配成为云计算所要解决的重要问题，也是云计算研究的热点问题。为了更好的合理利用资源和提高资源的利用率，本文提出了一种基于改进粒子群优化算法的资源分配和调价策略。这种改进的粒子群算法叫做粒子数量动态可变的粒子群优化算法。因为在云计算环境下资源是海量的，数以万计的用户需求，如何在短时间内合理的分配资源是我们所期望的。通过与传统的粒子群算法进行比较，粒子数量动态可变的粒子群算法在时间复杂度上要远优于传统的粒子群算法，这样就大大节省了云计算环境下对资源分配的时间。根据负载的特性，构造了体现其对所获资源的满意度和符合自身经济利润的效用函数，利用资源代理不断调价的方法来达到合理利用资源的目的，同时也最大化了每个负载的效益。通过仿真实验并证明了改进粒子群优化算法在云计算环境下资源分配的有效性、可行性、鲁棒性以及大大缩短了计算时间。 云计算数据中心是由大量的异构的计算资源池、存储资源池以及网络资源池等构成。当所有的服务器都启动时，会消耗大量的电能，产生大量的废气污染物给我们的环境造成极大的污染。因此，我们需要绿色节能的解决方法，不但能够最小化执行的代价还减小了对环境的影响。本文根据以上问题提出了资源分配的高效节能的算法。这种算法的基本思想是通过支持VMs的运转在不同的物理节点之间，然后根据执行的需求，动态的迁移VMs。当VMs不利用主机提供给的资源的时候，它就会动态的改变或整合到最小数目的物理节点上，而闲置的物理节点就会被关掉，消除了闲置机器能量的消耗，最终达到减小总能量的消耗。最后通过和其他策略的资源分配算法进行比较，得出本文提出的算法是有效的，在节能方面是最优的。 不管是在哪一种计算模式下，资源分配都是需要亟待解决的问题，在云计算环境下也不例外。本文做了两方面关于资源分配的研究，第一方面是基于市场机制的资源分配策略，从服务提供商和服务Qos以及均衡负载多方面考虑，提出了一套行之有效的资源分配方法。第二方面是从绿色节能的角度出发，从现实生活中得到启发，提出了如何整合资源节约能量，最终达到环保的目的。
[Abstract]:By 2010, we can divide the past into the personal computer era and the Internet era, and the future is cloud computing, the Internet of things and the three networks of the age. Cloud computing, a new computing model, has revolutionized the ICT world. Cloud computing has become the focus of attention of the industry as soon as it appears. Its design philosophy is that users can obtain various services just as they use hydropower, use them on demand, and pay for their use. This computing model gives everyone access to the top ICT technology, ICT business giants Microsoft, Google, IBM, Amazon Salesforce, and so on, at very low cost, all of which have launched their own cloud computing products. And cloud computing as its future development of one of the main strategic objectives. How to allocate resources effectively becomes an important problem in cloud computing, and it is also a hot issue in cloud computing research. In order to make better use of resources and improve the utilization of resources, this paper proposes a strategy of resource allocation and price adjustment based on improved particle swarm optimization algorithm. This improved particle swarm optimization algorithm is called particle swarm optimization with variable particle number. Because in the cloud computing environment the resources are massive, tens of thousands of user needs, how to allocate resources reasonably in a short time is what we expect. Compared with traditional particle swarm optimization (PSO), the time complexity of PSO with variable particle number is much better than that of PSO, which greatly saves the time of resource allocation in cloud computing environment. According to the characteristics of the load, the utility function, which reflects its satisfaction with the acquired resources and conforms to its own economic profit, is constructed, and the method of continuously adjusting the price of the resource agent is used to achieve the purpose of making rational use of the resources. It also maximizes the benefits of each load. The simulation results show that the improved particle swarm optimization algorithm is effective, feasible, robust and greatly shortens the computing time in cloud computing environment. Cloud computing data center is composed of a large number of heterogeneous computing resource pool, storage resource pool and network resource pool. When all servers start up, it will consume a lot of electric energy and produce a large amount of exhaust gas pollutants to cause great pollution to our environment. Therefore, we need green energy saving solutions, which not only minimize the cost of implementation, but also reduce the impact on the environment. In this paper, an efficient energy-saving algorithm for resource allocation is proposed. The basic idea of this algorithm is to dynamically migrate VMs according to the requirements of execution by supporting the operation of VMs between different physical nodes. When VMs does not take advantage of the resources provided by the host, it dynamically changes or integrates into a minimum number of physical nodes, and the idle physical nodes are shut down, eliminating the energy consumption of idle machines. Finally, the total energy consumption is reduced. Finally, by comparing with the resource allocation algorithm of other strategies, it is concluded that the algorithm proposed in this paper is effective and optimal in the aspect of energy saving. No matter in any computing mode, resource allocation is a problem that needs to be solved urgently, and it is no exception in cloud computing environment. In this paper, two aspects of resource allocation are studied. The first is the resource allocation strategy based on market mechanism. Considering service provider and service Qos and load balancing, a set of effective resource allocation method is proposed. The second part is from the point of view of green energy saving, from the real life inspiration, put forward how to integrate the resources to save energy, and finally achieve the goal of environmental protection.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP3
本文编号：2192712
[Abstract]:By 2010, we can divide the past into the personal computer era and the Internet era, and the future is cloud computing, the Internet of things and the three networks of the age. Cloud computing, a new computing model, has revolutionized the ICT world. Cloud computing has become the focus of attention of the industry as soon as it appears. Its design philosophy is that users can obtain various services just as they use hydropower, use them on demand, and pay for their use. This computing model gives everyone access to the top ICT technology, ICT business giants Microsoft, Google, IBM, Amazon Salesforce, and so on, at very low cost, all of which have launched their own cloud computing products. And cloud computing as its future development of one of the main strategic objectives. How to allocate resources effectively becomes an important problem in cloud computing, and it is also a hot issue in cloud computing research. In order to make better use of resources and improve the utilization of resources, this paper proposes a strategy of resource allocation and price adjustment based on improved particle swarm optimization algorithm. This improved particle swarm optimization algorithm is called particle swarm optimization with variable particle number. Because in the cloud computing environment the resources are massive, tens of thousands of user needs, how to allocate resources reasonably in a short time is what we expect. Compared with traditional particle swarm optimization (PSO), the time complexity of PSO with variable particle number is much better than that of PSO, which greatly saves the time of resource allocation in cloud computing environment. According to the characteristics of the load, the utility function, which reflects its satisfaction with the acquired resources and conforms to its own economic profit, is constructed, and the method of continuously adjusting the price of the resource agent is used to achieve the purpose of making rational use of the resources. It also maximizes the benefits of each load. The simulation results show that the improved particle swarm optimization algorithm is effective, feasible, robust and greatly shortens the computing time in cloud computing environment. Cloud computing data center is composed of a large number of heterogeneous computing resource pool, storage resource pool and network resource pool. When all servers start up, it will consume a lot of electric energy and produce a large amount of exhaust gas pollutants to cause great pollution to our environment. Therefore, we need green energy saving solutions, which not only minimize the cost of implementation, but also reduce the impact on the environment. In this paper, an efficient energy-saving algorithm for resource allocation is proposed. The basic idea of this algorithm is to dynamically migrate VMs according to the requirements of execution by supporting the operation of VMs between different physical nodes. When VMs does not take advantage of the resources provided by the host, it dynamically changes or integrates into a minimum number of physical nodes, and the idle physical nodes are shut down, eliminating the energy consumption of idle machines. Finally, the total energy consumption is reduced. Finally, by comparing with the resource allocation algorithm of other strategies, it is concluded that the algorithm proposed in this paper is effective and optimal in the aspect of energy saving. No matter in any computing mode, resource allocation is a problem that needs to be solved urgently, and it is no exception in cloud computing environment. In this paper, two aspects of resource allocation are studied. The first is the resource allocation strategy based on market mechanism. Considering service provider and service Qos and load balancing, a set of effective resource allocation method is proposed. The second part is from the point of view of green energy saving, from the real life inspiration, put forward how to integrate the resources to save energy, and finally achieve the goal of environmental protection.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP3
【引证文献】
相关硕士学位论文 前1条
1 陈盈盈;具有云服务特征的远程在线考试系统的设计与实现[D];北京邮电大学;2013年
本文编号：2192712
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