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计算机代写|云计算代写cloud computing代考|Background

Multi-server energy consumption strategy aims at reducing the power usage of the data center by using the least amount of resources that are available in the PMs while switching off inactive PMs at the data center. Man et al. [9] and Ajiro et al. [10] used different methods, such as “First Fit,” “Best Fit,” to solve VMs allocation problem. A global optimum solution is obtained from the heuristic approaches. “Best fit heuristic solution” is inaccessible because it takes more convergence time, and the proposed work has one more limitation based on a “single objective function.” Beloglazov et al. [11] recommended a “Modified Best Fit Decreasing (MBFD) algorithm” by arranging the ascending order of PMs and descending order of VMs based on their processing capability. In the “First Fit Decreasing (FFD),” VM allocation on PMs is done after VMs and PMs are sorted. The drawbacks are: Distinct allocation goal for VMs and MBFD is not flexible as huge requested VMs get influenced at the data center. The VM allocation issue is solved by using different types of algorithms. Many researchers employed bio-inspired and evolution-inspired algorithms for the cloud data center for allocation of VMs, such as “Genetic Algorithm (GA) [12],” “Particle swarm optimization (PSO),” etc. Xiong et al. [13] used PSO to resolve the issue of allocating energy-efficient VMs. The authors considered a single VM type as it is the major drawback of this research.

“Ant colony”-based VM is proposed for VM allocation by the cloud data centers by Gao et al. [14]. The authors used only a single VM and PM combination which is the drawback of their work. Wang et al. [15] recommended the use of PSO to locate efficient VMs in a data center. The drawback of this research is that the allocations of VMs change the particle velocity that requires more iterations and gives an inaccurate result. “Particle Swarm Optimization (PSO) algorithm” [16] is derived from bird flocking, its a bio-inspired algorithm [17]. It is through PSO that each particle in the swarm gives a solution with four dimensions, its present location, the position, its speed, and the location found among all the particles in the population. Its location is set in the search area based on the position its neighborhood has reached. The limitations of PSO algorithms suffer from the partial optimism that triggers less accurate velocity and distance control. PSO is unable to address particle issues in the field of energy consumption. Sharma et al. proposed “Genetic algorithm and Particle Swarm Optimization (GAPSO)” [6, 8], hybridization of GA and PSO results in increasing the fitness value of the parent chromosomes, and thus allocation of VMs to the PMs is achieved in lesser time.The limitation of GAPSO algorithm takes more convergence time.

计算机代写|云计算代写cloud computing代考|Problem Formulation

For each round of scheduling, the essence of the problem is to find a mapping of tasks to worker nodes such that the communicating tasks are packed as compact as possible. In addition, the resource constraints need to be met-the resource requirements of the allocated tasks should not exceed the resource availability in each worker node. Since the compact assignment of tasks also leads to reducing the number of used machines, we model the scheduling problem as a variant of the bin-packing problem and formulate it using the symbols illustrated in Table $5.1$.
In this work, the resource consumptions and availability are examined in two dimensions-CPU and memory. Though memory resources can be intuitively measured in terms of megabytes, the quantification of CPU resources is usually vague and imprecise due to the diversity of $\mathrm{CPU}$ architectures and implementations. Therefore, following the convention in literature [13], we specify the amount of CPU resources with a point-based system, where 100 points are given to represent the full capacity of a Standard Compute Unit (SCU). The concept of SCU is similar to the EC2 Compute Unit (ECU) introduced by Amazon Web Services (AWS). It is then the responsibility of the IaaS provider to define the computing power of an SCU, so that developers can compare the CPU capacity of different instance types with consistency and predictability regardless of the hardware heterogeneity presented in the infrastructure. As a relative measure, the definition of an SCU can be updated through benchmarks and tests after introducing new hardware to the data centre infrastructure.

In this chapter, we assume that the IaaS cloud provider has followed the example of Amazon to create a vCPU as a hyperthread of an Intel Xeon core, ${ }^6$ where 1 SCU is defined as the CPU capacity of a vCPU. Therefore, every single core in the provisioned virtual machine is allocated with 100 points. A multi-core instance can get a capacity of num_of_cores* 100 points, and a task that accounts for $p \%$ CPU usages reported by the monitoring system has a resource demand of $p$ points.

计算机代写|云计算代写cloud computing代考|CSE546

云计算代考

计算机代写|云计算代写cloud computing代考|Background

.背景信息

多服务器能耗策略的目的是在关闭数据中心不活动的pm的同时,尽可能减少pm的可用资源,从而降低数据中心的功耗。Man et al.[9]和Ajiro et al.[10]分别采用“First Fit”、“Best Fit”等不同的方法解决虚拟机分配问题。采用启发式方法求解全局最优解。“最优拟合启发式解”无法实现,因为它需要更多的收敛时间,而且所提出的工作基于“单一目标函数”还有一个限制。Beloglazov et al.[11]推荐了一种“Modified Best Fit reduction (MBFD)算法”,该算法根据PMs的处理能力排列升序和虚拟机的降序。在“First Fit reduction (FFD)”中,VM在pm上的分配是在VM和pm排序后完成的。缺点是:虚拟机分配目标不同,MBFD不灵活,大量请求的虚拟机在数据中心受到影响。VM分配问题可以通过使用不同类型的算法来解决。许多研究人员在云数据中心使用了受生物启发和受进化启发的算法来分配vm,如“遗传算法(GA)[12]”、“粒子群优化(PSO)”等。Xiong等人[13]使用粒子群算法解决了分配节能vm的问题。作者认为单一虚拟机类型是本研究的主要缺陷

Gao等人提出了基于“蚁群”的虚拟机用于云数据中心的虚拟机分配。作者只使用了单一的VM和PM组合,这是他们工作的缺点。Wang等人[15]推荐使用PSO在数据中心中定位高效的vm。该研究的缺点是vm的分配改变了粒子速度,需要更多的迭代,得到的结果不准确。“粒子群优化(PSO)算法”[16]是由蜂群进化而来的一种仿生算法[17]。正是通过粒子群算法,群中的每个粒子给出了一个四维解,包括它的当前位置、位置、速度以及在群体中所有粒子中找到的位置。它的位置是在搜索区域中基于它的邻居已经到达的位置设置的。粒子群算法的局限性在于局部乐观,导致速度和距离控制不够精确。粒子群算法无法解决能量消耗领域的粒子问题。Sharma等人提出了“遗传算法和粒子群优化(GAPSO)”[6,8], GA和PSO杂交可以提高亲本染色体的适应度值,从而在更短的时间内将vm分配给PMs。GAPSO算法的局限性是需要更多的收敛时间

计算机代写|云计算代写cloud computing代考|Problem Formulation

.问题表述


对于每一轮调度,问题的本质是找到任务到工作节点的映射,以便通信任务尽可能紧凑。此外,还需要满足资源约束—分配任务的资源需求不应超过每个工作节点中的资源可用性。由于任务的紧凑分配也会导致使用的机器数量的减少,我们将调度问题建模为装箱问题的变体,并使用表$5.1$中所示的符号来表示它。虽然内存资源可以直观地以兆字节来衡量,但是由于$\mathrm{CPU}$体系结构和实现的多样性,CPU资源的量化通常是模糊和不精确的。因此,按照文献[13]中的约定,我们使用基于积分的系统指定CPU资源的数量,其中给出100分表示标准计算单元(SCU)的全部容量。SCU的概念类似于Amazon Web Services (AWS)引入的EC2计算单元(ECU)。然后,IaaS提供者负责定义SCU的计算能力,以便开发人员能够以一致性和可预测性比较不同实例类型的CPU容量,而不管基础设施中呈现的硬件异构性如何。作为一种相对度量,在向数据中心基础设施引入新硬件后,可以通过基准测试和测试更新SCU的定义


在本章中,我们假设IaaS云提供商已经效仿Amazon创建了一个vCPU作为Intel Xeon核的超线程,${ }^6$其中1 SCU定义为vCPU的CPU容量。因此,提供的虚拟机中的每个核都分配了100个点。一个多核实例的容量为num_of_cores* 100个点,而监控系统报告的占$p \%$ CPU使用率的任务的资源需求为$p$个点

计算机代写|操作系统代写operating systems代考

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