计算机代写|云计算代写cloud computing代考|ECE4150

2022年10月8日

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计算机代写|云计算代写cloud computing代考|Destination PM Selection for Migrating VMs from O-UPMs

SRTDVMC algorithm develops a set, denoted as vmsToMigrate, comprised of all migrating VMs from $O-U P M s$ (Line 6-9 of Algorithm 3.1), as these VMs of vmsToMigrate are sorted in decreasing order of VMRT (Line 10 of Algorithm 3.1). The migrating VMs are attempted to host in PMs, which are neither $O-U P M s$, nor in sleep state or turned off state. Such set of PMs, which are not $O-U P M s$ and not in either turned off or sleep state is referred to as $N O P$ (Line 11 of Algorithm 3.1). SRTDVMC algorithm then keeps invoking DPSVO algorithm, presented as Algorithm $3.3$ to determine the destination PM for each of the migrating VMs of vmsToMigrate starting from the largest VM to the smallest VM in terms of VMRT (Line 12-23 of Algorithm 3.1).

In order to determine a PM from $N O P$ as destination host for a migrating $\mathrm{VM}$ of vmsToMigrate, the DPSVO algorithm first sorts the PMs of $N O P$ in increasing order of SPMRT (Line 1 of Algorithm 3.3). The smallest PM in terms of SPMRT from the sorted $N O P$ is first checked whether it is suitable to accommodate the migrating VM or not (Line 2 of Algorithm 3.3). The PST algorithm presented in Algorithm $3.4$ is invoked to check the suitability of a PM as a potential destination PM (Line 3 of Algorithm 3.3). A PM is considered suitable, if RC (3.5) and MUTC (3.7) constraints are not violated (Line 1-4 of Algorithm 3.4). If that PM is found as suitable as per PST algorithm, then it is selected as the new destination PM for the migrating VM and the destination PM selection process ends (Line 4-6 of Algorithm 3.3). In case the PM is found as unsuitable, suitability of the second smallest PM in terms of $S P M R T$ from the sorted $N O P$ is checked and then the third smallest PM and so forth until a suitable PM is found (Line 2-7 of Algorithm 3.3). If no PM from $N O P$ can accommodate that particular migrating $\mathrm{VM}$, then the most energy-efficient and suitable PM from the set of PMs, which are in either sleep or turned-off state, referred to as $S P$ is awoke and selected as destination PM (Line 8 of Algorithm 3.3). If the destination PM is selected from $S P$, then that PM is removed from the set $S P$, since it is no more in sleep or turned-off state and its utilization and capacity values across all resource types are adjusted (Line 15-20 of Algorithm 3.1). Furthermore, the PM is added in the set $N O P$, so that it can be considered as a potential destination PM for following migrating VMs of vmsToMigrate (Line 21-22 of Algorithm 3.1).

计算机代写|云计算代写cloud computing代考|Migrating VM and Destination PM Selection

SRTDVMC algorithm invokes the DPSVU algorithm (Algorithm 3.5) to select migrating VMs from $U$-UPMs and corresponding new destination PMs. Once a $U-U P M$ from sorted candidateSources is selected as source $U$-UPM, the hosted VMs in that source $U-U P M$ is sorted in decreasing order of VMRT (Line 1 of Algorithm 3.5). The VMs starting from the largest to the smallest in terms of VMRT are attempted to migrate out (Line 2 of Algorithm 3.5). The reason of selecting VMs in descending order of VMRT is that migrating out the largest VM can reduce the $S P M R T$ of the source PM leading towards energy consumption minimization. If for any VM, a suitable new destination $U-U P M$ cannot be found, the migrating VM(s) selection from a source $U-U P M$ terminates (Line 18-20 inside of Line 2-21 from Algorithm 3.5). In the following, we have discussed the process of determining the new destination PM for such migrating VM.

In order to select the destination PM for a migrating VM of $U-U P M, S R T-$ $D V M C$ algorithm first creates a set of potential destination PMs, referred to as candidateDestinations. The PMs of $S P, O P$ and the source $U-U P M s$ hosting the migrating VMs are excluded from candidateDestinations, since a source PM cannot be the new destination PM of its own VMs and to avoid increasing the likelihood of turning the PMs from $O P$ into $O-U P M s$ again (Line 25, 27 and 28 of Algorithm 3.1). The DPSVU algorithm (Algorithm 3.5) is then invoked to select the destination PM from candidateDestinations (Line 29 of Algorithm 3.1). The PMs of candidateDestinations are first sorted in increasing order of $S P M R T$ (Line 4 of Algorithm 3.5) and then the suitability of these PMs from sorted candidateDestinations are sequentially checked starting from the smallest to the largest PM in terms of SPMRT (Line 5-6 of Algorithm 3.5). If a PM is found as suitable satisfying both RC (3.5) and MUTC (3.7) constraints as per PST Algorithm (Algorithm 3.4), then net energy gain for the potential VM migration is estimated from the difference between reduced energy consumption of source $U-U P M$ and increased energy consumption of new destination $U$ – $U P M$. If net energy gain is found as positive, then that PM is selected as the new destination PM (Line 7-17 of Algorithm 3.5). In the following section, we have discussed the characteristics of SRTDVMC algorithm.

云计算代考

计算机代写|云计算代写cloud computing代考|从O-UPMs迁移虚拟机的目标PM选择

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SRTDVMC算法开发了一个集合，称为vmsToMigrate，由所有从$O-U P M s$迁移的虚拟机(算法3.1的第6-9行)组成，因为这些vmsToMigrate的虚拟机按照VMRT的递减顺序排序(算法3.1的第10行)。迁移的虚拟机试图在pm(既不是$O-U P M s$)中托管，也不是处于休眠状态或关闭状态。这样一组pm，既不是$O-U P M s$，也不是处于关闭或休眠状态，被称为$N O P$(算法3.1的第11行)。SRTDVMC算法继续调用DPSVO算法，表示为算法$3.3$，以确定vmsToMigrate的每个迁移虚拟机的目标PM，根据VMRT从最大的虚拟机开始到最小的虚拟机(算法3.1的第12-23行)

计算机代写|云计算代写cloud computing代考|迁移虚拟机和目标PM的选择

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SRTDVMC算法调用DPSVU算法(算法3.5)从$U$ – upm和相应的新目标pm中选择迁移的虚拟机。一旦从已排序的candidateSources中选择$U-U P M$作为源$U$ -UPM，源$U-U P M$中的托管虚拟机将按VMRT的递减顺序排序(算法3.5的第1行)。尝试将VMRT从大到小的虚拟机迁移出去(算法3.5的第2行)。按照VMRT降序选择VM的原因是，迁移出最大的VM可以减少源PM的$S P M R T$，从而实现能耗最小化。如果对于任何虚拟机，无法找到合适的新目的地$U-U P M$，则从源$U-U P M$选择迁移的虚拟机将终止(算法3.5中第2-21行中的第18-20行)。在接下来的文章中，我们讨论了为这种迁移的VM确定新的目标PM的过程

有限元方法代写

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MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。