An efficient host overload detection algorithm for cloud data center based on exponential weighted moving average,International Journal of Communication Systems

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An efficient host overload detection algorithm for cloud data center based on exponential weighted moving average
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2021-01-10 , DOI: 10.1002/dac.4708
Sudhanshu Kulshrestha ₁ , Sanjeev Patel ₂

Affiliation

The major aim of data center (DC) is to optimize the three parameters, viz., energy consumption, resource utilization, and quality of service (QoS). Our objective is to balance these three parameters by managing the virtual machine (VM) efficiently. Therefore, this paper presents a holistic view of VM consolidation and explains its constituent subprocesses: (i) host overload detection, (ii) VM selection for migration from the overloaded host, and (iii) VM placement for provisioning selected VMs on a new set of hosts. Later, this paper attempts to optimize the parameters mentioned above by proposing a host overload detection algorithm based on exponential weighted moving average (EWMA) and its variants. The performance of the proposed EWMA is compared with the state‐of‐the‐art algorithms based on (i) local regression, (ii) median absolute deviation, and (iii) interquartile range. The efficacy of the proposed EWMA is evaluated in combination with four different existing VM selection policies: (i) minimum migration time, (ii) minimum utilization, (iii) maximum correlation, and (iv) random selection. The evaluation metrics comprise energy consumption, VM migration count from the overloaded hosts representing the resource utilization, service level agreement (SLA) violations describing the QoS, and the average execution time. Simulation experiments are carried out on the CloudSim simulator using PlanetLab real cloud trace as workload. The proposed EWMA consumes 18.33% less energy, causes 47.81% less VM migration, makes 9.91% less SLA violation, and takes 43.44% less average‐time for the execution of the entire workload as compared to state‐of‐the‐art algorithms.

中文翻译：

基于指数加权移动平均值的云数据中心主机过载检测算法

数据中心（DC）的主要目标是优化三个参数，即能耗，资源利用率和服务质量（QoS）。我们的目标是通过有效管理虚拟机（VM）来平衡这三个参数。因此，本文提供了虚拟机整合的整体视图，并解释了其组成子过程：（i）主机过载检测，（ii）从过载主机迁移的虚拟机选择，以及（iii）在新集合上配置所选虚拟机的虚拟机放置的主机。随后，本文尝试通过提出一种基于指数加权移动平均值的主机过载检测算法来优化上述参数。（EWMA）及其变体。将拟议的EWMA的性能与基于（i）局部回归，（ii）中位数绝对偏差和（iii）四分位数间距的最新算法进行比较。结合四种不同的现有VM选择策略评估了提出的EWMA的功效：（i）最小迁移时间，（ii）最小利用率，（iii）最大相关性，以及（iv）随机选择。评估指标包括能耗，代表资源利用率的超载主机的VM迁移计数，描述QoS的服务水平协议（SLA）违规以及平均执行时间。使用PlanetLab在CloudSim模拟器上进行模拟实验真正的云跟踪作为工作负载。与最新算法相比，拟议的EWMA能耗降低18.33％，VM迁移减少47.81％，SLA违规减少9.91％，平均全部工作执行时间减少43.44％。

更新日期：2021-02-03

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