A learning‐based approach for virtual machine placement in cloud data centers

Abstract

In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on bestfit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well-familiar baseline VM allocation algorithms.

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

“…Furthermore, we also have one more experiment with one of the recent ensemble learning-based prediction algorithms that recently used for online VM consolidation in our allocation algorithm (ie, LA-based ensemble). 4 Table 11 describes the false placement of migrating VMs on underloaded and overloaded components and prediction accuracy. Table 12 shows energy consumption and SLAV overhead of each prediction algorithm paired with proposed VM allocation algorithm.…”

“…By this way, we are able to calculate median of half upper and half lower values (Q1 and Q3). According to achieved statistics, we can now calculate interquartile range (IQR) of the history values (see lines [4][5][6].…”

“…The less the amount of power consumed, the lower is the amount of monetary costs which has to be paid pack by cloud providers. 3,4 Regarding power and cost efficiency, the amount of carbon footprint has increased drastically due to the increasing amount of power consumption. Hence, reducing power consumption not only reduces the amount of monetary costs for cloud operations but also reduces the threat of a natural disaster.…”

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

“…Task scheduling is the main problem in the fog computing, which selects the appropriate fog resources which are existent for performing the received tasks to minimize the overall execution time. The significant reason behind task scheduling is finding the best sequence of the different task, which can be performed to give a suitable result to the user in a CPS …”

“…The significant reason behind task scheduling is finding the best sequence of the different task, which can be performed to give a suitable result to the user in a CPS. 8 In fog computing, fog nodes are a set of networked servers consisting of heterogeneous hosts provided with a large range of computation resources. The virtualization technology in fog and cloud data center eliminates the server heterogeneity, provides the server consolidation, and raises the effectiveness of server use.…”