Optimizing the Topology and Energy-Aware VM Migration in Cloud Computing

Optimizing the Topology and Energy-Aware VM Migration in Cloud Computing

Nitin S. More (Pune Institute of Computer Technology (PICT), SPPU, Pune, India) and Rajesh B. Ingle (Pune Institute of Computer Technology (PICT), SPPU, Pune, India)
Copyright: © 2020 |Pages: 24
DOI: 10.4018/IJACI.2020070103
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The advancements in virtual machine migration (VMM) have been trending due to its effective load balancing features in cloud infrastructure. Previously, data centers were used for handling VMs organized in racks. These racks are arranged in a spanning tree topology with a high bandwidth. Thus, the cost for moving the data between servers is highest when the racks are far from each other. This work addresses this issue and proposed VMM strategy based on self-adaptive D-Crow algorithm (S-DCrow) that incorporates adaptive constants in Dragonfly-based Crow (D-Crow) optimization algorithm based on the proposed topology model. The proposed S-DCrow describes a migrating model, which is based on topology, energy consumption, load, and migration cost. Here, the network is organized in a spanning tree topology and is adapted by proposed S-DCrow for optimal VMM. The performance of the proposed S-DCrow shows superior performance in terms of load, energy consumption, and migration cost with the values of 0.1417, 0.1009, and 0.1220, respectively.
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1. Introduction

The Cloud computing platform facilitates resource sharing on the basis of middleware, scalable infrastructures, and platforms for application development. The advanced computing technologies, like virtualization, high power enterprise, and service-oriented architecture, have the ability to manage the cloud computing platforms effectively. The services offered in the clouds are categorized into three categories: Platform as a service (PaaS), Infrastructure as a service (IaaS), and Software as a service (SaaS). The cloud computing permits cloud users to store and process the data on the web using the Internet, in which security is considered as a major issue (Jensen et al., 2009). Security must be integrated into every aspect of cloud computing platforms to make users trust that their data is secure (Christodorescu et al., 2009). The biggest challenge of security issues while designing a cloud computing platform is the virtual machine (VM) interconnectivity. There may be possibilities that a VM can monitor another VM or access the underlying network interfaces, which lead to break of isolation (Wu et al., 2010). The large-scale distributed cloud computing paradigm is motivated by economies of scale, where dynamically scalable computing functions are distributed on demand to customers via Internet (Foster et al., 2008).

The cloud computing is based on virtualization of computing resources, which are essential for building logical functions from the physical resources (Duan et al., 2012). Virtualization integrated to cloud computing produces several security issues and causes severe damage to the system. The security in virtualization deals with two factors, which involve individual safety of virtualized technology and the adoption of new security issues based on virtualization (Luo et al., 2011). Securing virtualization in cloud computing platform is a major problem due to the adoption of private cloud computing techniques. The cloud owner adopts virtualization technology for attaining multi-tenant architecture to offer effective distribution of resources and security (Luo et al., 2011). The server consolidation using virtualization technology plays an important role to enhancing the energy of data centres (Meng et al., 2010). The purpose of adopting server consolidation technology is to transfer VMs to energy efficient physical machines (PMs). The server consolidation faces problems related to computations and is considered as a VM placement problem (Tang & Pan, 2015).

The techniques, such as load balancing and server consolidation, are important techniques in managing the resources. The virtualized infrastructure contains several applications, which are executed on VM and further, mapped to a data center of a PM. The capacity to host different applications on same PM is the major challenge. The resource sharing, unpredicted escalation from resource demands and load balancing are some of the challenges that are to be focused, while solving the issues regarding VM placement. The assurance is required to ensure that the computing resources are used in an effective manner for distributing the workloads to reduce the energy consumption (Choudhary et al., 2016). The Virtual Machine Consolidation (VMC) is a mechanism for reducing the utilization of resources and energy consumption of data centres by mapping a group of VMs to PMs. The major issue in VMC is to determine an appropriate PM for a group of VM to increase the utilization of resources and to minimize the energy consumption of data centers while satisfying the requirements of QoS (Aryania et al., 2018).

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