Decentralized Predictive Secure VS Placement in Cloud Environment

International Journal for Research in Applied Science and Engineering Technology -IJRASET, 2020

Energy conservation in data centers has been an active research area in cloud computing in recent times. Effective energy conservation can be achieved using server consolidation, which aims at utilizing server resources efficiently and minimizing the number of active Physical Machines (APMs) running in a data center. Effective placement of virtual machines is necessary to optimize server consolidation. Virtual machine placement techniques provide a suitable mapping of hosts to VMs to reduce energy consumption and minimize SLA violation in data centers. This paper presents a comprehensive survey of different Virtual Machine placement techniques utilized in cloud computing, revealing the advantages and limitations of the algorithms. I. INTRODUCTION Cloud computing provides access to on demand computing resources to the users in a pay-as-you-use pricing model. Three different models are being offered by cloud service providers such as IaaS (Infrastructure as a service), PaaS (Platform as a service) and SaaS (Software as a service). One of the significant challenges for cloud service providers is to reduce energy consumption in data centers. The cloud service providers spend a significant amount in setting up data centers in the beginning. They have to incur data center management costs later to maintain data centers. This includes power costs, software and hardware maintenance costs etc. According to a recent study [1], 13% of the overall data center management cost is incurred by power consumption. So, it is essential to optimize power consumption in data centers to reduce the operational cost for cloud service providers. To prevent wastage of resources in data centers, Virtual Machines (VM) are packed on to the fewest possible physical machines and idle physical machines are later shut down, thereby reducing energy consumption. This process of consolidating VMs on to the servers is called server consolidation. It comprises of 4 steps: 1) Host underload detection, where hosts with utilization under a certain threshold are selected, all the VMs on the host are migrated to other servers, and underloaded hosts are shutdown. 2) Host overload detection, where hosts with utilization greater than a certain threshold are detected and some of the VMs are migrated to other hosts. 3) VM selection, where appropriate VMs are selected for migration from over utilized hosts. 4) VM placement, where VMs selected for migration in the 3 rd step is mapped to different Physical Machines (PM). In this paper, we focus on Virtual Machine Placement algorithms. Virtual machine placement (VMP) is the process of mapping Virtual machines to Physical machines in order to reduce energy consumption and minimize SLA violation in data centers. VMP has been an active research area in cloud computing throughout the last decade. Many VMP algorithms have been proposed to maximize utilization and to reduce power consumption, in turn reducing operational costs in data centers. VMP algorithms can be traffic-aware, load-aware, application-aware, power-aware or a combination of these. To achieve better performance, VMs are migrated to other hosts when servers become over utilized or underutilized. So, when the resource demands of a Virtual machine cannot be fulfilled by the physical machine on which the VM is hosted, VMs are migrated to another PM for the fulfillment of the demands.. VMs are migrated from over utilized hosts to prevent Service level Agreement violation. In the case of underutilized hosts, all the VMs hosted on the PM are migrated and the host is shut down. The remainder of this paper is organized as follows. Section II describes the classification of VM placement algorithms. Section III presents a detailed discussion of different approaches used in VM placement algorithms and Section IV presents concluding remarks and future research directions. II. VM PLACEMENT CLASSIFICATION A. Power and Quality of Service 1) Power based: The objective of power based virtual machine placement algorithm is to map virtual machines to physical machines in a manner to reduce energy consumption in data centers. Virtual machines are aggressively packed in physical machines and underutilized physical machines are shut down to reduce power consumption [2]. 2) QoS based: The objective of this approach is to meet the quality of service guaranteed by cloud service providers. Service Level Agreement (SLA) is signed between the user and cloud service provider when users opt for cloud services. Service provider will have to pay the penalty when they fail to deliver quality of service. QoS based approaches are used to minimize SLA violation, in turn ensure quality of service to the customers [3].

Virtual machine placement is one of the most important features in virtual machine technology. It plays a crucial role in resource utilization, load balancing, and in reducing energy consumption. This paper represents the survey of various types of virtual machine placement techniques and algorithms in cloud computing .

International Journal on Cloud Computing: Services and Architecture, 2014

In traditional data center numbers of services are run onto the dedicated physical servers. Most of the time, these data centers are not used their full capacity in term of resources. Virtualization allows the movement of VM from one host to the another host ,which is called virtual machine migration, so these data centers can consolidate their services onto lesser number of physical servers than originally required. Virtual machine placement is the part of the VM migration. To map the virtual machines to the physical machines is called the VM placement. In other word, VM placement is the process to select the appropriate host for the given VM. For the efficient utilization of the physical resources, VM should be placed on to the suitable host. So many virtual machine placement algorithms have been proposed by different researchers that run under cloud computing environment. Most of the VM placement algorithms try to achieve some goal. This goal can either saving energy by shutting down some severs or it can be maximizing the resources utilization. Four steps are involved in the VM machine migration process. First step is to select the PM which is overload or undreloaded, next step is to select one or more VM, and then select the PM where selected VM can be placed and last step is to transfer the VM. Selecting the suitable host is one of the challenging task in the migration process, because wrong selection of host can increased the number of migration, resource wastage and energy consumption. This paper only focuses to the third step that is selecting a suitable PM that can host the VM. It shows an analysis of different existing Virtual Machine's placement algorithms with their anomalies.

Computational Intelligence and Neuroscience, 2022

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several speci cations for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. e tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. is algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. e methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. e results are compiled and then compared graphically with respect to other existing energy-e cient VM placement algorithms.

2016

Load balancing is one of the critical issues in cloud due to the change in user requirement at run time. Cloud provider allots resources to the user with the help of virtualization which allows dividing the physical resources in the form of virtual machine (VM). User services are running on these VM which is hosted inside the physical machine (PM). If the VM is not distributed properly then it will degrade the performance of the physical and virtual machine. Hence load balancing is the core management function of the cloud provider. Three steps are involved in the migration process i.e., source PM selection, VM selection and the last step is target PM selection. The study of previous work on the VM migration says that VM selection and VM placement are the two challenging task in the cloud environment and the performance of the load balancing approach is totally dependent on the VM selection and placement. Further performance of the load balancing approach can be controlled by selecting the suitable physical and virtual machine. Plenty of work on the load balancing in cloud computing environment are presented in the last few decade and mostly they are differ in the VM selection and VM placement policies. This paper presents various existing VM selection and placement approaches with their anomalies.

Emerging Research in Cloud Distributed Computing Systems, 2015

With the pragmatic realization of computing as a utility, Cloud Computing is has recently emerged as a highly successful alternative IT paradigm through on-demand resource provisioning and almost perfect reliability. The rapidly growing customer demands for computing and storage resources are responded by the Cloud providers with the deployment of large scale data centers across the globe. Efficiency and scalability of these data centers, as well as the performance of the hosted applications highly depend on the allocations of the physical resource (e.g., CPU, memory, storage, and network bandwidth). Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the related background information and a taxonomy that characterizes and classifies the various components of network-aware VM placement and migration techniques. An elaborate survey and comparative analysis of the state of the art techniques is also put forward. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms recently proposed by the research community, the survey further identifies the limitations of the existing techniques and discusses on the future research directions.

IEEE Systems Journal

To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.

2021

To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static an...

Efficient resource allocation is one of the critical performance challenges in an Infrastructure as a Service (IaaS) cloud. Virtual machine (VM) placement and migration decision making methods are integral parts of these resource allocation mechanisms. We present a novel virtual machine placement algorithm which takes performance isolation amongst VMs and their continuous resource usage into account while taking placement decisions. Performance isolation is a form of resource contention between virtual machines interested in basic low level hardware resources (CPU, memory, storage, and networks bandwidth). Resource contention amongst multiple co-hosted neighbouring VMs form the basis of the presented novel approach. Experiments are conducted to show the various categories of applications and effect of performance isolation and resource contention amongst them. A per-VM 3-dimensional Resource Utilization Vector (RUV) has been continuously calculated and used for placement decisions w...

—The Cloud computing forges the shape of the current era and the following ones based on delocalized IT infrastructure and sharing resources. However, the rebellious rise of cloud computing comes with concerns over energy consumption. Numerous reports which inspected Cloud energy consumption showed that the Cloud is an energy monster, specifically the data centers that holds 2% of overall energy consumed in the world on 2011 [6]. More closely, it has been proven that the servers (Physical machines PM) are the most energy-hungry elements of the data center [7]. Server consolidation based on virtualization is a key mechanism for energy consumption taming. Within this context, our aim in this paper is to propose a VM Placement Algorithm Based on Recruitment process within Ant Colonies proposed by Bonabeau [12] that seeks to maximize PM resources exploitation along with maximizing resources balance. The exprimental results showed that our algorithm generates always a significantly good solution.