A Survey of Virtual Machine Placement Techniques and VM Selection Policies in Cloud Datacenter

The large scale virtualized data centers have been established due to the requirement of rapid growth in computational power driven by cloud computing model . The high energy consumption of such data centers is becoming more and more serious problem .In order to reduce the energy consumption, server consolidation techniques are used .But aggressive consolidation of VMs can lead to performance degradation. Hence another problem arise that is, the Service Level Agreement(SLA) violation. The optimized consolidation is achieved through optimized VM placement and VM selection policies . A comparative study of virtual machine placement and VM selection policies are presented in this paper for improving the energy efficiency

Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine Placement

With the increasing expansion of cloud data centers and the demand for cloud services, one of the major problems facing these data centers is the “increasing growth in energy consumption ". In this paper, we propose a method to balance the burden of virtual machine resources in order to reduce energy consumption. The proposed technique is based on a four-adaptive threshold model to reduce energy consumption in physical servers and minimize SLA violation in cloud data centers. Based on the proposed technique, hosts will be grouped into five clusters: hosts with low load, hosts with a light load, hosts with a middle load, hosts with high load and finally, hosts with a heavy load. Virtual machines are transferred from the host with high load and heavy load to the hosts with light load. Also, the VMs on low hosts will be migrated to the hosts with middle load, while the host with a light load and hosts with middle load remain unchanged. The values of the thresholds are obtained on the basis of the mathematical modeling approach and the -Means Clustering Algorithm is used for clustering of hosts. Experimental results show that applying the proposed technique will improve the load balancing and reduce the number of VM migration and reduce energy consumption

Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation

Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users. But, the growing popularity of MC is affecting the climate. Because multimedia cloud data centers consume an enormous amount of energy to provide services, it harms the environment due to carbon dioxide emissions. Virtual machine (VM) migration can effectively address this issue; it reduces the energy consumption of multimedia cloud data centers. Due to the reduction of Energy Consumption (EC), the Service Level Agreement violation (SLAV) may increase. An efficient VM selection plays a crucial role in maintaining the stability between EC and SLAV. This work highlights a novel VM selection policy based on identifying the Maximum value among the differences of the Sum of Squares Utilization Rate (MdSSUR) parameter to reduce the EC of multimedia cloud data centers with minimal SLAV. The proposed MdSSUR VM selection policy has been evaluated using real workload traces in CloudSim. The simulation result of the proposed MdSSUR VM selection policy demonstrates the rate of improvements of the EC, the number of VM migrations, and the SLAV by 28.37%, 89.47%, and 79.14%, respectively

An optimal VM Placement, Energy Efficient and SLA at Cloud Environment - A Comparative Analysis

In the cloud computing framework, computing resources can be increased or decreased in response to the users’ different application loads. The data is stored and the applications are running on the servers in the clouds. Users do not have to worry about lost or corrupt data. The clouds can distribute computing resources according to the users’ needs or preferences to provide fl exible management. Users do not have to buy expensive computing devices. They only need to pay for the computing services provided by the clouds. Cloud computing provides a platform for computational experiments with abundant computing and storage resources. The system can be considered as a whole and the control and management decisions are sent as services to agents. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level

Energy Efficient Virtual Machine Migration in Cloud Data Centers

Cloud computing services have been on the rise over the past few decades, which has led to an increase in the number of data centers worldwide which increasingly consume more and more amount of energy for their operation, leading to high carbon dioxide emissions and also high operation costs. Cloud computing infrastructures are designed to support the accessibility and deployment of various service oriented applications by the users. The resources are the major source of the power consumption in data centers along with air conditioning and cooling equipment. Moreover the energy consumption in the cloud is proportional to the resource utilization and data centers are almost the worlds highest consumers of electricity. It is therefore, the need of the hour to devise efficient consolidation schemes for the cloud model to minimize energy and increase Return of Investment(ROI) for the users by decreasing the operating costs. The consolidation problem is NP-complete in nature, which requires heuristic techniques to get a sub-optimal solution. The complexity of the problem increases with increase in cloud infrastructure. We have proposed a new consolidation scheme for the virtual machines(VMs) by improving the host overload detection phase of the scheme. The resulting scheme is effective in reducing the energy and the level of Service Level Agreement(SLA) violations both, to a considerable extent. For testing the performance of our implementation on cloud we need a simulation environment that can provide us an environment with system and behavioural modelling of the actual cloud computing components, and can generate results that can help us in the analysis so that we can deploy them on actual clouds. CloudSim is one such simulation toolkit that allows us to test and analyse our allocation and selection algorithms. In this thesis we have used CloudSim version 3.0.3 to test and analyse our policies and modifications in the current policies. The advantages of using CloudSim 3.0.3 is that it takes very less effort and time to implement cloud-based application and we can test the performance of application services in heterogeneous Cloud environments. The observations are validated by simulating the experiment using the CLoudSim framework and the data provided by PlanetLab

Service Level Agreement-based GDPR Compliance and Security assurance in (multi)Cloud-based systems

Compliance with the new European General Data Protection Regulation (Regulation (EU) 2016/679) and security assurance are currently two major challenges of Cloud-based systems. GDPR compliance implies both privacy and security mechanisms definition, enforcement and control, including evidence collection. This paper presents a novel DevOps framework aimed at supporting Cloud consumers in designing, deploying and operating (multi)Cloud systems that include the necessary privacy and security controls for ensuring transparency to end-users, third parties in service provision (if any) and law enforcement authorities. The framework relies on the risk-driven specification at design time of privacy and security level objectives in the system Service Level Agreement (SLA) and in their continuous monitoring and enforcement at runtime.The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644429 and No 780351, MUSA project and ENACT project, respectively. We would also like to acknowledge all the members of the MUSA Consortium and ENACT Consortium for their valuable help

Efficient Energy Management in Cloud Data center using VM Consolidation

Cloud computing is a model which can fast provisioned and released the computing resources by using minimum number of management effort. This can be done by the user without doing any communication with the cloud service providers. Cloud provide the computing resources, on-demand network access which is pooled together and it can be provisioned dynamically according to the user needs. Due to the large application, more number of computing nodes are required. A large amount of electrical energy is consumed due to the establishment of the data center. There is a problem of carbon dioxide emissions and increasing cost of operation due to the formation of large data center. A consolidation of virtual machines technique is proposed in our thesis to reduce the energy consumption and to maximize the utilization of the computing resources in the data center. Several virtual machines are taken together into a single physical machine in the consolidation technique and it helps to decrease the consumption of energy by putting idle server into inactive mode. A number of active hosts is minimized by continuously reallocating VMs using live migration. In each migration, Service Level Agreement(SLA) violations may occur, hence it is required to reduce the number of migrations.In order to satisfy quality of services in cloud computing environment, our proposed techniques mainly performs the following functions:(i)reducing the consumption of energy, (ii) minimize the number of migrations and (iii) minimize the percentage of SLA violations. Initially we detect whether any host is overloaded or not. The Overloaded host is detected by considering CPU utilization as a threshold Value. If an overloaded host is detected then some virtual machines are migrated from it by using VM selection policy. After selection of the VMs, the next step is to place the new VMs. For VM placement, the greedy algorithms such as Best Fit Decreasing(BFD) and Modified First Fit Decreasing(MFFD) are used in this thesis. The proposed techniques are compared with the existing EEDVM and PALVM techniques. Using proposed AUTREC technique there is 8% improved in energy consumption, 3% in number of migrations, 10% in SLA violation and 12% in host shutdown as compared to EEDVM technique. Using proposed DUTREC technique there is 9% improved in energy consumption, 6% in number of migrations, 20% in SLA violation and 13% in host shutdown as compared to PALVM technique