Virtual Machines In Cloud Environment Research Articles

Greedy-based task scheduling algorithm for minimising energy and power consumption for virtual machines in cloud environment

Greedy-based task scheduling algorithm for minimising energy and power consumption for virtual machines in cloud environment

Greedy-based task scheduling algorithm for minimising energy and power consumption for virtual machines in cloud environment

Greedy-based task scheduling algorithm for minimising energy and power consumption for virtual machines in cloud environment

An Improved Approach for Load Balancing among Virtual Machines in Cloud Environment

Cloud computing has shown noteworthy evolution in information technology. Users can enjoy various services of cloud technology only if internet connection is available. In cloud computing, load balancing considered as a fundamental issue that has confronted researchers in this domain. Load balancing basically works by allotting fair and efficient work among computing resources which ultimately achieve high user satisfaction and raises systems productivity. Many load-balancing techniques made efforts to resolve this problem using metaheuristics algorithm, and amplify the operation and efficiency of systems. In this paper, existing load balancing techniques has been discussed and research gaps in existing lirture hasbeeb discussed. Also a new technique has been proposed called IG-GWO - Inquisitive Genetic Grey Wolf Optimization algorithm using combination of Grey wolf optimization (GWO) algorithm and Genetic algorithm.

Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

Solving fully dynamic bin packing problem for virtual machine allocation in the cloud environment by the futuristic greedy algorithm

Many scientists apply fully dynamic bin packing problem solving for resource allocation of virtual machines in cloud environments. The goal of problem-solving is to reduce the number of allocated hosts (bins) and virtual machines (items) migration rates for reducing energy consumption. This study demonstrates a greedy futuristic algorithm (proposed algorithm) for fully dynamic bin packaging with an average asymptotic approximation ratio of 1.231, better than other existing algorithms. The proposed algorithm identifies inappropriate local selections using special futuristic conditions to prevent them as much as possible. Eventually, suitable choices determine and discard the improper ones. The proposed algorithm illustrates an asymptotic approximation ratio of (t/ (t-1)) OPT, where the value of t depends on the distribution of the arrived and departed items. Also, OPT is the number of bins by an optimal solution. Finally, in experiments of datasets using a maximum utilization of 80% of each bin, the average migration rate is 0.338. Using the proposed method for allocating resources in the cloud environment can allocate hosts to a virtual machine using almost optimal use. This allocation can reduce the cost of maintaining and purchasing hosts. Also, this method can reduce the migration rate of virtual machines. As a result, decreasing migration improves the energy consumption cost in the cloud environment.

A tree based mechanism for the load balancing of virtual machines in cloud environments

Cloud computing is a paradigm for providing resources as a utility over the Internet. With the cloud computing, new administrations in data innovation has risen up in terms of business and innovation as a viewpoint which facilitates client’s access to IT assets whenever and anyplace utilizing pay-per-utilize model. Along these, it provides prominent working environment to the client and at the same time should be invaluable for the cloud specialist organization. Load balancing is a major aspect of cloud computing which balances overloaded and under-loaded nodes or less burden node. Load balancing improves the quality of service metrics of multi-objectives like response-time, cost, vitality, and throughput. This paper presents, paired tree algorithm to improve the performance of makespan and migration. The paired tree algorithm on 25 cloudlets provides task migration of 7 and 91.32% of makespan.

Analysis of Energy Efficient Load Balancing of Virtual Machine in Cloud Environment

Cloud computing is the hottest trend in distributed computing. It provides the services based on demand. The services are distributed such as applications, software’s and data bases. The data center handles 53% of load during the peak time. It consumes around 0.67 Zeta bytes of data and the energy usage is 0.96 billion kilo watts of power during the peak time. The main challenge in the cloud data center is to manage load during the peak time. To reduce the energy consumption, the virtual machine needs to be reconfigured based on the memory, CPU usage and workload conditions. In this paper, the Virtual Machine allocation is done using proposed method viz Multiplier algorithm and performance during different workload conditions are analyzed and it is found that the energy efficiency increases from 5% to 6% in the cloud data center.

Energy-efficient strategy for virtual machine consolidation in cloud environment

An important issue of energy efficiency in cloud environment is to perform more jobs while consuming less amount of power. Virtual machine consolidation remains the most deployed strategy to manage both performance and energy consumption. Most of existing energy efficiency techniques save energy against the cost on performance degradation. Consolidation techniques leverage thresholds to detect overloaded and underloaded hosts that could be vacated to achieve optimal balance between host utilization and energy consumption. In this research, we propose an energy-efficient strategy (EES) to consolidate virtual machines in cloud environment with an aim of reducing the energy consumption while completing more tasks with the highest throughput. Our proposal makes use of the performance-to-power ratio to set upper thresholds for overload detection. In addition, EES considers the overall data center workload utilization to set lower thresholds, which can reduce the number of virtual machine migrations. The simulation results show that EES leads to energy-efficient workload consolidation with the minimal number of migrations and less energy consumption. The results conclude that EES saves energy consumption without compromising user’s workload requirement.

An Improved Genetic Algorithm for Document Clustering on the Cloud

This article presents a modified genetic algorithm for text document clustering on the cloud. Traditional approaches of genetic algorithms in document clustering represents chromosomes based on cluster centroids, and does not divide cluster centroids during crossover operations. This limits the possibility of the algorithm to introduce different variations to the population, leading it to be trapped in local minima. In this approach, a crossover point may be selected even at a position inside a cluster centroid, which allows modifying some cluster centroids. This also guides the algorithm to get rid of the local minima, and find better solutions than the traditional approaches. Moreover, instead of running only one genetic algorithm as done in the traditional approaches, this article partitions the population and runs a genetic algorithm on each of them. This gives an opportunity to simultaneously run different parts of the algorithm on different virtual machines in cloud environments. Experimental results also demonstrate that the accuracy of the proposed approach is at least 4% higher than the other approaches.

A Hybrid Heuristic for QoS Aware Matching of User's Job and Virtual Machines in Cloud Environment

Cloud computing is a technological advancement that provides services in the form of utility on a pay-per-use basis. As the cloud market is expanding, numerous service providers are joining the cloud platform with their services. This creates an indecision amongst the users to choose an appropriate service provider especially when the cloud provider provisions diverse type of virtual machines. The problem becomes more challenging when the user has different jobs requiring specific quality of service. To address the aforementioned problem, this article applies a hybrid heuristic using College Admission Problem and Analytical Hierarchical Process for stable matching of the users' job with the cloud's virtual machines. The case study depicts the effectiveness of the proposed model.

Performance analysis of local, network and distributed file systems running inside user’s virtual machines in cloud environment

Performance analysis of local, network and distributed file systems running inside user’s virtual machines in cloud environment

Multi-objective dynamic management of virtual machines in cloud environments

The management strategy of a data center needs access to sufficient resources in order to handle different requests of applications, while minimizing the consumed energy. With regard to high and varying resource demands, Virtual Machines (VM) management necessitates dynamic strategies. Dynamic management of VMs includes VM placement and VM migration. The management approach presented in this paper aimed to reduce the energy consumption and the violation of service level agreements (SLA) simultaneously in data centers. The simulation results indicate that proposed approach improved the VM management 40% compared to the previous single-goal approaches based on the energy consumption and SLA violation rates.

An Efficient Decentralized Energy and Communication Aware Workload Management

In this paper, a research work is carried out to improve workload management by minimize energy consumption of node, localizing traffic in a network and reduce communication delay between virtual machines in cloud environment. Cloud computing consists numerous virtual machine to store huge volume of data. The usage of virtual machines is to provide separate guest operating system which is popular now-a-days. The workload management across different virtual machines in cloud is really a difficult process. Decentralized approaches were proposed in the literature for energy efficient management of virtual machines. But still it has some issues like failed to consider the communication between virtual machines and it leads to traffic in a network. Thus it reduces the performance of work load management.

A hybrid security model for virtual machines in cloud environment

In cloud environment, the communication between virtual machines (VMs) residing on the same physical system is inevitable, which brings convenience for malicious software to attack other VMs through the communication channels between VMs. In order to reduce the security risk of the VMs in cloud environment, a novel hybrid security model (HSMVM) is presented, which combines the features of confidentiality, integrality, and organisation isolation of generic security model. The limitations and the advantages of the existing security models are thoroughly analysed in this paper. Afterwards, the basic definitions of HSMVM, judgment method for the system security state, and the state transformation rules are described in detail. The prototype system of HSMVM is developed and tested on Xen system. Experimental results indicate that the security model can effectively prevent the attacks between the VMs with reasonably low resource utilisation.

A hybrid security model for virtual machines in cloud environment

A hybrid security model for virtual machines in cloud environment

A Comparative Analysis of Task Scheduling Algorithms of Virtual Machines in Cloud Environment

Cloud computing is an interesting and beneficial area in modern distributed computing. It enables millions of users to use the offered services through their own devices or terminals. Cloud computing offers an environment with low cost, ease of use and low power consumption by utilizing server virtualization in its offered services (e.g., Infrastructure as a Service). The pool of Virtual Machines (VMs) in a cloud computing Data Center (DC) needs to be managed through an efficient task scheduling algorithm to maintain quality of service and resource utilization and thus ensure the positive impact of energy consumption in the cloud computing environment. In this study, an experimental comparative study is carried out among three task scheduling algorithms in cloud computing, namely, random resource selection, round robin and green scheduler. Based on the analysis of the simulation result, we can conclude which algorithm is the best for scheduling in terms of energy and performance of VMs. The evaluation of these algorithms is based on three metrics: Total power consumption, DC load and VM load. A number of experiments with various aims are completed in this empirical comparative study. The results showed that there is no algorithm that is superior to the others. Each has its own pros and cons. Based on the simulation performed, the green scheduler gives the best performance with respect to energy consumption. On the other hand, the random scheduler showed the best performance with respect to both VM and DC load. The round robin scheduler gives better VM and DC load than the green scheduler but have more energy consumption than both random and green schedulers. However, since the RR scheduler distributes the tasks fairly, the network traffic is balanced and neither the server nor the network node will get overloaded or congested.

Live Migration of Virtual Machines in Cloud Environment: A Survey

Virtualization is a useful tool for administrators of data centers and clusters. Migration process of VMs mitigates the overloaded condition of data centers and provides the uninterrupted services. The main objective of migration process is to achieve less down time and migration time. Various migration techniques like pre-copy, post-copy and suspend-copy are available to perform live migration of VMs. Pre-copy migration method has optimum performance compare to the other techniques. A great deal of research and study on pre-copy methods has been proposed by researchers. This paper gives a comprehensive survey investigation of different pre-copy live migration methods based on their working principles along with pros and cons.Keywords: Cloud Computing, Live Migration, Pre-Copy, Virtualization, Virtual Machine (VM)

A weighted throttled load balancing approach for virtual machines in cloud environment

In a cloud environment, load balancing techniques play a vital role in reducing costs associated with file management systems and maximise availability of resources by reducing the amount of downtime that affects the system during outages. Virtualisation technology provides an effective solution to the management of dynamic web-based resources and this prompted us to propose a virtual machine load balancing algorithm. In this paper, we propose a weighted throttled load balancing algorithm, a modification of throttled load balancing algorithm by assigning a weight to each virtual machine VM. For throttled load balancing algorithm, virtual machines VMs are either busy or available and if there is no VM available to serve a request, the request is queued. In our proposed algorithm a request is not queued instead it is assigned a busy VM that has a higher weight in comparison to other VMs. The proposed approach improves system availability through minimising latency, and reduces processing time through proper allocation of requests to the most powerful VM available. The algorithm is implemented using CloudSim simulator and the experimental results achieve better response time and processing time.

Cloud Scheduling Optimization: A Reactive Model to Enable Dynamic Deployment of Virtual Machines Instantiations

This study proposes a model for supporting the decision making process of the cloud policy for the deployment of virtual machines in cloud environments. We explore two configurations, the static case in which virtual machines are generated according to the cloud orchestration, and the dynamic case in which virtual machines are reactively adapted according to the job submissions, using migration, for optimizing performance time metrics. We integrate both solutions in the same simulator for measuring the performance of various combinations of virtual machines, jobs and hosts in terms of the average execution and total simulation time. We conclude that the dynamic configuration is prosperus as it offers optimized job execution performance.