Energy Consumption In Cloud Research Articles

AN IMPLEMENTATION OF CARBON EFFICIENT VM PLACEMENT AND MIGRATION TECHNIQUE IN CLOUD ENVIRONMENT

Electricity consumption is set to rise 76% from 2007 to 2030 and datacenters are the main contributors of an important portion of this increase, emphasizes the importance of reducing energy consumption in clouds. Increase in the level of carbon dioxide in our ecosystem is another consequence of this increasing amount of energy consumption by the datacenters. According to Gartner, the Information and communication industry produces 2% of global carbon dioxideemission [10]. Hence, there is a great requirement of making use of more environmentally friendly computing called “Green Cloud Computing” to minimize operational and energy consumption costs and also to reduce the environmentalimpact. In this paper, we have implemented the carbon efficient VM placement and migration technique in cloud sim simulator.

Analysis of power consumption in heterogeneous virtual machine environments

Reduction of energy consumption in Cloud computing datacenters today is a hot a research topic, as these consume large amounts of energy. Furthermore, most of the energy is used inefficiently because of the improper usage of computational resources such as CPU, storage and network. A good balance between the computing resources and performed workload is mandatory. In the context of data-intensive applications, a significant portion of energy is consumed just to keep alive virtual machines or to move data around without performing useful computation. Moreover, heterogeneity of resources increases the difficulty degree, when trying to achieve energy efficiency. Power consumption optimization requires identification of those inefficiencies in the underlying system and applications. Based on the relation between server load and energy consumption, we study the efficiency of data-intensive applications, and the penalties, in terms of power consumption, that are introduced by different degrees of heterogeneity of the virtual machines characteristics in a cluster.

QoS constraints-based energy-efficient model in cloud computing networks for multimedia clinical issues

For many applications of multimedia medical devices in clinical and medical issues, cloud computing becomes a very useful way. However, high energy consumption of cloud computing networks for these...

A PERFORMANCE OPTIMIZATION MODEL OF TASK SCHEDULING TOWARDS GREEN CLOUD COMPUTING

Cloud computing becomes a powerful trend in the development of ICT services. It allows dynamic resource scaling from infinite resource pool for supporting Cloud users. Such scenario leads to necessity of larger size of computing infrastructure and increases processing power. Demand on the cloud computing is continually growth that makes it changes to scope of green cloud computing. It aims to reduce energy consumption in Cloud computing while maintaining a better performance. However, there is lack of performance metric that analyzing trade-off between energy consumption and performance. Considering high volume of mixed users’ requirements and diversity of services offered; an appropriate performance model for achieving better balance between Cloud performance and energy consumption is needed. In this work, we focus on green Cloud Computing through scheduling optimization model. Specifically, we investigate a relationship between performance metrics that chosen in scheduling approaches with energy consumption for energy efficiency. Through such relationship, we develop an energy-based performance model that provides a clear picture on parameter selection in scheduling for effective energy management. We believed that better understanding on how to model the scheduling performance will lead to green Cloud computing.

Energy Efficient Load Balancing Strategy for Mobile Cloud Computing

Cloud Computing combines cloud computing and mobile computing. It has turned into one of the business trendy expressions and a real exchange string in the IT world. As MCC is still at the early phase of advancement, it is important to handle a careful understanding of the innovation to call attention to the bearing of future research. In this paper, a new algorithm is proposed load balancing for mobile clouds has been presented. The results has shown that the load balancing during job placement plays a critical role in energy consumption of cloud computing environment. The comparisons has clearly shown that the proposed technique outperforms over available techniques. KeywordsBalancing, Mobile Cloud Computing

A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach

Cloud computing slowly gained an important role in scientific application, on-demand facility of virtualized resources is provided as a service with the help of virtualization without any additional waiting time. Energy consumption is reduced for job scheduling problems based on makespan constraint which in turn leads to significant decrease in the energy cost. Additionally, there is an increase in complexity for scheduling problems mainly because the application is not based on makespan constraint. In this paper we propose a new Hybrid algorithm combining the benefits of ACO and cuckoo search algorithm. It is focused on the voltage scaling factor for reduction of energy consumption. Performance of the Hybrid algorithm is considerably increased from 45 tasks onward when compared to ACO. Energy consumed by Hybrid algorithm is measured and energy improvement is evaluated up to 35 tasks. Energy consumption is the same as ACO algorithm because as the number of tasks increases (45 to 70) there is a considerable decrease in the energy consumption rate. Makespan of Hybrid algorithm based on number of tasks is compared with ACO algorithm. Further we have analyzed the energy consumption for a number of processors and its improvement rate – up to 6 processors, energy consumption is considerably reduced and the energy consumption tends to be in steady state with further increase in the number of processors.

Optimal Energy Efficient Scheduling based on VM Energy Consumption in Cloud

Cloud computing has emerged as a flexible and powerful computing platform which offers different types of services to users. However, the energy consumption and carbon emission in cloud data centre have massive impact on global environment triggering intense research in this area. Growth in demand for cloud based services is resulting in increasing of utilization of cloud resources and consequent increase in the energy consumption. While there is a need to maximize utilization of the resources, the energy consumption needs to be reduced. Therefore we need to determine the operational optimal point which reduces the energy consumption for a desired value of resource utilization. In this paper, we propose a technique to find such an optimal operational point in Cloud computing environment. The technique is based on measuring the energy consumption of VMs created for a task, classifying the VMs based on their energy consumption and resource utilization, and then performing scheduling of VMs. The time efficiency of this algorithm is . The effectiveness of the proposed technique has been verified by simulating on CloudSim. Experimental results confirm that the technique proposed here can significantly reduce energy consumption for the desired value of resource utilization.

A Genetic Algorithmic Approach for Energy Efficient Task Consolidation in Cloud Computing

In cloud, processing loads arrive from many users at random time instants in the form of task. A proper resource allocation policy attempts to assign this task to available VMs on different host so to complete the execution of the tasks in the shortest possible time with minimum power consumption. The complexity of the resource allocation problem with cloud increases with the number of hosts and becomes difficult to solve effectively. The resource allocation problem is a combinatorial problem and known to be NP-complete. The exponential solution space of the load balancing problem can be searched using heuristic techniques based on Genetic algorithms to obtain a sub - optimal solution in acceptable time. The novel genetic algorithm framework has been proposed for task scheduling to minimize the energy consumption in cloud computing infrastructure. The performance of the proposed GA resource allocation strategy has been compared Random and Round Robin scheduling using in house simulator. The experimental results show that the GA based scheduling model outperforms the existing Random and Round Robin scheduling models.

Cloud Computing and its Environmental Effects

Cloud computing is providing basic level of computing facility to meet the daily needs of general or public community.in this paper we have selected the topic cloud computing and its environmental effects. Global warming, greenhouse effect, increasing temperature of the whole environment are major concerns regarding to rise in temperature and pollution of the environment of the whole world. No doubt cloud computing is providing basic facility of computing to general community but at the same time it is playing major role in increasing temperature of the whole world and becoming the source of pollution for the environment. In this paper different strategies have been adopted to control this problem. Virtualization technique is being utilized to control the problem of energy consumption and emission of carbon dioxide gas. So many techniques regarding to virtualization like live migration of VM, Dynamic Resource Allocation of different resources, virtualization of network resources like routers, routing protocols, virtualization of hardware resources like server, storage, memory and other devices. An effort is being exercised to reduce the problem of carbon emission and energy consumption of cloud computing to large extent.

Implementation of energy efficient green computing in cloud computing

Clouds are rapidly becoming an important platform for scientific applications. For executing large programs in cloud, the class of programs can be decomposed into multiple sequences of tasks that can be executed in different VMs. Users have to pay for the resources they use according to some pricing model. Cloud applications consume huge amount of energy, this also cause high operational cost. So this work introduces green computing in cloud called green cloud. Green computing is the study of efficient and eco-friendly computing resources in order to reduce energy consumption, carbon emission, etc. In recent years, companies in the IT industry have come to realise that going green is in their best interest that is reduced costs. Energy consumption at different levels in cloud computing system was discussed. This work discuses several techniques for reducing energy consumption in cloud and evaluates the CPU energy with power consumption. It also works on memory consumption techniques and presents the results for that. It compares the memory consumption with power consumption and present results for the same.

SEATS: smart energy-aware task scheduling in real-time cloud computing

Mitigating energy consumption in Clouds reduces operational costs for providers. Power management policies which aim to reduce total energy consumed in data-centers pose challenges in both hardware technologies and resource management policies. We introduce an optimal utilization level of a host to execute a certain number of instructions to minimize energy consumption of the host. We also propose a virtual machine (VM) scheduling algorithm based on the unsurpassed utilization level to come up with optimal energy consumption while meeting a given QoS. In other words, our proposed algorithm regulates allocated computing resources of VMs on a host which results in reaching an optimal energy level in the host. The simulation results show that our proposed method not only reduces total energy consumption of a Cloud by 60 %, but also has a profound impact on turnaround times of real-time tasks by 94 %. It also increases the acceptance rate of arrival tasks by 96 %. Moreover, it takes a leading part in accepting lengthy tasks which have short deadlines.

Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment

Green cloud computing has become a major concern in both industry and academia, and efficient scheduling approaches show promising ways to reduce the energy consumption of cloud computing platforms while guaranteeing QoS requirements of tasks. Existing scheduling approaches are inadequate for real-time tasks running in uncertain cloud environments, because those approaches assume that cloud computing environments are deterministic and pre-computed schedule decisions will be statically followed during schedule execution. In this paper, we address this issue. We introduce an interval number theory to describe the uncertainty of the computing environment and a scheduling architecture to mitigate the impact of uncertainty on the task scheduling quality for a cloud data center. Based on this architecture, we present a novel scheduling algorithm (PRS11Proactive and Reactive Scheduling.) that dynamically exploits proactive and reactive scheduling methods, for scheduling real-time, aperiodic, independent tasks. To improve energy efficiency, we propose three strategies to scale up and down the system's computing resources according to workload to improve resource utilization and to reduce energy consumption for the cloud data center. We conduct extensive experiments to compare PRS with four typical baseline scheduling algorithms. The experimental results show that PRS performs better than those algorithms, and can effectively improve the performance of a cloud data center.

Artificial bee colony based energy‐aware resource utilization technique for cloud computing

SummaryCloud computing is a form of distributed computing, which promises to deliver reliable services through next‐generation data centers that are built on virtualized compute and storage technologies. It is becoming truly ubiquitous and with cloud infrastructures becoming essential components for providing Internet services, there is an increase in energy‐hungry data centers deployed by cloud providers. As cloud providers often rely on large data centers to offer the resources required by the users, the energy consumed by cloud infrastructures has become a key environmental and economical concern. Much energy is wasted in these data centers because of under‐utilized resources hence contributing to global warming. To conserve energy, these under‐utilized resources need to be efficiently utilized and to achieve this, jobs need to be allocated to the cloud resources in such a way so that the resources are used efficiently and there is a gain in performance and energy efficiency. In this paper, a model for energy‐aware resource utilization technique has been proposed to efficiently manage cloud resources and enhance their utilization. It further helps in reducing the energy consumption of clouds by using server consolidation through virtualization without degrading the performance of users’ applications. An artificial bee colony based energy‐aware resource utilization technique corresponding to the model has been designed to allocate jobs to the resources in a cloud environment. The performance of the proposed algorithm has been evaluated with the existing algorithms through the CloudSim toolkit. The experimental results demonstrate that the proposed technique outperforms the existing techniques by minimizing energy consumption and execution time of applications submitted to the cloud. Copyright © 2014 John Wiley & Sons, Ltd.

Using priced timed automaton to analyse the energy consumption in cloud computing environment

According to the fact that cloud servers have different energy consumption on different running states, as well as the energy waste problem caused by the mismatching between cloud servers and cloud tasks, we carry out researches on the energy optimal method achieved by a priced timed automaton for the cloud computing center in this paper. The priced timed automaton is used to model the running behaviors of the cloud computing system. After introducing the matching matrix of cloud tasks and cloud resources as well as the power matrix of the running states of cloud servers, we design a generation algorithm for the cloud system automaton based on the generation rules and reduction rules given ahead. Then, we propose another algorithm to settle the minimum path energy consumption problem in the cloud system automaton, therefore obtaining an energy optimal solution and an energy optimal value for the cloud system. A case study and repeated experimental analyses manifest that our method is effective and feasible.

Research on Resource Scheduling Algorithm in Cloud Computing Data Center

In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low. 3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. Resource scheduling is a major research direction of cloud computing. First, we make a in-depth investigation and analysis of the research status of cloud computing resource scheduling, and then focus on resource scheduling method to reduce the energy consumption of cloud computing data center. Finally we set an important future research direction of cloud computing resource management research in order to provide a useful reference for cloud computing research.

A multi-start local search heuristic for an energy efficient VMs assignment on top of the OpenNebula cloud manager

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a large-scale cloud. Minimizing energy consumption can significantly reduce the amount of energy bills and the greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present an Energy-aware Multi-start Local Search algorithm (EMLS-ONC) that optimizes the energy consumption of an OpenNebula-based Cloud. Moreover, we propose a Pareto Multi-Objective version of the EMLS-ONC called EMLS-ONC-MO dealing with both the energy consumption and the Service Level Agreement (SLA). The objective is to find a Pareto tradeoff between reducing the energy consumption of the cloud while preserving the performance of Virtual Machines (VMs). The different schedulers have been experimented using different arrival scenarios of VMs and different hardware configurations (artificial and real). The results show that EMLS-ONC and EMLS-ONC-MO outperform the other energy- and performance-aware algorithms in addition to the one provided in OpenNebula by a significant margin on the considered criteria. Besides, EMLS-ONC and EMLS-ONC-MO are proved to be able to assign at least as many VMs as the other algorithms.