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Virtual machine consolidation and scheduling influence directly the cloud cost and performance. They play an important role in cloud service granting helping to achieve execution efficiency, user Service Level Agreement (SLA) compliance, utilization of resources, energy saving, and the increasing profit of cloud providers. In this paper the authors propose the Optimization using Simulated Annealing...
As a new computing paradigm, cloud computing has significantly contributed to the rapid development of massive data centers. However, the corresponding energy issue becomes increasingly challenging. In this paper, we focus on the energy saving issue for virtual machine (VM) selections on an overloaded host in a cloud computing environment. We analyze the energy influencing factors during a VM migration,...
We propose a reinforcement learning algorithm, Megh, for live migration of virtual machines that simultaneously reduces the cost of energy consumption and enhances the performance. Megh learns the uncertain dynamics of workloads as-it-goes. Megh uses a dimensionality reduction scheme to projectthe combinatorially explosive state-action space to a polynomial dimensional space. These schemes enable...
This paper studies user-centric cluster scheme in cloud radio access network (Cloud-RAN), where distributed Radio Remote Heads (RRHs) are connected to a centralized Based Band Units (BBUs) pool and all baseband processing is performed via high-bandwidth low-latency backhaul network. In user-centric cluster scheme, BBU schedules multiple RRHs for each user to form the serving cluster, and then BBU...
With the development of cloud computing, on-demand resource provision has become a key feature of it. However, most cloud service providers have their own interface type, energy consumption level and other service features. A service broker in cloud computing plays an intermediate role between providers and users. It is designed to find out the suitable services from the various service providers...
In a Cloud Computing environment, a pool of resources in multiple physical machines is shared among virtual machines. Those virtual machines are deployed to host client applications and communicate together to run the appropriate tasks. Therefore communication between VMs should be taken into consideration when allocating VMs across servers. Recently, research works on network communication prove...
Cloud computing is emerging technology due to pay-as-you-go pricing model. It is spreading globally due to its easy and simple service oriented model. Some people are having perception that cloud computing is just another name of Internet. The numbers of users accessing the cloud are rising day by day. Cloud is based on data centers, which are powerful to handle large number of users, who can access...
Datacentres consume incredible amounts of energy for data processing, storage and communication, which negatively impacts the environment through carbon emissions. This paper proposes a novel scheduling algorithm aimed at reducing energy consumption in cloud computing datacentres, with the objective to save the environment. It optimises Virtual Machines' (VMs') allocation and consolidation so as to...
Computational offloading can improve the energy efficiency of mobile devices, by executing some tasks of a mobile application in the cloud. In this paper, a new algorithm called 'Dynamic Programming with Randomization' (DPR) is presented. The DPR algorithm iteratively improves an offloading decision vector, by generating random bit strings with a biased probability of generating 0s, which represent...
In cloud data center deployment of Virtual Machine (VM) reduces the active number of Physical Servers. An efficient VM Scheduling scheme requires to reduce the energy consumption as well as to improve the network performance. In contrast to the existing solution, VM placement and VM migration is proposed in this work. In VM placement Physical Machine (PM) resources are formulated as Bin Packing Problem...
Cloud computing has overtaken the traditional computing technologies by providing virtualized resources on demand. Cloud data centers consume a huge amount of power and emit much carbon dioxide, resulting in two challenging problems: high energy consumption and global warming. Our prior work has derived an enhanced energy model that considered energy consumed in computing, migration and host reactivating,...
The advent of cloud computing has radically changed the development of the future Internet of Services. Cloud data centers accommodating numerous tenant requests for cloud applications discharge massive quantities of energy, contributing to high operational expenditures and carbon dioxide (CO2) diffusion into the environment. In order to curtail this, there is a need to conserve energy for future...
In order to perform optimal Virtual Machine (VM) consolidation under QoS constraints based on energy consumption in Cloud Data Centres (CDCs) containing heterogeneous physical resources, one must build a framework that combines many subsystem algorithms, including prediction, selection, placement, etc. Several energy minimization strategies can be used in CDCs, but the most importantly is one in which...
Monitoring ultrascale systems such as Clouds requires collecting enormous amount of data by periodically reading metric values from a system. Current approaches tend to select a static frequency for sampling monitoring data. On one hand, over-sampling the data by collecting it at high frequencies results in data redundancy during steady runs of the system. On the other hand, under-sampling with low...
Nowadays, data centers consume about 2% of the worldwide energy production, originating more than 43 million tons of CO2 per year. Cloud providers need to implement an energy-efficient management of physical resources in order to meet the growing demand for their services and ensure minimal costs. From the application-framework viewpoint, Cloud workloads present additional restrictions as 24/7 availability,...
Code offloading has been proposed to improve the performance and energy-efficiency of mobile devices by sending heavy computation tasks to resourceful cloud, instead of executing all tasks on local mobile devices. Unfortunately, current code offloading techniques are not efficient enough because of high communication cost. In this paper, we propose a novel code offloading strategy with cellular traffic...
Cloud computing is widely being adopted by many companies because it allows to maximize the utilization of resources. However, the complexity of cloud computing systems with the existence of many cloud providers makes infeasible for the end user the optimal or near-optimal resource provisioning and utilization, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence,...
Cloud computing is a novel perspective for large scale distributed computing and parallel processing. It provides computing as a utility service on a pay per use basis. The performance and efficiency of cloud computing services always depends upon the performance of the user tasks submitted to the cloud system. Scheduling of the user tasks plays significant role in improving performance of the cloud...
Improving energy efficiency is a multidimensional challenge regarding cloud computing environments management, which can directly reduce the operating costs and carbon dioxide emissions, while increasing the system reliability. An energy-efficient resource allocation approach for real-time cloud services is proposed in this paper. Several policies for provisioning of virtual machines (VMs) and hosts...
Cloud data centers consume an enormous amount of energy. Virtual Machine (VM) migration technology can be applied to reduce energy consumption by consolidating VMs onto the minimal number of servers and turn idle servers into power-saving modes. While most existing energy models consider mainly computing energy, an enhanced energy consumption model is formulated, which includes energy consumption...
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