706 research outputs found
Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines
Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos
Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines
Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos
An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads
Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads
A Framework for QoS-aware Execution of Workflows over the Cloud
The Cloud Computing paradigm is providing system architects with a new
powerful tool for building scalable applications. Clouds allow allocation of
resources on a "pay-as-you-go" model, so that additional resources can be
requested during peak loads and released after that. However, this flexibility
asks for appropriate dynamic reconfiguration strategies. In this paper we
describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for
executing workflows involving Web Services hosted in a Cloud environment. SAVER
allows execution of arbitrary workflows subject to response time constraints.
SAVER uses a passive monitor to identify workload fluctuations based on the
observed system response time. The information collected by the monitor is used
by a planner component to identify the minimum number of instances of each Web
Service which should be allocated in order to satisfy the response time
constraint. SAVER uses a simple Queueing Network (QN) model to identify the
optimal resource allocation. Specifically, the QN model is used to identify
bottlenecks, and predict the system performance as Cloud resources are
allocated or released. The parameters used to evaluate the model are those
collected by the monitor, which means that SAVER does not require any
particular knowledge of the Web Services and workflows being executed. Our
approach has been validated through numerical simulations, whose results are
reported in this paper
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
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
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
An Optimization of Energy Saving in Cloud Environment
Cloud computing is a technology in distributed computing which facilitates pay per model based on user demand and requirement. Cloud can be defined as a collection of virtual machines. This includes both computational and storage facility. The goal of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud Computing is developing day by day and faces many challenges; one of them is i) Load Balancing and ii) Task scheduling. Load balancing is defined as division of the amount of work that a system has to do between two or more systems so that more work gets done in the same amount of time and all users get served faster. Load balancing can be implemented with hardware, software, or a combination of both. Load balancing is mainly used for server clustering. Task Scheduling is a set of policies to control the work order to be performed by a system. It is also a technique which is used to improve the overall execution time of the job. Task Scheduling is responsible for selection of best suitable resources for task execution, by taking some parameters into consideration. A good task scheduler adapts its scheduling strategy according to the changing environment and the type of task. In this paper, the Energy Saving Load Balancing (ESLB) Algorithm and Energy Saving Task Scheduling (ESTS) algorithm was proposed. The various scheduling algorithms (FCFS, RR, PRIORITY, and SJF) are reviewed and compared. The ESLB algorithm and ESTS algorithm was tested in cloudsim toolkit and the result shows better performance
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