Ceaseless Virtual Appliance Streaming

2016 ◽  
pp. 70-84
Author(s):  
Shahid Nawaz ◽  
Asad Waqar Malik ◽  
Raihan ur Rasool
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Video On Demand ◽  
Resource Base ◽  
Storage Area Networks ◽  
Network Utilization ◽  
Virtual Appliance ◽  
Storage Area ◽  
Network Interfaces ◽  
Server Clusters

Cloud computing is modus operandi of manipulating server clusters hosted at secluded sites on Internet for storage, processing, and retrieval of data. It tenders suppleness, disaster recovery, competitiveness, and cutback in capital and operational cost for ventures, principally small and medium ones, which hold meager resource base. Virtualization at plinth of cloud computing sanctions utilizing physical hardware stratum to frame and administer virtualized infrastructure, storage areas, and network interfaces. Virtual machines, administered on clouds to seize inherent advantages of virtualization, are fabricated on storage area networks (Armbrust et al., 2009). But whenever user endeavors to access them from remote location it resulted in hundreds of megabytes of data reads and ensuing congestion in network. Question is how to instigate virtual machines and load their applications in minimal time. The ingenious Ceaseless Virtual Appliance Streaming system assures virtual machine's streaming just like video on demand. It trims down burden over existing resources and offers improved network utilization.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals Redundant VoD Streaming Service in a Private Cloud: Availability Modeling and Sensitivity Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Rosangela Maria De Melo ◽  
Maria Clara Bezerra ◽  
Jamilson Dantas ◽  
Rubens Matos ◽  
Ivanildo José De Melo Filho ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Cloud Computing ◽  
Hierarchical Modeling ◽  
Video On Demand ◽  
Differential Sensitivity ◽  
Private Cloud ◽  
Streaming Service ◽  
Critical Components ◽  
Modeling Strategy ◽  
Vod Streaming

For several years cloud computing has been generating considerable debate and interest within IT corporations. Since cloud computing environments provide storage and processing systems that are adaptable, efficient, and straightforward, thereby enabling rapid infrastructure modifications to be made according to constantly varying workloads, organizations of every size and type are migrating to web-based cloud supported solutions. Due to the advantages of the pay-per-use model and scalability factors, current video on demand (VoD) streaming services rely heavily on cloud infrastructures to offer a large variety of multimedia content. Recent well documented failure events in commercial VoD services have demonstrated the fundamental importance of maintaining high availability in cloud computing infrastructures, and hierarchical modeling has proved to be a useful tool for evaluating the availability of complex systems and services. This paper presents an availability model for a video streaming service deployed in a private cloud environment which includes redundancy mechanisms in the infrastructure. Differential sensitivity analysis was applied to identify and rank the critical components of the system with respect to service availability. The results demonstrate that such a modeling strategy combined with differential sensitivity analysis can be an attractive methodology for identifying which components should be supported with redundancy in order to consciously increase system dependability.

Implementation of a 6$\,\times\,$6 Free-Space Optical Fiber Ribbon Switch for Storage Area Networks

2012 ◽  
Vol 30 (11) ◽  
pp. 1719-1725 ◽  
Author(s):  
Hsi-Hsir Chou ◽  
F. Zhang ◽  
T. D. Wilkinson ◽  
N. Collings ◽  
W. A. Crossland
Keyword(s):  
Optical Fiber ◽  
Free Space ◽  
Free Space Optical ◽  
Storage Area Networks ◽  
Storage Area

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

Multidimensional Resource Scheduling in Cloud Data Centers

2014 ◽  
Vol 1008-1009 ◽  
pp. 1513-1516
Author(s):  
Hai Na Song ◽  
Xiao Qing Zhang ◽  
Zhong Tang He
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Three Dimensional ◽  
Resource Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

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