Performance Evaluations of a Cloud Computing Physical Machine with Task Reneging and Task Resubmission (Feedback)

2020 ◽  
pp. 185-198
Author(s):  
Godlove Suila Kuaban ◽  
Bhavneet Singh Soodan ◽  
Rakesh Kumar ◽  
Piotr Czekalski
Keyword(s):  
Cloud Computing ◽  
Performance Evaluations ◽  
Physical Machine ◽  
And Task

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 A Smart Collaborative Policy for Mobile Fog Computing in Rural Vitalization

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yutong Zhou ◽  
Wei Shi ◽  
Fei Song
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Mobile Devices ◽  
Policy Design ◽  
Fog Computing ◽  
Performance Evaluations ◽  
Round Trip Time ◽  
Round Trip ◽  
Trip Time ◽  
Smart Mobile

Mobile Fog Computing (MFC), as a crucial supplement to cloud computing, has its own special traits in many aspects. As smart mobile devices grow and vary in shapes and formats over the years, the need for real-time interactions and an easy-to-use network is imminent. In this paper, we propose a smart collaborative policy for MFC scenarios by considering the target of rural vitalization. The challenges and drawbacks of extending cloud to fog are reviewed at the beginning. Then, the analysis of policy design is presented from the perspectives of feature comparisons, urgent requirements, and possible solutions. The details of policy establishment are introduced with necessary examples. Finally, performance evaluations are provided based on simulation platforms. Validation results related to round trip time and transmission time illustrate the significant improvements of our proposal in certain ways compared to the original candidate, which enables larger deployment in impoverished areas.

scholarly journals An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
C. Saravanakumar ◽  
M. Geetha ◽  
S. Manoj Kumar ◽  
S. Manikandan ◽  
C. Arun ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
High Availability ◽  
Performance Parameters ◽  
Task Execution ◽  
User Tasks ◽  
Cloud User ◽  
Computing Models ◽  
Bucket Size ◽  
And Task

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

scholarly journals Features, Challenges and Issues of Fog Computing: A Comprehensive Review

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
End Users ◽  
Computing Technology ◽  
Privacy And Security ◽  
Comprehensive Review ◽  
Advantages And Disadvantages ◽  
Security Control ◽  
Control And Management ◽  
And Task

Fog computing is a promising technology that is used by many organizations and end-users. It has characteristics and advantages that offer services such as computing, storage, communication, and application services. It facilitates these services to end-users and allows to increase the number of devices that can connect to the network. In this paper, we provide a survey of Fog computing technology in terms of its architecture, features, advantages and disadvantages. We provide a comparison of this model with Cloud Computing, Mobile-Edge Computing, and Cloudlet Computing. We also present challenges and issues that face Fog Computing such as privacy and security, control and management, fog networking and task scheduling. Finally, we discuss aspects of Fog computing security and the benefits of integration between Fog computing and other techniques like Internet of Things and Cloud Computing.

scholarly journals An efficient Task Scheduling and Load Balancing in Cloud Computing using KD-Tree Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 2426-2433
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Research Work ◽  
Tree Algorithm ◽  
Cloud Resource Management ◽  
High Resource ◽  
Cloud Resource ◽  
And Task

Cloud Computing provides the sharing ability and access for available cloud host and various distributed environments, namely Load Balancing (LB), virtualization technologies and scheduling techniques. The satisfaction of both users and cloud providers are the major issues for effective LB and task scheduling algorithms in cloud resource management, where the requirements namely high resource utilization, low monetary costs and minimum makespan. Many researchers tried to develop various heuristic and meta-heuristic algorithms to attain the aforementioned user requirements. But, when the number of tasks grows exponentially, these algorithms failed to achieve LB, lower running time, and it faces the high time complexity. In this research work, a KD-Tree algorithm is developed to address the issues of heuristic algorithms and provide efficient LB by partitioning the environments into several tasks. According to the deadline of task execution, the remaining tasks are adjusted dynamically by the proposed KD-tree algorithm in the virtual environment. The experiments are conducted to evaluate the efficiency of KD-Tree algorithm with existing heuristic techniques by using makespan, energy consumption and task migrations. When the number of tasks is 20, the proposed KD-Tree algorithm achieved 71.33% makespan and 5% task migrations.

scholarly journals An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

2021 ◽  
Author(s):  
Jianying Miao
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Simulation Environment ◽  
Attribute Matching ◽  
Resource Allocation Strategy ◽  
And Task

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

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