On-Demand Resource Allocation for Service Level Guarantee in Grid Environment

2005 ◽  
pp. 678-689 ◽  
Author(s):  
Hailan Yang ◽  
Gongyi Wu ◽  
Jianzhong Zhang
Keyword(s):  
Resource Allocation ◽  
Service Level ◽  
Grid Environment ◽  
On Demand

Resource Allocation Policies in Cloud Computing Environment

2017 ◽  
pp. 115-132 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Different Types ◽  
Cloud Datacenter

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.

Provisioning Virtual Resources Adaptively in Elastic Compute Cloud Platforms

2011 ◽  
Vol 8 (3) ◽  
pp. 54-69 ◽  
Author(s):  
Fan Zhang ◽  
Junwei Cao ◽  
Hong Cai ◽  
Cheng Wu
Keyword(s):  
Resource Allocation ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Major Difficulty ◽  
Cloud Platform ◽  
On Demand ◽  
Conflicting Objectives ◽  
The Cost ◽  
Virtual Resources

Provisioning Virtual machines on demand is significant in elastic compute cloud for reliable service delivery. The importance and major difficulty lies in satisfying the conflicting objectives of satisfying contracted service level agreement while lowering used resource costs. In this paper, the authors propose a mathematical multi-tier framework for adaptive virtual resource allocation problem. The framework captures the performance of the virtualized cloud platform gracefully. The authors first use simulations to derive virtual resource allocation policies, and later use real benchmarking applications, to verify the effectiveness of this framework. Experimental results show that the model can be simply and effectively used to satisfy the response time requirement as well as lowering the cost of using the virtual machine resources.

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.

On Demand Resource Allocation for LTE Uplink Transmission Based on Logical Channel Groups

2017 ◽  
Vol 22 (5) ◽  
pp. 868-879 ◽  
Author(s):  
Fang-Chang Kuo ◽  
Kuo-Chang Ting ◽  
Hwang-Cheng Wang ◽  
Chih-Cheng Tseng
Keyword(s):  
Resource Allocation ◽  
On Demand ◽  
Logical Channel ◽  
Lte Uplink

Resource Allocation Mechanism with New Models for Grid Environment

2013 ◽  
Vol 5 (2) ◽  
pp. 1-26
Author(s):  
Kuppani Sathish ◽  
A. Rama Mohan Reddy
Keyword(s):  
Resource Allocation ◽  
Waiting Time ◽  
Vital Role ◽  
Allocation Mechanism ◽  
Grid Resource ◽  
Grid Environment ◽  
Grid Resources ◽  
Utilization Cost ◽  
Resource Allocation Mechanism ◽  
Grid Resource Allocation

Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

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