A Novel Multi-Dimension Resource Recycling Mechanism for Cloud Data Centers

2015 ◽  
Vol 7 (2) ◽  
pp. 50-64 ◽  
Author(s):  
Hong-Yi Chang ◽  
Tu-Liang Lin ◽  
Cheng-Kai Huang
Keyword(s):  
Resource Allocation ◽  
Cloud Service ◽  
Allocation Mechanism ◽  
Cloud Data Center ◽  
Resource Allocation Algorithm ◽  
Cloud Data ◽  
Resource Recycling ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Cloud Servers

Cloud servers can be started with ineffective resource arrangement, and extra costs are produced if unnecessary servers are started. This is a substantial fee for the cloud service provider. Therefore, each cloud data center needs an efficient resource allocation mechanism to prevent unnecessary cloud servers from being started. In general, resource allocation problems can be classified into either one-dimension or multi-dimension aspects. In 2013, the authors have proposed a multi-dimension resource allocation algorithm that can improve the utilization of cloud servers by as much as 97%. However, most of previous studies are more concerned with solving the resource allocation problem. In fact, after a period of time, the applications will finish their jobs, and resources been occupied on the servers will be released, thus decreasing the utilization of cloud servers. Therefore, this paper proposes a novel multi-dimension resource recycling algorithm to optimize the server resource again, to recycle the excess cloud resources and to reduce the unnecessary operating cloud servers.

scholarly journals ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

2017 ◽  
Vol 10 (13) ◽  
pp. 162
Author(s):  
Amey Rivankar ◽  
Anusooya G
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Energy Efficient ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

2016 ◽  
Vol 8 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Li Mao ◽  
De Yu Qi ◽  
Wei Wei Lin ◽  
Bo Liu ◽  
Ye Da Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Resource Scheduling ◽  
Constraint Satisfaction Problems ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

scholarly journals Cloud Computing Virtualization of Resources Allocation for Distributed Systems

2020 ◽  
Vol 1 (3) ◽  
pp. 98-105 ◽  
Author(s):  
Hanan Shukur ◽  
Subhi Zeebaree ◽  
Rizgar Zebari ◽  
Diyar Zeebaree ◽  
Omar Ahmed ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Resource Provisioning ◽  
Third Party ◽  
Cloud Provider ◽  
Cloud Data Center ◽  
Resource Allocation Algorithm ◽  
Cloud Data

Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques.  The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

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