scholarly journals A Cluster-Based Energy-Efficient Resource Management Scheme With QoS Requirement for Ultra-Dense Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 182412-182421
Author(s):  
Yanzan Sun ◽  
Ge Guo ◽  
Shunqing Zhang ◽  
Shugong Xu ◽  
Tao Wang ◽  
...  
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Dense Networks ◽  
Management Scheme ◽  
Efficient Resource ◽  
Qos Requirement

scholarly journals A Cluster-Based Energy-Efficient Resource Management Scheme for Ultra-Dense Networks

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 6823-6832 ◽  
Author(s):  
Liang Liang ◽  
Wen Wang ◽  
Yunjian Jia ◽  
Shu Fu
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Dense Networks ◽  
Management Scheme ◽  
Efficient Resource

scholarly journals Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yu Cui ◽  
Shunfu Jin ◽  
Wuyi Yue ◽  
Yutaka Takahashi
Keyword(s):  
Resource Management ◽  
Performance Optimization ◽  
Energy Efficient ◽  
Data Centers ◽  
Network Resource ◽  
System Cost ◽  
Cloud Data ◽  
Management Scheme ◽  
Efficient Resource ◽  
Sleep Mechanism

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines (PMs) into two pools: a hot (running) pool and a warm (turned on, but in dynamic sleep) pool. Each PM is configured with a resource search engine (RSE) that finds an available virtual machine (VM) for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm (SSA) is presented to minimize the system cost and optimize the sleep parameter.

scholarly journals A Novel Energy Efficient Resource Management System in Cloud Computing Environment.

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1030
Author(s):  
S. K. Sonkar ◽  
M. U.Kharat
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Resource Management ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Service Providers ◽  
Optimization Techniques ◽  
Cloud Provider ◽  
Efficient Resource ◽  
Machine Allocation

Primary target of cloud provider is to provide the maximum resource utilization and increase the revenue by reducing energy consumption and operative cost. In the service providers point of view, resource allocation, resource sharing, migration of resources on demand, memory management, storage management, load balancing, energy efficient resource usage, computational complexity handling in virtualization are some of the major tasks that has to be dealt with. The major issue focused in this paper is to reduce the energy consumption problem and management of computation capacity utilization.  For the same, an energy efficient resource management method is proposed to grip the resource scheduling and to minimize the energy utilized by the cloud datacenters for the computational work. Here a novel resource allocation mechanism is proposed, based on the optimization techniques. Also a novel dynamic virtual machine (VM) allocation method is suggested to help dynamic virtual machine allocation and job rescheduling to improve the consolidation of resources to execute the jobs. Experimental results indicated that proposed strategy outperforms as compared to the existing systems.  

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