Energy-Efficient Resource Management Techniques in Wireless SensorNetworks

2009 ◽  
pp. 439-468 ◽  
Author(s):  
Xiao-Hui Lin ◽  
Yu-Kwong Kwok ◽  
Hui Wang
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Efficient Resource ◽  
Management Techniques

A Survey and Taxonomy of Energy Efficient Resource Management Techniques in Platform as a Service Cloud

2017 ◽  
pp. 410-454 ◽  
Author(s):  
Sareh Fotuhi Piraghaj ◽  
Amir Vahid Dastjerdi ◽  
Rodrigo N. Calheiros ◽  
Rajkumar Buyya
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Energy Efficient ◽  
Power Efficiency ◽  
Large Scale ◽  
Data Centers ◽  
Cloud Service ◽  
Efficient Resource ◽  
Management Techniques ◽  
Increasing Demand

The numerous advantages of cloud computing environments, including scalability, high availability, and cost effectiveness have encouraged service providers to adopt the available cloud models to offer solutions. This rise in cloud adoption, in return encourages platform providers to increase the underlying capacity of their data centers so that they can accommodate the increasing demand of new customers. Increasing the capacity and building large-scale data centers has caused a drastic growth in energy consumption of cloud environments. The energy consumption not only affects the Total Cost of Ownership but also increases the environmental footprint of data centers as CO2 emissions increases. Hence, energy and power efficiency of the data centers has become an important research area in distributed systems. In order to identify the challenges in this domain, this chapter surveys and classifies the energy efficient resource management techniques specifically focused on the PaaS cloud service models.

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 Deep-Reinforcement-Learning-Based Energy-Efficient Resource Management for Social and Cognitive Internet of Things

2020 ◽  
Vol 7 (6) ◽  
pp. 5677-5689 ◽  
Author(s):  
Helin Yang ◽  
Wen-De Zhong ◽  
Chen Chen ◽  
Arokiaswami Alphones ◽  
Xianzhong Xie
Keyword(s):  
Reinforcement Learning ◽  
Resource Management ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Efficient Resource ◽  
Cognitive Internet Of Things

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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