Energy-Efficient Data Processing in Cloud Computing Centers

2014 ◽  
Vol 910 ◽  
pp. 397-400
Author(s):  
Yang Ping Li ◽  
Shao Fen Zhong ◽  
Xiao Heng Pan ◽  
Hua Qiang Yuan
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Data Processing ◽  
Rapid Growth ◽  
Energy Efficient ◽  
Software Designers ◽  
Efficient Data ◽  
Computing Centers

Rapid growth in the cloud computing centers worldwide is posing serious challenges to both hardware and software designers on the energy efficiency issues. This paper explores particular challenges and potential promises on the part of data processing in these centers.

scholarly journals SNMP for Cloud Environment Energy Efficiency

2021 ◽  
Author(s):  
Marta Chinnici ◽  
Asif Iqbal ◽  
ah lian kor ◽  
colin pattinson ◽  
eric rondeau
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Power Consumption ◽  
Empirical Evidence ◽  
Rapid Growth ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Second Stage ◽  
The Core ◽  
Snmp Protocol

Abstract Cloud computing has seen rapid growth and environments are now providing multiple physical servers with several virtual machines running on those servers. Networks have grown larger and have become more powerful in recent years. A vital problem related to this advancement is that it has become increasingly complex to manage networks. SNMP is one standard which is applied as a solution to this management of networks problem. This work utilizes SNMP to explore the capabilities of SNMP protocol and its features for monitoring, control and automation of virtual machines and hypervisors. For this target, a stage-wise solution has been formed that obtains results of experiments from the first stage uses SNMPv3 and feed to the second stage for further processing and advancement. The target of the controlling experiments is to explore the extent of SNMP capability in the control of virtual machines running in a hypervisor, also in terms of energy efficiency. The core contribution based on real experiments is conducted to provide empirical evidence for the relation between power consumption and virtual machines.

Energy-Efficient Data Processing at Sweet Spot Frequencies

2014 ◽  
pp. 154-171 ◽  
Author(s):  
Sebastian Götz ◽  
Thomas Ilsche ◽  
Jorge Cardoso ◽  
Josef Spillner ◽  
Uwe ASSmann ◽  
...  
Keyword(s):  
Data Processing ◽  
Energy Efficient ◽  
Sweet Spot ◽  
Efficient Data

Secure Energy Efficient Data Collection Scheme Over Wireless Multimedia Sensor Networks

2016 ◽  
Vol 13 (10) ◽  
pp. 7375-7384
Author(s):  
Hyunsung Kim ◽  
Sung Woon Lee
Keyword(s):  
Energy Efficiency ◽  
Sensor Networks ◽  
Data Collection ◽  
Energy Efficient ◽  
Wireless Multimedia Sensor Networks ◽  
Sensor Nodes ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Efficient Data ◽  
Efficient Data Collection

A secure data collection in wireless multimedia sensor networks (WMSNs) has given attention to one of security issues. WMSNs pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. For the energy efficiency, WMSNs adopt mobile sinks to collect data from sensor nodes. Thus, how to gather data securely and efficiently is an important issue WMSNs. In this paper, we propose a secure energy efficient data collection scheme over WMSNs, which are based on Bilinear pairing and symmetric key cryptosystem. First of all, we devise a security model based on a hierarchical key structure for the security mechanisms, authentication, key agreement, confidentiality, and integrity. Based on the model, we propose a secure energy efficient data collection scheme, which could establish secure session in one round. The proposed scheme could efficiently remedy security and efficiency problems in the previous data collection schemes over WMSNs. It has only about 18% of overhead for the security but also has energy efficiency compared with the other related schemes.

ERIDIS: ENERGY-EFFICIENT RESERVATION INFRASTRUCTURE FOR LARGE-SCALE DISTRIBUTED SYSTEMS

2011 ◽  
Vol 21 (02) ◽  
pp. 133-154 ◽  
Author(s):  
ANNE-CECILE ORGERIE ◽  
LAURENT LEFEVRE
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Distributed Systems ◽  
Energy Efficient ◽  
Large Scale ◽  
Generic Framework ◽  
Peer To Peer Systems ◽  
Dedicated Networks ◽  
Electrical Consumption ◽  
Hardware Designs

At the age of petascale machines, cloud computing and peer-to-peer systems, large-scale distributed systems need an ever-increasing amount of energy. These systems urgently require effective and scalable solutions to manage and limit their electrical consumption. As of now, most efforts are focused on energy-efficient hardware designs. Thus, the challenge is to coordinate all these low-level improvements at the middleware level to improve the energy efficiency of the overall systems. Resource-management solutions can indeed benefit from a broader view to pool the resources and to share them according to the needs of each user. In this paper, we propose ERIDIS, an Energy-efficient Reservation Infrastructure for large-scale DIstributed Systems. It provides a unified and generic framework to manage resources from Grids, Clouds and dedicated networks in an energy-efficient way.

scholarly journals Research and Application of Massive Data Processing in Oil Services

2012 ◽  
Vol 6-7 ◽  
pp. 1036-1040
Author(s):  
Bao An Li
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Rapid Growth ◽  
Massive Data ◽  
Oil Drilling ◽  
Data Problem ◽  
Traditional Industry ◽  
Massive Data Processing

Big data problem has caused widespread concern from industry to academia in recent years. As the amount of data produced by various industries and sectors of rapid growth, increasing demands on data processing and analysis capabilities, how to face the challenges of data, discover new opportunities, the issue has received wide attention. As a traditional industry, the oil drilling or refinery enterprise is facing the operational status of the system to produce large amounts of data. This text introduced an approach to massive data processing for oil enterprise based on cloud computing and Internet of Things.

scholarly journals Energy Efficiency Analysis Between Green Computing and Cloud Computing

2019 ◽  
Vol 8 (3S2) ◽  
pp. 254-258
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Data Center ◽  
Energy Efficient ◽  
Green Computing ◽  
Energy Efficient Computing ◽  
Green Data Center ◽  
Computer Resources ◽  
Server System ◽  
Energy Efficiency Analysis

Usually cloud computing is based under effective data computing but green cloud computing focus on energy efficiency of device and computing mainly based on computing architecture. Here cloud computing is also known as green computing due its better effective energy. The purpose of green computing is to decrease the usage of harmful resources, maximizing energy efficiency throughout the living span of the good and also to promote the recyclability or bio degradability of useless goods plus misuse materials of the factory. In order to simulate the hardware by utilizing software, green computing plays a major role for various technologies like virtualization, green data center, cloud and grid computing and power optimization. Here computer resources are effectively utilized by replacing the data center standalone server system by artificial server so as to run the software by less number for large computers called as virtualized. Finally this paper is fulfilled by the advancement of recognized energy efficient computing

scholarly journals Energy-Efficient Multi-Job Scheduling Model for Cloud Computing and Its Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaoli Wang ◽  
Yuping Wang ◽  
Hai Zhu
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Energy Efficient ◽  
Job Scheduling ◽  
Point Of View ◽  
Scheduling Model ◽  
Fitness Value ◽  
New Energy ◽  
Searching Ability ◽  
Local Search Operator

For the problem that the energy efficiency of the cloud computing data center is low, from the point of view of the energy efficiency of the servers, we propose a new energy-efficient multi-job scheduling model based on Google’s massive data processing framework. To solve this model, we design a practical encoding and decoding method for the individuals and construct an overall energy efficiency function of the servers as the fitness value of each individual. Meanwhile, in order to accelerate the convergent speed of our algorithm and enhance its searching ability, a local search operator is introduced. Finally, the experiments show that the proposed algorithm is effective and efficient.

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