User Power Consumption Cluster Analysis Based on Cloud Computing and Improved K-means Algorithm

2019 ◽  
Author(s):  
Yan Song ◽  
Yun Su ◽  
Hongshan Yang
Keyword(s):  
Cluster Analysis ◽  
Cloud Computing ◽  
Power Consumption

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.

Design and Application of Innovative 3DVC in AI Server System

2021 ◽  
Author(s):  
Xianguang Tan ◽  
Yongzhan He ◽  
Bin Liu ◽  
Jiang Yu ◽  
Ahuja Nishi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Power Consumption ◽  
Heat Dissipation ◽  
Air Cooling ◽  
Computing Power ◽  
Advanced Phase ◽  
Cooling Technology ◽  
Saving Effect ◽  
Server System

Abstract With the accelerated application of cloud computing and artificial intelligence, the computing power and power consumption of chips are greatly enhanced, which brings severe challenges to heat dissipation. Based on this, Baidu has adopted advanced phase change cooling technology and successfully developed an innovative 3dvc air cooling scheme for AI server system. This paper introduces the design, test and verification of the innovative scheme in detail. The results show that the scheme can reduce the GPU temperature by more than 5 °C compared with the traditional heat pipe cooling scheme, save 30%+ of the fan power consumption, and achieve good cooling and energy saving effect.

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 16-38
Author(s):  
Pushpa R. ◽  
M. Siddappa
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Vm Placement ◽  
Migration Strategy ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Migration Cost ◽  
Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

On IoT and Its Integration With Cloud Computing

2021 ◽  
pp. 37-64
Author(s):  
Iram Abrar ◽  
Sahil Nazir Pottoo ◽  
Faheem Syeed Masoodi ◽  
Alwi Bamhdi
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Power Consumption ◽  
Rapid Growth ◽  
Storage Capacity ◽  
The Other ◽  
Computational Power ◽  
New Paradigm ◽  
Other Hand

Internet of things witnessed rapid growth in the last decade and is considered to be a promising field that plays an all-important role in every aspect of modern-day life. However, the growth of IoT is seriously hindered by factors like limited storage, communication capabilities, and computational power. On the other hand, cloud has the potential to support a large amount of data as it has massive storage capacity and can perform complex computations. Considering the tremendous potential of these two technologies and the manner in which they complement one another, they have been integrated to form what is commonly referred to as the cloud of things (CoT). This integration is beneficial as the resulting system is more robust, intelligent, powerful, and offers promising solutions to the users. However, the new paradigm (CoT) is faced with a significant number of challenges that need to be addressed. This chapter discusses in detail various challenges like reliability, latency, scalability, heterogeneity, power consumption, standardization, etc. faced by the cloud of things.

Application of cluster analysis for enhancing power consumption awareness in smart grids

2018 ◽  
pp. 397-414
Author(s):  
Guido Coletta ◽  
Alfredo Vaccaro ◽  
Domenico Villacci ◽  
Ahmed F. Zobaa
Keyword(s):  
Cluster Analysis ◽  
Power Consumption ◽  
Smart Grids

scholarly journals Power consumption prediction in cloud data center using machine learning

2020 ◽  
Vol 10 (2) ◽  
pp. 1524 ◽  
Author(s):  
Deepika T. ◽  
Prakash P.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Business World ◽  
Cloud Data ◽  
Cloud Data Centers

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

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