K-mMA VM selection in dynamic VM consolidation for improving energy efficiency at cloud data centre

Allotted computing is a blasting innovation that tenders effective assets, and smooth accessibility through web based processing. however, the growing wishes of clients for such administrations are convincing the cloud professional corporations to send huge portions of strength hungry server farms which element awful effect to the earth with the aid of the usage of plenteous Carbon Dioxide discharge. To limit control usage and strengthen the quality of service (QoS) inside the server farm assesses the strength usage in an assortment of plans in IaaS of dispensed computing situation. Dynamic Virtual Machines’ Consolidation and Placement(DVMCP) is an in a position strategies for enhancing using assets and proficient power usage in Cloud DataCenters. in this exploration, we proposed a calculation, Energy Conscious Greeny Cloud Dynamic (ECGCD) set of rules that accomplishes live VM relocation that is turning off the inert has or located it to lowcontrol mode (i.e., rest or hibernation),that builds up power productivity and succesful usage of property in the dynamic hosts. The take a look at stop result confirmations with duplicate that, the proposed calculation achieves good sized diploma of lower in electricity usage in correlation with the modern-day-day VM combination calculations.

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Virtual Machine Sizing in Virtualized Public Cloud Data Centres

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1953124 ◽

2019 ◽

pp. 583-590

Author(s):

Kenga Mosoti Derdus ◽

Vincent Oteke Omwenga ◽

Patrick Job Ogao

Keyword(s):

Virtual Machine ◽

Energy Savings ◽

Service Providers ◽

Cloud Service ◽

Public Cloud ◽

Dynamic Nature ◽

Cloud Data ◽

Data Centre ◽

Data Centres ◽

Vm Consolidation

Virtual machine (VM) consolidation in data centres is a technique that is used to ensure minimum use of physical servers (hosts) leading to better utilization of computing resources and energy savings. To achieve these goals, this technique requires that the estimated VM size is on the basis of application workload resource demands so as to maximize resources utilization, not only at host-level but also at VM-level. This is challenging especially in Infrastructure as a Service (IaaS) public clouds where customers select VM sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the amount of resources their applications need. More often, the resources are overprovisioned and thus go to waste, yet these resources consume power and are paid for by the customers. In this paper, we propose a technique for determining fixed VM sizes, which satisfy application workload resource demands. Because of the dynamic nature of cloud workloads, we show that any resource demands that exceed fixed VM resources can be addressed via statistical multiplexing. The proposed technique is evaluated using VM usage data obtained from a production data centre consisting of 49 hosts and 520 VMs. The evaluations show that the proposed technique reduces energy consumption, memory wastage and CPU wastage by at least 40%, 61% and 41% respectively.

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Dynamic VM Consolidation Enhancement for Designing and Evaluation of Energy Efficiency in Green Data Centers Using Regression Analysis

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.6.14966 ◽

2018 ◽

Vol 7 (3.6) ◽

pp. 179

Author(s):

A V. Sajitha ◽

A C. Subhajini

Keyword(s):

Energy Efficiency ◽

Regression Analysis ◽

Data Centers ◽

Virtual Machines ◽

Computation Time ◽

Experimental Result ◽

Analysis Model ◽

Bandwidth Utilization ◽

Cloud Data ◽

Vm Consolidation

Enhancement of dynamic Virtual Machines (VM) consolidation is an efficient means to improve the energy efficiency via effective resources utilization in Cloud data centers. In this paper, we propose an algorithm, Energy Conscious Greeny Cloud Dynamic Algorithm, which considers multiple factors such as CPU, memory and bandwidth utilization of the node for empowering VM consolidation by using regression analysis model. This algorithm is the combination of several adaptive algorithms such as EnCoReAn (UPReAn) for Predicting the Utility of a host), Overload and Under-load detection), VM Selection and Allocation algorithms, which helps to achieve live VM migration by switching-off unused servers to low-power mode (i.e., sleep or hibernation), thus saves energy and efficient resource utilization. This approach reduces the operational cost, computation time and increase the scalability. The experimental result proves that, the proposed algorithm attains significant percentage in reduction of energy consumption rather than existing VM consolidation strategies.

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Improvement of Energy Efficiency at Cloud Data Center Based on Fuzzy Markov Normal Algorithm VM Selection in Dynamic VM Consolidation

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v11i6.9473 ◽

2016 ◽

Vol 11 (6) ◽

pp. 511 ◽

Cited By ~ 6

Author(s):

Guruh Fajar Shidik ◽

Azhari Azhari ◽

Khabib Mustofa

Keyword(s):

Energy Efficiency ◽

Data Center ◽

Cloud Data Center ◽

Cloud Data ◽

Normal Algorithm ◽

Vm Consolidation

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Cleaner, greener data centres

ITNOW ◽

10.1093/itnow/bwab103 ◽

2021 ◽

Vol 63 (4) ◽

pp. 18-20

Author(s):

John Booth

Keyword(s):

Climate Change ◽

Energy Efficiency ◽

Energy Use ◽

Data Centre ◽

Eu Directives ◽

Data Centres

Abstract John Booth MBCS, Data Centre Energy Efficiency and Sustainability Consultant at Carbon3IT, explores the detrimental trajectory of data centre energy use, against a backdrop of COP26, climate change and proposed EU directives.

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Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽

10.3390/electronics7120389 ◽

2018 ◽

Vol 7 (12) ◽

pp. 389 ◽

Cited By ~ 6

Author(s):

Aisha Fatima ◽

Nadeem Javaid ◽

Tanzeela Sultana ◽

Waqar Hussain ◽

Muhammad Bilal ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Particle Swarm ◽

Vm Placement ◽

Swarm Optimization ◽

Cloud Data ◽

Cloud Data Centers ◽

Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

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