Resource clustering algorithm for cloud data centers

2021 ◽  
pp. 1-11
Author(s):  
Cail Song ◽  
Bin Liang ◽  
Jiao Li
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Clustering Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Good Effect ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Physical Machine ◽  
Cloud Data Centers

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider’s earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine’s use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine’s use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine’s use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent.

scholarly journals An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chunxia Yin ◽  
Jian Liu ◽  
Shunfu Jin
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Sleep Mode ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Response Performance

In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.

scholarly journals SDN Based Energy Efficient Cloud Data Center Networks

2019 ◽  
Vol 8 (12) ◽  
pp. 4250-4256
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Easy Access ◽  
Primary Concern ◽  
Data Center Networks ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing has led to the tremendous growth of IT organizations, which serves as the means of delivering services to large number of consumers globally, by providing anywhere, anytime easy access to resources and services. The primary concern over the increasing energy consumption by cloud data centers is mainly due to the massive emission of greenhouse gases, which contaminate the atmosphere and tend to worsen the environmental conditions. The major part of huge energy consumption comes from large servers, high speed storage devices and cooling equipment, present in cloud data centers. These serve as the basis for fulfilling the increasing need for computing resources. These in turn bestow additional cost of resources. The goal is to focus on energy savings through effective utilization of resources. This necessitates the need for developing a green-aware, energy-efficient framework for cloud data center networks. The Software Defined Networking (SDN) are chosen as they aid in studying the behaviour of networks from the overall perspective of software layer, rather than decisions from each individual device, as in case of conventional networks. The central objective of this paper is dedicated to survey on various existing SDN based energy efficient cloud data center networks.

scholarly journals An Efficient Cloud Data Center Allocation to the Source of Requests

2020 ◽  
Vol 32 (3) ◽  
pp. 23-36
Author(s):  
Kanniga Devi R. ◽  
Murugaboopathi Gurusamy ◽  
Vijayakumar P.
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Service Consumer ◽  
Cloud Data Centers

A Cloud data center is a network of virtualized resources, namely virtualized servers. They provision on-demand services to the source of requests ranging from virtual machines to virtualized storage and virtualized networks. The cloud data center service requests can come from different sources across the world. It is desirable for enhancing Quality of Service (QoS), which is otherwise known as a service level agreement (SLA), an agreement between cloud service requester and cloud service consumer on QoS, to allocate the cloud data center closest to the source of requests. This article models a Cloud data center network as a graph and proposes an algorithm, modified Breadth First Search where the source of requests assigned to the Cloud data centers based on a cost threshold, which limits the distance between them. Limiting the distance between Cloud data centers and the source of requests leads to faster service provisioning. The proposed algorithm is tested for various graph instances and is compared with modified Voronoi and modified graph-based K-Means algorithms that they assign source of requests to the cloud data centers without limiting the distance between them. The proposed algorithm outperforms two other algorithms in terms of average time taken to allocate the cloud data center to the source of requests, average cost and load distribution.

scholarly journals Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

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.

scholarly journals Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772199721
Author(s):  
Mueen Uddin ◽  
Mohammed Hamdi ◽  
Abdullah Alghamdi ◽  
Mesfer Alrizq ◽  
Mohammad Sulleman Memon ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data Center ◽  
Server Consolidation ◽  
Cloud Data ◽  
Task Scheduling Algorithm ◽  
Cloud Data Centers

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

Energy Efficient Load Balancing in Cloud Data Center Using Clustering Technique

2019 ◽  
Vol 15 (1) ◽  
pp. 84-100 ◽  
Author(s):  
N. Thilagavathi ◽  
D. Divya Dharani ◽  
R. Sasilekha ◽  
Vasundhara Suruliandi ◽  
V. Rhymend Uthariaraj
Keyword(s):  
Response Time ◽  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Resource Requirement ◽  
Server Consolidation ◽  
Cloud Data ◽  
Initial Allocation

Cloud computing has seen tremendous growth in recent days. As a result of this, there has been a great increase in the growth of data centers all over the world. These data centers consume a lot of energy, resulting in high operating costs. The imbalance in load distribution among the servers in the data center results in increased energy consumption. Server consolidation can be handled by migrating all virtual machines in those underutilized servers. Migration causes performance degradation of the job, based on the migration time and number of migrations. Considering these aspects, the proposed clustering agent-based model improves energy saving by efficient allocation of the VMs to the hosting servers, which reduces the response time for initial allocation. Middle VM migration (MVM) strategy for server consolidation minimizes the number of VM migrations. Further, randomization of extra resource requirement done to cater to real-time scenarios needs more resource requirements than the initial requirement. Simulation results show that the proposed approach reduces the number of migrations and response time for user request and improves energy saving in the cloud environment.

scholarly journals Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

2021 ◽  
Vol 11 (13) ◽  
pp. 5849
Author(s):  
Nimra Malik ◽  
Muhammad Sardaraz ◽  
Muhammad Tahir ◽  
Babar Shah ◽  
Gohar Ali ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Tasks Scheduling ◽  
Benchmark Datasets ◽  
Cloud Data Centers

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

scholarly journals A Novel Physical Machine Overload Detection Algorithm Combined with Quiescing for Dynamic Virtual Machine Consolidation in Cloud Data Centers

2019 ◽  
Vol 17 (3) ◽  
pp. 358-366
Author(s):  
Loiy Alsbatin ◽  
Gürcü Öz ◽  
Ali Ulusoy
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Energy Performance ◽  
Detection Algorithm ◽  
Vm Migration ◽  
Cloud Data ◽  
Detection Algorithms ◽  
Physical Machine ◽  
Cloud Data Centers

Further growth of computing performance has been started to be limited due to increasing energy consumption of cloud data centers. Therefore, it is important to pay attention to the resource management. Dynamic virtual machines consolidation is a successful approach to improve the utilization of resources and energy efficiency in cloud environments. Consequently, optimizing the online energy-performance trade off directly influences Quality of Service (QoS). In this paper, a novel approach known as Percentage of Overload Time Fraction Threshold (POTFT) is proposed that decides to migrate a Virtual Machine (VM) if the current Overload Time Fraction (OTF) value of Physical Machine (PM) exceeds the defined percentage of maximum allowed OTF value to avoid exceeding the maximum allowed resulting OTF value after a decision of VM migration or during VM migration. The proposed POTFT algorithm is also combined with VM quiescing to maximize the time until migration, while meeting QoS goal. A number of benchmark PM overload detection algorithms is implemented using different parameters to compare with POTFT with and without VM quiescing. We evaluate the algorithms through simulations with real world workload traces and results show that the proposed approaches outperform the benchmark PM overload detection algorithms. The results also show that proposed approaches lead to better time until migration by keeping average resulting OTF values less than allowed values. Moreover, POTFT algorithm with VM quiescing is able to minimize number of migrations according to QoS requirements and meet OTF constraint with a few quiescings.

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