An Optimal Strategy for Resource Utilization in Cloud Data Centers

In this article, the authors studied hotspots in cloud data centers, which are caused due to a lack of resources to satisfy the peak immediate requests from clients. The nature of resource utilization in cloud data centers are totally dynamic in context and may lead to hotspots. Hotspots are unfavorable situations which cause SLA violations in some scenarios. Here they use trend aware regression (TAR) methods as a load prediction model and perform linear regression analysis to detect the formation of hotspots in physical servers of cloud data centers. This prediction model provides an alarm period for the cloud administrators either to provide enough resources to avoid hotspot situations or perform interference aware virtual machine migration to balance the load on servers. Here they analyzed the physical server resource utilization model in terms of CPU utilization, memory utilization and network bandwidth utilization. In the TAR model, the authors consider the degree of variation between the current points in the prediction window to forecast the future points. The TAR model provides accurate results in its predictions.

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Resource Utilization-Aware Scheduling Technique Based on Dynamic Cache Refresh Scheme in Large-Scale Cloud Data centers

2017 International Conference on Computational Science and Computational Intelligence (CSCI) ◽

10.1109/csci.2017.272 ◽

2017 ◽

Cited By ~ 1

Author(s):

HeeSeok Choi ◽

Jihun Kang ◽

Daewon Lee ◽

KwangSik Chung ◽

Heonchang Yu

Keyword(s):

Resource Utilization ◽

Large Scale ◽

Data Centers ◽

Cloud Data ◽

Cloud Data Centers ◽

Scheduling Technique

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

Applied Sciences ◽

10.3390/app11135849 ◽

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.

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Joint Optimization of Server and Network Resource Utilization in Cloud Data Centers

GLOBECOM 2017 - 2017 IEEE Global Communications Conference ◽

10.1109/glocom.2017.8254037 ◽

2017 ◽

Author(s):

Biyu Zhou ◽

Jie Wu ◽

Lin Wang ◽

Fa Zhang ◽

Zhiyong Liu

Keyword(s):

Resource Utilization ◽

Data Centers ◽

Joint Optimization ◽

Network Resource ◽

Cloud Data ◽

Cloud Data Centers

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Optimal virtual machine placement with multiple resources constraints in cloud data centers

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189001 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5161-5170

Author(s):

Zhenxiang He ◽

Zhenjiang Li ◽

Shengcai Zhang ◽

Jun Lu

Keyword(s):

Resource Utilization ◽

Virtual Machine ◽

Data Centers ◽

Virtual Machine Placement ◽

Utilization Efficiency ◽

Ant Colony Optimization Algorithm ◽

Cloud Data ◽

Multiple Resources ◽

Cloud Data Centers ◽

Resource Utilization Efficiency

In cloud data centers, different virtual machine placement strategies will affect the resource utilization efficiency of the whole system. How to improve the overall resource utilization of the system by adjusting the virtual machine placement strategy under limited resources is the focus of current researchers. In this paper, a new virtual machine placement strategy is proposed, which is based on the comprehensive constraints of various system resources. In the process of solving the problem, a multi-objective ant colony optimization algorithm is used. Simulation results show that this method can effectively reduce the number of physical machines activated in the data center, and the accuracy is high.

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EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

Symmetry ◽

10.3390/sym13040690 ◽

2021 ◽

Vol 13 (4) ◽

pp. 690

Author(s):

Muhammad Ibrahim ◽

Muhammad Imran ◽

Faisal Jamil ◽

Yun Jung Lee ◽

Do-Hyeun Kim

Keyword(s):

Energy Consumption ◽

Resource Utilization ◽

Large Scale ◽

Data Centers ◽

Cloud Services ◽

Migration Strategy ◽

Cloud Data ◽

Sla Violation ◽

Cloud Data Centers ◽

Vm Consolidation

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

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