Energy and Carbon Footprint-Aware Management of Geo-Distributed Cloud Data Centers

Cloud computing provides on-demand access to computing resources for users across the world. It offers services on a pay-as-you-go model through data center sites that are scattered across diverse geographies. However, cloud data centers consume huge amount of electricity and leave high amount of carbon footprint in the ecosystem. This makes data centers responsible for 2% of the global CO2 emission. Therefore, having energy and carbon-efficient techniques for resource management in distributed cloud data centers is inevitable. This chapter presents a taxonomy and classifies the existing research works based on their target system, objective, and the technique they use for resource management in achieving a green cloud computing environment. Finally, it discusses how each work addresses the issue of energy and carbon-efficiency and also provides an insight into future directions.

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Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

Applied Sciences ◽

10.3390/app9173550 ◽

2019 ◽

Vol 9 (17) ◽

pp. 3550 ◽

Cited By ~ 2

Author(s):

A-Young Son ◽

Eui-Nam Huh

Keyword(s):

Energy Efficiency ◽

Cloud Computing ◽

Response Time ◽

Data Centers ◽

Cloud Data ◽

Multi Objective ◽

Service Placement ◽

Service Response Time ◽

Cloud Data Centers ◽

Distributed Cloud

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

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Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers

International Journal of Intelligent Systems and Applications ◽

10.5815/ijisa.2021.06.01 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1-12

Author(s):

Leila Helali ◽

◽

Mohamed Nazih Omri

Keyword(s):

Cloud Computing ◽

Data Centers ◽

Virtual Machines ◽

Heuristic Algorithms ◽

Energy Aware ◽

Computing Systems ◽

Cloud Data ◽

Software Licenses ◽

Cloud Data Centers ◽

Distributed Cloud

Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated,mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 𝑫𝑺𝟑𝑪. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.

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Hierarchical Agent-Based Architecture for Resource Management in Cloud Data Centers

2014 IEEE 7th International Conference on Cloud Computing ◽

10.1109/cloud.2014.128 ◽

2014 ◽

Cited By ~ 1

Author(s):

Fahimeh Farahnakian ◽

Tapio Pahikkala ◽

Pasi Liljeberg ◽

Juha Plosila

Keyword(s):

Resource Management ◽

Data Centers ◽

Agent Based ◽

Cloud Data ◽

Cloud Data Centers

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Multidimensional Resource Scheduling in Cloud Data Centers

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.1513 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 1513-1516

Author(s):

Hai Na Song ◽

Xiao Qing Zhang ◽

Zhong Tang He

Keyword(s):

Cloud Computing ◽

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Three Dimensional ◽

Resource Scheduling ◽

Computing Environment ◽

Cloud Computing Environment ◽

Cloud Data ◽

Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

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An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

Scientific Programming ◽

10.1155/2019/3204346 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16

Author(s):

Chi Zhang ◽

Yuxin Wang ◽

Yuanchen Lv ◽

Hao Wu ◽

He Guo

Keyword(s):

Energy Consumption ◽

Resource Management ◽

Management Strategy ◽

Data Centers ◽

Minimum Energy ◽

Service Level Agreement ◽

Cloud Data ◽

Cpu Utilization ◽

Cloud Data Centers ◽

Detecting Method

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.

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