An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Gholipour, Niloofar; Shoeibi, Niloufar; Arianyan, Ehsan

doi:10.1007/978-3-030-53829-3_26

Cited by 5 publications

(3 citation statements)

References 65 publications

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“…In the work by Zubar et al [28] , an approach for green computing process optimization using a machine learning algorithm is presented. Handling the information is an important task in the healthcare sector; for this reason, data mining techniques are the correct alternative to solve the complex problems in this sector.…”

Section: Overview Of Existing Work On Intelligent Systems Applied To Green Computingmentioning

confidence: 99%

Review on the Interactions of Green Computing and Computational Intelligence techniques and their Applications to Real-World Problems

Castillo¹,

Melín²

2021

JSEGC

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The present paper describes a comprehensive review on the current state of art on the interactions of the green computing and computational intelligence areas and their applications to real world-problems. Green computing can be viewed as the responsible use of computers and their corresponding resources for achieving environmental and ecological goals. On the other hand, computational intelligence is an area consisting of methods and models for building intelligent systems, such as neural network models, fuzzy logic systems and genetic algorithms. The interaction of the two areas could produce intelligent systems that can achieve environmentally responsible and eco-friendly applications, which can help really achieve the main goals of green computing. The present review summarizes the most relevant existing works on these areas, as well as the current applications that have been done to the moment. In addition, an analysis on the trends of current existing works and a discussion of possible research trends are also offered. The significance of this paper is the presentation in a unified way of the main concepts of green computing and computational intelligence, as well their current and future applications. We strongly believe that this paper could help lay the foundation for a new hybrid area combining green computing with computational intelligence, which would be very important because up to now both areas have been flourishing apart and they could both benefit by interacting with each other.

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Section: Overview Of Existing Work On Intelligent Systems Applied To Green Computingmentioning

confidence: 99%

Review on the Interactions of Green Computing and Computational Intelligence techniques and their Applications to Real-World Problems

Castillo¹,

Melín²

2021

JSEGC

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“…The plan is to use as few physical computers as possible to host as many virtual machines and containers as possible. Previous studies have implemented container and virtual machine consolidation in a segregated fashion 16 …”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have implemented container and virtual machine consolidation in a segregated fashion. 16 Few studies have looked at container consolidation as a collective optimization problem, which considers both the selection of VMs and their placement. ZA Mann in Reference 17, suggested a "COMBINED" strategy to solve this issue, emphasizing decision effectiveness rather than solution quality.…”

mentioning

confidence: 99%

FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

Patra,

Sahoo,

Turuk

2024

Concurrency and Computation

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Containers in cloud computing provide a logical packaging technique for applications to be isolated from the computing environment in which they actually execute, allowing for efficient sharing of memory, processor, storage, and network resources at the Operating System (OS) level. Since they are so compact, container‐based clouds have recently gained significant popularity. In order to maximize resource usage and minimize energy consumption, the container consolidation technique is widely employed in the cloud environment. This work introduces container consolidation in cloud computing that exploits Fractional Pelican Hawks Optimization (FPHO). In Container as a Service (CaaS) model, containers are placed in the Virtual Machines (VMs), and virtual machines are hosted in Physical Machines (PMs) or servers. The proposed method for container consolidation consists of two modules, namely, the host status module and the consolidation module. In the host status module, the PM's load is predicted using Long Short Term Memory (LSTM) and checked whether the PM is overloaded or underloaded using a threshold. If it is overloaded, the container selection algorithm is performed, and the migration list is also generated. In the consolidation module, the created migration list which is employed for the destination list to be created by an overloaded destination selector. In the same way, the underloaded list is also generated by the underloaded destination selector. Finally, the container and VM migration is carried out by considering the multi‐objectives such as predicted load, migration cost, resource utilization, energy consumption, network, and bandwidth which are optimally selected by the proposed FHPO. Here, FHPO is the combination of Fractional Pelican Optimization (FPO) and Fire Hawk Optimizer (FHO). The designed model achieved the measures with minimum energy consumption, resource utilization, Service Level Agreement (SLA), and Makespan as 0.066, 0.019, 0.054, and 0.066, respectively for setup one.

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Recent Advances in Energy-Efficient Resource Management Techniques in Cloud Computing Environments

2012

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An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

Cited by 5 publications

References 65 publications

Review on the Interactions of Green Computing and Computational Intelligence techniques and their Applications to Real-World Problems

Review on the Interactions of Green Computing and Computational Intelligence techniques and their Applications to Real-World Problems

FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

Recent Advances in Energy-Efficient Resource Management Techniques in Cloud Computing Environments

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