Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques

Khattar, Nagma; Sidhu, Jagpreet; Singh, Jaiteg

doi:10.1007/s11227-019-02764-2

Cited by 96 publications

(25 citation statements)

References 123 publications

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“…Hardware strategies employ parallel architectures, multicore architectures, voltage and frequency scaling, and dynamic component consolidation and deactivation to reduce energy consumption of hardware in cloud data centers. e DVFS introduced above is the most popular one among them [12]. By employing this technique, the CPU can adjust its performance dynamically.…”

Section: Hardware Strategiesmentioning

confidence: 99%

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

Zhang

Wang

et al. 2019

Scientific Programming

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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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Section: Hardware Strategiesmentioning

confidence: 99%

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

Zhang

Wang

et al. 2019

Scientific Programming

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“…Later plans have racks of computers cooled by fluids that are pumped through the racks, servers and even chips. Yet, these proposed solutions are costly to implement and give a limited reduction in energy consumption [5][6][7].…”

Section: Review Of Research Efforts In Cloud Computing Data Center Enmentioning

confidence: 99%

“…(3) Software-based optimizations include employing job scheduling algorithms in the application level of the data center, which have been broadly utilized for reducing energy consumption. Some researchers used heuristics as a base of a scheduling algorithm to map the task on the heterogeneous system while minimizing energy consumption [5].…”

Section: Review Of Research Efforts In Cloud Computing Data Center Enmentioning

confidence: 99%

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

Diouani¹,

Medromi²

2019

IJIES

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Given the development of the Cloud Computing recently, clients and customers using the Cloud for both individual and business needs have expanded to an uncommon scale. This has normally prompted the expanded deployments of Cloud data centers over the globe. As a result, Cloud data centers are seen to be monstrous energy consumers and natural polluters. They require an extraordinary measure of regular energy which has made an effect on the energy supply and natural conditions of the environment. This is the reason why the vulnerability of persistent energy supply, later on, is being referred to. In this way, there is a need of an energy-aware cloud-based system which automatically and efficiently manages and optimize cloud computing data center resources by considering energy consumption as an essential Quality of Service (QoS) parameter. This paper, focus on the energy utilization of the data centers and how this can be limited so as to make the cloud computing greener. Thus, a new autonomic resource optimization manager has been proposed to avail the most optimum level of resources with reduced server energy consumption. The proposed framework has been verified theoretically and tested experimentally. The experimental analysis has demonstrated that the effectiveness of the proposed solution is greater than the state-ofthe-art methods in terms of the achieved results related to reducing energy consumption and response time in cloud computing data centers.

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“…The nature-inspired load-balancing algorithms can be classified into three different types: heuristic, metaheuristic, and hybrid. The purpose of designing heuristics is to achieve the optimal response in a specified period [13][14][15]. Met-heuristic algorithms require more execution time to achieve the optimal response, and these algorithms have a more extensive response space than heuristics [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes

Tahmasebi-Pouya

Sarram

Mostafavi

2022

Wireless Communications and Mobile Computing

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In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user’s requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double- Q -learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods.

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Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques

Cited by 96 publications

References 123 publications

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

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

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes

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