GreenSched: An intelligent energy aware scheduling for deadline-and-budget constrained cloud tasks

Kaur, Tarandeep; Chana, Inderveer

doi:10.1016/j.simpat.2017.11.008

Cited by 20 publications

(6 citation statements)

References 33 publications

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“…Regarding scheduling, we can find works such as [ 142 ], in which multi-stage multi-machine multi-product scheduling problem is resolved using the Bat Algorithm. In [ 143 ] energy-aware cloud task scheduling is studied by resorting to the same method. Finally, a task scheduler on diverse computing systems is described in [ 144 ].…”

Section: A Joint Perspective On Bio-inspired Computation and Big Datamentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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Section: A Joint Perspective On Bio-inspired Computation and Big Datamentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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“…During the past years, numerous researches have focused on saving energy in cloud center [3], [4], [16]- [19], [25]- [28]. Some of them reduced energy consumption by optimizing the task scheduling process [25], [27] and resource allocation issues [4], [16], [17], [26], [28]. In [16], a task scheduling method using a combination of cultural and ACO algorithm was presented in order to optimize energy consumption.…”

Section: Related Research a In Terms Of Energy Consumptionmentioning

confidence: 99%

“…However, the complexity of the combination algorithm is too high and they only consider the task makespan without evaluating the algorithm calculation overhead. In [26], a GreenSched model was proposed to schedule tasks on identified less energy consuming or energy aware nodes. Instead of turning off low-energy nodes, they use them as mission gathering places.…”

Section: Related Research a In Terms Of Energy Consumptionmentioning

confidence: 99%

An Experience-Based Scheme for Energy-SLA Balance in Cloud Data Centers

Zhou

Liu

et al. 2019

IEEE Access

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The proliferation of cloud computing has resulted in the establishment of large-scale data centers containing thousands of computing nodes and consuming enormous amounts of electrical energy. However, the low-cost and high-efficiency slogans are getting louder and louder, and the IT industry is also striving for this pursuit. Therefore, it is vital to minimizing the energy consumption for cloud providers while ensuring the quality of service for cloud users. In this paper, we propose several heuristic strategies to optimize these two metrics based on a two-level management model under a heterogeneous cloud environment. First, to detect whether a physical node is continuously overloaded, we propose an empirical forecast algorithm, which predicts the future state of the host by statistically analyzing the historical utilization data of the host. Second, we propose a weighted priority virtual machine (VM) selection algorithm. For each VM on the overloaded host, we weight several utilization factors and calculate its migration priority. Then, we simulate the proposed approach and compare it with the existing overloaded hosts detection algorithms with different VM selection policies under different workloads.

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“…Today, research community's top priorities are energy conservation and effectiveness. The issue of excessive energy utilization arises as a result of unexpected and rapid changes in the environment around the globe [5]. The levels of carbon footprint and Green House Gases (GHG) in the environment have rapidly increased.…”

Section: Introductionmentioning

confidence: 99%

A systematic review on effective energy utilization management strategies in cloud data centers

2022

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Data centers are becoming considerably more significant and energy-intensive due to the exponential growth of cloud computing. Cloud computing allows people to access computer resources on demand. It provides amenities on the pay-as-you-go basis across the data center locations spread over the world. Consequently, cloud data centers consume a lot of electricity and leave a proportional carbon impact on the environment. There is a need to investigate efficient energy-saving approaches to reduce the massive energy usage in cloud servers. This review paper focuses on identifying the research done in the field of energy consumption (EC) using different techniques of machine learning, heuristics, metaheuristics, and statistical methods. Host CPU utilization prediction, underload/overload detection, virtual machine selection, migration, and placement have been performed to manage the resources and achieve efficient energy utilization. In this review, energy savings achieved by different techniques are compared. Many researchers have tried various methods to reduce energy usage and service level agreement violations (SLAV) in cloud data centers. By using the heuristic approach, researchers have saved 5.4% to 90% of energy with their proposed methods compared with the existing methods. Similarly, the metaheuristic approaches reduce energy consumption from 7.68% to 97%, the machine learning methods from 1.6% to 88.5%, and the statistical methods from 5.4% to 84% when compared to the benchmark approaches for a variety of settings and parameters. So, making energy use more efficient could cut down the air pollution, greenhouse gas (GHG) emissions, and even the amount of water needed to make power. The overall outcome of this review work is to understand different methods used by researchers to save energy in cloud data centers.

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GreenSched: An intelligent energy aware scheduling for deadline-and-budget constrained cloud tasks

Cited by 20 publications

References 33 publications

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

An Experience-Based Scheme for Energy-SLA Balance in Cloud Data Centers

A systematic review on effective energy utilization management strategies in cloud data centers

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