Energy-Efficient Service Allocation Techniques in Cloud: A Survey

Mishra, Sambit Kumar; Sahoo, Sampa; Sahoo, Bibhudatta; Jena, Sanjay Kumar

doi:10.1080/02564602.2019.1620648

Cited by 17 publications

(8 citation statements)

References 32 publications

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“…Joint Optimization-Aware Resource Allocation: This method aims to optimize both computing and network resources simultaneously. Recently, one of the biggest problems facing researchers is energy optimization [88] and network performance [89] in a CDC. It is worth noting that power optimization that deals with servers and DCN separately causes limitations on the network because consolidation of a server without taking into account the DCN could lead to traffic congestion and lower network performance.…”

Section: Scope Methodsmentioning

confidence: 99%

Software-defined networks for resource allocation in cloud computing: A survey

et al. 2021

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Section: Scope Methodsmentioning

confidence: 99%

Software-defined networks for resource allocation in cloud computing: A survey

et al. 2021

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“…The authors of [57] unveiled a new concept for cloud computing energy efficiency that tracks how the emissions of different greenhouse gasses from data centers have caused, how much ozone depletion, and how eco-friendly our technology can be [69][70][71][72][73][74][75][76][77][78][79]. It also addresses the numerous main problems when these energy-saving strategies are applied in cloud data centers.…”

Section: Evolution Of Energy Efficiency Techniquesmentioning

confidence: 99%

“…According to the observations by the authors, the vast majority of energy-saving approaches ignore the quality of service (quality of service) factors, scalability, and network lifetime. The total energy usage by a cloud system can be reduced by using service allocation mechanisms outlined in [74].…”

Section: Related Surveysmentioning

confidence: 99%

“…For maintaining a steady temperature and humidity level, a considerable amount of energy is required. ASHRAETC9, the American Society of Heating, Refrigerating, and Air-Conditioning Engineers, specified the IT server temperature between 18-27 • C, and therefore allowable temperature ranges from 15-32 • C (class 1) and 10-35 • C (class 2) with an acceptable relative humidity range from 20-80% [74]. The server's internal parts are kept steady to minimize server inlet temperature effects by increasing the fan speed.…”

Section: Self-designed Simulatormentioning

confidence: 99%

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A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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“…It achieves the optimization of both objectives by taking the estimated time to compute matrix for each task along with the total utilization matrix over all VMs and normalizes the sum of each entry for each VM and finds the minimum value to schedule the task to the VM corresponding to the index of the minimum value. Mishra et al (2019) [21] survey the current energy efficient service allocation techniques in cloud systems and divide the various techniques based on a taxonomy. The techniques consider tasks of either real-time or non-real time.…”

Section: Related Workmentioning

confidence: 99%

ESPS: Energy Saving Power Spectrum-Aware Scheduling to Leverage Differences in Power Ratings of Physical Hosts in Datacenters

Gourisaria

Khilar

Patra

2021

IJCAI

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Cloud Computing has seen massive growth over the past couple of decades, leading to exponential growth in energy consumption at data centres. Data centres consuming high amounts of energy leave a carbon footprint of the same scale, hence Cloud Service Providers (CSPs) have been looking for energy-efficient solutions to task scheduling in cloud to reduce the amount of carbon dioxide emission. Saving energy not only helps reduce the carbon footprint datacentres have on the environment, but also helps cover the costs of running multiple datacentres on the CSP's end. In this paper, we propose an energy saving task scheduling heuristic for heterogeneous cloud systems which selects the optimal physical host containing virtual machines with the additional consideration of the utilization of any incoming task on that particular virtual machine. We compare the energy efficiency of our proposed heuristic with recent algorithms including ECTC, MaxUtil, Random, and FCFS on several benchmark and synthetic datasets to display its superiority in energy-efficient task scheduling in heterogeneous cloud environments. FCFS, MaxUtil, Random, and ECTC respectively consume approximately 38.65%, 33.59%, 53.02%. and 46.96% more energy in a heterogeneous cloud environment as compared to our proposed heuristic namely Energy Saving Power Spectrum-Aware Scheduling (ESPS). Povzetek: Računalništvo v oblaku beleži močno rast, kar vodi do eksponentne rasti porabe energije v podatkovnih centrih. V tem prispevku je predlagano hevristično načrtovanje naloge varčevanja z energijo za heterogene sisteme v oblaku, ki izbere optimalnega fizičnega gostitelja, ki vsebuje navidezne stroje. Energijska učinkovitost predlagane hevristike je primerjana z nedavnimi algoritmi, vključno z ECTC, MaxUtil, Random in FCFS na več primerjalnih in sintetičnih naborih podatkov.

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Energy-Efficient Service Allocation Techniques in Cloud: A Survey

Cited by 17 publications

References 32 publications

Software-defined networks for resource allocation in cloud computing: A survey

Software-defined networks for resource allocation in cloud computing: A survey

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

ESPS: Energy Saving Power Spectrum-Aware Scheduling to Leverage Differences in Power Ratings of Physical Hosts in Datacenters

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