Efficient Energy Utilization in Cloud Fog Environment

Hayat, Babur; Nauman, Muhammad; Yousaf, Sheraz; Mehmood, Mudassar; Saleem, Hammad

doi:10.14569/ijacsa.2019.0100476

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Nevertheless, they neglected the energy consumption during tasks processing at the server. Also, withing a cloud Fog environment in [11], the problem of resource allocation to optimize energy consumption with load balancing is studied.…”

Section: Introductionmentioning

confidence: 99%

Delay-Aware and User-Adaptive Offloading of Computation-Intensive Applications with Per-Task Delay in Mobile Edge Computing Networks

Chanyour¹,

Hmimz²,

El³

et al. 2020

IJACSA

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Mobile-edge computing (MEC) is a new paradigm with a great potential to extend mobile users capabilities becauseof its proximity. It can contribute efficiently to optimize the energy consumption to preserve privacy, and reduce the bottlenecks of the network traffic. In addition, intensive-computation offloading is an active research area that can lessen latencies and energy consumption. Nevertheless, within multiuser networks with a multi-task scenario, select the tasks to offload is complex and critical. Actually, these selections and the resources' allocation have to be carefully considered as they affect the resulting energies and delays. In this work, we study a scenario considering a user-adaptive offloading where each user runs a list of heavy computation-tasks. Every task has to be processed in its associated MEC server within a fixed deadline. Hence, the proposed optimization problem target the minimization of a weighted-sum normalized function depending on three metrics. The first is energy consumption, the second is the total processing delays, and the third is the unsatisfied processing workload. The solution of the general problem is obtained using the solutions of two sub-problems. Also, all solutions are evaluated using a set of simulation experiments. Finally, the execution times are very encouraging for moderate sizes, and the proposed heuristic solutions give satisfactory results in terms of users cost function in pseudo-polynomial times.

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Section: Introductionmentioning

confidence: 99%

Delay-Aware and User-Adaptive Offloading of Computation-Intensive Applications with Per-Task Delay in Mobile Edge Computing Networks

Chanyour¹,

Hmimz²,

El³

et al. 2020

IJACSA

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Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Baburao

Pavankumar

Prabhu³

2021

Appl Nanosci

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Fog computing is the new technology era, which is deployed as a middle layer computing system between Internet of Things (IoT) devices and cloud computing systems, where data are acquired and analyzed at the border of the system. Cloud computing offers many advantages, and drawbacks of network congestions due to the huge amount of information coming from various sources, which causes higher latency for immediate responsive devices. To conquer these problems fog computing provides solutions as they are deployed near the edge of end users. The load examination concern arises in fog computing when a great amount of new IoT user applications are connected to the fog nodes. To efficiently handle load balancing, a particle swarm optimization-based Enhanced Dynamic Resource Allocation Method (EDRAM) has been proposed which in turn reduces task waiting time, latency and network bandwidth consumption and improves the Quality of Experience (QoE). The Enhanced Dynamic Resource Allocation Method (EDRAM), which in turns helps for allocating the required resource by removing the long-time inactive, unreferenced and sleepy services from the Random-Access Memory.

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Task scheduling approach for better energy optimization in fog computing

Patel,

Modi

2023

INTERNATIONAL CONFERENCE ON SCIENCE, ENGINEERING, AND TECHNOLOGY 2022: Conference Proceedings

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Efficient Energy Utilization in Cloud Fog Environment

Cited by 4 publications

References 11 publications

Delay-Aware and User-Adaptive Offloading of Computation-Intensive Applications with Per-Task Delay in Mobile Edge Computing Networks

Delay-Aware and User-Adaptive Offloading of Computation-Intensive Applications with Per-Task Delay in Mobile Edge Computing Networks

Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Task scheduling approach for better energy optimization in fog computing

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