Congestion-Aware and Energy-Efficient MEC Model with Low Latency for 5G

Ismail, Alshimaa H.; Soliman, Tarek Abed; Salama, Gerges M.; El‐Bahnasawy, Nirmeen A.; Hamed, Hesham F. A.

doi:10.1109/jac-ecc48896.2019.9051312

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…[9], [10] introduced power saving problem on IoT devices. [11] proposed a hand-off model between a specific model named (AGCM) to reduce the waste of energy at both cloud and mobile edge.…”

Section: Related Workmentioning

confidence: 99%

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

2022

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Mobile Edge Computing (MEC) technology philosophy inspires the next generation mobile networks to provide cloud computing capabilities in addition to a diverse range of Information Technology (IT) services with ultra-low latency and higher bandwidth at the edge. One of the most common challenges of 5G-MEC is the management and orchestration across all networks and infrastructure resources as well as end-to-end quality of experience. The decentralized architecture of MEC with independent and noncollaborative servers results in the situation of having underutilized servers with wasted energy. Moreover, the consequences of having highly utilized servers with highly consumed energy are not only the incapability to accommodate all the load of the computing jobs and the dramatic increase in the total OPEX cost, but it also creates some environmental problems. Orchestrating servers' workload and control offloading the computation jobs is one of the technical advantages of MEC since it satisfies the increasing requirements of modern mobile applications while optimizing the energy consumption and cost. In this work, we consider cluster-based energy-aware offloading framework. The proposed work consists of dual-tier domain divided into clusters of Edge Servers ES s . We have presented the results of our simulation as a proof of our concept that the formulated adaptive strategy to minimize the optimization problem calculation per cluster reduces the energy consumption and enhances the quality of experience while achieving the conservation of the related computing and storage resources cost.

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“…[9], [10] introduced power saving problem on IoT devices. [11] proposed a hand-off model between a specific model named (AGCM) to reduce the waste of energy at both cloud and mobile edge.…”

Section: Related Workmentioning

confidence: 99%

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

2022

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show abstract

“…Computation offloading strategy has been extensively studied in mobile edge computing (MEC) [11] and mobile cloud computing (MCC) [12]. Most studies focus on one [13][14][15] or two [16][17][18] aspects of energy consumption and latency. In [19], the author designed a deadline and priority-aware task offloading (DPTO) strategy to schedule and process offloaded tasks to suitable computing devices.…”

Section: A Related Workmentioning

confidence: 99%

“…Here, d represents the offloading deadline which is equal to the length of the time slot. -17) indicates the computing time and transmission time cannot exceed a time slot (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). indicates the offloading energy consumption cannot exceed the maximum instantaneous discharge threshold.…”

mentioning

confidence: 99%

Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Bai

Han

et al. 2021

IEEE Access

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Fog computing is considered to be an effective method to solve the problem of high latency and high energy consumption of IoT devices. A suitable computation offloading strategy can provide a low offloading cost to the user device. Most researches on computation offloading in fog computing focus on one or two targets to improve system performance, however, the actual system needs to meet a comprehensive demand. Therefore, the joint optimization of multi-objective in multiple scenarios is a very meaningful problem. Inspired by this, the paper highlights the joint optimization research for fog computing, which proposes a Joint Computation offloading, Data compression, Energy harvesting, and Application scenarios (JCDEA) algorithm. The related mathematical model is constructed and the cost expressions of local computing, fog computing, and cloud computing are derived. Through the proposed algorithm, solving the computation offloading strategy is transformed into solving the minimum cost and is simplified by controlling strategy factors. Moreover, five simulation experiments are conducted and the meaningful conclusions are drawn, which contain that (1) the cost of fog computing is lower than that of local and cloud computing in most time slots and cloud computing can compensate for fog computing in complex environments; (2) the cost increases approximately linear with the amount of offloaded data; (3) the number of user devices and the compression ratio affect the fog-to-cloud ratio (FCR), while the FCR affects the cost; and (4) the related offloading strategy distribution and the cost are obtained for different scenarios. The JCDEA algorithm always outperforms than that of the random selection algorithm in all scenarios.

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“…Nowadays, ultra‐low latency, green computing, and scalable framework for MEC networks is an attractive technology for IoT smart devices. An active queue management‐based green cloud model introduced to minimise the latency and energy wastage [42]. In [43], a hybrid artificial neural network‐based particle swarm optimisation algorithm investigated to improve user quality of experience in cloud‐edge computing platform.…”

Section: Related Workmentioning

confidence: 99%

Congestion‐aware adaptive decentralised computation offloading and caching for multi‐access edge computing networks

et al. 2020

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Congestion-Aware and Energy-Efficient MEC Model with Low Latency for 5G

Cited by 6 publications

References 13 publications

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing

Congestion‐aware adaptive decentralised computation offloading and caching for multi‐access edge computing networks

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