Multiuser <scp>context‐aware</scp> computation offloading in mobile edge computing based on Bayesian learning automata

Farahbakhsh, Fariba; Shahidinejad, Ali; Ghobaei‐Arani, Mostafa

doi:10.1002/ett.4127

“…To overcome communication and latency bottleneck of MCCs and reduce the burden on the backbone of the network, MEC approaches emerged. They allow mobile devices to offload computations to a middle-tear of compute resources; namely Fog servers or cloudlets [31]- [34]. In [32], authors propose a meta-heuristic task offloading scheduler, namely ant colony optimization and particle swarm optimization, to balance computation load across Fog servers.…”

Section: Scheduling Offloading To Cloud and Fogmentioning

confidence: 99%

MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments

Gedawy

¹

,

Elgazar

²

,

Harras

³

2022

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Many novel IoT-based applications demand low latency, large compute resources and high privacy. These requirements have motivated the emergence of fog and edge computing to complement the low-privacy and high-latency cloud. The idea of Fog computing is to place computational servers closer to the user, typically within the city's vicinity, to reduce latency. However, because of the high deployment cost of these servers at scale, and unreliable network infrastructures in many countries and areas, edge computing was proposed. Edge computing advocates leveraging compute resources, typically 0-hops away, on distributed ensembles of colocated devices called FemtoClouds. In this paper, we propose Maestro, a system that enables users to offload computational jobs to multiple FemtoClouds in their immediate vicinity. For Maestro, we propose an integrated architecture that includes two new scheduling algorithms for assigning computing workloads to FemtoClouds. Each of our scheduling algorithms is designed to allow the system to operate more efficiently given poor or strong network infrastructure. We implement a full prototype of our system to assess its performance on our experimental testbed. The results indicate that in communication-challenged environments, our specialized scheduler outperforms state-of-the-art schedulers by up to 55%, while in communication-friendly environments our other specialized scheduler outperforms state-of-the-art schedulers by up to 67%.

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“…In the past few years, we can find a plethora of work towards offloading problems from mobile devices to the remote server or cloud. Similar to other domains, in cloud computing, Deep Learning (DL) has been widely applied [8][9][10][11][12][13]. At the same time, a tremendous amount of efforts have been made towards reducing the overload of DL tasks to make it feasible on smartphones [14].…”

Section: Related Workmentioning

confidence: 99%

“…E offloading_phone = P wait_cloud × t cloud + E wifi + E bluetooth (9) In order to improve the energy efficiency of smartphones, the decision to offload is made through the following two comparisons.…”

Section: Smartphone's Decision Modelmentioning

confidence: 99%

Computation offloading technique for energy efficiency of smart devices

Ko

¹

,

Choi

²

,

Paul

³

2021

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The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading. Despite major development of wearable devices and offloading techniques, there are several concerns such as latency, battery power, and computation capability that requires significant development. In this paper, we focus on the fact that most smart wearable devices have Bluetooth pairing with smartphones, and Bluetooth communication is significantly energy-efficient compare to 3G/LTE or Wi-Fi. We propose a computation offloading technique that offloads from the smartphone to the cloud server considering the decision model of both wearable devices and smartphones. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. In our model, we increase the energy efficiency of smart devices. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly.

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“…Bluetooth energy (E bluetooth ) and Wi-Fi energy (E wif i ) are consumed to return to the wearable device and smartphones, respectively. E of f loading phone = P wait cloud × t cloud + E wif i + E bluetooth (9) In order to improve the energy efficiency of smartphones, the decision to offload is made through the following two comparisons.…”

Section: Smartphone's Decision Modelmentioning

confidence: 99%

Computation Offloading Technique for Energy Efficiency of Smart Devices

Ko

¹

,

Choi

²

,

Paul

³

2021

Preprint

0

View full text Add to dashboard Cite

The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading. Despite major development of wearable devices and offloading techniques, there are several concerns such as latency, battery power, and computation capability that requires significant development. In this paper, we focus on the fact that most smart wearable devices have Bluetooth pairing with smartphones, and Bluetooth communication is significantly energy-efficient compare to 3G/LTE or Wi-Fi. We propose a computation offloading technique that offloads from the smartphone to the cloud server considering the decision model of both wearable devices and smartphones. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. In our model, we increase the energy efficiency of smart devices. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly

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Multiuser context‐aware computation offloading in mobile edge computing based on Bayesian learning automata

Cited by 44 publications

References 38 publications

MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments

MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments

Computation offloading technique for energy efficiency of smart devices

Computation Offloading Technique for Energy Efficiency of Smart Devices

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