Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Firdose, Saeik; Avgeris, Marios; Spatharakis, Dimitrios; Santi, Nina; Dechouniotis, Dimitrios; Violos, John; Leivadeas, Aris; Αθανασόπουλος, Νικόλαος; Mitton, Nathalie; Papavassiliou, Symeon

doi:10.1016/j.comnet.2021.108177

Cited by 129 publications

(59 citation statements)

References 243 publications

(302 reference statements)

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“…The problem of task offloading falls into the knapsack resource allocation category which is NP-hard in general [13]. Most of the proposed approaches follow a partial or full offloading technique, according to whether the tasks are separated or not, with the goal to minimize the overall latency and/or energy [14]. Furthermore, they propose static resource allocation schemes on the edge infrastructure.…”

Section: Related Workmentioning

confidence: 99%

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Avgeris

Spatharakis

Dechouniotis

et al. 2022

Sensors

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Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

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Section: Related Workmentioning

confidence: 99%

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Avgeris

Spatharakis

Dechouniotis

et al. 2022

Sensors

Self Cite

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“…6.2 Future Directions 6.2.1 Task offloading. Task offloading is the transfer of computational responsibility to more resource-rich devices such as Edge, Fog, or Cloud computing services [65]. Encompassing the limitations of embedded devices and existing work to formulate effective Online learning solutions, offloading learning/training, or other intensive processing tasks to resource-rich devices needs investigation.…”

Section: Challengesmentioning

confidence: 99%

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

Rajapakse¹,

Karunanayake²,

Nadeem³

2022

Preprint

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The rapid miniaturization of Machine Learning (ML) for low powered processing has opened gateways to provide cognition at the extreme edge (E.g., sensors and actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research field proposes to democratize the use of Machine Learning (ML) and Deep Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly energy-efficient pervasive devices capable of operating with less than a few Milliwatts of power. Nevertheless, many solutions assume that TinyML can only run inference. Despite this, growing interest in TinyML has led to work that makes them reformable, i.e., work that permits TinyML to improve once deployed. In line with this, roadblocks in MCU based solutions in general, such as reduced physical access and long deployment periods of MCUs, deem reformable TinyML to play a significant part in more effective solutions. In this work, we present a survey on reformable TinyML solutions with the proposal of a novel taxonomy for ease of separation. Here, we also discuss the suitability of each hierarchical layer in the taxonomy for allowing reformability. In addition to these, we explore the workflow of TinyML and analyze the identified deployment schemes and the scarcely available benchmarking tools. Furthermore, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges and future directions.

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“…This often urges the offloading of computational hefty IoT tasks to a remote infrastructure, a process called task offloading [3]. Edge Computing [4] is a viable solution for the task offloading as it allows to offer the necessary networking and computational resources at the edge of the network enabling at the same time the real time processing VOLUME 4, 2016 of the IoT data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for IoT Traffic Multi-Classification in a Smart-City Scenario

2022

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As the number of Internet of Things (IoT) devices and applications increases, the capacity of the IoT access networks is considerably stressed. This can create significant performance bottlenecks in various layers of an end-to-end communication path, including the scheduling of the spectrum, the resource requirements for processing the IoT data at the Edge and/or Cloud, and the attainable delay for critical emergency scenarios. Thus, a proper classification or prediction of the time varying traffic characteristics of the IoT devices is required. However, this classification remains at large an open challenge. Most of the existing solutions are based on machine learning techniques, which nonetheless present high computational cost, whereas they are not considering the fine-grained flow characteristics of the traffic. To this end, this paper introduces the following four contributions. Firstly, we provide an extended feature set including, flow, packet and device level features to characterize the IoT devices in the context of a smart environment. Secondly, we propose a custom weighting based preprocessing algorithm to determine the importance of the data values. Thirdly, we present insights into traffic characteristics using feature selection and correlation mechanisms. Finally, we develop a two-stage learning algorithm and we demonstrate its ability to accurately categorize the IoT devices in two different datasets. The evaluation results show that the proposed learning framework achieves 99.9% accuracy for the first dataset and 99.8% accuracy for the second. Additionally, for the first dataset we achieve a precision and recall performance of 99.6% and 99.5%, while for the second dataset the precission and recall attained is of 99.6% and 99.7% respectively. These results show that our approach clearly outperforms other well-known machine learning methods. Hence, this work provides a useful model deployed in a realistic IoT scenario, where IoT traffic and devices' profiles are predicted and classified, while facilitating the data processing in the upper layers of an end-to-end communication model.

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Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Cited by 129 publications

References 243 publications

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

A Deep Learning Approach for IoT Traffic Multi-Classification in a Smart-City Scenario

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