PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

Gao, Xin; Huang, Xi; Bian, Simeng; Shao, Ziyu; Yang, Yang

doi:10.1109/icc.2019.8762031

Cited by 23 publications

(20 citation statements)

References 18 publications

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“…In connection with minimising delay, computational offloading has been proposed in the literature [4,8,9,[11][12][13][14][15][16]24]. Computational offloading can be deployed offline or online.…”

Section: Resultsmentioning

confidence: 99%

“…Offloading computing tasks to a cloud data centre and/or neighbouring fog computing servers has received considerable attention in other publications [4,8,9,[12][13][14][15]. Due to the differences in system models, existing works can be categorised as outlined below:…”

Section: Computational Offloadingmentioning

confidence: 99%

“…The associated fog nodes can choose to offload part of their computational workload to another fog node or to the cloud, thereby exploiting fog and cloud resources to process end-user tasks. Gao et al [9] investigated dynamic offloading and resource allocation, formulating the problem as a stochastic network optimisation to minimise delay and power consumption while ensuring the stability of all queues in the system. They present a predictive offloading and resource allocation approach that focuses on the trade-off between energy consumption and delay.…”

Section: Computation Offloading In Fog-cloud Computing Systemsmentioning

confidence: 99%

“…However, FNs are characterised as resource-limited devices [4,7] because they cannot handle all requests emanating from IoT devices located in their radius of coverage. To overcome this problem, computational offloading within the fog computing paradigm is one of the solutions that helps to improve the utilisation of available resources at FNs and allows for the processing of end-user tasks [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…In fog computing, computational offloading refers to the cooperation between fog nodes in the same layer or with upper layers in which overloaded FNs delegate part of the workload to underloaded FNs within their proximity [9,11] (we refer to such proximal fog nodes as forming a "neighbourhood"). Offloading, in this sense, refers to the sharing of the workload amongst the nodes to minimise the overall latency of end-user tasks and improve QoS for user applications [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Minimising Delay and Energy in Online Dynamic Fog Systems

Alenizi¹,

Rana²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The increasing use of Internet of Things (IoT) devices generates a greater demand for data transfers and puts increased pressure on networks. Additionally, connectivity to cloud services can be costly and inefficient. Fog computing provides resources in proximity to user devices to overcome these drawbacks. However, optimisation of quality of service (QoS) in IoT applications and the management of fog resources are becoming challenging problems. This paper describes a dynamic online offloading scheme in vehicular traffic applications that require execution of delay-sensitive tasks. This paper proposes a combination of two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC) that aim to minimise overall delay, enhance throughput of user tasks and minimise energy consumption at the fog layer while maximising the use of resource-constrained fog nodes. Compared to other schemes, our experimental results show that these algorithms can reduce the delay by up to 80.79% and reduce energy consumption by up to 66.39% in fog nodes. Additionally, this approach enhances task execution throughput by 40.88%.

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Section: Resultsmentioning

confidence: 99%

Section: Computational Offloadingmentioning

confidence: 99%

Section: Computation Offloading In Fog-cloud Computing Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimising Delay and Energy in Online Dynamic Fog Systems

Alenizi¹,

Rana²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Smart admission control strategy utilizing volunteer‐enabled fog‐cloud computing

Jangu,

Raza

2023

Concurrency and Computation

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Fog computing has become an effective platform for computing delay‐sensitive IoT tasks. However, the increased scalability of IoT devices () makes it difficult for fog nodes to perform better. Volunteer computing (VC) has emerged as a supportive technology in which resource‐capable s, such as computers and laptops, share their idle resources to compute the IoT tasks. However, in VC‐based approaches, improper selection of volunteer nodes (VNs) may result in an increased failure rate and delay. To address these challenges, this work proposes a Smart Admission Control strategy utilizing volunteer‐enabled Fog‐Cloud computing (SAC‐VFC). The VNs are selected based on grey TOPSIS ranking. The incoming tasks are classified based on priority and delay and then scheduled using the Improved Jellyfish Algorithm (IJFA). Smart gateway (SGW) and fog manager (FM) act as mediators for allocating tasks among voluntary, fog, and cloud resources. FM performs a similarity‐based clustering of fog nodes using the enhanced Fuzzy C Means clustering (EFCM) algorithm to manage resources. Simulation study suggests the superior performance of SAC‐VFC over peers under comparison in terms of average delay, average makespan, success rate of tasks and tasks satisfying the deadline metrics.

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Task offloading in vehicular fog computing: State-of-the-art and open issues

Hamdi

Hussain

2022

Future Generation Computer Systems

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PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

Cited by 23 publications

References 18 publications

Minimising Delay and Energy in Online Dynamic Fog Systems

Minimising Delay and Energy in Online Dynamic Fog Systems

Smart admission control strategy utilizing volunteer‐enabled fog‐cloud computing

Task offloading in vehicular fog computing: State-of-the-art and open issues

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