Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

Wang, Yanyan; Wang, Lin; Zheng, Ruijuan; Zhao, Xuhui; Liu, Muhua

doi:10.3390/s21072347

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…They apply non-cooperative game method using real-time update computation offloading (RUCO) algorithm that uses Nash equilibrium, and a multi-user probabilistic offloading decision algorithm to address this problem. A backpressure algorithm based task-offloading algorithm to minimize delay of latency-sensitive tasks in smart homes is presented in [21]. This algorithm minimize the queue length of tasks by minimizing Lyapunov drift optimization algorithm in each slot to improve the stability of the queue and offloading performance.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization Based Distributed Task Offloading in Fog Computing

Rani,

Jamil,

Ijaz

2024

Research Journal Of Computer Science And Information Technology

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Due to an increasing number of IoT devices, a massive amount of data is generating daily. Initially, Cloud-centric Internet of Things (CIoT) based architecture is used for data processing, storage, and analysis. But it is difficult for the CIoT to handle the huge amount of data produced by these devices. Furthermore, cloud data centers are far away from IoT devices so transmitting data to the cloud will result in more bandwidth consumption, cost, and latency issues that’s why cloud computing is not suitable for real-time applications. To overcome these problems the concept of fog computing is introduced that extends cloud computing by moving the facilities to the edge of the network. Fog computing provides low latency, real-time processing near the edge of the network. Fog computing contains heterogeneous and intrinsically resource-constrained devices. Due to resource-constrained devices at the edge of the network resource management is a necessary issue to efficiently distribute resources and move some tasks to the other entity for execution to balance the load it’s called offloading. Different techniques of task offloading are used in fog computing that’s main purpose is to maximize and effective resource utilization, minimizing latency, cost, and energy consumption. This research is concerned with the design and implementation of a multi-objective optimization-based distributed task offloading algorithm in which we will optimize more than two objectives simultaneously like optimization of performance and resource utilization metrics.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization Based Distributed Task Offloading in Fog Computing

Rani,

Jamil,

Ijaz

2024

Research Journal Of Computer Science And Information Technology

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“…They proposed a task scheduling model, considering the role of containers and reallocation mechanisms, that can reduce task delays and improve concurrency using fog nodes. In Reference 12, the authors proposed a computational offloading strategy in edge‐cloud computing for smart homes by minimizing the delay. The strategy, which uses the back‐pressure algorithm, involves minimizing the queue length of tasks while ensuring queue stability to improve offloading performance.…”

Section: Related Workmentioning

confidence: 99%

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

Yadav

Vidyarthi

2023

Concurrency and Computation

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In a hierarchical edge‐fog‐cloud architecture, edge devices possess limited resources and energy. To contain with, it can offload some tasks generated by the Internet of Things (IoT) to the fog and cloud. Several factors influence this task‐offloading decision, including hardware features, network conditions, and application characteristics. Most of the research studies, in task offloading systems, have confined to changing parameter values, whereas very few have considered fuzzy‐based dynamic approaches for resource allocation. This work proposes a fuzzy‐based task offloading technique (FBOT) for scheduling the tasks (both compute and data‐intensive) to the appropriate nodes of the fog‐cloud system. The proposed method incorporates a vague logic‐based fuzzy task scheduler for scheduling various tasks at the fog layer. This helps to reduce the waiting time for the compute‐intensive tasks and minimizes the starvation problem of data‐intensive tasks. In addition, the proposed technique decreases the task completion time and selects the best computing nodes for each tasks. To validate the performance of the proposed technique, extensive simulation studies have been carried out on various parameters. Compared with the baseline algorithms, the results show that the proposed method offers an average improvement of 30% in terms of the total time of the task.

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“…To leverage the DVFS capability, the back-pressure algorithm was used in [58] to determine the offloading decision and the number of tasks that might be offloaded, and the authors presented a computational offloading technique for reducing latency. The authors built a system with several local MDs work queues and numerous edge processor task queues.…”

Section: -Computation and Communicationmentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

Cited by 8 publications

References 45 publications

Multi-Objective Optimization Based Distributed Task Offloading in Fog Computing

Multi-Objective Optimization Based Distributed Task Offloading in Fog Computing

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

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