Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications

Mukherjee, Mithun; Kumar, Suman; Mavromoustakis, Constandinos X.; Mastorakis, George; Matam, Rakesh; Kumar, Vikas; Zhang, Qi

doi:10.1109/tii.2019.2957129

Cited by 74 publications

(16 citation statements)

References 24 publications

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“…Effectively scheduling tasks requested by terminal devices can reduce service delay and energy consumption [19]. In the field of intelligent manufacturing, Mithun et al [20] proposed a solution to the fog computing task offloading problem. This solution modelled the optimization problem mathematically and used quadratic constraint quadratic programming to solve the de-weighting problem, and finally solved the optimization problem by the semideterministic relaxation method.…”

Section: Fog Computing Task Schedulingmentioning

confidence: 99%

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

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Section: Fog Computing Task Schedulingmentioning

confidence: 99%

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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“…It should be noted that the goal which is selected or achieved in the so-called "failed experience" should be related to the final goal in some way. And the learning performance will be very poor if the similarity between two goals is very low [25,26]. e pseudocode for a deep reinforcement learning algorithm based on multiple objectives and experience replay is shown in Algorithm 1.…”

Section: Dqn-based Offloading Strategymentioning

confidence: 99%

Resource Allocation Strategy Using Deep Reinforcement Learning in Cloud-Edge Collaborative Computing Environment

Jun-jie

2022

Mobile Information Systems

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With the development of technologies such as IoT and 5G, the exponential explosion in the amount of new data has put more stringent requirements on ultrareliable and low-delay communication of services. To better meet these requirements, a resource allocation strategy using deep reinforcement learning in a cloud-edge collaborative computing environment is proposed. First, a collaborative mobile edge computing (MEC) system model, which combines the core cloud center with MEC to improve the network interaction ability, is constructed. The communication model and computation model of the system are considered at the same time. Then, the goal of minimizing system delay is modeled as a Markov decision process, and it is solved by using the deep Q network (DQN) which is improved by hindsight experience replay (HER), so as to realize the resource allocation with the minimum system delay. Finally, the proposed method is analyzed based on the simulation platform. The results show that when the number of user terminals is 80, the maximum user delay is 1150 ms, which is better than other comparison strategies and can effectively reduce the system delay in complex environment.

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“…However, in this study, the fog layer was viewed as a relay layer between the application layer and the cloud layer, despite the fact that this layer can perform some tasks, particularly data-driven tasks, which require data to be passed through this layer before reaching the cloud. Mukherjee et al [10] have investigated horizontal collaboration between many nodes and vertical collaboration with the cloud for parallel task data offloading in order to reduces the overall latency for data-driven tasks. However, this mechanism has considered offloading data as one piece either to another node or to cloud, while the required data availability in each node has been ignored.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach

Aladwani

Alghamdi

Kolomvatsos

et al. 2022

2022 9th International Conference on Future Internet of Things and Cloud (FiCloud)

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Dynamic data-driven applications such as tracking and surveillance have emerged in Internet of Things (IoT) environments. Such applications rely heavily on data generated by connected devices (e.g., sensors). Consequently, leveraging these data in building data-driven predictive analytics tasks improves the Quality of Service (QoS) and, as a result, Quality of Experience (QoE). Such data support various data-driven tasks such as regression and classification. Analytics tasks require data and resources to be executed at the edge since transferring them to the cloud negatively affects response times and QoS. However, the network edge is characterized by limited resources compared to the cloud, being the subject of constraints that are violated upon offloading data-driven tasks to improper edge nodes. We contribute with an analytics task management mechanism based on the context of the requested data, the task delay sensitivity and the VM utilization. We introduce a novel Fuzzy inference mechanism for determining whether data-driven tasks should be executed locally, offloaded to peer edge servers, or sent to cloud. We showcase how our fuzzy reasoning mechanism efficiently derives such decisions by calculating the offloading probability per task. The derived optimal actions are compared against benchmark models in Edge Computing (EC) environments.

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Latency-Driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications

Cited by 74 publications

References 24 publications

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Resource Allocation Strategy Using Deep Reinforcement Learning in Cloud-Edge Collaborative Computing Environment

Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach

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