scholarly journals PRIORITIZED TASK SCHEDULING IN FOG COMPUTING

2018 ◽  
Author(s):  
TEJASWINI CHOUDHARI
Keyword(s):  
Task Scheduling ◽  
Fog Computing

scholarly journals Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

2021 ◽  
Vol 11 (22) ◽  
pp. 10996
Author(s):  
Jongbeom Lim
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Industrial Revolution ◽  
Virtual Machines ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Virtualization Technology ◽  
Computing Environments ◽  
One Machine

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

Task scheduling approaches for fog computing

2021 ◽  
Author(s):  
Lina Benchikh ◽  
Lemia Louail
Keyword(s):  
Task Scheduling ◽  
Fog Computing

scholarly journals A Multi-Objective Task Scheduling Method for Fog Computing in Cyber-Physical-Social Services

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 65085-65095
Author(s):  
Ming Yang ◽  
Hao Ma ◽  
Shuang Wei ◽  
You Zeng ◽  
Yefeng Chen ◽  
...  
Keyword(s):  
Social Services ◽  
Task Scheduling ◽  
Fog Computing ◽  
Multi Objective ◽  
Scheduling Method

Adaptive task scheduling in IoT using reinforcement learning

2020 ◽  
Vol 13 (3) ◽  
pp. 261-282
Author(s):  
Mohammad Khalid Pandit ◽  
Roohie Naaz Mir ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Real Time ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Clustering Algorithm ◽  
Fog Computing ◽  
Feed Forward Neural Network ◽  
Content Type ◽  
Communication Costs

PurposeThe intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.Design/methodology/approachTo realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.FindingsExperimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.Originality/valueThe proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

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