Neural Network-Based Task Scheduling with Preemptive Fan Control

2016 ◽  
Author(s):  
Bilge Acun ◽  
Eun Kyung Lee ◽  
Yoonho Park ◽  
Laxmikant V. Kale
Keyword(s):  
Neural Network ◽  
Task Scheduling

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.

scholarly journals Dynamic Task Scheduling using Trained Neural Network and Genetic Algorithm

IJARCCE ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 124-131
Author(s):  
Suhani Kumari ◽  
Himanshu Yadav ◽  
Chetan Agrawal
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Task Scheduling ◽  
Dynamic Task ◽  
Trained Neural Network ◽  
Dynamic Task Scheduling

scholarly journals The Task Scheduling Problem: A NeuroGenetic Approach

2014 ◽  
Vol 12 (4) ◽  
pp. 327
Author(s):  
Anurag Agarwal ◽  
Selcuk Colak ◽  
Jason Deane ◽  
Terry Rakes
Keyword(s):  
Neural Network ◽  
Task Scheduling ◽  
Computational Time ◽  
Test Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Machine ◽  
Better Than

This paper addresses the task scheduling problem which involves minimizing the makespan in scheduling n tasks on m machines (resources) where the tasks follow a precedence relation and preemption is not allowed. The machines (resources) are all identical and a task needs only one machine for processing. Like most scheduling problems, this one is NP-hard in nature, making it difficult to find exact solutions for larger problems in reasonable computational time. Heuristic and metaheuristic approaches are therefore needed to solve this type of problem. This paper proposes a metaheuristic approach - called NeuroGenetic - which is a combination of an augmented neural network and a genetic algorithm. The augmented neural network approach is itself a hybrid of a heuristic approach and a neural network approach. The NeuroGenetic approach is tested against some popular test problems from the literature, and the results indicate that the NeuroGenetic approach performs significantly better than either the augmented neural network or the genetic algorithms alone.

Machine Learning Approach to Task Scheduling in Cloud Computing

2020 ◽  
Vol 17 (9) ◽  
pp. 4213-4218
Author(s):  
H. S. Madhusudhan ◽  
T. Satish Kumar ◽  
G. Mahesh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Machine Learning Approach ◽  
Artificial Neural Network Ann

Cloud computing provides on demand service on internet using network of remote servers. The pivotal role for any cloud environment would be to schedule tasks and the virtual machine scheduling have key role in maintaining Quality of Service (QOS) and Service Level Agreement (SLA). Task scheduling is the process of scheduling task (user requests) to certain resources and it is an NP-complete problem. The primary objectives of scheduling algorithms are to minimize makespan and improve resource utilization. In this research work an attempt is made to implement Artificial Neural Network (ANN), which is a methodology in machine learning technique and it is applied to implement task scheduling. It is observed that neural network trained with genetic algorithm will outperforms default genetic algorithm by an average efficiency of 25.56%.

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