Adaptive task scheduling in IoT using reinforcement learning

Huang

Mathematical Problems in Engineering

2021

Distributed stream processing frameworks (DSPFs) are the vital engine, which can handle real-time data processing and analytics for IoT applications. How to prioritize DSPFs and select the most suitable one for special IoT applications is an open issue. To help developers of IoT applications to solve this complex issue, a novel probabilistic hesitant fuzzy multicriteria decision making (MCDM) model is put forward in this paper. To characterize the requirements for large-scale IoT data stream processing, a novel evaluation criteria system including qualitative and quantitative criteria is established. To accurately model the collective opinions from skilled developers and consider their psychological distance, the definition of probabilistic hesitant fuzzy sets (PHFSs) is used. To derive the importance degrees of criteria, a novel probabilistic hesitant fuzzy best-worst (PHFBW) method is proposed based on the score value. To prioritize the DSPFs and choose the most suitable one, a novel probabilistic hesitant fuzzy MULTIMOORA method is put forward. Finally, a practical case composed of four Apache stream processing frameworks, namely, Storm, Flink, Spark, and Samza, is studied. The obtained results indicate that throughput, latency, and reliability are considered to be the three most important criteria, and Flink is the most suitable stream framework.

Section: Introductionmentioning

confidence: 99%

Probabilistic Hesitant Fuzzy Methods for Prioritizing Distributed Stream Processing Frameworks for IoT Applications

Huang

Mathematical Problems in Engineering

2021

Trans Emerging Tel Tech

“…In this paper, 75 task scheduling policy (RL based) is described in fog‐enabled IoT system. The proposed algorithms use NN‐based (second level) task scheduling algorithm.…”

Section: Technical Classification Of Task Schedulingmentioning

confidence: 99%

A systematic review of task scheduling approaches in fog computing

Bansal

Aggarwal

2022

Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

“…The purpose of this research aimed by its researchers was to present a novel variant of the optimistic concurrency control protocol. Partially deployed augmented validation protocol can make it possible to locally process the read-only IoT transactions at the fog node [ 16 ].…”

Section: The Iot Applications Organizationmentioning

confidence: 99%

Real-Time Scheduling in IoT Applications: A Systematic Review

Khajeh

Saberikamarposhti

Rahmani

2022

Sensors

The Internet of Things (IoT) is a telecommunication network in the next generation of applications with the rapid progress of wireless sensor network techniques that have touched many spheres of life today. Hardware, telephony, communications, storage, secure platforms, software and services, and data processing platforms are all part of the IoT environment. IoT sensors collect data from their environment and share it by connecting to the Internet gateway. These sensors often perform tasks without human intervention. This article aims to review real-time scheduling in the IoT to fully understand the issues raised in this area published from 2018 to 2022. A classification for IoT applications based on practical application is provided for selected studies. Selected studies include healthcare, infrastructure, industrial applications, smart city, commercial applications, environmental protection, and general IoT applications. Studies are sorted into groups based on related applications and compared based on indicators such as performance time, energy consumption, makespan, and assessment environments depending on the provided classification. Finally, this paper discusses all reviewed studies’ main concepts, disadvantages, advantages, and future work.