Task scheduling in edge-fog-cloud architecture: a multi-objective load balancing approach using reinforcement learning algorithm

Computing, Journal Year: 2023, Volume and Issue: 105(6), P. 1337 - 1359

Published: Jan. 5, 2023

Language: Английский

---

Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment

Journal of Computational Science, Journal Year: 2022, Volume and Issue: 64, P. 101828 - 101828

Published: Aug. 19, 2022

Language: Английский

---

Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments

Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 152, P. 55 - 69

Published: Oct. 28, 2023

Edge/fog computing, as a distributed computing paradigm, satisfies the low-latency requirements of ever-increasing number IoT applications and has become mainstream paradigm behind applications. However, because large require execution on edge/fog resources, servers may be overloaded. Hence, it disrupt also negatively affect applications' response time. Moreover, many are composed dependent components incurring extra constraints for their execution. Besides, environments inherently dynamic stochastic. Thus, efficient adaptive scheduling in heterogeneous is paramount importance. limited computational resources imposes an burden applying optimal but computationally demanding techniques. To overcome these challenges, we propose Deep Reinforcement Learning-based application Scheduling algorithm, called DRLIS to adaptively efficiently optimize time balance load servers. We implemented practical scheduler FogBus2 function-as-a-service framework creating edge-fog-cloud integrated serverless environment. Results obtained from extensive experiments show that significantly reduces cost by up 55%, 37%, 50% terms balancing, time, weighted cost, respectively, compared with metaheuristic algorithms other reinforcement learning

Language: Английский

---

IRATS: A DRL-based intelligent priority and deadline-aware online resource allocation and task scheduling algorithm in a vehicular fog network

Ad Hoc Networks, Journal Year: 2023, Volume and Issue: 141, P. 103090 - 103090

Published: Jan. 12, 2023

Cloud computing platforms support the Internet of Vehicles, but main bottlenecks are high latency and massive data transmission in cloud-based processing. Vehicular fog has emerged as a promising paradigm to accommodate increasing computational needs vehicles. It provides low network services that most important for latency-sensitive tasks. The dynamic nature VFC, having vehicles with heterogeneous resources, vehicle mobility, diverse tasks different priorities challenges vehicular networks. In can share their idle compute resources other task-generating So, scheduling on resource-limited is very important. Existing solutions use heuristic approach solve this issue lack generalizability adaptability. paper, we describe PPO-based intelligent, priority deadline-aware online distributed resource allocation task algorithm, called IRATS, IRATS formulates problem Markov decision process minimize waiting time delay For sharing design scheduler orderly execution received according using multi-level queues. We conducted extensive simulations SUMO, OMNeT++, Veins, veins-gym validate effectiveness presented algorithm. simulation results confirm proposed algorithm improves percentage in-time completed decreases packet loss, time, end-to-end compared random, A2C, DQN algorithms considering link duration

Language: Английский

---

Delay and energy aware task scheduling mechanism for fog-enabled IoT applications: A reinforcement learning approach

Computer Networks, Journal Year: 2023, Volume and Issue: 224, P. 109603 - 109603

Published: Feb. 3, 2023

Language: Английский

---

An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment

Computing, Journal Year: 2023, Volume and Issue: 106(1), P. 109 - 137

Published: Aug. 26, 2023

Language: Английский

---

A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments

Journal of Network and Computer Applications, Journal Year: 2023, Volume and Issue: 214, P. 103617 - 103617

Published: March 2, 2023

Language: Английский

---

Resource allocation in Fog–Cloud Environments: State of the art

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 227, P. 103891 - 103891

Published: April 28, 2024

Language: Английский

---

Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic review

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 28, 2025

Language: Английский

---

A systematic review of task scheduling approaches in fog computing

Transactions on Emerging Telecommunications Technologies, Journal Year: 2022, Volume and Issue: 33(9)

Published: May 2, 2022

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

Language: Английский

---