Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application

Rahbari, Dadmehr

doi:10.3390/a15110397

Cited by 12 publications

(3 citation statements)

References 54 publications

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“…Initialize positions of jellyfish Initialize Q-table Ǫ(𝒔, 𝖆) to zeros for all state-action pairs using eqn. (19) Parameters:…”

Section: Beginmentioning

confidence: 99%

RL-JSO: A Hybrid Q-Learning and Jellyfish Search Optimizer for Task Scheduling in Smart Homes Using a Fog-Assisted Cloud Architecture

BHAKHAR,

Chhillar

2024

Preprint

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Smart homes are becoming increasingly complex with the increase in the number of various sensors and connected devices. This complexity introduces challenges in task scheduling that ensures optimal performance and user satisfaction. Traditional cloud-based solutions, widely used for data processing and task scheduling, face limitations in meeting the real-time demands of smart home applications. To address the challenges inherent in smart home environments, fog computing has emerged as an innovative paradigm for optimizing task scheduling. This paper presents a fog-cloud framework for task scheduling in smart home environments. It also introduces a hybrid algorithm which uses Q-learning and jellyfish search optimizer. The proposed framework classifies the users’ tasks based on their sensitivity to latency. Real-time tasks are allotted to the fog layer, which consists of strategically placed fog computing nodes within the smart home environment, while non-real-time tasks are forwarded to the cloud layer for processing. The hybrid algorithm developed by integrating Q-learning and jellyfish search optimizer is dynamic in nature, ensures minimal latency. The simulation study conducted in MATLAB shows the better performance of Reinforcement Learning based Jellyfish search optimizer (RL-JSO) over existing algorithms in terms of execution time, energy consumption, load ratio and resource utilization metrics.

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“…Initialize positions of jellyfish Initialize Q-table Ǫ(𝒔, 𝖆) to zeros for all state-action pairs using eqn. (19) Parameters:…”

Section: Beginmentioning

confidence: 99%

RL-JSO: A Hybrid Q-Learning and Jellyfish Search Optimizer for Task Scheduling in Smart Homes Using a Fog-Assisted Cloud Architecture

BHAKHAR,

Chhillar

2024

Preprint

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“…Finally from generated results of CSHA it proved that resource cost, penalty is minimized over existing approaches. Authors in 16 also focused on total execution cost in Fog computing that effects scheduling. They developed a task scheduling mechanism which is based on hyper heuristic approach which tunes convergence of solutions.…”

Section: Existing Related Workmentioning

confidence: 99%

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Mangalampalli,

Karri,

Mohanty

et al. 2023

Sci Rep

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Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

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“…They efficiently allocated the tasks, satisfying deadline constraints with minimum cost. In another study, a Hyper Heuristic Scheduling (HHS) algorithm minimized execution cost, energy consumption, and latency [67]. However, transmission cost was not included in the cost.…”

Section: ) Other Heuristicsmentioning

confidence: 99%

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

Khan,

Aziz,

Osman

et al. 2023

IEEE Access

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Efficient task scheduling on the cloud is critical for optimal utilization of resources in data centers. It became even more challenging with the emergence of 5G and IoT applications that generate massive number of tasks with stringent latency requirements. This gives birth to fog/edge computing -a complementary layer to cloud. Tasks latency in fog computing can be reduced as processing in the network is done closer to the end devices and users, but every task cannot be scheduled in the fog due to limited resources availability. Conventional scheduling algorithms often fail to exploit the heterogeneous resources; therefore, specially designed and well-tuned scheduling algorithms are desired for achieving better quality of service. In this study, the state-of-the-art task scheduling algorithms in the cloud and fog environments are investigated in a diverse set of dimensions. Among the relevant studies published between 2018-2022 and indexed in the Web-of-Science (WOS), SCOPUS, and Google Scholar databases, eighteen studies are selected for both the cloud and fog domains from WOS and Scopus, while seventeen studies are chosen for both the cloud and fog domains from Google Scholar. Thus, a total of 106 studies are included in this survey for the detail investigation. The scheduling algorithms are broadly classified into three categories such as heuristic, meta-heuristic, and hybrid meta-heuristic followed by detailed critical analysis. It has been observed that most of the scheduling algorithms are dynamic and non-preemptive in nature, while the higher fraction of the tasks is independent in comparison to bag of tasks and workflows. Similarly, 97% of the studies focus on multiple objectives and 68% of the techniques are non-deterministic. Further, a total of twenty different scheduling objectives are identified with makespan, resource utilization, delay, load balancing, and energy consumption as the most significant metrics. The evaluation methods including simulations (51%), real experiments (4%), analytical equations (2%), and datasets (43%) etc. are surveyed. At the end, the open issues, challenges, and future directions are argued. Cloud computing, fog computing, task scheduling, workflow, Internet of Things (IoT). INDEX TERMS I. INTRODUCTION Cloud computing offers different kinds of scalable resources to end users based on pay-per-use basis by providing costThe associate editor coordinating the review of this manuscript and approving it for publication was Zhiwu Li . savings, security, flexibility, and automatic software updates etc. The users can concentrate on their personal and business objectives without bothering about the infrastructure and supporting services as the vendor is responsible for managing it. There are three service models and four deployment models provided by the cloud providers [1]. Though it is

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Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application

Cited by 12 publications

References 54 publications

RL-JSO: A Hybrid Q-Learning and Jellyfish Search Optimizer for Task Scheduling in Smart Homes Using a Fog-Assisted Cloud Architecture

RL-JSO: A Hybrid Q-Learning and Jellyfish Search Optimizer for Task Scheduling in Smart Homes Using a Fog-Assisted Cloud Architecture

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

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