An improved pathfinder algorithm using opposition-based learning for tasks scheduling in cloud environment

Talha, Adnane; Bouayad, Anas; Malki, Mohammed Ouçamah Cherkaoui

doi:10.1016/j.jocs.2022.101873

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…Real-world cloud workloads from CEA-Curie, HPC2N are utilized to assess how well scheduling methods perform using the CloudSim simulator. authors in [11] proposed workflow scheduling algorithms that address QoS Parameters i.e., time, cost, resource utilization. A Hybridized approach used by the authors to tackle scheduling problems.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

Rama Krishna,

Mangalampalli

2023

EAI Endorsed Trans IoT

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Task scheduling in cloud computing involves allocating tasks to virtual machines based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling, we have various scheduling algorithms that are nature-inspired, bio-inspired, and metaheuristic, but we still have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modelled by metaheuristics, nature-inspired algorithms, and machine learning, which address various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, and throughput, but do not address parameters like trust or fault tolerance. Trust and fault tolerance have an impact on task scheduling; trust is necessary for tasks and assigning responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling; therefore, this paper has analysed parameters like trust and fault tolerance and given research directions.

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Section: Related Workmentioning

confidence: 99%

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

Rama Krishna,

Mangalampalli

2023

EAI Endorsed Trans IoT

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“…Active mobility, on the other hand, is seen as successful local search space utilization. In order to define it, use the formula indicated in (18). 𝑋 𝑖 (𝑡 + 1) = 𝑋 𝑖 (𝑡) + 𝔠 × 𝑟𝑎𝑛𝑑(0,1) × (𝔘𝔟 − 𝔏𝔟) (12) 𝑋 𝑖 (𝑡 + 1) = 𝑋 𝑖 (𝑡) + 𝑟𝑎𝑛𝑑(0,1) × 𝔇(𝑖) ⃗⃗⃗⃗⃗⃗⃗⃗⃗ ( 13)…”

Section: Load Balancing Using Vms Optimizationmentioning

confidence: 99%

“…Nevertheless, no complete solution has been developed that adequately satisfies these criteria. Talha et al, [18] using the Pathfinder algorithm (PFA) and the oppositional-based learning algorithm (OBL), a new cloud workflow scheduling method. Three objectives were prioritized by the suggested scheduler.…”

Section: Introductionmentioning

confidence: 99%

A Novel Heterogenous Dominant Sequence Clustering for Task Scheduling and Optimal Load Balancing Using JSO in Cloud

Kalaiselvi¹

2023

cienc.eng.

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Cloud computing (CC) is rapidly increasing and being used more and more in information technology (IT) contexts. Among the hottest issues in the world of CC is task scheduling. In cloud datacentres, load balancing is accomplished using a variety of scheduling techniques, although lengthening the parallel time. By taking task duration and VM capacity into account, this study proposes a cluster-based task scheduling paradigm. The makespan and execution duration should be reduced, the suggested system engages in dynamic load balancing. In this study, we suggest a unique method called HDDSC-JSO, which employs the Jellyfish Swarm Optimization (JSO) algorithm for load balancing and heterogeneous Density based dominant sequence clustering (HDDSC) for job scheduling. Using the HDSC technique, It presents a graph of one or more groups representing user tasks, the tasks of users are first clustered. The Adapted Diverse Expeditious Termination Time (ADETT) method is used to score each work after task clustering. whenever the job with the greatest priority is scheduled first. Next, using a JSO algorithm, load balancing is carried out, distributing the load according to the weight and capacity of the server as well as the connection of the client to the server. Task allocation chooses a heavily weighted or unconnected server, lengthening the response time. Finally, measures including response time, makespan, resource consumption, and service dependability were used to assess the suggested architecture.

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“…A group of researchers in [47] worked on task scheduling in a cloud environment by an improved pathfinder algorithm using opposition-based learning (OBLPFA). Their approach improved total execution time, cost, and resource utilization compared to PSO, dragonfly, the Arithmetic Optimization Technique, the Reptile Search technique, the Aquila Optimization method, and Lion Optimization methods.…”

Section: Hybrid Heuristic Algorithmsmentioning

confidence: 99%

“…OBLPFA [47] Hybrid-heuristic Execution time, cost, and resource utilization Cloud/CloudSim Improved time complexity.…”

Section: Rss-in [25]mentioning

confidence: 99%

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

Rahbari

2022

Algorithms

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In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An application in FC is divided into several modules. The allocation of processing elements (PEs) to modules is a scheduling problem. In this paper, some heuristic and meta-heuristic algorithms are analyzed, and a Hyper-Heuristic Scheduling (HHS) algorithm is presented to find the best allocation with respect to low latency and energy consumption. HHS allocates PEs to modules by low-level heuristics in the training and testing phases of the input workflow. Based on simulation results and comparison of HHS with traditional, heuristic, and meta-heuristic algorithms, the proposed method has improvements in energy consumption, total execution cost, latency, and total execution time.

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An improved pathfinder algorithm using opposition-based learning for tasks scheduling in cloud environment

Cited by 12 publications

References 31 publications

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing

A Novel Heterogenous Dominant Sequence Clustering for Task Scheduling and Optimal Load Balancing Using JSO in Cloud

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

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