A hybrid bi-objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach

Alaie, Yeganeh Asghari; Shirvani, Mirsaeid Hosseini; Rahmani, Amir Masoud

doi:10.1007/s11227-022-04703-0

Cited by 32 publications

(10 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An optimal operating state is characterized by a greater utilization rate, which indicates maximum utilization and minimal idle resources. The approach suggested by [14] has the lowest utilization rate of all the approaches under consideration; in contrast, our method has the second-highest utilization rate, highlighting its effectiveness in resource allocation and consumption. A comparison of the expenses related to task execution for each of the five techniques is shown in Fig.…”

Section: Evaluation and Experiments Resultsmentioning

confidence: 84%

“…In [12], a Particle Swarm Optimization (PSO) algorithm has been proposed, which assigns jobs to virtual machines linked to physical data center equipment to maximize efficiency and prioritizes task scheduling in cloud computing depending on work length. In a heterogeneous machine-based data center, [14] used a parallel hybrid evolutionary algorithm with an island model for job migration, concentrating on energy-aware scheduling to minimize the makespan parameter. To reduce execution time and concentrate the fitness function on elitism and generation creation based on a threshold level, [15] presented a genetic algorithm that uses a roulette wheel selection approach.…”

Section: A Resource Scheduling Using Heuristicsmentioning

confidence: 99%

See 1 more Smart Citation

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Mittal,

Kumar,

Sharma

2024

IJCDS

View full text Add to dashboard Cite

The need for cloud computing has increased in the age of contemporary networked systems, driving the pursuit of optimal resource allocation and data processing. It is imperative in essential fields where security, such as transportation systems, depends on computing performance. Even after much research has been done on managing resources in cloud computing, finding algorithms that maximize job completion, minimize costs, and maximize resource consumption has remained a top priority. However, existing techniques have shown limitations, which calls for new ways. Our work shows the novel hybrid approach that has the potential to change the game completely. The Neural Network Task Classification (N2TC) is the result of merging neural networks with genetic algorithms. This ground-breaking method skillfully applies the Genetic Algorithm Task Assignment (GATA) for resource allocation while utilizing neural networks for task categorization. Notably, our algorithm carefully considers execution time, response time, costs, and system efficiency to promote fairness, a defense against resource scarcity. Our method achieves a remarkable 13.3% cost reduction, a stunning 12.1% increase in response time, and a 3.2% increase in execution time. These strong indicators act as a wake-up call, announcing our hybrid algorithm's power and revolutionary potential in transforming the paradigms around cloud-based task scheduling. This work represents a turning point in cloud computing, demonstrating an innovative combination of algorithms that not only overcomes current constraints but also ushers in a new era of efficacy and efficiency with far-reaching implications outside the domain of transportation systems.

show abstract

Section: Evaluation and Experiments Resultsmentioning

confidence: 84%

Section: A Resource Scheduling Using Heuristicsmentioning

confidence: 99%

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Mittal,

Kumar,

Sharma

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…On the other hand, there are continuous optimization and discrete optimization methods that have been proposed to address combinatorial problems. In the context of continuous optimization, Asghari et al [ 40 ] describe the scientific workflow scheduling problem as a bi-objective optimization problem using a makespan and reliability optimization strategy to resolve workflow scheduling problems from a makespan and reliability perspective. A hybrid bi-objective discrete cuckoo search algorithm (HDCSA) is suggested as a solution to this combinatorial challenge.…”

Section: Related Workmentioning

confidence: 99%

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Alweshah,

Aldabbas,

Abu-Salih

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…In Reference 61, a hybrid bi‐objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach is presented. his algorithm the scientific workflow scheduling issue to a bi‐objective optimization problem with makespan and reliability optimization approach because the users not only expect to have quick response, but also they need reliable executions.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

“…According to the above explanations, the classification of workflow scheduling methods has been shown in Figure 4. In the first part, the classification of methods is based on the nature of scheduling algorithms, which includes super-heuristic 9 Meta Heuristic methods [5], [16], [21], [35], [36], [37], [50], [51], [52], [54], [55], [56], [57], [58], [61], [62], [63], [64], [65], [66], [67] 9 Heuristic methods [1], [2], [13], [17], [18], [19], [22], [25], [26], [28], [29], [30], [31], [32], [33], [34], [42], [46], [47], [48], [49] 9 F I G U R E 4 Classification of workflow scheduling methods. 12 methods and heuristic methods.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The advent of distributed computing and mobile clouds made it possible to transfer and distribute the heavy processes of complex workflows to the cloud. Managing and with the help of the mobile cloud, virtualization and shared resources managing the executing large‐scale workflows is possible. Since virtual resources are used, scheduling and resource allocation are important and key research topics in public cloud environment and mobile clouds. The cloud model of mobile devices with public or virtual cloud, which consists of temporary mobile devices, is an interesting topic to be investigated. Workflow scheduling and availability of resources in mobile clouds by high mobility and Energy efficient, it is one of the challenges and problems that must be investigated one of the main challenges in this research area. The purpose of scheduling algorithms is to improve service quality criteria by observing the constraints. In this article, the main goal is to extensively review the scheduling algorithms for the complex scientific workflow in public and mobile clouds. Furthermore, a review of the use of heuristic and meta‐heuristic techniques in dynamic and static states, with various constraints for task scheduling in cloud and mobile computing has been performed. This article accomplishes a review of using existing techniques for scheduling tasks in mobile clouds. We also present a comprehensive analysis and systematic comparison between these scheduling algorithms.

show abstract

A hybrid bi-objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach

Cited by 32 publications

References 49 publications

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Contact Info

Product

Resources

About