A novel technique to optimize quality of service for directed acyclic graph (DAG) scheduling in cloud computing environment using heuristic approach

Rajak, Ranjit; Kumar, Shrawan; Prakash, Shiv; Rajak, Nidhi; Dixit, Pratibha

doi:10.1007/s11227-022-04729-4

Cited by 12 publications

(2 citation statements)

References 44 publications

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“…The Growable Genetic Algorithm (GGA) was presented by [17], combined a Random Multi-Weight algorithm with a Heuristic-based Local Search Algorithm (HLSA) to provide a growth stage to the genetic algorithm, allowing individuals to evolve along several growth paths. To augment the QoS parameters, [18][19] created a Directed Acyclic Graph (DAG) scheduling model. This model uses resource provisioning and heuristic techniques to efficiently assign tasks to points and arrange the running sequence of jobs.…”

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

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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.

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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

“…In recent times, researchers have introduced swarm intelligence optimization algorithms, like ant colonies [10], bat algorithms [11], and sparrow search [12], to achieve workload balancing across virtual machines (VMs) by efficiently assigning tasks to appropriate VMs. However, some of these cuttingedge metaheuristic approaches encounter difficulties such as slow convergence [13][14][15][16]. Consequently, grey wolf and sunflower optimization algorithms have garnered increased interest from scholars due to their superior optimization performance compared to other swarm intelligence optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

Khaleel,

Safran,

Alfarhood

et al. 2023

Mathematics

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Scheduling jobs within a cloud environment is a critical area of research that necessitates meticulous analysis. It entails the challenge of optimally assigning jobs to various cloud servers, each with different capabilities, and is classified as a non-deterministic polynomial (NP) problem. Many conventional methods have been suggested to tackle this difficulty, but they often struggle to find nearly perfect solutions within a reasonable timeframe. As a result, researchers have turned to evolutionary algorithms to tackle this problem. However, relying on a single metaheuristic approach can be problematic as it may become trapped in local optima, resulting in slow convergence. Therefore, combining different metaheuristic strategies to improve the overall system enactment is essential. This paper presents a novel approach that integrates three methods to enhance exploration and exploitation, increasing search process efficiency and optimizing many-objective functions. In the initial phase, we adopt cooperative game theory with merge-and-split techniques to train computing hosts at different utilization load levels, determining the ideal utilization for each server. This approach ensures that servers operate at their highest utilization range, maximizing their profitability. In the second stage, we incorporate the mean variation of the grey wolf optimization algorithm, making significant adjustments to the encircling and hunting phases to enhance the exploitation of the search space. In the final phase, we introduce an innovative pollination operator inspired by the sunflower optimization algorithm to enrich the exploration of the search domain. By skillfully balancing exploration and exploitation, we effectively address many-objective optimization problems. To validate the performance of our proposed method, we conducted experiments using both real-world and synthesized datasets, employing CloudSim software version 5.0. The evaluation involved two sets of experiments to measure different evaluation metrics. In the first experiment, we focused on minimizing factors such as energy costs, completion time, latency, and SLA violations. The second experiment, in contrast, aimed at maximizing metrics such as service quality, bandwidth utilization, asset utilization ratio, and service provider outcomes. The results from these experiments unequivocally demonstrate the outstanding performance of our algorithm, surpassing existing state-of-the-art approaches.

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Reducing energy footprint in cloud computing: a study on the impact of clustering techniques and scheduling algorithms for scientific workflows

Saadi

Jounaidi²,

Kafhali

et al. 2023

Computing

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A novel technique to optimize quality of service for directed acyclic graph (DAG) scheduling in cloud computing environment using heuristic approach

Cited by 12 publications

References 44 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

A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

Reducing energy footprint in cloud computing: a study on the impact of clustering techniques and scheduling algorithms for scientific workflows

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