Heuristic Algorithms for Task Scheduling in Cloud Computing: A Survey

Soltani, Nasim; Soleimani, Behzad; Barekatain, Behrang

doi:10.5815/ijcnis.2017.08.03

Cited by 24 publications

(11 citation statements)

References 23 publications

(14 reference statements)

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“…In this sub-section, we present the review and comparison of diferent heuristic algorithms used by researchers to solve resource scheduling problems along with their focused metrics. Heuristic algorithms are lexible and well-suited methods for performance optimization of the scheduling problems [107] that aim to provide an optimal = metrics focused in proposed technique; × = metrics ignored; RS = Resource Scheduling; WS = Worklow Scheduling solution in a short time. These algorithms help in solving NP-complete problems.…”

Section: Heuristicsmentioning

confidence: 99%

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

et al. 2022

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The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, etc. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services like storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The paper is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey paper is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.

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Section: Heuristicsmentioning

confidence: 99%

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

et al. 2022

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“…The survey of various heuristic scheduling algorithms for task scheduling is accomplished here [9]. The survey suggests the improvements for the emerging scheduling requirements, which include multidimensional, multi objective and dynamic scheduling.…”

Section: Related Workmentioning

confidence: 99%

“…7,8,9 present an analysis of actual to predicted algorithms for optimizing makespan, throughput, cost, degree of imbalance in the view of task scheduling dynamically in the simulated cloud environment. Comparing the results…”

mentioning

confidence: 99%

Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

Shetty¹

2021

TURCOMAT

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The flexibility provided by the cloud service provider at reduced cost popularized the cloud tremendously. The cloud service provider must schedule the incoming requests dynamically. In a cloud environment tasks must be scheduled such that proper resource utilization is achieved. Hence task scheduling plays a significant role in the functionality and performance of cloud computing systems. While there exist many approaches for boosting the task scheduling in the cloud, it is still an unresolved issue. In this proposed framework we attempt to optimize the usage of cloud computing resources by applying machine learning techniques. The new proposed framework dynamically selects the scheduling algorithm for the incoming request rather than arbitrary assigning a task to the scheduling algorithm. The scheduling algorithm is predicted dynamically using a neural network which is the best for the incoming request. The proposed framework considers scheduling parameters namely cost, throughput, makespan and degree of imbalance. The algorithms chosen for scheduling are 1) MET 2) MCT 3) Sufferage 4)Min-min 5) Min-mean 6) Min-var. The framework includes 4 neural networks to predict the best algorithm for each scheduling parameters considered for optimization. PCA algorithm is used for extracting relevant features from the input data set. The proposed framework shows the scope for the overall system performance by dynamically selecting precise scheduling algorithms for each incoming request from the user.

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“…Chromosomes are reproduced by the crossover and the mutation and create a new generation. There are many methods how to select the best chromosomes, for example roulette wheel selection, tournament selection, rank selection and some others [20][21][22].…”

Section: B Genetic Algorithm (Ga)mentioning

confidence: 99%

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

Neysiani¹,

Soltani²,

Mofidi³

et al. 2019

IJITCS

Self Cite

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Recommender systems that possess adequate information about users and analyze their information, are capable of offering appropriate items to customers. Collaborative filtering method is one of the popular recommender system approaches that produces the best suggestions by identifying similar users or items based on their previous transactions. The low accuracy of suggestions is one of the major concerns in the collaborative filtering method. Several methods have been introduced to enhance the accuracy of this method through the discovering association rules and using evolutionary algorithms such as particle swarm optimization. However, their runtime performance does not satisfy this need, thus this article proposes an efficient method of producing cred associations rules with higher performances based on a genetic algorithm. Evaluations were performed on the data set of MovieLens. The parameters of the assessment are: run time, the average of quality rules, recall, precision, accuracy and F1-measurement. The experimental evaluation of a system based on our algorithm outperforms show than the performance of the multiobjective particle swarm optimization association rule mining algorithm, finally runtime has dropped by around 10%.

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Heuristic Algorithms for Task Scheduling in Cloud Computing: A Survey

Cited by 24 publications

References 23 publications

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

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