MLBox: Machine learning box for asymptotic scheduling

Vasile, Mihaela-Andreea; Pop, Florin; Nita, Mihaela-Catalina; Cristea, Valentin

doi:10.1016/j.ins.2017.01.005

Cited by 13 publications

(2 citation statements)

References 24 publications

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“…Under certain conditions, they could easily discover the best scheduling algorithm, prove its optimality and compute its asymptotic throughput. Vasile et al [27] proposed a scheduling algorithm for different types of computation requests: independent tasks, like a bag of tasks model, or tasks with dependencies modeled as directed acyclic graphs, and they will be scheduled for execution in a cloud data center. The tasks in the requests are scheduled based on the available resources using the scheduling algorithm suitable for each request.…”

Section: Related Workmentioning

confidence: 99%

Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

Priya¹,

Mehata²,

Banu³

2018

JTIT

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This paper proposes a fuzzy Manhattan distance-based similarity for gang formation of resources (FMDSGR) method with priority task scheduling in cloud computing. The proposed work decides which processor is to execute the current task in order to achieve efficient resource utilization and effective task scheduling. FMDSGR groups the resources into gangs which rely upon the similarity of resource characteristics in order to use the resources effectively. Then, the tasks are scheduled based on the priority in the gang of processors using gang-based priority scheduling (GPS). This reduces mainly the cost of deciding which processor is to execute the current task. Performance has been evaluated in terms of makespan, scheduling length ratio, speedup, efficiency and load balancing. CloudSim simulator is the toolkit used for simulation and for demonstrating experimental results in cloud computing environments.

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Section: Related Workmentioning

confidence: 99%

Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

Priya¹,

Mehata²,

Banu³

2018

JTIT

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“…Since machine learning could serve to make complex algorithms concise and efficient, more studies [9][10][11][12] tend to tackle the scheduling problems with machine learning-based methods in recent years. Many scholars are interested in studying the combination of the two, and put forward some practical cases [13][14][15]. Machine learning-based methods could be classified into many categories, the best known of which are reinforcement learning [16][17][18], random forests [19,20], and neural networks [21,22].…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based scheduling: a bibliometric perspective

Cao

et al. 2021

IET Collab Intel Manufact

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In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non‐linear in nature, time varying, multi‐objective, and subject to interruptions. Ergo, the machine learning‐based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning‐based algorithms. To reveal the value of machine learning‐based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning‐based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.

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