MONWS: Multi-Objective Normalization Workflow Scheduling for Cloud Computing

Pillareddy, Vamsheedhar Reddy; Karri, Ganesh Reddy

doi:10.3390/app13021101

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…This process is normalization, so that the intelligent well monitoring data processed above are in the same dimension and concentrated in the [0,1] interval. According to the min max normalization method x * = x − x min x max − x min Where x * is the numerical value after data normalization, x is the original production data of the horizontal well, x min is the minimum value of the sample data, and x max is the maximum value. The processing results are shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

A New Method to Predict Water Breakthrough in Intelligent Completion Horizontal Well

Cai,

Chen,

et al. 2023

Energy Fuels

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With the oilfield production entering a later period, the yield will be seriously affected if the problem is not addressed. This article first determines through analysis that the evaluation parameters for horizontal well water output are reservoir permeability and porosity. Second, the relationship between the real-time monitoring data and the above parameters was defined, along with the collected raw data of temperature, pressure, and flow rate. Using different artificial intelligence algorithms for historical reconstruction again, the error of the BP neural network calculation is minimum and the permeability, porosity, and water yield possibility are within 0.8%. Further improvement of the BP neural network algorithm and establishment of a new neural network prediction model were achieved. Then, the water production situation of a certain horizontal well was predicted, and the calculation error was 0.6%. It shows that water entry was forecast to occur 42 months after production. Finally, we designed a reasonable water control and oil increase plan that can extend the anhydrous oil recovery period by over 900 days. The result shows that the prediction method proposed in this article is reasonable and feasible; the predicted results of effluent are accurate; and the designed water control plan is effective. It can slow the rate of water breakthrough in horizontal wells and improve oilfield recovery efficiency.

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Section: Resultsmentioning

confidence: 99%

A New Method to Predict Water Breakthrough in Intelligent Completion Horizontal Well

Cai,

Chen,

et al. 2023

Energy Fuels

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“…The evaluation shows that this proposed method can increase the usage of green energy for the execution of industrial workflow up to three times with a slight increase of cost. By determining the dynamic threshold value for scheduling jobs on virtual machines, [66] suggested a task's dynamic priority for workflow scheduling using MONWS, which applies the min-max algorithm to reduce finish time and maximize resource utilization. MONWS produced 35% improvements in makespan, 8% increases in maximum average cloud utilization, and 4% cost reductions when compared to existing algorithms based on the testing results.…”

Section: Surveymentioning

confidence: 99%

Taxonomy and survey of scientific workflow scheduling in infrastructure-as-a-service cloud computing systems

Stéphane,

Corneille,

Yassa

et al. 2024

JACST

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Scientific workflows are groups of scientific application tasks organized in oriented graphs. These scientific workflows are characterized by a large number of tasks requiring sufficient resources for their execution. Tasks with all available input data can be computed simultaneously. Cloud computing is an appropriate environment for the implementation of scientific workflows. Although the cloud computing environment has unlimited resources and can run some scientific workflow tasks simultaneously, scheduling scientific workflow tasks using pay-as-you-go cloud computing resources is an NP-complete problem. This difficulty is due to constraints on the part of the cloud resource provider and the part of the user (customer). The algorithm tries to find efficient schedules that take into account several requirements of client service (QoS) such as deadlines, budgets, and resource providers’ profits, i.e., the minimization of energy consumption and many others. There have been several papers recommending effective solutions to workflow schedule problems. This paper reviews existing and more recent papers on the plan of scientific workflows in pay-as-you-go IaaS cloud computing environments, focusing on future directions for algorithms that can improve the optimal solution.

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“…It evaluated against the existing algorithm cuckoo search cuckoo search and differential evolution algorithm, particle swarm optimization (CSPO), hybrid oppositional differential evolution-enabled whale optimization algorithm results are shown minimizing the makespan, execution time, cost, and energy consumption. In [24] the author developed a dynamic priority for workflow scheduling using multi-objective normalization workflow scheduling. The entire simulation was done in cloud Sim, the results are compared existing algorithms DLS, HEFT, maxmin, min-min proposed to improve in makespan, increases in maximum average cloud utilization and decrease in cost.…”

Section: Related Workmentioning

confidence: 99%

An Effective analysis on various task scheduling algorithms in Fog computing

Choppara,

Mangalampalli

2023

EAI Endorsed Trans IoT

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Fog computing involved as an extension of cloud and distributed systems fog nodes allowing data to be processed closer to the edge device and reduces the latency and bandwidth, storage capacity of IoT tasks. Task scheduling in fog computing involves allocating the tasks in fog nodes 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 and bio-inspired algorithms but still we have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modeled by metaheuristic, nature inspired and machine learning which address the various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, throughput but still parameters like trust, fault tolerance not addressed by many of the existing authors. Trust and fault tolerance gives an impact and task scheduling trust is necessary to tasks and assign 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 done analysis on parameters like trust, fault tolerance and given research directions.

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MONWS: Multi-Objective Normalization Workflow Scheduling for Cloud Computing

Cited by 10 publications

References 33 publications

A New Method to Predict Water Breakthrough in Intelligent Completion Horizontal Well

A New Method to Predict Water Breakthrough in Intelligent Completion Horizontal Well

Taxonomy and survey of scientific workflow scheduling in infrastructure-as-a-service cloud computing systems

An Effective analysis on various task scheduling algorithms in Fog computing

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