Inductive Complexity of the P Versus Np Problem

Calude, Cristian S.; Calude, Elena; Queen, Melissa S.

doi:10.1142/s0129626413500072

Cited by 3 publications

(1 citation statement)

References 15 publications

(30 reference statements)

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“…In cloud computing, the problem of task scheduling has become a hot issue. At the same time, it is also a NP hard problem [3,4].…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

Zhou¹,

Hu²

2015

TOCSJ

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Abstract:In view of Min-Min algorithm prefers scheduling small tasks and Max-Min algorithm prefers scheduling big tasks led to the problem of load imbalance in cloud computing, a new algorithm named Min-Max is proposed. Min-Max makes good use of time of greedy strategy, small tasks and big tasks are put together for scheduling in order to solve the problem of load imbalance. Experimental results show that the Min-Max improves the utilization rate of the entire system and saves 9% of the overall execution time compared with Min-Min. As compared to Max-Min, Min-Max improves the utilization rate of the entire system and the total completion time and average response time are saved by 7% and 9%, respectively.

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“…In cloud computing, the problem of task scheduling has become a hot issue. At the same time, it is also a NP hard problem [3,4].…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

Zhou¹,

Hu²

2015

TOCSJ

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Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management

et al. 2021

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The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e., Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision-making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial) lead to shifts in planning and budgeting, but most importantly, reduce confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This paper presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.

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On the resource trade-off of flow update in software-defined networks

Liu

Zhang

et al. 2016

IEEE Commun. Mag.

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Inductive Complexity of the P Versus Np Problem

Cited by 3 publications

References 15 publications

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

Task Scheduling Algorithm based on Greedy Strategy in Cloud Computing

Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management

On the resource trade-off of flow update in software-defined networks

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