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data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
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Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment
Published 2022“…Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. …”
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Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog- Cloud Computing Environment
Published 2019Subjects: Get full text
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An Uncertainty Based Genetic Algorithm Approach for Project Resource Scheduling
Published 2016“…The work presented in thesis add to the existing literature in a proposing the use of a genetic algorithm uncertain approach to resource- scheduling in projects. …”
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GENETIC SCHEDULING OF TASK GRAPHS
Published 2020“…A genetic algorithm for scheduling computational task graphs is presented. …”
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GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing
Published 2024“…In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), a novel optimization approach tailored for task scheduling in cloud computing environments. …”
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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
Published 2016Get full text
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Optimizing Energy Consumption of Cloud Computing IaaS
Published 2017Subjects: Get full text
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A deterministic heuristic algorithm for optimal multiprocessor scheduling. (c1995)
Published 1995Get full text
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Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…The objective is to minimize different cost types while satisfying constraints such as resources availability, customer service level, and tasks dependency relation. The algorithm proved its effectiveness by allocating tasks with higher priority and weight while taking into consideration customers’ Service Level Agreement, time, and different types of costs, which led to a lower total cost of the batching process. …”
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Proactive Fault Tolerance and Minimizing Task Execution Failure in A Cloud Data Center
Published 2024Get full text
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A simulated evolution approach to task-matching and scheduling in heterogeneous computing environments
Published 2020“…Abstract This paper applies a simulated evolution (SE) approach to the problem of matching and scheduling dependent tasks in a heterogeneous suite of computers interconnected via a high-speed network. …”
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Reinforcement R-learning model for time scheduling of on-demand fog placement
Published 2020“…We also demonstrate the ability of our model to improve over time by adapting the new demand of users. Experiments comparing the decisions of our model with two other potential fog placement approaches used for task/service scheduling (threshold based and random based) show that the number of processed requests performed by the cloud decreases from 100 to 30% with a limited number of fogs to push. …”
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A discrete-time learning control algorithm
Published 1994Get full text
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