Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments Using A3C Learning and Residual Recurrent Neural Networks [PDF]
IEEE Transactions on Mobile Computing, 2020 The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such
Shreshth Tuli +3 more
semanticscholar +1 more source
Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling
IEEE Transactions on Cybernetics, 2021 Evolutionary multitask learning has achieved great success due to its ability to handle multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which aims at generating a heuristic for a class of problems rather than ...
Fangfang Zhang +4 more
semanticscholar +1 more source
Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing
ACM Trans. Internet Techn., 2021 Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas.
Qianmu Li +7 more
semanticscholar +1 more source
Dynamic scheduling method for data relay satellite networks considering hybrid system disturbances
Complex & Intelligent Systems, 2023 System disturbances, such as the change of required service durations, the failure of resources, and temporary tasks during the scheduling process of data relay satellite network (DRSN), are difficult to be predicted, which may lead to unsuccessful ...
Zongling Li +4 more
doaj +1 more source
Dynamic Appointment Scheduling
, 2021 This paper considers appointment scheduling in a setting in which at every client arrival the schedule of all future clients can be adapted. Starting our analysis with an explicit treatment of the case of exponentially distributed service times, we then develop a phase-type-based approach to also cover cases in which the service times' squared ...
Mahes, Roshan +3 more
openaire +2 more sources
The synthesis of a hardware scheduler for Non-Manifest Loops [PDF]
, 2002 This paper addresses the hardware implementation of a dynamic scheduler for non-manifest data dependent periodic loops. Static scheduling techniques which are known to give near optimal scheduling-solutions for manifest loops, fail at scheduling non ...
Krol, Thijs +2 more
core +2 more sources
Dynamic Scheduling of Crane by Embedding Deep Reinforcement Learning into a Digital Twin Framework
Information, 2022 This study proposes a digital twin (DT) application framework that integrates deep reinforcement learning (DRL) algorithms for the dynamic scheduling of crane transportation in workshops.
Zhenyu Xu +3 more
doaj +1 more source
Dynamic Task Scheduling Method for Space Crowdsourcing [PDF]
Jisuanji kexue, 2022 Space crowdsourcing is used to solve offline crowdsourcing tasks with time and space constraints,and it has developed rapidly in recent years.Task scheduling is an important research direction of space crowdsourcing.The difficulty lies in the dynamic ...
SHEN Biao, SHEN Li-wei, LI Yi
doaj +1 more source
A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling [PDF]
, 2013 The scheduling and execution of bag-of-tasks applications (BoTs) in Clouds is performed on sets of virtualized Cloud resources that start being exhausted right after their allocation disregarding whether tasks are being executed. In addition, BoTs may be
Gutierrez-Garcia, J. Octavio +1 more
core +1 more source
Dynamic Face Video Segmentation via Reinforcement Learning
, 2021 For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods ...
Cheng, Shiyang +5 more
core +1 more source