Adaptive Scheduling for Joint CommRadar: Optimizing Tradeoff Among Data Throughput, Queueing Delay, and Detection Opportunities

2021 ◽  
Author(s):  
Honghao Ju ◽  
Yan Long ◽  
Xuming Fang ◽  
Rong He
Keyword(s):  
Adaptive Scheduling ◽  
Data Throughput ◽  
Queueing Delay

A Study on Adaptive Scheduling Priority Control for Layered Cell Configuration

2017 ◽  
Vol E100.B (2) ◽  
pp. 372-379
Author(s):  
Atsushi NAGATE ◽  
Teruya FUJII ◽  
Masayuki MURATA
Keyword(s):  
Adaptive Scheduling ◽  
Cell Configuration

scholarly journals Towards workload-adaptive scheduling for HPC clusters

2020 ◽  
Author(s):  
Alexander V. Goponenko ◽  
Ramin Izadpanah ◽  
Jim M. Brandt ◽  
Damian Dechev
Keyword(s):  
Adaptive Scheduling

scholarly journals Evolution of the ALICE Software Framework for Run 3

2019 ◽  
Vol 214 ◽  
pp. 05010 ◽  
Author(s):  
Giulio Eulisse ◽  
Piotr Konopka ◽  
Mikolaj Krzewicki ◽  
Matthias Richter ◽  
David Rohr ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Model ◽  
Message Passing ◽  
Software Framework ◽  
Distributed Software ◽  
Central Collisions ◽  
Modular Software ◽  
Level Trigger ◽  
Data Throughput ◽  
High Level

ALICE is one of the four major LHC experiments at CERN. When the accelerator enters the Run 3 data-taking period, starting in 2021, ALICE expects almost 100 times more Pb-Pb central collisions than now, resulting in a large increase of data throughput. In order to cope with this new challenge, the collaboration had to extensively rethink the whole data processing chain, with a tighter integration between Online and Offline computing worlds. Such a system, code-named ALICE O2, is being developed in collaboration with the FAIR experiments at GSI. It is based on the ALFA framework which provides a generalized implementation of the ALICE High Level Trigger approach, designed around distributed software entities coordinating and communicating via message passing. We will highlight our efforts to integrate ALFA within the ALICE O2 environment. We analyze the challenges arising from the different running environments for production and development, and conclude on requirements for a flexible and modular software framework. In particular we will present the ALICE O2 Data Processing Layer which deals with ALICE specific requirements in terms of Data Model. The main goal is to reduce the complexity of development of algorithms and managing a distributed system, and by that leading to a significant simplification for the large majority of the ALICE users.

Adaptive scheduling in the cloud - SLA for Hadoop job scheduling

2015 ◽  
Author(s):  
Deveeshree Nayak ◽  
Venkata Swamy Martha ◽  
David Threm ◽  
Srini Ramaswamy ◽  
Summer Prince ◽  
...  
Keyword(s):  
Job Scheduling ◽  
Adaptive Scheduling

Adaptive scheduling method for dynamic robotic cell based on pattern classification algorithm

2018 ◽  
Vol 09 (05) ◽  
pp. 1850040 ◽  
Author(s):  
Chuyuan Wang ◽  
Linxuan Zhang ◽  
Chongdang Liu
Keyword(s):  
Real Time ◽  
Pattern Classification ◽  
Feature Selection Method ◽  
Classification Algorithm ◽  
Adaptive Scheduling ◽  
Gradient Boosting ◽  
Production Environment ◽  
Robotic Cell ◽  
Extreme Gradient Boosting ◽  
Scheduling Method

In order to deal with the dynamic production environment with frequent fluctuation of processing time, robotic cell needs an efficient scheduling strategy which meets the real-time requirements. This paper proposes an adaptive scheduling method based on pattern classification algorithm to guide the online scheduling process. The method obtains the scheduling knowledge of manufacturing system from the production data and establishes an adaptive scheduler, which can adjust the scheduling rules according to the current production status. In the process of establishing scheduler, how to choose essential attributes is the main difficulty. In order to solve the low performance and low efficiency problem of embedded feature selection method, based on the application of Extreme Gradient Boosting model (XGBoost) to obtain the adaptive scheduler, an improved hybrid optimization algorithm which integrates Gini impurity of XGBoost model into Particle Swarm Optimization (PSO) is employed to acquire the optimal subset of features. The results based on simulated robotic cell system show that the proposed PSO-XGBoost algorithm outperforms existing pattern classification algorithms and the newly learned adaptive model can improve the basic dispatching rules. At the same time, it can meet the demand of real-time scheduling.

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