DS-Harmonizer: A Harmonization Service on Spatiotemporal Data Stream in Edge Computing Environment

Abundant sensors in various types are widely used in modern cities to comprehend the current situations in real time. The raw data in open conditions is always in low quality and is hard to employ directly due to its imperfect or missing records. Traditional data preprocessing methods focus on the offline historical data and remain a dilemma between the efficiency and the overhead. In this paper, a data harmonization service DS-Harmonizer is proposed on spatiotemporal data stream in the edge computing environment. Through the online cleaning and complementing steps of the hierarchical service instances, the records’ validity and continuity can be guaranteed in an efficient way. On the simulated data in a practical project, the service shows high performance, low latency, and acceptable precision in extensive conditions.

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Programmable Chip Based High Performance MEC Router for Ultra-Low Latency and High Bandwidth Services in Distributed Computing Environment

IEICE Transactions on Information and Systems ◽

10.1587/transinf.2020pal0001 ◽

2020 ◽

Vol E103.D (12) ◽

pp. 2525-2527

Author(s):

SeokHwan KONG ◽

Saikia DIPJYOTI ◽

JaiYong LEE

Keyword(s):

Distributed Computing ◽

High Performance ◽

Low Latency ◽

Computing Environment ◽

High Bandwidth

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Deep Learning and Edge Computing Solutions for High Performance Computing

10.1007/978-3-030-60265-9 ◽

2021 ◽

Keyword(s):

Deep Learning ◽

High Performance Computing ◽

High Performance ◽

Edge Computing ◽

Performance Computing

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Deep reinforcement learning for computation offloading in mobile edge computing environment

Computer Communications ◽

10.1016/j.comcom.2021.04.028 ◽

2021 ◽

Author(s):

Miaojiang Chen ◽

Tian Wang ◽

Shaobo Zhang ◽

Anfeng Liu

Keyword(s):

Reinforcement Learning ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Computing Environment

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Reliability-Optimal Offloading in Low-latency Edge Computing Networks: Analytical and Reinforcement Learning Based Designs

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2021.3073791 ◽

2021 ◽

pp. 1-1

Author(s):

Yao Zhu ◽

Yulin Hu ◽

Tianyu Yang ◽

Tao Yang ◽

Jannik Vogt ◽

...

Keyword(s):

Reinforcement Learning ◽

Edge Computing ◽

Low Latency

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Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽

10.1093/genetics/165.4.2269 ◽

2003 ◽

Vol 165 (4) ◽

pp. 2269-2282

Author(s):

D Mester ◽

Y Ronin ◽

D Minkov ◽

E Nevo ◽

A Korol

Keyword(s):

Discrete Optimization ◽

High Performance ◽

Large Scale ◽

Simulated Data ◽

Real Data ◽

Genetic Maps ◽

Chromosome 1 ◽

Evolutionary Strategy ◽

Group A ◽

The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

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