scholarly journals DTHMM ExaLB: discrete-time hidden Markov model for load balancing in distributed exascale computing environment

2020 ◽  
Vol 7 (1) ◽  
pp. 1743404
Author(s):  
Ulphat Bakhishoff ◽  
Ehsan Mousavi Khaneghah ◽  
Araz R. Aliev ◽  
Amirhossein Reyhani Showkatabadi ◽  
Stefania Tomasiello
Keyword(s):  
Load Balancing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Discrete Time ◽  
Hidden Markov ◽  
Computing Environment ◽  
Exascale Computing

Hidden Markov Model-based Load Balancing in Data Center Networks

2019 ◽  
Vol 63 (10) ◽  
pp. 1449-1462
Author(s):  
Binjie He ◽  
Dong Zhang ◽  
Chang Zhao
Keyword(s):  
Load Balancing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Data Center ◽  
Data Centers ◽  
Hidden Markov ◽  
Path Selection ◽  
Data Center Networks ◽  
Data Flows ◽  
Load Balancing Algorithm

Abstract Modern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.

Discrete-time optimal asset allocation under Higher-Order Hidden Markov Model

2017 ◽  
Vol 66 ◽  
pp. 223-232 ◽  
Author(s):  
Dong-Mei Zhu ◽  
Jiejun Lu ◽  
Wai-Ki Ching ◽  
Tak-Kuen Siu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Discrete Time ◽  
Asset Allocation ◽  
Hidden Markov ◽  
Higher Order ◽  
Time Optimal

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing
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