TY - GEN
T1 - Request Distribution for Heterogeneous Database Server Clusters with Processing Time Estimation
AU - Omori, Minato
AU - Nishi, Hiroaki
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - Recently, data traffic on the Internet has increased due to the rapid growth of various Internet-based services. The convergence of user requests means that servers are overloaded. To solve this problem, service providers generally install multiple servers and distribute requests using a load balancer. The existing load balancing algorithms do not estimate the size of the load of unknown requests. However, the requested contents are heterogeneous and complex, so the size of the load is dependent on the servers and the contents of the requests. In this study, we propose a load balancing algorithm that distributes the requests based on estimates of the processing time, which avoids mismatches between the characteristics of servers and the request contents. The processing time for requests is estimated based on the requested contents by online machine learning, and a strategy to cover the latency of machine learning is proposed and partially conducted. To test the algorithm, we built a model of multiple database servers and performed an experiment using real log data for database requests. The simulation results showed that the proposed algorithm reduced the average processing time for requests by 94.5% compared with round robin and by 28.3% compared with least connections.
AB - Recently, data traffic on the Internet has increased due to the rapid growth of various Internet-based services. The convergence of user requests means that servers are overloaded. To solve this problem, service providers generally install multiple servers and distribute requests using a load balancer. The existing load balancing algorithms do not estimate the size of the load of unknown requests. However, the requested contents are heterogeneous and complex, so the size of the load is dependent on the servers and the contents of the requests. In this study, we propose a load balancing algorithm that distributes the requests based on estimates of the processing time, which avoids mismatches between the characteristics of servers and the request contents. The processing time for requests is estimated based on the requested contents by online machine learning, and a strategy to cover the latency of machine learning is proposed and partially conducted. To test the algorithm, we built a model of multiple database servers and performed an experiment using real log data for database requests. The simulation results showed that the proposed algorithm reduced the average processing time for requests by 94.5% compared with round robin and by 28.3% compared with least connections.
KW - Database server
KW - Load balancing
KW - Online machine learning
KW - Processing time estimation
UR - http://www.scopus.com/inward/record.url?scp=85055505123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055505123&partnerID=8YFLogxK
U2 - 10.1109/INDIN.2018.8471931
DO - 10.1109/INDIN.2018.8471931
M3 - Conference contribution
AN - SCOPUS:85055505123
T3 - Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
SP - 278
EP - 283
BT - Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Industrial Informatics, INDIN 2018
Y2 - 18 July 2018 through 20 July 2018
ER -