TY - GEN
T1 - Forecast Extreme CPU Usages Under Peak Load Using Envelop EM Semi-supervised Learning
AU - Khosla, Nitin
AU - Sharma, Dharmendra
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The main objective of an expected maximization (EM) based envelop semi-supervised neural net learning approach in this paper is to develop a model for forecasting peak CPU usages under unpredictable web traffic (load conditions) in a large enterprise applications environment with several hundred live applications. This proposed approach forecasts the likelihood of extreme peak response time because of the stressed CPU due to a burst in incoming web traffic from the live IT applications and then predicts the CPU utilization under extreme load (peak) conditions. The enterprise complex IT infrastructure consists of many applications running simultaneously in real-time. Features are extracted after analyzing the CPU-load patterns of demand which are mainly hidden in the data related to key transactions of the IT applications. This method generates synthetic CPU load profiles by simulating virtual users and execute the key transactions in the test environment. This model is used to predict the excessive peak utilization under peaked CPU conditions. We have used envelope expectation maximization classifier method with forced learning, attempting to extract and analyze the parameters that maximize the likelihood of the model after marginalizing out the unknown labels. This has resulted in mitigating the risks of system failures and enabled us to manage the IT capacity within 3 days from 7 days. This model has helped in IT capacity planning and optimal usages of existing IT infrastructure with minimal risk.
AB - The main objective of an expected maximization (EM) based envelop semi-supervised neural net learning approach in this paper is to develop a model for forecasting peak CPU usages under unpredictable web traffic (load conditions) in a large enterprise applications environment with several hundred live applications. This proposed approach forecasts the likelihood of extreme peak response time because of the stressed CPU due to a burst in incoming web traffic from the live IT applications and then predicts the CPU utilization under extreme load (peak) conditions. The enterprise complex IT infrastructure consists of many applications running simultaneously in real-time. Features are extracted after analyzing the CPU-load patterns of demand which are mainly hidden in the data related to key transactions of the IT applications. This method generates synthetic CPU load profiles by simulating virtual users and execute the key transactions in the test environment. This model is used to predict the excessive peak utilization under peaked CPU conditions. We have used envelope expectation maximization classifier method with forced learning, attempting to extract and analyze the parameters that maximize the likelihood of the model after marginalizing out the unknown labels. This has resulted in mitigating the risks of system failures and enabled us to manage the IT capacity within 3 days from 7 days. This model has helped in IT capacity planning and optimal usages of existing IT infrastructure with minimal risk.
KW - Machine learning
KW - Neural nets
KW - Performance and peak load testing
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85123290236&partnerID=8YFLogxK
UR - https://icdlair2020.iaasse.org/index.html
U2 - 10.1007/978-3-030-85365-5_3
DO - 10.1007/978-3-030-85365-5_3
M3 - Conference contribution
AN - SCOPUS:85123290236
SN - 9783030853648
T3 - Lecture Notes in Networks and Systems
SP - 25
EP - 34
BT - Advances in Deep Learning, Artificial Intelligence and Robotics - Proceedings of the 2nd International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2020
A2 - Troiano, Luigi
A2 - Vaccaro, Alfredo
A2 - Tagliaferri, Roberto
A2 - Kesswani, Nishtha
A2 - Díaz Rodriguez, Irene
A2 - Brigui, Imene
A2 - Parente, Domenico
PB - Springer
CY - Switzerland
T2 - 2nd International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2020
Y2 - 7 December 2020 through 18 December 2020
ER -