Software Aging Forecasting Using Time Series Model
With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also hou...
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Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2017-12-01.
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Online Access: | Get fulltext |
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LEADER | 02471 am a22003133u 4500 | ||
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001 | ijeecs7956_7675 | ||
042 | |a dc | ||
100 | 1 | 0 | |a Umesh, I M |e author |
100 | 1 | 0 | |e contributor |
700 | 1 | 0 | |a Srinivasan, G N |e author |
700 | 1 | 0 | |a Torquato, Matheus |e author |
245 | 0 | 0 | |a Software Aging Forecasting Using Time Series Model |
260 | |b Institute of Advanced Engineering and Science, |c 2017-12-01. | ||
500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/7956 | ||
520 | |a With the emergence of virtualization and cloud computing technologies, several services are housed on virtualization platform. Virtualization is the technology that many cloud service providers rely on for efficient management and coordination of the resource pool. As essential services are also housed on cloud platform, it is necessary to ensure continuous availability by implementing all necessary measures. Windows Active Directory is one such service that Microsoft developed for Windows domain networks. It is included in Windows Server operating systems as a set of processes and services for authentication and authorization of users and computers in a Windows domain type network. The service is required to run continuously without downtime. As a result, there are chances of accumulation of errors or garbage leading to software aging which in turn may lead to system failure and associated consequences. This results in software aging. In this work, software aging patterns of Windows active directory service is studied. Software aging of active directory needs to be predicted properly so that rejuvenation can be triggered to ensure continuous service delivery. In order to predict the accurate time, a model that uses time series forecasting technique is built. | ||
540 | |a Copyright (c) 2017 Institute of Advanced Engineering and Science | ||
540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
546 | |a eng | ||
690 | |||
690 | |||
655 | 7 | |a info:eu-repo/semantics/article |2 local | |
655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
655 | 7 | |2 local | |
786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 8, No 3: December 2017; 589-596 | |
786 | 0 | |n 2502-4760 | |
786 | 0 | |n 2502-4752 | |
786 | 0 | |n 10.11591/ijeecs.v8.i3 | |
787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/7956/7675 | |
856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/7956/7675 |z Get fulltext |