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http://hdl.handle.net/10603/594144
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DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | Customizing the container orchestration and improving the performance of clustered iot services | |
dc.date.accessioned | 2024-10-10T09:16:10Z | - |
dc.date.available | 2024-10-10T09:16:10Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/594144 | - |
dc.description.abstract | Container technology is the latest lightweight virtualization technology and it is an alternate solution for virtual machines. It is highly significant in many technologies and communication systems. Among the container technologies, Docker is widely acknowledged and most popular one. Containers, in general, have gained significant popularity due to their suitability for various IT environments including dynamic development, packaging and transferring from one environment to another environment. To maximize the container efficiency, scaling is essential to dynamically adjust the instances for optimal resource usage and adaptability to the changing workloads. The proposed research work focuses on four main aspects. The first work aims to develop a load-balancing scheduling algorithm that efficiently distributes the containers across the hosts especially in scenarios with a high number of running containers. In order to achieve the efficiency in load balancing and resource allocation of multiple containers, an Ant Colony Optimisation-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm is proposed. This algorithm is specifically designed for scheduling various processing requests in a system. The performance metrics such as node load, response time (ms), Mean Square Error (MSE), Central Processing Unit usage (CPU) and Memory performance of the proposed algorithm are evaluated and compared with the existing baseline algorithms such as least connection and round robin algorithms. The quantitative analysis shows that the proposed ACO-LWC scheme achieves better performance in terms of all the metrics compared to the existing baseline algorithms.The second work focuses on container service migration which integrates with fog servers. newline | |
dc.format.extent | xvii,160p. | |
dc.language | English | |
dc.relation | p.144-159 | |
dc.rights | university | |
dc.title | Customizing the container orchestration and improving the performance of clustered iot services | |
dc.title.alternative | ||
dc.creator.researcher | Aruna, K | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Hardware and Architecture | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | lightweight virtualization | |
dc.subject.keyword | orchestration | |
dc.description.note | ||
dc.contributor.guide | Pradeep, G | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Information and Communication Engineering | |
dc.date.registered | ||
dc.date.completed | 2024 | |
dc.date.awarded | 2024 | |
dc.format.dimensions | 21cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 230.11 kB | Adobe PDF | View/Open |
02_prelim_pages.pdf | 2.48 MB | Adobe PDF | View/Open | |
03_content.pdf | 16.3 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 9.1 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 888.41 kB | Adobe PDF | View/Open | |
06_chapter2.pdf | 405.93 kB | Adobe PDF | View/Open | |
07_chapter3.pdf | 1.2 MB | Adobe PDF | View/Open | |
08_chapter4.pdf | 847.41 kB | Adobe PDF | View/Open | |
09_chapter5.pdf | 1.04 MB | Adobe PDF | View/Open | |
10_chapter6.pdf | 353.49 kB | Adobe PDF | View/Open | |
11_annexures.pdf | 148.78 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 486.9 kB | Adobe PDF | View/Open |
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