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Architectural trade-offs: comparative analysis across K3s, serverless, and traditional server deployments.

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Title: Architectural trade-offs: comparative analysis across K3s, serverless, and traditional server deployments.
Authors: P., Prajwal1 prajwalp.cs20@rvce.edu.in, Teli, Naveen B.1 naveenbteli.cs20@rvce.edu.in, H. N., Nishal1 nishalhn.cs20@rvce.edu.in, Dey, Nimisha1 nimishadey.cs20@rvce.edu.in, Deenadhayalan, Pratiba1 pratibad@rvce.edu.in, Pattar, Ramakanth Kumar1 ramakanthkp@rvce.edu.in, Hadagali, Pavithra1 pavithrah@rvce.edu.in, P. R., Skanda1 skandapr.cs22@rvce.edu.in
Source: International Journal of Electrical & Computer Engineering (2088-8708). Apr2026, Vol. 16 Issue 2, p873-882. 10p.
Subjects: Software architecture, Containers, On-demand computing, Client/server computing, Benchmark problems (Computer science)
Abstract: In modern software architecture, combining serverless computing, microservices, and containers improves scalability, performance, observability, and resilience. However, choosing the right deployment strategy is crucial. Current individual deployment methods often limit productivity because of poor integration options. This study looks at three deployment approaches: Kubernetes cluster, AWS Lambda (serverless), and Traditional Java Server. We tested performance under different workloads using virtual machines and simulations. The results show that the K3s cluster provides high throughput and low latency because it manages resources directly. AWS Lambda's pay-as-you-go model, along with its built-in cost optimization, works well for event-driven workloads. In contrast, Java Microservice is cost-effective but needs manual tuning to control latency and error rates. Bringing these scenarios together into a single service mesh architecture could help optimize costs, performance, and system resilience. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Architectural trade-offs: comparative analysis across K3s, serverless, and traditional server deployments.
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  Data: <searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Containers%22">Containers</searchLink><br /><searchLink fieldCode="DE" term="%22On-demand+computing%22">On-demand computing</searchLink><br /><searchLink fieldCode="DE" term="%22Client%2Fserver+computing%22">Client/server computing</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink>
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  Data: In modern software architecture, combining serverless computing, microservices, and containers improves scalability, performance, observability, and resilience. However, choosing the right deployment strategy is crucial. Current individual deployment methods often limit productivity because of poor integration options. This study looks at three deployment approaches: Kubernetes cluster, AWS Lambda (serverless), and Traditional Java Server. We tested performance under different workloads using virtual machines and simulations. The results show that the K3s cluster provides high throughput and low latency because it manages resources directly. AWS Lambda's pay-as-you-go model, along with its built-in cost optimization, works well for event-driven workloads. In contrast, Java Microservice is cost-effective but needs manual tuning to control latency and error rates. Bringing these scenarios together into a single service mesh architecture could help optimize costs, performance, and system resilience. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.11591/ijece.v16i2.pp873-882
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        Text: English
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      – SubjectFull: Containers
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      – SubjectFull: On-demand computing
        Type: general
      – SubjectFull: Client/server computing
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      – SubjectFull: Benchmark problems (Computer science)
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              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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