Browsing by Author "Samaratunge Arachchillage, U. S. S"

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationEmbargo
    Expert System for Kubernetes Cluster Autoscaling and Resource Management
    (IEEE, 2022-12-09) Hettiarachchi, L.S; Jayadeva, S.V; Bandara, R. A. V; Palliyaguruge, D; Samaratunge Arachchillage, U. S. S; Kasthurirathna, D
    The importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools’ failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to conFigure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes.
  • Thumbnail Image
    PublicationOpen Access
    Source Code based Approaches to Automate Marking in Programming Assignments
    (Science and Technology Publications, 2021) Kuruppu, T; Tharmaseelan, J; Silva, C; Samaratunge Arachchillage, U. S. S; Manathunga, K; Reyal, S; Kodagoda, N; Jayalath, T
    With the embarkment of this technological era, a significant demand over programming modules can be observed among university students in larger volume. When figures grow exponentially, manual assessments and evaluations would be a tedious and error-prone activity, thus marking automation has become fast growing necessity. To fulfil this objective, in this review paper, authors present literature on automated assessment of coding exercises, analyse the literature from four dimensions as Machine Learning approaches, Source Graph Generation, Domain Specific Languages, and Static Code Analysis. These approaches are reviewed on three main aspects: accuracy, efficiency, and user-experience. The paper finally describes a series of recommendations for standardizing the evaluation and benchmarking of marking automation tools for future researchers to obtain a strong empirical footing on the domain, thereby leading to further advancements in the field.
  • Thumbnail Image
    PublicationOpen Access
    Source Code based Approaches to Automate Marking in Programming Assignments.
    (Science and Technology Publications, 2021) Kuruppu, T; Tharmaseelan, J; Silva, C; Samaratunge Arachchillage, U. S. S; Manathunga, K; Reyal, S; Kodagoda, N
    With the embarkment of this technological era, a significant demand over programming modules can be observed among university students in larger volume. When figures grow exponentially, manual assessments and evaluations would be a tedious and error-prone activity, thus marking automation has become fast growing necessity. To fulfil this objective, in this review paper, authors present literature on automated assessment of coding exercises, analyse the literature from four dimensions as Machine Learning approaches, Source Graph Generation, Domain Specific Languages, and Static Code Analysis. These approaches are reviewed on three main aspects: accuracy, efficiency, and user-experience. The paper finally describes a series of recommendations for standardizing the evaluation and benchmarking of marking automation tools for future researchers to obtain a strong empirical footing on the domain, thereby leading to further advancements in the field.