Research Publications

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Open Access
A cross-category analysis of high impact low occurrence (HILO) disasters
(Elsevier Ltd, 2026-03-19) Samaraweera, U; Kulatunga, U; Dias, P
This paper explores six High Impact Low Occurrence (HILO) disasters, generating insights from five different categories associated with them, namely causes (geophysical, technological, biological, sociological), phases (preparedness, response, recovery), dimensions (socio-economics, governance, equity), sectors (health, education, infrastructure, economy) and national contexts with differing levels of economic development. The process involved the generation of a questionnaire, based on a literature review; and the subsequent analysis and discussion of the questionnaire responses made by six experts nominated by six academies of science in Asia. The findings highlight the limitations of probabilistic, frequency-based risk models for HILO disasters and instead emphasise the importance of scenario-based (worst-case) analyses; mechanisms that preserve inter‐generational knowledge, institutional continuity and community‐based early‐response networks; strengthening community resilience while ensuring equity; and making appropriate investments for increasing preparedness, if not through structural interventions, at least through sustained awareness programs and periodic drills. Theoretical contributions include arguments that institutional capacity, governance quality, and social resilience are more decisive determinants of HILO event outcomes than probabilistic risk analyses; and that effective preparedness depends more on anticipatory planning, scenario-based training and institutionalised memory rather than experiential learning; thus advancing HILO theory beyond event-centred and frequency-driven interpretations towards a more governance- and resilience-oriented understanding.
Embargo
A Network Science-Based Approach for an Optimal Microservice Governance
(IEEE, 2020-12-10) Siriwardhana, G. S; De Silva, N; Jayasinghe, L. S; Vithanage, L; Kasthurirathna, D
In today's world of software application development, Kubernetes has emerged as one of the most effective microservice deployment technologies presently available due to its exceptional ability to deploy and orchestrate containerized microservices. However, a common issue faced in such orchestration technologies is the employment of vast arrays of disjoint monitoring solutions that fail to portray a holistic perspective on the state of microservice deployments, which consequently inhibit the creation of more optimized deployment policies. In response to this issue, this publication proposes the use of a network science-based approach to facilitate the creation of a microservice governance model that incorporates the use of dependency analysis, load prediction, centrality analysis, and resilience evaluation to effectively construct a more holistic perspective on a given microservice deployment. Furthermore, through analysis of the factors mentioned above, the research conducted, then proceeds to create an optimized deployment strategy for the deployment with the aid of a developed optimization algorithm. Analysis of results revealed the developed governance model aided through the utilization of the developed optimization algorithm proposed in this publication, proved to be quite effective in the generation of optimized microservice deployment policies.

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