arXiv subjects

Samodha Pallewatta

Publications and source records attributed to Samodha Pallewatta.

7 recordsLinked to original sources

arXiv2025

A Multivocal Review of MLOps Practices, Challenges and Open Issues

MLOps has emerged as a key solution to address many socio-technical challenges of bringing ML models to production, such as integrating ML models with non-ML software, continuous monitoring, maintenance, and retraining of deployed models. Despite the utility of MLOps, an integrated body of knowledge regarding MLOps remains elusive because of its extensive scope due to the diversity of ML productionalization challenges it addresses. Whilst the existing literature reviews provide valuable snapshots of specific practices, tools, and research prototypes related to MLOps at various times, they focus on particular facets of MLOps, thus fail to offer a comprehensive and invariant framework that can weave these perspectives into a unified understanding of MLOps. This paper presents a Multivocal Literature Review that systematically analyzes a corpus of 150 peer-reviewed and 48 grey literature to synthesize a unified conceptualization of MLOps and develop a snapshot of its best practices, adoption challenges, and solutions.

cs.SE↗

arXiv2024

Towards Secure Management of Edge-Cloud IoT Microservices using Policy as Code

IoT application providers increasingly use MicroService Architecture (MSA) to develop applications that convert IoT data into valuable information. The independently deployable and scalable nature of microservices enables dynamic utilization of edge and cloud resources provided by various service providers, thus improving performance. However, IoT data security should be ensured during multi-domain data processing and transmission among distributed and dynamically composed microservices. The ability to implement granular security controls at the microservices level has the potential to solve this. To this end, edge-cloud environments require intricate and scalable security frameworks that operate across multi-domain environments to enforce various security policies during the management of microservices (i.e., initial placement, scaling, migration, and dynamic composition), considering the sensitivity of the IoT data. To address the lack of such a framework, we propose an architectural framework that uses Policy-as-Code to ensure secure microservice management within multi-domain edge-cloud environments. The proposed framework contains a "control plane" to intelligently and dynamically utilise and configure cloud-native (i.e., container orchestrators and service mesh) technologies to enforce security policies. We implement a prototype of the proposed framework using open-source cloud-native technologies such as Docker, Kubernetes, Istio, and Open Policy Agent to validate the framework. Evaluations verify our proposed framework's ability to enforce security policies for distributed microservices management, thus harvesting the MSA characteristics to ensure IoT application security needs.

cs.CR↗

arXiv2024

An Empirically Grounded Reference Architecture for Software Supply Chain Metadata Management

With the rapid rise in Software Supply Chain (SSC) attacks, organisations need thorough and trustworthy visibility over the entire SSC of their software inventory to detect risks early and identify compromised assets rapidly in the event of an SSC attack. One way to achieve such visibility is through SSC metadata, machine-readable and authenticated documents describing an artefact's lifecycle. Adopting SSC metadata requires organisations to procure or develop a Software Supply Chain Metadata Management system (SCM2), a suite of software tools for performing life cycle activities of SSC metadata documents such as creation, signing, distribution, and consumption. Selecting or developing an SCM2 is challenging due to the lack of a comprehensive domain model and architectural blueprint to aid practitioners in navigating the vast design space of SSC metadata terminologies, frameworks, and solutions. This paper addresses the above-mentioned challenge by presenting an empirically grounded Reference Architecture (RA) comprising of a domain model and an architectural blueprint for SCM2 systems. Our proposed RA is constructed systematically on an empirical foundation built with industry-driven and peer-reviewed SSC security frameworks. Our theoretical evaluation, which consists of an architectural mapping of five prominent SSC security tools on the RA, ensures its validity and applicability, thus affirming the proposed RA as an effective framework for analysing existing SCM2 solutions and guiding the engineering of new SCM2 systems.

cs.CR↗

arXiv2023

AI Augmented Edge and Fog Computing: Trends and Challenges

In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems.

cs.DC↗

arXiv2023

MicroFog: A Framework for Scalable Placement of Microservices-based IoT Applications in Federated Fog Environments

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel microservice placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework providing a scalable, easy-to-configure control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition. The framework is evaluated using multiple use cases. The results demonstrate that MicroFog is a scalable, extensible and easy-to-configure framework that can integrate and evaluate novel placement policies for deploying microservice-based applications within multi-fog multi-cloud environments. We integrate multiple microservice placement policies to demonstrate MicroFog's ability to support horizontally scaled placement, thus reducing the application service response time up to 54%.

cs.DC↗

arXiv2023

Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions

The Fog computing paradigm utilises distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of fast-evolving IoT applications. Due to the fine-grained modularity of the microservices and their independently deployable and scalable nature, MSA exhibits great potential in harnessing Fog and Cloud resources, thus giving rise to novel paradigms like Osmotic computing. The loosely coupled nature of the microservices, aided by the container orchestrators and service mesh technologies, enables the dynamic composition of distributed and scalable microservices to achieve diverse performance requirements of the IoT applications using distributed Fog resources. To this end, efficient placement of microservice plays a vital role, and scalable placement algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. Thus, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications placement within Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the placement problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.

cs.DC↗

arXiv2021

IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments

Internet of Things (IoT) has already proven to be the building block for next-generation Cyber-Physical Systems (CPSs). The considerable amount of data generated by the IoT devices needs latency-sensitive processing, which is not feasible by deploying the respective applications in remote Cloud datacentres. Edge/Fog computing, a promising extension of Cloud at the IoT-proximate network, can meet such requirements for smart CPSs. However, the structural and operational differences of Edge/Fog infrastructure resist employing Cloud-based service regulations directly to these environments. As a result, many research works have been recently conducted, focusing on efficient application and resource management in Edge/Fog computing environments. Scalable Edge/Fog infrastructure is a must to validate these policies, which is also challenging to accommodate in the real-world due to high cost and implementation time. Considering simulation as a key to this constraint, various software has been developed that can imitate the physical behaviour of Edge/Fog computing environments. Nevertheless, the existing simulators often fail to support advanced service management features because of their monolithic architecture, lack of actual dataset, and limited scope for a periodic update. To overcome these issues, we have developed multiple simulation models for service migration, dynamic distributed cluster formation, and microservice orchestration for Edge/Fog computing in this work and integrated with the existing iFogSim simulation toolkit for launching it as iFogSim2. The performance of iFogSim2 and its built-in policies are evaluated using three use case scenarios and compared with the contemporary simulators and benchmark policies under different settings. Results indicate that the proposed solution outperform others in service management time, network usage, ram consumption, and simulation time.

cs.DC↗