A Survey on Machine Learning Based Fault Tolerant Mechanisms in Cloud Towards Uncertainty Analysis

Nivitha, K.; Pabitha, P.

doi:10.1007/978-3-030-43192-1_2

Cited by 4 publications

(1 citation statement)

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“…Furthermore, proactive methods can be employed to introduce more foresight such as: self-healing capabilities to automatically detect and fix faults [113]; preemptive migration to shift tasks from potentially failing nodes [114]; or fault prediction through using patterns to anticipate and prevent future issues [115]. Going a step further, resilient methods employ machine learning, particularly reinforcement learning, to interact with the environment and dynamically adapt fault-handling strategies [116]. This learning-oriented approach can enhance the middleware's ability to cope with unexpected challenges efficiently.…”

Section: F Resilience and Fault Tolerancementioning

confidence: 99%

Smart City Middleware: A Survey and a Conceptual Framework

Goumopoulos

2024

IEEE Access

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Smart city middleware serves as a foundational tool in the evolution of urban digitalization, acting as an intermediary software layer that simplifies the development, deployment, and management of applications tailored for smart urban environments. However, the development of effective middleware for smart cities is challenging. The present research embarks on a comprehensive exploration of the smart city middleware landscape, unraveling the intricacies of its development and the challenges faced therein. Rooted in the assessment of 20 distinct middleware solutions, our study highlights the pivotal technologies, features and functionalities that are imperative for a middleware to effectively support a city's digital transformation. The functional and non-functional requirements form the nucleus of our evaluation. We also explore the architectural styles pivotal to middleware development and the programming paradigms shaping smart city application development. Our study highlights challenges in using middleware for smart city applications, such as interoperability, scalability, security amidst big data, context management, reliability, quality of service, energy efficiency, and compliance with technological standards and regulations. Based on the detailed analysis, we propose a conceptual framework for smart city middleware, shaped by the challenges and requirements identified in existing literature and middleware solutions. This framework is designed to reflect the diverse demands and complexities of urban digital transformation, and guide smart city middleware development accordingly. As a result, this research stands as a reference study for software developers, urban planners, and researchers, outlining the current state and future directions in the domain of smart city middleware.

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