In the evolving landscape of distributed computing, the integration of edge devices with traditional cloud infrastructures necessitates innovative approaches to harness their combined computational prowess. Osmotic computing, a paradigm that promises such integration, has transitioned from theoretical frameworks to tangible implementations. This chapter provides a comprehensive examination of osmotic computing, tracing its journey from conceptual underpinnings to its current real-world applications. Central to osmotic computing is the deployment of microservices—modular, autonomous units of computation—strategically positioned across the edge-cloud continuum based on immediate needs and resource availabilities. This review elucidates the foundational principles of osmotic computing, its distinguishing characteristics, the challenges encountered in its practical adoption, and its demonstrable benefits in current computing scenarios.
Top1. Introduction
In today's digital era, the proliferation of devices, from smartphones to IoT sensors, has radically transformed the computational landscape. Traditional cloud-based processing models, while powerful, are increasingly challenged by the sheer volume of data generated at the edge of the network, raising concerns about latency, bandwidth consumption, and data privacy. Enter osmotic computing, a paradigm shift aiming to seamlessly blend the capabilities of edge devices with cloud infrastructures.
The capacity to shift computational processes from the Cloud to IoT devices positions. Osmotic Computing as an ideal fit for rapidly changing environments like smart cities. Industry 5.0 represents the next phase in manufacturing where humans and advanced technology (Rani, S., & Srivastava, G., 2024) work together to transform workflows. It's crucial for the evolution of the next-generation smart cities. Designing and operating smart infrastructure for Industrial Internet of Things (IIoT) involves various decision-making processes across different domains. Today, complex systems can be thought of as a network of serverless services (or workflows) distributed across the Cloud and Edge layers. However, implementing applications in this complicated context while still assuring built-in security remains difficult. Osmotic Computing has recently emerged as a novel computational method. It may provide a solid foundation for improving security in serverless programs throughout the Cloud-Edge spectrum. Osmotic Computing concepts to modify the structure of OpenWolf, a novel serverless engine, resulting in a 65% reduction in the execution time of encrypted data. In such settings, intelligent services, attuned to specific contexts, frequently manage environmental variables and discern patterns in people's actions.
At its core, osmotic computing envisions a dynamic computational environment where tasks are efficiently distributed across the edge-cloud continuum. This is achieved through the strategic deployment of microservices, which are self-contained units of computation that can be easily migrated based on contextual needs and resource availabilities. While the theoretical foundations of osmotic computing promise a revolution in distributed computing, it's in the practical implementations that its true potential is being rigorously tested. Many Cloud providers are already embracing serverless computing via the Function as a Service (FaaS) concept. This strategy, which is based on (Morabito, Get et al., 2023) dynamically allocating serverless services, has significantly altered how Cloud applications are constructed.
There are numerous studies in the literature that address naming geographical regions in a manner easily understood by humans. However, some of these methods result in geocode conflicts, while others don't support the designation of areas of varying sizes. Additionally, some solutions are proprietary, limiting their widespread adoption or integration into open-source initiatives. This review seeks to traverse the journey of osmotic computing from its early conceptualizations to its current real-world applications.
By merging consortium blockchain (also known as supervisory blockchain) and fog computing, the authors have produced a novel approach. This suggested system is divided into three layers: the application layer, the fog layer, and the blockchain security layer. The authors (Almaiah, M. A., & Alkdour, T., 2023). present a new consensus technique dubbed the Proof of Enhanced Concept (PoEC) to successfully implement this concept. This method secures transactions before delegating them to the fog layer or fog devices via homomorphic encryption. This method aids in the mitigation of numerous security risks such as collusion attacks, phishing attacks, and replay attacks, while also increasing the resilience of each layer against such intrusions. The model presented by the authors uses a hybrid-deep learning approach to protect electronic medical records against potential breaches. Additionally, by employing a decentralized fog computing system (Kochovski, P, et al.,2023) it reduces latency and enhances security measures.