A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks <s> Preliminaries: Osmotic Computing <s> Osmotic computing is a new paradigm to support the efficient execution of Internet of Things (IoT) services and applications at the network edge. This paradigm is founded on the need for a holistic distributed system abstraction enabling the deployment of lightweight microservices on resource-constrained IoT platforms at the network edge, coupled with more complex microservices running on large-scale datacenters. This paradigm is driven by the significant increase in resource capacity/capability at the network edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with datacenter-based services. This installment of "Blue Skies" discusses osmotic computing features, challenges, and future directions. <s> BIB001 </s> A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks <s> Preliminaries: Osmotic Computing <s> Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with the significant recent take up of latency-sensitive applications, such as streaming and real-time data processing. Fog computing provides a solution to such types of applications by bridging the gap between the user and public/private cloud infrastructure via the inclusion of a "fog" layer. Such approach is capable of reducing the overall processing latency, but the issues of redundancy, cost-effectiveness in utilizing such computing infrastructure and handling services on the basis of a difference in their characteristics remain. This difference in characteristics of services because of variations in the requirement of computational resources and processes is termed as service heterogeneity. A potential solution to these issues is the use of Osmotic Computing -- a recently introduced paradigm that allows division of services on the basis of their resource usage, based on parameters such as energy, load, processing time on a data center vs. a network edge resource. Service provisioning can then be divided across different layers of a computational infrastructure, from edge devices, in-transit nodes, and a data center, and supported through an Osmotic software layer. In this paper, a fitness-based Osmosis algorithm is proposed to provide support for osmotic computing by making more effective use of existing Fog server resources. The proposed approach is capable of efficiently distributing and allocating services by following the principle of osmosis. The results are presented using numerical simulations demonstrating gains in terms of lower allocation time and a higher probability of services being handled with high resource utilization <s> BIB002 </s> A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks <s> Preliminaries: Osmotic Computing <s> Pervasive social networking (PSN) aims at bridging the gap between the services and users by providing a platform for social communication irrespective of the time and location. With the advent of a new era of high-speed telecommunication services, mobile users have evolved to a large extent demanding secure, private, and trustworthy services. Online social networks have evolved as pervasive online social networks (POSNs), which uses a common platform to connect users from hybrid applications. Trust has always been a concern for these networks. However, existing approaches tend to provide application-specific trust management, thus resulting in the cost of excessive network resource utilization and high computations. In this paper, a pervasive trust management framework is presented for POSNs, which is capable of generating high trust value between the users with a lower cost of monitoring. The proposed approach uses a flexible mixture model to develop the system around six different properties, and then utilizes the concept of osmotic computing to perform computational offloading, which reduces the number of computations as well as computational time. The novel concepts of lock door policy and intermediate state management procedure are used to allow trust visualization by providing efficient identification of trustworthy and untrustworthy users. The proposed approach is capable of predicting user ratings efficiently with extremely low errors, which are in the range of ±2. The effectiveness of the proposed approach is demonstrated using theoretical and numerical analyses along with data set-based simulations. <s> BIB003 </s> A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks <s> Preliminaries: Osmotic Computing <s> The rapid evolution of Internet of Things (IoT) devices (e.g., sensors and gateways) and the almost ubiquitous connectivity (e.g., 4G, Wi-Fi, RFID/NFC, Bluetooth, IEEE 802.15.4) are forcing us to radically rethink how to effectively deal with massive volume, velocity, and variety of big data produced by such IoT devices. There are currently 6.4 billion IoT devices in use around the world and their number, capabilities, as well as the scope of their use, keeps growing rapidly. <s> BIB004 </s> A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks <s> Preliminaries: Osmotic Computing <s> Emerging availability (and varying complexity and types) of Internet of Things (IoT) devices, along with large data volumes that such devices (can potentially) generate, can have a significant impact on our lives, fuelling the development of critical next-generation services and applications in a variety of application domains (e.g. healthcare, smart grids, finance, disaster management, agriculture, transportation and water management). Deep learning technology, which has in the past been used successfully in computer vision and language modelling is now finding application in new domains driven by availability of diverse and large datasets. One such example is the advances in medical diagnostics and prediction by using Deep Learning technologies to improve human health. However, transferring large data streams (a requirement of Deep Learning technologies for achieving high accuracy) to centralised locations such as Cloud datacentre environments, in a timely and reliable manner, is being seen as a key limitation of expanding the application horizons of such technologies. To this end, various paradigms, including Osmotic Computing, have been proposed that promotes distribution of data analysis tasks across Cloud and Edge computing environments. However, these existing paradigms fail to provide a detailed account of how technologies such as deep learning can be orchestrated and take advantage of the cloud, edge and mobile edge environments in a holistic manner. In other words, the focus of this Blue Skies piece is to analyze the research challenges involved with developing a new class of holistic distributed deep learning algorithms that are “resource and data aware”, and which are able to account for underlying heterogeneous data and data models, resource (cloud vs. edge vs. mobile edge) models and data availability while executing – trading accuracy for execution time, etc. <s> BIB005 </s> A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks <s> Preliminaries: Osmotic Computing <s> Message-oriented middleware is a key technology in todays Internet of Things (IoT). Centralized message brokers facilitate decoupled device-to-device communication and can transparently scale to handle many millions of messages per second. However, Cloud-based solutions, such as AWS IoT or Azure IoT Hub, cannot satisfy the stringent QoS and privacy requirements of many modern IoT scenarios. Instead, distributed middleware needs to leverage the ever- increasing amount of resources at the edge of the network to provide reliable, ultra-low-latency, and privacy-aware message routing. But the heterogeneity and volatility inherent to Edge resources, and the unpredictability of mobile clients, make it extremely challenging to provide resilient coordination mechanisms and guaranteed message delivery. Applying Osmotic Computing principles to message-oriented middleware opens new opportunities for solving these challenges. <s> BIB006 </s> A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks <s> Preliminaries: Osmotic Computing <s> In recent years, the rapid growth of new Cloud technologies acted as an enabling factor for the adoption of microservices based architecture that leverages container virtualization in order to build modular and robust systems. As the number of containers running on hosts increases, it becomes essential to have tools to manage them in a simple, straightforward manner and with a high level of abstraction. Osmotic Computing is an emerging research field that studies the migration, deployment and optimization of microservices from the Cloud to Fog, Edge, and Internet of Things (IoT) environments. However, in order to achieve Osmotic Computing environments, connectivity issues have to be addressed. This paper investigates these connectivity issues leveraging different network overlays. In particular, we analyze the performance of four network overlays that are OVN, Calico, Weave, and Flannel. Our results give a concrete overview in terms of overhead and performances for each proposed overlay solution, helping us to understand which the best overlay solution is. Specifically, we deployed CoAP and FTP microservices which helped us to carry out these benchmarks and collect the results in terms of transfer times. <s> BIB007
Osmotic computing is a network integration paradigm inspired by the chemical process of osmosis. In chemistry, the osmosis is a spontaneous mechanism of transferring or movement of the solvent through a semipermeable membrane into solute concentrated solution. The solution is a mixture in which one substance is dissolved in another, the solute is the substance that is getting dissolved, and the solvent is the substance that is causing the dissolving. The standard mechanism of osmosis was molded with respective of available information by Villari et al. BIB001 to form this new paradigm of osmotic computing. This type of computing supports load balancing by enabling the movement of micro-services between a data center and the edge devices. It also reduces the latency of the overall application, however, in the initial drafts, the authors do not fully identify the mechanisms that can be used to support and facilitate such service migrations as stated in Sharma et al BIB002 . As per the standard mechanism of osmosis, the model contains a solution of the solute and the solvent and a semi-permeable membrane for filtering the solute. The participants of any model are interchanged with the respective terminologies to allow applicability of osmotic computing, like information and services, refer loads, process time, and energy of the system. The server can play the role of solvent for the interchangeable services through a semi-permeable membrane and is configured as a controller for the movement of services between the senders and the receivers. The basic principle of osmosis is used for load balancing of incoming requests. In case a server receives a lot of requests and is unable to handle in those instances, the service provider can act as a semi-permeable membrane and can shift the load to another server. To further enhance the understanding, the standard procedures without osmosis and load balancing with the osmosis principle are conceptualized in Fig. 3 . The primary objective of osmotic computing is to balance the load and resource utilization among servers without affecting the connectivity of services and performance. The real-time distribution of services as per the requirements helps to enable the new service migration concepts with the osmotic computing. Maksimović  defined the osmotic computing as "It enables the dynamic arrangement and migration of services and microservices across cloud data centers and Edge resources according to differ-ent infrastructure demands and software." The author also conceptualizes the role of osmotic computing in the IoT and discusses the issues associated with the efficient execution of IoT services and applications across different computing infrastructures. In the era of osmotic computing development, there have been limited but qualitative works that have highlighted the significance of using this new computing paradigm. Sharma et al. BIB002 focused on the efficient distribution and allocation of services via a concept of osmosis. The authors present a fitness-based Osmosis algorithm which is used to provide support for osmotic computing. The algorithm utilizes a fitness function to distribute and allocate the services into micro and macro-components. As per the authors, security concerns of the osmotic computing are still an open issue and future aspects should be considered with these issues. Furthermore, Sharma et al. BIB003 presented an efficient implementation of the osmotic computing, which is used for the pervasive trust management framework to perform computational offloading. The authors used three solutions which include the models-fitness-based movement, probabilistic movement, and threshold-based movement. Nardelli et al. BIB004 presented the Osmotic Flow model, which supports multiple types and the mix of data transformation tasks on shared EDC+CDC infrastructures. The proposed model helps to assign the Edge and Cloud nodes among multiple and concurrent data transformation functions. As per the authors, the concept of machine learning with the distribution of data analysis tasks in various cloud environments imposes various solutions in a holistic manner. Morshed et al. BIB005 emphasized the deep learning concept in osmotic computing. The authors discussed the implementation issues and challenges of deep learning formations in the osmotic set-up. Rausch et al. BIB006 emphasized the applicability of osmotic computing to the message-oriented middleware for enhancing reliability, ultra-lowlatency, and privacy-aware message routing. The proposed solution enables the facilities for brokers to enable or diffuse the edge resources on the demand and support device to device communication in the IoT environment. Despite the facilities associated with osmotic computing, lots of challenges are also linked with the emerging technologies. Buzachis et al. BIB007 addressed the issues of connectivity in the environment which imposes network degradation and failures concerning osmotic nodes.