Latency minimization is a pivotal aspect in provision of real time services while adhering to Quality of Experience (QoE) parameters for assuring spectral efficiency. Edge Cloud Computing, being a potential research dimension in the realm of 5G networks, targets to enhance the network efficiency by harnessing effectiveness of both cloud computing and mobile devices in user's proximity. Keeping in view the far ranging impact of Edge Cloud Computing in future mobile generations, a comprehensive review of the prevalent Edge Cloud Computing frameworks and approaches is presented with a detailed comparison of its classifications through various QoS metrics (pertinent to network performance and overheads associated with deployment/migration). Considering the knowledge accumulated, procedures analysed and theories discussed, the paper provides a comprehensive overview on sate-of-the-art and future research directions for multi-access mobile edge computing.

Recently, cloud computing has been recognized as effective computing model, being adopted by both large corporations as well as SMEs (Small and Medium Enterprises). Due to its on-demand provisioning nature number of cloud based services are growing gradually and rapidly. The availability of open-source cloud computing platforms plays a vital role to enable researchers to go beyond state of the art for development of cloud applications. In this paper, we have provided an overview of cloud computing along with its services. We have presented a comparative performance analysis of different cloud IaaS platforms while covering different aspects of their application and service models. We have also proposed cloud test-bed using open-source cloud platform. A comparative performance analysis of open-source cloud platforms will assist researchers to select an appropriate tool to validate their research and experimentation.

6G networks are expected to have a breakthrough by enabling the emergence of collaborative cognitive communication services over heterogeneous environments for industry 5.0 applications. These applications are required to adapt human-centric approach to make the most of human intuition and intelligence in Industry 4.0 automation.It calls for a transdisciplinarity research domain to investigate innovative systems with overlapping realms of Psychology, Sociology, Communication networks, Artificial Intelligence , Natural Language Processing and Collaborative Computing. The author at the Cognitive Systems Research Centre, London South Bank University has coined the expression "3C Systems" to refer to such artifacts which stands for "Collaborative Cognitive Communication Systems". In this paper, an innovative framework for 3C Systems is proposed that is able to analyze and predict both the human as well as machine behaviors. It proactively diagnoses issues and recommends solutions without requiring any human intervention. The proposed concept of 3C Systems would potentially contribute towards 6G standardization. The automation and orchestration aspects of this research have variety of applications stretched across city infrastructures, retail, business, tourism, health, law, education and travel. A thorough insight to a broad view of 6G vision has been presented towards envisioned 3C Systems, while covering its enabling technologies. The experimental results for the proof of concept implementation has been presented. Results affirm the technical capabilities of the concept, to contribute to several industry 5.0 applications including, but not limited to holographic communication, self-driving vehicles, context-aware infrastructure and personalized interfaces.

Twenty‐first century global job market competition requires Science, Technology, Engineering, and Mathematics (STEM) university curricula to support both state‐of‐the‐art technical and soft skills learning to improve graduate employment. This necessitates the transformation of the current teaching and learning methodology powered by a social and collaborative platform to provide a social co‐learning environment. This social co‐learning will provide students with opportunities for self‐enrichment while supporting their technical skills and hands‐on needs. The platform must also provide the required lab infrastructure for hands‐on experimentation. This paper proposes the design and implementation of a cloud based platform called Edu‐Cloud. The Edu‐Cloud has been designed to provide automated resource provisioning and perform on‐the‐fly deployment of scalable virtual network functions to stream multimedia content closer to the global learners. This would help to meet the specific learning needs of a group of global interconnected students with similar learning skills and abilities. The benchmarking performance results show that the proposed framework works efficiently while reducing primary network traffic by deploying resources closer to the users and support scalability for a global deployment scenario.

Hardware and software lightweight solutions became the mainstream for current and future emerging technologies. Container-based virtualization provides more efcient and faster solutions than traditional virtual machines, offering good scalability, flexibility, and multi-tenancy. They are capable of serving in a heterogeneous and dynamic environment across multiple domains, including IoT, cloud, fog, and multi-access edge computing. In this paper, we propose a lightweight solution for LCC (Live Container Cloud) that permits the user to access live/remote cloud resources faster. LCC can be embedded as a fog/edge node to permit the users to allocate and deallocate cloud resources. The performance of such a containerization technology is presented.

The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%.

The challenge of disaster recovery management for cloud based services is constantly evolving. The costs of cloud service downtime in the event of disaster striking is the subject of much international research. The key issue to resolve is developing suitably resilient and seamless live/realtime mechanisms for disaster recovery. In this paper, we have implemented a proof of concept for an autonomous and fault tolerant carrier cloud brokerage solution with resilient provisioning of on-the-fly cloud resources. When a disaster strikes, the proposed solution will trigger the migration of an entire IaaS from one cloud to another without causing any disruption to the business. In the event of non-availability of hosts for the deployment of virtual network functions for different business processes, an on-the-fly host selection mechanism is proposed and implemented to locate other active compute hosts without any disruptions. In order to evaluate the performance of the proposed solution, we defined several usecase scenarios for each cloud service. This proposed solution will not only reduce the capital expenditure but also provides a reliable and efficient way to access the data during disaster.