Characteristics and the promise of 5G technology; Macroeconomic benefits of 5G; 5G Value Chain and stakeholders : Education institutes importance in preparing next wave economy development talents; Potential Use cases contribution on UN sustainable development goals (SDGs). Stakeholders shall align and cooperate to fully realize the socio-economic value that 5G can deliver through its defining key features and to unlock various use cases across multiple industry sectors, using the 5G Ecosystem Cycle as a framework by defining key actions that stakeholders can take to contribute to the successful deployment of 5G.

Recent years have seen a growing interest among users in the migration of their applications to the Cloud computing and Internet-of-Things environments. However, due to high complexity, Cloud-based and Internet-of-Things infrastructures need advanced components for supporting applications and advanced management techniques for increasing the efficiency. Adaptivity and autonomous learning abilities become extremely useful to support configuration and dynamic adaptation of these infrastructures to the changing needs of the users as well as to create adaptable applications. This self-adaptation ability is increasingly essential especially for non expert managers as well as for application designers and developers with limited competences in tools for achieving this ability. Artificial intelligence is a set of techniques which greatly can improve both the creation of applications and the management of these infrastructures. This talk will discuss the use of artificial intelligence in supporting the creation of applications in cloud and IoT infrastructures as well as their use in the various aspects of infrastructure management.

Cloud computing has become an emerging area of research for many years now. Particularly important in cloud computing is to run delay-sensitive applications. It is essential that effective scheduling techniques are utilized ensuring timeliness. This can be achieved due to cloud's high-performance computing capacity for real-time processing. In recent years there is an expansion of the Internet of Things (IoT). IoT applications generate huge amounts of data and it is critical to process these data in real-time and provide immediate decisions. As a result, fog computing has been introduced as a computing paradigm extending the cloud to the edge of the network, thus reducing the latency of IoT data transmission. The potential of the research on cloud and fog computing is strong due to the challenges in dealing with real-time applications in the IoT domain. However, the computational capacity of fog servers is usually restricted, therefore it is necessary to explore alternative techniques that involve the collaboration between the cloud and fog resources. Consequently, appropriate scheduling of time-sensitive applications is required to fully exploit the capabilities of cloud and fog computing so that the deadlines are met. In this keynote we will present various aspects of cloud and fog computing, from the perspective of scheduling real-time applications and we will conclude with future research directions in the cloud and fog computing areas.

The benefits of Cloud computing include reduced costs, high reliability, as well as the immediate availability of additional computing resources as needed. Despite such advantages, Cloud Service Provider (CSP) consumers need to be aware that the Cloud poses its own set of unique risks that are not typically associated with storing and processing one's own data internally using privately owned infrastructure. Recent years have seen a number of such incidents occur, whereby customer data hosted on the Cloud has been leaked. The ideal solution to achieving an optimal balance of data security and functionality within the Cloud involves the CSP having the ability to search and operate on data while it is in encrypted form. New techniques such as Fully Homomorphic Encryption and Searchable Encryption have arisen to make this a reality. Fully-Homomorphic Encryption supports computations over data in encrypted form but an efficient Fully-Homomorphic Encryption remains someway off. Searchable Encryption however, despite being a relatively obscure form of Cryptography is now at the point that it can be deployed and used within the Cloud. Searchable Encryption can allow CSP customers to store their data in encrypted form, while retaining the ability to search that data without disclosing the associated decryption keys to CSPs. Symmetric Search Encryption (SSE) represents one of the few forms of Searchable Encryption that is achievable using established standardised encryption algorithms. This talk will discuss a Searchable Symmetric Encryption scheme which is efficient enough to be deployed in a Cloud environment to achieve industry acceptable search speeds whilst maintaining Data Privacy.

Automation has been the holy grail of network management research for decades; it aims at achieving autonomous networks, i.e., networks capable to autonomously monitor their status, analyze problems, make decisions, and execute corrective actions. Despite several attempts to achieve autonomous networks in the past, their practical deployments have largely remained unrealized. Several factors are attributed to this, including the existence of many stakeholders with conflicting goals, reliance on proprietary solutions, the inability to process network monitoring data at scale, and the lack of global visibility restricting network-wide optimizations. The stars are now aligned to realize the vision of network automation thanks to (i) advances in network softwarization; (ii) recent breakthroughs in machine learning; and (iii) the availability of large-scale data processing platforms. However, a number of challenges must be addressed in order to create the synergy between these different technology domains and achieve autonomous networks. This talk will discuss some of these challenges with particular focus on programmable network monitoring leveraging network softwarization, predictive machine learning for automated management decision making, and on-demand orchestration of network services.