Orchestrating resources in 5G and beyond-5G systems will be substantially more complex than it used to be in previous generations of mobile networks. In order to take full advantage of the unprecedented possibilities for dynamic reconfiguration offered by network softwarization and virtualization technologies, operators have to embed intelligence in network resource orchestrators. We advocate that the automated, data-driven decisions taken by orchestrators must be guided by considerations on the cost that such decisions involve for the operator. We show that such a strategy can be implemented via a deep learning architecture that forecasts capacity rather than plain traffic, thanks to a novel loss function named α-OMC. We investigate the convergence properties of α-OMC, and provide preliminary results on the performance of the learning process in case studies with real-world mobile network traffic.
Industry and academia are the two major pillars that drive innovation and research, and historically, significant cross-pollination of ideas and expertise occurs between them. The goal of this panel is to discuss shared experiences, identify time-tested and new strategies, as well as role responsibilities in situations when industry and academia work together towards driving ambitious, open-ended research. The panel includes representatives from major research laboratories associated with the leading networking companies, startups, as well as industry consortiums for large wireless platform development projects committed to working with academic partners.
Cloud computing provides a kind of dynamic and scalable service on demand. However, clouds consume huge amountsof electrical energy. Meanwhile, delivering the negotiated QoS defined as Service Level Agreement (SLA) to users is necessary. A virtual machine placement strategy based on the equilibrium between energy and SLA is proposed. Aiming at dynamical changes of application workloads, an adaptive placement strategy RLWR based on robust local weight regression is presented, which decides the overload time of hosts dynamically according to the historical resource occupation of application workload. Then, two virtual machine migration selection algorithms, MPM and MNM are presented.The migrated virtual machines are deployed using bin-packing algorithm PBFDH. Contrasting to static algorithms such as STH, MPA and DVFS, virtual machines are not only deployed on fewer hosts in our algorithm, which promotes energy efficiency, but the load prediction can bring high-reliable QoS delivery and avoid overmuch SLA violations. Experimental results show that our strategy has an obvious effect on decreasing SLA violation under ensuring energy efficiency.
We consider an authentication process that makes use of biometric data or the output of a physical unclonable function (PUF), respectively, from an information theoretical point of view. We analyse different definitions of achievability for the authentication model. For the secrecy of the key generated for authentication, these definitions differ in their requirements. In the first work on PUF based authentication, weak secrecy has been used and the corresponding capacity regions have been characterized. The disadvantages of weak secrecy are well known. The ultimate performance criteria for the key are perfect secrecy together with uniform distribution of the key. We derive the corresponding capacity region. We show that, for perfect secrecy and uniform distribution of the key, we can achieve the same rates as for weak secrecy together with a weaker requirement on the distribution of the key. In the classical works on PUF based authentication, it is assumed that the source statistics are known perfectly. This requirement is rarely met in applications. That is why the model is generalized to a compound model, taking into account source uncertainty. We also derive the capacity region for the compound model requiring perfect secrecy. Additionally, we consider results for secure storage using a biometric or PUF source that follow directly from the results for authentication. We also generalize known results for this problem by weakening the assumption concerning the distribution of the data that shall be stored. This allows us to combine source compression and secure storage.
Network slicing to enable resource sharing among multiple tenants -network operators and/or services-is considered a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the 'share-constrained proportional allocation' mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' allocation. This results in a network slicing game in which each tenant reacts to the user allocations of the other tenants so as to maximize its own utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, performance than under a static partitioning of resources, hence providing the same level of protection as such static partitioning. We further analyze the efficiency and fairness of the resulting allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fill a gap in the literature regarding the analysis of this resource allocation model under strategic players.
