The heterogeneity in use cases and the need to support diverse requirements from vertical markets are the main drivers for new design principles of 5G communication systems. In this paper, we review the notion of plastic architecture and propose an end to end network slicing concept to serve new 5G capabilities and features in a flexible and efficient manner. Besides the concept definition, we address in particular the key issue of how 5G devices may be enabled to discover, select and access the most appropriate E2E network slices. To solve the issue, we developed a novel Device Triggered Network Control mechanism and evaluated its performance and implementation cost with respect to alternative available schemes, where the slicing concept was applied only to the core network domain of the communication system. Simulation results showed two digits gains in terms of attachment delay and signalling overhead. Copyright © 2016 John Wiley & Sons, Ltd.

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This paper presents a comparative analysis of replication algorithms for DHT (Distributed Hash Table) architectures. These algorithms are applicable to all existing structured peer-to-peer systems, and can be implemented on top of any DHT. The performance of these algorithms is examined using emulation through virtualization. Significant differences are identified in terms of delays, control overhead, success rate, and overlay route length.

We present the design and early implementation of p4rl, a system that uses reinforcement learning-guided fuzz testing to execute the verification of P4 switches automatically at runtime. p4rl system uses our novel user-friendly query language, p4q to conveniently specify the intended properties in simple conditional statements (if-else) and check the actual runtime behavior of the P4 switch against such properties. In p4rl, user-specified p4q queries with the control plane configuration, Agent, and the Reward System guide the fuzzing process to trigger runtime bugs automatically during Agent training. To illustrate the strength of p4rl, we developed and evaluated an early prototype of p4rl system that executes runtime verification of a P4 network device, e.g., L3 (Layer-3) switch. Our initial results are promising and show that p4rl automatically detects diverse bugs while outperforming the baseline approach.

Modern services consist of interconnected components, e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge).We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76 %) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.