AIaaS for ORAN-based 6G Networks: Multi-time Scale Slice Resource Management with DRL

This paper addresses how to handle slice resources for 6G networks at different time scales in an architecture based on an open radio access network (ORAN). The proposed solution includes artificial intelligence (AI) at the edge of the network and applies two control-level loops to obtain optimal performance compared to other techniques. The ORAN facilitates programmable network architectures to support such multi-time scale management using AI approaches. The proposed algorithms analyze the maximum utilization of resources from slice performance to take decisions at the inter-slice level. Inter-slice intelligent agents work at a non-real-time level to reconfigure resources within various slices. Further than meeting the slice requirements, the intra-slice objective must also include the minimization of maximum resource utilization. This enables smart utilization of the resources within each slice without affecting slice performance. Here, each xApp that is an intra-slice agent aims at meeting the optimal quality of service (QoS) of the users, but at the same time, some inter-slice objectives should be included to coordinate intra- and inter-slice agents. This is done without penalizing the main intra-slice objective. All intelligent agents use deep reinforcement learning (DRL) algorithms to meet their objectives. We have presented results for enhanced mobile broadband (eMBB), ultra-reliable low latency (URLLC), and massive machine type communication (mMTC) slice categories

Fog-enabled Scalable C-V2X Architecture for Distributed 5G and Beyond Applications

The Internet of Things (IoT) ecosystem, as fostered by fifth generation (5G) applications, demands a highly available network infrastructure. In particular, the internet of vehicles use cases, as a subset of the overall IoT environment, require a combination of high availability and low latency in big volumes support. This can be enabled by a network function virtualization architecture that is able to provide resources wherever and whenever needed, from the core to the edge up to the end user proximity, in accordance with the fog computing paradigm. In this article, we propose a fog-enabled cellular vehicle-to-everything architecture that provides resources at the core, the edge and the vehicle layers. The proposed architecture enables the connection of virtual machines, containers and unikernels that form an application-as-a-service function chain that can be deployed across the three layers. Furthermore, we provide lifecycle management mechanisms that can efficiently manage and orchestrate the underlying physical resources by leveraging live migration and scaling functionalities. Additionally, we design and implement a 5G platform to evaluate the basic functionalities of our proposed mechanisms in real-life scenarios. Finally, the experimental results demonstrate that our proposed scheme maximizes the accepted requests, without violating the applications’ service level agreement.This work has been supported in part by the research projects SPOTLIGHT (722788), AGAUR (2017-SGR-891), 5G-DIVE (859881), SPOT5G (TEC2017-87456-P), MonB5G (871780) and 5G-Routes (951867)

SCHEMA: Service Chain Elastic Management with distributed reinforcement learning

As the demand for Network Function Virtualization accelerates, service providers are expected to advance the way they manage and orchestrate their network services to offer lower latency services to their future users. Modern services require complex data flows between Virtual Network Functions, placed in separate network domains, risking an increase in latency that compromises the offered latency constraints. This shift requires high levels of automation to deal with the scale and load of future networks. In this paper, we formulate the Service Function Chaining (SFC) placement problem and then we tackle it by introducing SCHEMA, a Distributed Reinforcement Learning (RL) algorithm that performs complex SFC orchestration for low latency services. We combine multiple RL agents with a Bidding Mechanism to enable scalability on multi-domain networks. Finally, we use a simulation model to evaluate SCHEMA, and we demonstrate its ability to obtain a 60.54% reduction of average service latency when compared to a centralised RL solution.Peer ReviewedPostprint (author's final draft

SLIP-IN architecture: a new hybrid optical switching scheme

In this paper, we present a new hybrid switching architecture, termed as SLIP-IN, that combines electronic packet/burst with optical circuit switching. SLIP-IN architecture takes advantages of the pre-transmission idle periods of optical lightpaths and slips into them packets or bursts of packets. In optical circuit switching (wavelength-routing) networks, capacity is immediately hard-reserved upon the arrival of a setup message, but is only used after a round-trip time delay. This idle period is significant for optical multi-gigabit networks and can be used to transmit traffic of a lower class of service. In this paper, we present the main features and dependencies of the proposed hybrid switching architecture, and further we perform a detailed evaluation by conducting network wide simulation experiments on the NSFnet backbone topology. For this purpose, we have developed an extensive network simulator, where the basic features of the architecture were modeled. The extensive network study revealed that SLIP-IN architecture can achieve and sustain an adequate data rate with a finite worst case delay

CORE: A Clustering Optimization algorithm for Resource Efficiency in LTE-A Networks

In a fluctuating mobile environment where operators have to confront the ever increasing demands of their subscribers, insufficient spectrum poses capacity limitations. This is more evident in the downlink (DL) direction, since DL resources are over-utilized compared to the uplink (UL) ones as a result of asymmetry in the generated traffic and intense interference. In this framework, we propose the creation of Device-to-Device (D2D) based clusters of users where intra-cluster communication will be achieved over UL resources. The minimization of the required resources (equivalent to the maximization of the spectral efficiency), is formulated as an integer (binary) linear optimization problem. Finally, a low- complexity clustering optimization algorithm for resource efficiency (CORE), is devised. Illustrative results prove that CORE, manages to increase the spectral efficiency and the network's capacity

Smart home's energy management applying the deep deterministic policy gradient and clustering

<p>Smart buildings, equipped with controllable devices and energy management systems are expected to be substantial parts of the future energy grids. In this paper, a Reinforcement Learning (RL)-based method is developed for the energy scheduling of a smart home's energy storage system, which is also equipped with a photovoltaic system. The proposed scheme aims to minimize the electricity cost of the smart home; the overall problem is formulated as a Markov decision process, and it is solved by applying the Deep Deterministic Policy Gradient (DDPG). The main advantage of the proposed method is that increases the degree of similarity between the train set and the test set, through data clustering, achieving superior energy schedules than the existing RL-based approaches.</p&gt

Real-time dynamic network slicing for the 5G radio access network

The 5G networks are expected to satisfy diverse use cases and business models with significant advancements in terms of capacity, reliability, and latency. The allocation and provisioning of network resources pose a challenge for this novel architecture to guarantee higher flexibility and quality of service. As a potential enabler, network slicing was proposed as an innovative approach for the control of the network resources. Although a static slicing approach can be suitable for the transport and core network, the stochastic behavior of the wireless channel requires fast and secure slicing techniques for resource allocation. In this paper, we propose a dynamic slicing approach for the radio access network, where the network resources are carefully assigned to guarantee the service level agreements and increase the number of served users. To prove the performance of our approach, we implemented a fronthaul testbed to emphasize the strength of our method in terms of throughput and resource utilization, compared to static slicing.This work has been funded by 5G STEP-FWD (722429), SPOT5G (TEC2017-87456-P), 5GSolutions (856691) and SGR (2014-SGR-1551).Peer ReviewedPostprint (published version

Enhancing wireless communications

DIWINE considers wireless communications in a dense relay/node scenario where wireless network coding messages are flooded via dense massively air-interacting nodes in the self-contained cloud while the physical-layer air-interface between the terminals (sources/destinations) and the Cloud is simple and uniform. A complex infrastructure cloud creates an equivalent air-interface to the terminal, which is as simple as possible. Source and destination air interfaces are completely blind to the Cloud’s network structure. The Cloud has its own self-contained organising and processing capability