The Capacitated Vehicle Routing Problem (CVRP) is one of the most relevant optimization problems in operations research and logistics nowadays. While current state-of-the-art solutions to this problem implement evolutionary approaches such as Genetic Algorithms (GAs) on GPUs and multi-core CPUs, this work focuses on implementing a GA for CVRP on a high-performance FPGA to achieve an improved calculation efficiency: While maintaining a comparable solution quality and speed, the FPGA consumes only a fraction of the energy required by traditional computing platforms. This is achieved through leveraging algorithmic parallelism by a custom-fitted hardware architecture, combined with customized data representation for efficient processing.

Edge computing brings added benefits for different elements in the overall system (e.g., users, operators and service providers). However, currently there are no proper interfaces and mechanisms to instantiate third-party services within the operators' infrastructure (e.g., as a MEC application), thus hindering edge computing to reach its full potential. To fill this gap, this paper presents architectural enhancements, interfaces and mechanisms to enable dynamic and efficient third-party service deployment within the operators' domain. A simulation-based analysis is presented to showcase the relevance of the proposed solution. Results highlighted the benefits of optimal migration of third-party services into a distributed setting, compared to the unveiled drawbacks of a centralized approach. In addition, the key components of the solution are implemented and experimentally validated through a proof-of-concept prototype showcasing the performance impact of the proposed approach as well as the suitability of its implementation.

Multiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for ultralarge-scale MEC networks. We develop a new multicriteria decision-making algorithm: Technique for order of preference by similarity to ideal solution with attribute-based Niche count, named TOPANSIS, which showcases its strength to provide an optimal solution for service migration in large-scale deployments towards optimal data rate, latency, and load balancing. We further decentralize the operation of TOPANSIS to release the traffic burden from central datacenters by leveraging local decision making by edge nodes, while relying on central cloud coordination to account for the overall network information. Simulation results showcase that the proposed architecture outperforms the selected benchmarks with an average improvement of 39.41% for latency, 2.92% for data rate, as well as 10.53% and 6.26% for RAM and CPU load balancing, respectively. Moreover, the feasibility of the proposed solution is validated by means of a proof-of-concept implementation and experimental assessments.

In this paper, we modeled the emerging edge-computing-enabled live service migration as a multi-criteria problem optimization, tackling migration costs and benefits, as well as discussion of service providers' data privacy, simultaneously. Based on the optimization formulation, we conducted a small-scale analytical feasibility test, considering widely-utilized multi-criteria decision making algorithms, based on which we proposed a new TOPSIS based service migration algorithm. The algorithm was evaluated using simulations, whose results show that the proposed algorithm is sufficient to support live service migration for heterogeneous edge computing, while outperforming benchmarks in with respect to reducing migration costs and increasing achieved benefits, by 34.52% and 60.21%, respectively.

This paper presents an implementation of the Zenoh pub-sub stack using P4. Traditional pub-sub protocols generally rely on a centralized broker to forward messages. Using P4, one could make every network equipment able to assume such role, with the whole system behaving as a distributed broker. By deploying network tasks at the edge, this approach can reduce end-to-end latency and the overhead caused by packet retransmission due to packet loss. The proposed implementation accounts for the offloading of some control tasks into the data plane. The prototype is validated and the impact of the task offloading features is assessed. Results show that offloading can be beneficial and improve network performance. Still, our system is outperformed by traditional Zenoh Routers, which is explained by the lack of performance of the used BMv2 software switch. To account for these limitations, future developments from this work will target hardware-based validation.

Research on Future Internet has gained traction in recent years, with a variety of clean-slate network architectures being proposed. The realization of such proposals may lead to a period of coexistence with the current Internet, creating a heterogeneous Future Internet. In such a vision, mobile nodes (MNs) can move across access networks supporting different network architectures, while being able to maintain the access to content during this movement. In order to support such scenarios, this paper proposes an inter-network architecture mobility framework that allows MNs to move across different network architectures without losing access to the contents being accessed. The usage of the proposed framework is exemplified and evaluated in a mobility scenario targeting IP and NDN network architectures in a content retrieval use case. The obtained results validate the proposed framework while highlighting the impact on the overall communication between the MN and content source.

Information-Centric Networking (ICN) has recently emerged as a promising Future Internet architecture that aims to cope with the increasing demand for highly scalable and efficient distribution of content. Moving away from the Internet communication model based in addressable hosts, ICN leverages in-network storage for caching, multi-party communication through replication, and interaction models that decouple senders and receivers. This novel networking approach has the potential to outperform IP in several dimensions, besides just content dissemination. Concretely, the rise of the Internet of Things (IoT), with its rich set of challenges and requirements placed over the current Internet, provide an interesting ground for showcasing the contribution and performance of ICN mechanisms. This work analyses how the in-network caching mechanisms associated to ICN, particularly those implemented in the Content-Centric Networking (CCN) architecture, contribute in IoT environments, particularly in terms of energy consumption and bandwidth usage. A simulation comparing IP and the CCN architecture (an instantiation of ICN) in IoT environments demonstrated that CCN leads to a considerable reduction of the energy consumed by the information producers and to a reduction of bandwidth requirements, as well as highlighted the flexibility for adapting current ICN caching mechanisms to target specific requirements of IoT.