CLOSER: A Collaborative Locality-aware Overlay SERvice

Current Peer-to-Peer (P2P) file sharing systems make use of a considerable percentage of Internet Service Providers (ISPs) bandwidth. This paper presents the Collaborative Locality-aware Overlay SERvice (CLOSER), an architecture that aims at lessening the usage of expensive international links by exploiting traffic locality (i.e., a resource is downloaded from the inside of the ISP whenever possible). The paper proves the effectiveness of CLOSER by analysis and simulation, also comparing this architecture with existing solutions for traffic locality in P2P systems. While savings on international links can be attractive for ISPs, it is necessary to offer some features that can be of interest for users to favor a wide adoption of the application. For this reason, CLOSER also introduces a privacy module that may arouse the users' interest and encourage them to switch to the new architectur

Time-Driven Access and Forwarding for Industrial Wireless Multihop Networks

The deployment of wireless technologies in industrial networks is very promising mainly due to their inherent flexibility. However, current wireless solutions lack the capability to provide the deterministic, low delay service required by many industrial applications. Moreover, the high level of interference generated by industrial equipment limits the coverage that ensures acceptable performance. Multi-hop solutions, when combining frame forwarding with higher node density, have the potential to provide the needed coverage while keeping radio communication range short. However, in multi-hop solutions the medium access time at each of the nodes traversed additively contributes to the end-to-end delay and the forwarding delay (i.e., the time required for packets to be processed, switched, and queued) at each node is to be added as well. This paper describes Time-driven Access and Forwarding (TAF), a solution for guaranteeing deterministic delay, at both the access and forwarding level, in wireless multi-hop networks, analyzes its properties, and assesses its performance in industrial scenario

Resource Management Policies for Cloud-based Interactive 3D Applications

The increasing interest for the cloud computing paradigm is leading several different applications and services moving to the 'cloud'. Those range from general storage and computing services to document management systems and office applications. A new challenge is the migration to the cloud of interactive 3D applications, especially those designed for professional usage (e.g., scientific data visualizers, CAD instruments, 3D medical modeling applications). Among the several hurdles rising from some specific hardware and software requirements, an important issue to address is the definition of novel management policies that can properly support these applications, namely, that ensure efficient resource utilization together with a sufficient quality perceived by users. This paper presents some preliminary results in this direction and discusses some possible future work in this field. Our work is part of a wider project aiming at developing a complete architecture to offer interactive 3D applications in a cloud computing environment. Hence, we refer to this particular solution in this stud

PIT Overload Analysis in Content Centric Networks

Content Centric Networking represents a paradigm shift in the evolution and definition of modern network protocols. Many research efforts have been made with the purpose of proving the feasibility and the scalability of this proposal. Our main contribution is to provide an analysis of the Pending Interest Table memory requirements in real deployment scenarios, especially considering the impact of distributed denial of service attacks. In fact, the state that the protocol maintains for each resource request makes the routers more prone to resources exhaustion issues than in traditional stateless solutions. Our results are derived by using a full custom simulator and considering the different node architectures that have been proposed as valid reference models. The main outcomes point out differentiated weaknesses in each architecture we investigated and underline the need for improvements in terms of security and scalabilit

A Federated Learning Approach to Routing in Challenged SDN-Enabled Edge Networks

The edge computing paradigm allows computationally intensive tasks to be offloaded from small devices to nearby (more) powerful servers, via an edge network. The intersection between such edge computing paradigm and Machine Learning (ML), in general, and deep learning in particular, has brought to light several advantages for network operators: from automating management tasks, to gain additional insights on their networks. Most of the existing approaches that use ML to drive routing and traffic control decisions are valuable but rarely focus on challenged networks, that are characterized by continually varying network conditions and the high volume of traffic generated by edge devices. In particular, recently proposed distributed ML-based architectures require either a long synchronization phase or a training phase that is unsustainable for challenged networks. In this paper, we fill this knowledge gap with Blaster, a federated architecture for routing packets within a distributed edge network, to improve the application's performance and allow scalability of data-intensive applications. We also propose a novel path selection model that uses Long Short Term Memory (LSTM) to predict the optimal route. Finally, we present some initial results obtained by testing our approach via simulations and with a prototype deployed over the GENI testbed. By leveraging a Federated Learning (FL) model, our approach shows that we can optimize the communication between SDN controllers, preserving bandwidth for the data traffic

Resource Inference for Task Migration in Challenged Edge Networks with RITMO

Edge computing, combined with the proliferation of IoT devices, is generating new business model opportunities and applications. Among those applications, Unmanned Aerial Vehicles (UAVs) have been deployed in several scenarios, from surveillance and monitoring to disaster response, to precision agriculture. To support such applications, however, edge network managers and application programmers need to overcome a few challenges, e.g., unstable network conditions, high loss rate, and node failures. Existing solutions designed to mitigate such inefficiencies by predicting future network conditions are often computationally intensive and hence less portable on constrained devices. In this paper, we propose RITMO, a distributed and adaptive task planning algorithm that aims at solving these challenges while running on a network of UAV devices. We model our system as a network of queues, and we exploit a simple yet effective ARIMA regressor, to dynamically predict the length of future UAV task queues. Such prediction is then used to proactively migrate the tasks in case of a failure or unbalanced loads. Our simulation results demonstrate how RITMO helps to reduce the overall latency perceived by the application and anticipates the node overloading by avoiding agents that are likely to exhaust their computational resources

A Distributed Reinforcement Learning Approach for Energy and Congestion-Aware Edge Networks

The abiding attempt of automation has also pervaded computer networks, with the ability to measure, analyze, and control themselves in an automated manner, by reacting to changes in the environment (e.g., demand) while exploiting existing flexibilities. When provided with these features, networks are often referred to as "self-driving". Network virtualization and machine learning are the drivers. In this regard, the provision and orchestration of physical or virtual resources are crucial for both Quality of Service guarantees and cost management in the edge/cloud computing ecosystem. Auto-scaling mechanisms are hence essential to effectively manage the lifecycle of network resources. In this poster, we propose Relevant, a distributed reinforcement learning approach to enable distributed automation for network orchestrators. Our solution aims at solving the congestion control problem within Software-Defined Network infrastructures, while being mindful of the energy consumption, helping resources to scale up and down as traffic demands fluctuate and energy optimization opportunities arise

Restoring Application Traffic of Latency-Sensitive Networked Systems using Adversarial Autoencoders

The Internet of Things (IoT), coupled with the edge computing paradigm, is enabling several pervasive networked applications with stringent real-time requirements, such as telemedicine and haptic telecommunications. Recent advances in network virtualization and artificial intelligence are helping solve network latency and capacity problems, learning from several states of the network stack. However, despite such advances, a network architecture able to meet the demands of next-generation networked applications with stringent real-time requirements still has untackled challenges. In this paper, we argue that only using network (or transport) layer information to predict traffic evolution and other network states may be insufficient, and a more holistic approach that considers predictions of application-layer states is needed to repair the inefficiencies of the TCP/IP architecture. Based on this intuition, we present the design and implementation of Reparo. At its core, the design of our solution is based on the detection of a packet loss and its restoration using a Hidden Markov Model (HMM) empowered with adversarial autoencoders. In our evaluation, we considered a telemedicine use case, specifically a telepathology session, in which a microscope is controlled remotely in real-time to assess histological imagery. Our results confirm that the use of adversarial autoencoders enhances the accuracy of the prediction method satisfying our telemedicine application’s requirements with a notable improvement in terms of throughput and latency perceived by the user

On Control and Data Plane Programmability for Data-Driven Networking

The soaring complexity of networks has led to more and more complex methods to manage and orchestrate efficiently the multitude of network environments. Several solutions exist, such as OpenFlow, NetConf, P4, DPDK, etc., that allow network programmability at both control and data plane level, driving innovation in many focused high-performance networked applications. However, with the increase of strict requirements in critical applications, also the networking architecture and its operations should be redesigned. In particular, recent advances in machine learning have opened new opportunities to the automation of network management, exploiting existing advances in software-defined infrastructures. We argue that the design of effective data-driven network management solutions needs to collect, merge, and process states from both data and control planes. This paper sheds light upon the benefits of utilizing such an approach to support feature extraction and data collection for network automation