Discretized Distributed Optimization over Dynamic Digraphs

We consider a discrete-time model of continuous-time distributed optimization over dynamic directed-graphs (digraphs) with applications to distributed learning. Our optimization algorithm works over general strongly connected dynamic networks under switching topologies, e.g., in mobile multi-agent systems and volatile networks due to link failures. Compared to many existing lines of work, there is no need for bi-stochastic weight designs on the links. The existing literature mostly needs the link weights to be stochastic using specific weight-design algorithms needed both at the initialization and at all times when the topology of the network changes. This paper eliminates the need for such algorithms and paves the way for distributed optimization over time-varying digraphs. We derive the bound on the gradient-tracking step-size and discrete time-step for convergence and prove dynamic stability using arguments from consensus algorithms, matrix perturbation theory, and Lyapunov theory. This work, particularly, is an improvement over existing stochastic-weight undirected networks in case of link removal or packet drops. This is because the existing literature may need to rerun time-consuming and computationally complex algorithms for stochastic design, while the proposed strategy works as long as the underlying network is weight-symmetric and balanced. The proposed optimization framework finds applications to distributed classification and learning

Walkabout : an asynchronous messaging architecture for mobile devices

Modern mobile devices are prolific producers and consumers of digital data, and wireless networking capabilities enable them to transfer their data over the Internet while moving. Applications running on these devices may perform transfers to upload data for backup or distribution, or to download new content on demand. Unfortunately, the limited connectivity that mobile devices experience can make these transfers slow and impractical as the amount of data increases. This thesis argues that asynchronous messaging supported by local proxies can improve the transfer capabilities of mobile devices, making it practical for them to participate in large Internet transfers. The design of the Walkabout architecture follows this approach: proxies form store-and-forward overlay networks to deliver messages asynchronously across the Internet on behalf of devices. A mobile device uploads a message to a local proxy at rapid speed, and the overlay delivers it to one or more destination devices, caching the message until each one is able to retrieve it from a local proxy. A device is able to partially upload or download a message whenever it has network connectivity, and can resume this transfer at any proxy if interrupted through disconnection. Simulation results show that Walkabout provides better throughput for mobile devices than is possible under existing methods, for a range of movement patterns. Upload and end-to-end to transfer speeds are always high when the device sending the message is mobile. In the basic Walkabout model, a message sent to a mobile device that is repeatedly moving between a small selection of connection points experiences high download and end-to-end transfer speeds, but these speeds fall as the number of connection points grows. Pre-emptive message delivery extensions improve this situation, making fast end-to-end transfers and device downloads possible under any pattern of movement. This thesis describes the design and evaluation of Walkabout, with both practical implementation and extensive simulation under real-world scenarios

On Leveraging Partial Paths in Partially-Connected Networks

Mobile wireless network research focuses on scenarios at the extremes of the network connectivity continuum where the probability of all nodes being connected is either close to unity, assuming connected paths between all nodes (mobile ad hoc networks), or it is close to zero, assuming no multi-hop paths exist at all (delay-tolerant networks). In this paper, we argue that a sizable fraction of networks lies between these extremes and is characterized by the existence of partial paths, i.e. multi-hop path segments that allow forwarding data closer to the destination even when no end-to-end path is available. A fundamental issue in such networks is dealing with disruptions of end-to-end paths. Under a stochastic model, we compare the performance of the established end-to-end retransmission (ignoring partial paths), against a forwarding mechanism that leverages partial paths to forward data closer to the destination even during disruption periods. Perhaps surprisingly, the alternative mechanism is not necessarily superior. However, under a stochastic monotonicity condition between current v.s. future path length, which we demonstrate to hold in typical network models, we manage to prove superiority of the alternative mechanism in stochastic dominance terms. We believe that this study could serve as a foundation to design more efficient data transfer protocols for partially-connected networks, which could potentially help reducing the gap between applications that can be supported over disconnected networks and those requiring full connectivity.Comment: Extended version of paper appearing at IEEE INFOCOM 2009, April 20-25, Rio de Janeiro, Brazi

Forecasting for Network Management with Joint Statistical Modelling and Machine Learning

Forecasting is a task of ever increasing importance for the operation of mobile networks, where it supports anticipa tory decisions by network intelligence and enables emerging zero touch service and network management models. While current trends in forecasting for anticipatory networking lean towards the systematic adoption of models that are purely based on deep learning approaches, we pave the way for a different strategy to the design of predictors for mobile network environments. Specifically, following recent advances in time series prediction, we consider a hybrid approach that blends statistical modelling and machine learning by means of a joint training process of the two methods. By tailoring this mixed forecasting engine to the specific requirements of network traffic demands, we develop a Thresholded Exponential Smoothing and Recurrent Neural Network (TES-RNN) model. We experiment with TES RNN in two practical network management use cases, i.e., (i) anticipatory allocation of network resources, and (ii) mobile traffic anomaly prediction. Results obtained with extensive traffic workloads collected in an operational mobile network show that TES-RNN can yield substantial performance gains over current state-of-the-art predictors in both applications consideredThis work is partially supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no.101017109 DAEMON. This work is partially supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D 6GCLARION-OR and AEON-ZERO. The authors would like to thank Dario Bega for his contribution to developing the forecasting use case I, and Slawek Smyl for his feedback on the baseline ES-RNN model