Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Linux XIA: an interoperable meta network architecture to crowdsource the future Internet

With the growing number of proposed clean-slate redesigns of the Internet, the need for a medium that enables all stakeholders to participate in the realization, evaluation, and selection of these designs is increasing. We believe that the missing catalyst is a meta network architecture that welcomes most, if not all, clean-state designs on a level playing field, lowers deployment barriers, and leaves the final evaluation to the broader community. This paper presents Linux XIA, a native implementation of XIA [12] in the Linux kernel, as a candidate. We first describe Linux XIA in terms of its architectural realizations and algorithmic contributions. We then demonstrate how to port several distinct and unrelated network architectures onto Linux XIA. Finally, we provide a hybrid evaluation of Linux XIA at three levels of abstraction in terms of its ability to: evolve and foster interoperation of new architectures, embed disparate architectures inside the implementation’s framework, and maintain a comparable forwarding performance to that of the legacy TCP/IP implementation. Given this evaluation, we substantiate a previously unsupported claim of XIA: that it readily supports and enables network evolution, collaboration, and interoperability—traits we view as central to the success of any future Internet architecture.This research was supported by the National Science Foundation under awards CNS-1040800, CNS-1345307 and CNS-1347525

Towards Data-driven Simulation of End-to-end Network Performance Indicators

Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically impossible to reevaluate different approaches under the exact same conditions. However, as these methods massively simplify the effects of the radio environment and various cross-layer interdependencies, the results of end-to-end indicators (e.g., the resulting data rate) often differ significantly from real world measurements. In this paper, we present a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks. The proposed approach can be exploited for fast and close to reality evaluation and optimization of new methods in a controllable environment as it implicitly considers cross-layer dependencies between measurable features. Within an example case study for opportunistic vehicular data transfer, the proposed approach is validated against real world measurements and a classical system-level network simulation setup. Although the proposed method does only require a fraction of the computation time of the latter, it achieves a significantly better match with the real world evaluations

Routing on the Channel Dependency Graph:: A New Approach to Deadlock-Free, Destination-Based, High-Performance Routing for Lossless Interconnection Networks

In the pursuit for ever-increasing compute power, and with Moore's law slowly coming to an end, high-performance computing started to scale-out to larger systems. Alongside the increasing system size, the interconnection network is growing to accommodate and connect tens of thousands of compute nodes. These networks have a large influence on total cost, application performance, energy consumption, and overall system efficiency of the supercomputer. Unfortunately, state-of-the-art routing algorithms, which define the packet paths through the network, do not utilize this important resource efficiently. Topology-aware routing algorithms become increasingly inapplicable, due to irregular topologies, which either are irregular by design, or most often a result of hardware failures. Exchanging faulty network components potentially requires whole system downtime further increasing the cost of the failure. This management approach becomes more and more impractical due to the scale of today's networks and the accompanying steady decrease of the mean time between failures. Alternative methods of operating and maintaining these high-performance interconnects, both in terms of hardware- and software-management, are necessary to mitigate negative effects experienced by scientific applications executed on the supercomputer. However, existing topology-agnostic routing algorithms either suffer from poor load balancing or are not bounded in the number of virtual channels needed to resolve deadlocks in the routing tables. Using the fail-in-place strategy, a well-established method for storage systems to repair only critical component failures, is a feasible solution for current and future HPC interconnects as well as other large-scale installations such as data center networks. Although, an appropriate combination of topology and routing algorithm is required to minimize the throughput degradation for the entire system. This thesis contributes a network simulation toolchain to facilitate the process of finding a suitable combination, either during system design or while it is in operation. On top of this foundation, a key contribution is a novel scheduling-aware routing, which reduces fault-induced throughput degradation while improving overall network utilization. The scheduling-aware routing performs frequent property preserving routing updates to optimize the path balancing for simultaneously running batch jobs. The increased deployment of lossless interconnection networks, in conjunction with fail-in-place modes of operation and topology-agnostic, scheduling-aware routing algorithms, necessitates new solutions to solve the routing-deadlock problem. Therefore, this thesis further advances the state-of-the-art by introducing a novel concept of routing on the channel dependency graph, which allows the design of an universally applicable destination-based routing capable of optimizing the path balancing without exceeding a given number of virtual channels, which are a common hardware limitation. This disruptive innovation enables implicit deadlock-avoidance during path calculation, instead of solving both problems separately as all previous solutions

Feasibility of Using Passive Monitoring Techniques in Mesh Networks for the Support of Routing

In recent years, Wireless Mesh Networks (WMNs) have emerged as a promising solution to provide low cost access networks that extend Internet access and other networking services. Mesh routers form the backbone connectivity through cooperative routing in an often unstable wireless medium. Therefore, the techniques used to monitor and manage the performance of the wireless network are expected to play a significant role in providing the necessary performance metrics to help optimize the link performance in WMNs. This thesis initially presents an assessment of the correlation between passive monitoring and active probing techniques used for link performance measurement in single radio WMNs. The study reveals that by combining multiple performance metrics obtained by using passive monitoring, a high correlation with active probing can be achieved. The thesis then addresses the problem of the system performance degradation associated with simultaneous activation of multiple radios within a mesh node in a multi-radio environment. The experiments results suggest that the finite computing resource seems to be the limiting factor in the performance of a multi-radio mesh network. Having studied this characteristic of multi-radio networks, a similar approach as used in single radio mesh network analysis was taken to investigate the feasibility of passive monitoring in a multi-radio environment. The accuracy of the passive monitoring technique was compared with that of the active probing technique and the conclusion reached is that passive monitoring is a viable alternative to active probing technique in multi-radio mesh networks

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future