Enhancing QoS metrics estimation in multiclass networks

This paper discusses the problematic of QoS monitoring, suggesting the use of on-line multipurpose active monitoring in multiclass networks as a powerful tool to efficiently assist and enhance the control of multiple service levels. To improve the simultaneous estimation of one-way QoS metrics, we propose a flexible probing source able to adjust probing patterns to the measurement requirements of each service class, exploring pattern coloring to better sense packet loss. The proof-of-concept provided shows that the proposed solution improves the estimation accuracy of multiple QoS metrics significantly, with a reduced probing overhead.info:eu-repo/semantics/publishedVersio

Constant-time approximate sliding window framework with error control

Stream Processing is a crucial element for the Edge Computing paradigm, in which large amount of devices generate data at the edge of the network. This data needs to be aggregated and processed on-the-move across different layers before reaching the Cloud. Therefore, defining Stream Processing services that adapt to different levels of resource availability is of paramount importance. In this context, Stream Processing frameworks need to combine efficient algorithms with low computational complexity to manage sliding windows, with the ability to adjust resource demands for different deployment scenarios, from very low capacity edge devices to virtually unlimited Cloud platforms. The Approximate Computing paradigm provides improved performance and adaptive resource demands in data analytics, at the price of introducing some level of inaccuracy that can be calculated. In this paper we present the Approximate and Amortized Monoid Tree Aggregator (A 2 MTA). It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. It is very suitable for adverse stream processing environments, such as resource scarce multi-tenant edge computing. The framework can compute aggregations over multiple data dimensions, setting error bounds on any of them, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations.This project is partially supported by the European Research Council (ERC), Spain under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).Peer ReviewedPostprint (author's final draft

Stream Processing in the Context of CTS

The recent development of innovative technologies related to mobile computing combined with smart city infrastructures is generating massive, heterogeneous data and creating opportunities for novel applications in transportational computation science. The heterogeneous data sources provide streams of information that can be used to create smart cities. The knowledge on stream analysis is thus crucial and requires collaboration of people working in logistics, city planning, transportation engineering and data science. We provide a list of materials for a course on stream processing for computational transportation science. The objectives of the course are: Motivate data stream and event processing, its model and challenges. Acquire basic knowledge about data stream processing systems. Understand and analyze their application in the transportation domain..

Citywide Estimation of Traffic Dynamics via Sparse GPS Traces

Traffic congestion is a perpetual challenge in metropolitan areas around the world. The ability to understand traffic dynamics is thus critical to effective traffic control and management. However, estimation of traffic conditions over a large-scale road network has proven to be a challenging task for two reasons: first, traffic conditions are intrinsically stochastic; second, the availability and quality of traffic data vary to a great extent. Traditional traffic monitoring systems that exist mostly on major roads and highways are insufficient to recover the traffic conditions for an entire network. Recent advances in GPS technology and the resulting rich data sets offer new opportunities to improve upon such traditional means, by providing much broader coverage of road networks. Despite that, such data are limited by their spatial-temporal sparsity in practice. To address these issues, we have developed a novel framework to estimate travel times, traversed paths, and missing values over a large-scale road network using sparse GPS traces. Our method consists of two phases. In the first phase, we adopt the shortest travel time criterion based on Wardrop\u27s Principles in the map-matching process. With an improved traveltime allocation technique, we have achieved up to 52.5% relative error reduction in network travel times compared to a state-of-the-art method [1]. In the second phase, we estimate missing values using Compressed Sensing algorithm, thereby reducing the number of required measurements by 94.64%

A Backend Framework for the Efficient Management of Power System Measurements

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.Comment: Published in Electric Power Systems Research (2016), not available ye

Enhancing QoS metrics estimation in multiclass networks

This paper discusses the problematic of QoS monitoring, suggesting the use of on-line multipurpose active monitoring in multiclass networks as a powerful tool to efficiently assist and enhance the control of multiple service levels. To improve the simultaneous estimation of one-way QoS metrics, we propose a flexible probing source able to adjust probing patterns to the measurement requirements of each service class, exploring pattern coloring to better sense packet loss. The proof-of-concept provided shows that the proposed solution improves the estimation accuracy of multiple QoS metrics significantly, with a reduced probing overhead.info:eu-repo/semantics/publishedVersio