AccEq-DRT: Planning Demand-Responsive Transit to reduce inequality of accessibility

Accessibility measures how well a location is connected to surrounding opportunities. We focus on accessibility provided by Public Transit (PT). There is an evident inequality in the distribution of accessibility between city centers or close to main transportation corridors and suburbs. In the latter, poor PT service leads to a chronic car-dependency. Demand-Responsive Transit (DRT) is better suited for low-density areas than conventional fixed-route PT. However, its potential to tackle accessibility inequality has not yet been exploited. On the contrary, planning DRT without care to inequality (as in the methods proposed so far) can further improve the accessibility gap in urban areas. To the best of our knowledge this paper is the first to propose a DRT planning strategy, which we call AccEq-DRT, aimed at reducing accessibility inequality, while ensuring overall efficiency. To this aim, we combine a graph representation of conventional PT and a Continuous Approximation (CA) model of DRT. The two are combined in the same multi-layer graph, on which we compute accessibility. We then devise a scoring function to estimate the need of each area for an improvement, appropriately weighting population density and accessibility. Finally, we provide a bilevel optimization method, where the upper level is a heuristic to allocate DRT buses, guided by the scoring function, and the lower level performs traffic assignment. Numerical results in a simplified model of Montreal show that inequality, measured with the Atkinson index, is reduced by up to 34\%. Keywords: DRT Public, Transportation, Accessibility, Continuous Approximation, Network DesignComment: 15 page

Towards Inference Delivery Networks: Distributing Machine Learning with Optimality Guarantees

An increasing number of applications rely on complex inference tasks that are based on machine learning (ML). Currently, there are two options to run such tasks: either they are served directly by the end device (e.g., smartphones, IoT equipment, smart vehicles), or offloaded to a remote cloud. Both options may be unsatisfactory for many applications: local models may have inadequate accuracy, while the cloud may fail to meet delay constraints. In this paper, we present the novel idea of \emph{inference delivery networks} (IDNs), networks of computing nodes that coordinate to satisfy ML inference requests achieving the best trade-off between latency and accuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud). We propose a distributed dynamic policy for ML model allocation in an IDN by which each node dynamically updates its local set of inference models based on requests observed during the recent past plus limited information exchange with its neighboring nodes. Our policy offers strong performance guarantees in an adversarial setting and shows improvements over greedy heuristics with similar complexity in realistic scenarios

Elastic caching solutions for content dissemination services elastic caching solutions for content dissemination services of ip-based internet technologies prospective

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. The Information-Centric Networking (ICN) provides a new data dissemination Internet paradigm to support the communication services that will meet the end-users’ modern requirements. ICN focuses on transmitting data rather than physical locations. It offers a cache-able environment to fulfill future requirements and delivers communication services with less congestion and bandwidth in a network. The current Internet needs to enhance its architectural design for information distribution by reducing the end-to-end communication practices. ICN-based architecture aims to fulfill the end-users’ requirements and provide a better communication system compared to the current Internet system. ICN implements in-network caching (storage) to facilitate unicast and multicast mechanisms at the same time to deploy efficient and appropriate transmission of the desired information. In this situation, temporary storage is deployed all over the network to serve the requested objects (contents). In the last few years, ICN has shown up as engineering to replace the Internet design. In this paper, a comprehensive study about ICN-based caching mechanisms to enhance the IP-based Internet technologies is presented and analyzes the possible benefits using caching with the Internet of Things, Blockchain, Software Defined Network, 5G, genomic data sets, fog, and edge computing. In the end, the ICN-based caching strategies are mentioned that provide a diverse solution to deal with IP-based Internet technologies in an efficient way to deliver fast data dissemination