15 research outputs found
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