47,946 research outputs found
Formal Scheduling Constraints for Time-Sensitive Networks
In recent years, the IEEE 802.1 Time Sensitive Networking (TSN) task group
has been active standardizing time-sensitive capabilities for Ethernet networks
ranging from distributed clock synchronization and time-based ingress policing
to frame preemption, redundancy management, and scheduled traffic enhancements.
In particular the scheduled traffic enhancements defined in IEEE 802.1Qbv
together with the clock synchronization protocol open up the possibility to
schedule communication in distributed networks providing real-time guarantees.
In this paper we formalize the necessary constraints for creating
window-based IEEE~802.1Qbv Gate Control List schedules for Time-sensitive
Networks (TSN). The resulting schedules allow a greater flexibility in terms of
timing properties while still guaranteeing deterministic communication with
bounded jitter and end-to-end latency
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks
Emerging 5G systems will need to efficiently support both enhanced mobile
broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic.
In these systems, time is divided into slots which are further sub-divided into
minislots. From a scheduling perspective, eMBB resource allocations occur at
slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively
overlapped at the minislot timescale, resulting in selective
superposition/puncturing of eMBB allocations. This approach enables minimal
URLLC latency at a potential rate loss to eMBB traffic.
We study joint eMBB and URLLC schedulers for such systems, with the dual
objectives of maximizing utility for eMBB traffic while immediately satisfying
URLLC demands. For a linear rate loss model (loss to eMBB is linear in the
amount of URLLC superposition/puncturing), we derive an optimal joint
scheduler. Somewhat counter-intuitively, our results show that our dual
objectives can be met by an iterative gradient scheduler for eMBB traffic that
anticipates the expected loss from URLLC traffic, along with an URLLC demand
scheduler that is oblivious to eMBB channel states, utility functions and
allocation decisions of the eMBB scheduler. Next we consider a more general
class of (convex/threshold) loss models and study optimal online joint
eMBB/URLLC schedulers within the broad class of channel state dependent but
minislot-homogeneous policies. A key observation is that unlike the linear rate
loss model, for the convex and threshold rate loss models, optimal eMBB and
URLLC scheduling decisions do not de-couple and joint optimization is necessary
to satisfy the dual objectives. We validate the characteristics and benefits of
our schedulers via simulation
OSCAR: A Collaborative Bandwidth Aggregation System
The exponential increase in mobile data demand, coupled with growing user
expectation to be connected in all places at all times, have introduced novel
challenges for researchers to address. Fortunately, the wide spread deployment
of various network technologies and the increased adoption of multi-interface
enabled devices have enabled researchers to develop solutions for those
challenges. Such solutions aim to exploit available interfaces on such devices
in both solitary and collaborative forms. These solutions, however, have faced
a steep deployment barrier.
In this paper, we present OSCAR, a multi-objective, incentive-based,
collaborative, and deployable bandwidth aggregation system. We present the
OSCAR architecture that does not introduce any intermediate hardware nor
require changes to current applications or legacy servers. The OSCAR
architecture is designed to automatically estimate the system's context,
dynamically schedule various connections and/or packets to different
interfaces, be backwards compatible with the current Internet architecture, and
provide the user with incentives for collaboration. We also formulate the OSCAR
scheduler as a multi-objective, multi-modal scheduler that maximizes system
throughput while minimizing energy consumption or financial cost. We evaluate
OSCAR via implementation on Linux, as well as via simulation, and compare our
results to the current optimal achievable throughput, cost, and energy
consumption. Our evaluation shows that, in the throughput maximization mode, we
provide up to 150% enhancement in throughput compared to current operating
systems, without any changes to legacy servers. Moreover, this performance gain
further increases with the availability of connection resume-supporting, or
OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput
Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication
Future in-vehicular networks will be based on Ethernet. The IEEE
Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time
requirements in future car communication. Software-Defined Networking (SDN)
extends the Ethernet control plane with a programming option that can add much
value to the resilience, security, and adaptivity of the automotive
environment. In this work, we derive a first concept for combining
Software-Defined Networking with Time-Sensitive Networking along with an
initial evaluation. Our measurements are performed via a simulation that
investigates whether an SDN architecture is suitable for time-critical
applications in the car. Our findings indicate that the control overhead of SDN
can be added without a delay penalty for the TSN traffic when protocols are
mapped properly.Comment: To be published at IEEE VTC2019-Sprin
Optimizing Emergency Transportation through Multicommodity Quickest Paths
In transportation networks with limited capacities and travel times on the arcs, a class of problems attracting a growing scientific interest is represented by the optimal routing and scheduling of given amounts of flow to be transshipped from the origin points to the specific destinations in minimum time. Such problems are of particular concern to emergency transportation where evacuation plans seek to minimize the time evacuees need to clear the affected area and reach the safe zones. Flows over time approaches are among the most suitable mathematical tools to provide a modelling representation of these problems from a macroscopic point of view. Among them, the Quickest Path Problem (QPP), requires an origin-destination flow to be routed on a single path while taking into account inflow limits on the arcs and minimizing the makespan, namely, the time instant when the last unit of flow reaches its destination. In the context of emergency transport, the QPP represents a relevant modelling tool, since its solutions are based on unsplittable dynamic flows that can support the development of evacuation plans which are very easy to be correctly implemented, assigning one single evacuation path to a whole population. This way it is possible to prevent interferences, turbulence, and congestions that may affect the transportation process, worsening the overall clearing time. Nevertheless, the current state-of-the-art presents a lack of studies on multicommodity generalizations of the QPP, where network flows refer to various populations, possibly with different origins and destinations. In this paper we provide a contribution to fill this gap, by considering the Multicommodity Quickest Path Problem (MCQPP), where multiple commodities, each with its own origin, destination and demand, must be routed on a capacitated network with travel times on the arcs, while minimizing the overall makespan and allowing the flow associated to each commodity to be routed on a single path. For this optimization problem, we provide the first mathematical formulation in the scientific literature, based on mixed integer programming and encompassing specific features aimed at empowering the suitability of the arising solutions in real emergency transportation plans. A computational experience performed on a set of benchmark instances is then presented to provide a proof-of-concept for our original model and to evaluate the quality and suitability of the provided solutions together with the required computational effort. Most of the instances are solved at the optimum by a commercial MIP solver, fed with a lower bound deriving from the optimal makespan of a splittable-flow relaxation of the MCQPP
Airline Crew Scheduling with Potts Neurons
A Potts feedback neural network approach for finding good solutions to
resource allocation problems with a non-fixed topology is presented. As a
target application the airline crew scheduling problem is chosen. The
topological complication is handled by means of a propagator defined in terms
of Potts neurons. The approach is tested on artificial random problems tuned to
resemble real-world conditions. Very good results are obtained for a variety of
problem sizes. The computer time demand for the approach only grows like
\mbox{(number of flights)}^3. A realistic problem typically is solved within
minutes, partly due to a prior reduction of the problem size, based on an
analysis of the local arrival/departure structure at the single airportsComment: 9 pages LaTeX, 3 postscript figures, uufiles forma
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