Latency Bounds of Packet-Based Fronthaul for Cloud-RAN with Functionality Split

The emerging Cloud-RAN architecture within the fifth generation (5G) of wireless networks plays a vital role in enabling higher flexibility and granularity. On the other hand, Cloud-RAN architecture introduces an additional link between the central, cloudified unit and the distributed radio unit, namely fronthaul (FH). Therefore, the foreseen reliability and latency for 5G services should also be provisioned over the FH link. In this paper, focusing on Ethernet as FH, we present a reliable packet-based FH communication and demonstrate the upper and lower bounds of latency that can be offered. These bounds yield insights into the trade-off between reliability and latency, and enable the architecture design through choice of splitting point, focusing on high layer split between PDCP and RLC and low layer split between MAC and PHY, under different FH bandwidth and traffic properties. Presented model is then analyzed both numerically and through simulation, with two classes of 5G services that are ultra reliable low latency (URLL) and enhanced mobile broadband (eMBB).Comment: 6 pages, 7 figures, 3 tables, conference paper (ICC19

Coupled queues with customer impatience

Motivated by assembly processes, we consider a Markovian queueing system with multiple coupled queues and customer impatience. Coupling means that departures from all constituent queues are synchronised and that service is interrupted whenever any of the queues is empty and only resumes when all queues are non-empty again. Even under Markovian assumptions, the state space grows exponentially with the number of queues involved. To cope with this inherent state space explosion problem, we investigate performance by means of two numerical approximation techniques based on series expansions, as well as by deriving the fluid limit. In addition, we provide closed-form expressions for the first terms in the series expansion of the mean queue content for the symmetric coupled queueing system. By an extensive set of numerical experiments, we show that the approximation methods complement each other, each one being accurate in a particular subset of the parameter space. (C) 2017 Elsevier B.V. All rights reserved

A Maclaurin-series expansion approach to coupled queues with phase-type distributed service times

International audienc

Computationally Efficient Simulation of Queues: The R Package queuecomputer

Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model such systems accurately, we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter inference from data. Approximate Bayesian computation could offer a straightforward way to infer parameters for such networks if we could simulate data quickly enough. We present a computationally efficient method for simulating from a very general set of queueing networks with the R package queuecomputer. Remarkable speedups of more than 2 orders of magnitude are observed relative to the popular DES packages simmer and simpy. We replicate output from these packages to validate the package. The package is modular and integrates well with the popular R package dplyr. Complex queueing networks with tandem, parallel and fork/join topologies can easily be built with these two packages together. We show how to use this package with two examples: a call center and an airport terminal.Comment: Updated for queuecomputer_0.8.

Queues and risk models with simultaneous arrivals

We focus on a particular connection between queueing and risk models in a multi-dimensional setting. We first consider the joint workload process in a queueing model with parallel queues and simultaneous arrivals at the queues. For the case that the service times are ordered (from largest in the first queue to smallest in the last queue) we obtain the Laplace-Stieltjes transform of the joint stationary workload distribution. Using a multivariate duality argument between queueing and risk models, this also gives the Laplace transform of the survival probability of all books in a multivariate risk model with simultaneous claim arrivals and the same ordering between claim sizes. Other features of the paper include a stochastic decomposition result for the workload vector, and an outline how the two-dimensional risk model with a general two-dimensional claim size distribution (hence without ordering of claim sizes) is related to a known Riemann boundary value problem

Memory-aware sizing for in-memory databases

In-memory database systems are among the technological drivers of big data processing. In this paper we apply analytical modeling to enable efficient sizing of in-memory databases. We present novel response time approximations under online analytical processing workloads to model thread-level forkjoin and per-class memory occupation.We combine these approximations with a non-linear optimization program to minimize memory swapping in in-memory database clusters. We compare our approach with state-of-the-art response time approximations and trace-driven simulation using real data from an SAP HANA in-memory system and show that our optimization model is significantly more accurate than existing approaches at similar computational costs