19 research outputs found
Load Balancing via Random Local Search in Closed and Open systems
In this paper, we analyze the performance of random load resampling and
migration strategies in parallel server systems. Clients initially attach to an
arbitrary server, but may switch server independently at random instants of
time in an attempt to improve their service rate. This approach to load
balancing contrasts with traditional approaches where clients make smart server
selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants
thereof). Load resampling is particularly relevant in scenarios where clients
cannot predict the load of a server before being actually attached to it. An
important example is in wireless spectrum sharing where clients try to share a
set of frequency bands in a distributed manner.Comment: Accepted to Sigmetrics 201
Tight Load Balancing via Randomized Local Search
We consider the following balls-into-bins process with bins and
balls: each ball is equipped with a mutually independent exponential clock of
rate 1. Whenever a ball's clock rings, the ball samples a random bin and moves
there if the number of balls in the sampled bin is smaller than in its current
bin. This simple process models a typical load balancing problem where users
(balls) seek a selfish improvement of their assignment to resources (bins).
From a game theoretic perspective, this is a randomized approach to the
well-known Koutsoupias-Papadimitriou model, while it is known as randomized
local search (RLS) in load balancing literature. Up to now, the best bound on
the expected time to reach perfect balance was due to Ganesh, Lilienthal, Manjunath, Proutiere, and Simatos
(Load balancing via random local search in closed and open systems, Queueing
Systems, 2012). We improve this to an asymptotically tight
. Our analysis is based on the crucial observation
that performing "destructive moves" (reversals of RLS moves) cannot decrease
the balancing time. This allows us to simplify problem instances and to ignore
"inconvenient moves" in the analysis.Comment: 24 pages, 3 figures, preliminary version appeared in proceedings of
2017 IEEE International Parallel and Distributed Processing Symposium
(IPDPS'17
A Stochastic Model for Car-Sharing Systems
Vehicle-sharing systems are becoming important for urban transportation. In
these systems, users arrive at a station, pick up a vehicle, use it for a while
and then return it to another station of their choice. Depending on the type of
system, there might be a possibility to book vehicles before picking-up and/or
a parking space at the chosen arrival station. Each station has a finite
capacity and cannot host more vehicles and reserved parking spaces than its
capacity. We propose a stochastic model for an homogeneous car-sharing system
with possibility to reserve a parking space at the arrival station when
picking-up a car. We compute the performance of the system and the optimal
fleet size according to a specific metric. It differs from a similar model for
bike-sharing systems because of reservation that induces complexity, especially
when traffic increases
Stationary Distribution Analysis of a Queueing Model with Local Choice
The paper deals with load balancing between one-server queues on a circle by a local choice policy. Each one-server queue has a Poissonian arrival of customers. When a customer arrives at a queue, he joins the least loaded queue between this queue and the next one, ties solved at random. Service times have exponential distribution. The system is stable if the arrival-to-service rate ratio called load is less than one. When the load tends to zero, we derive the first terms of the expansion in this parameter for the stationary probabilities that a queue has 0 to 3 customers. We investigate the error, comparing these expansion results to numerical values obtained by simulations. Then we provide the asymptotics, as the load tends to zero, for the stationary probabilities of the queue length, for a fixed number of queues. It quantifies the difference between policies with this local choice, no choice and the choice between two queues chosen at random
On the flow-level stability of data networks without congestion control: the case of linear networks and upstream trees
In this paper, flow models of networks without congestion control are
considered. Users generate data transfers according to some Poisson processes
and transmit corresponding packet at a fixed rate equal to their access rate
until the entire document is received at the destination; some erasure codes
are used to make the transmission robust to packet losses. We study the
stability of the stochastic process representing the number of active flows in
two particular cases: linear networks and upstream trees. For the case of
linear networks, we notably use fluid limits and an interesting phenomenon of
"time scale separation" occurs. Bounds on the stability region of linear
networks are given. For the case of upstream trees, underlying monotonic
properties are used. Finally, the asymptotic stability of those processes is
analyzed when the access rate of the users decreases to 0. An appropriate
scaling is introduced and used to prove that the stability region of those
networks is asymptotically maximized
Mean field and propagation of chaos in multi-class heterogeneous loss models
We consider a system consisting of parallel servers, where jobs with different resource requirements arrive and are assigned to the servers for processing. Each server has a finite resource capacity and therefore can serve only a finite number of jobs at a time. We assume that different servers have different resource capacities. A job is accepted for processing only if the resource requested by the job is available at the server to which it is assigned. Otherwise, the job is discarded or blocked. We consider randomized schemes to assign jobs to servers with the aim of reducing the average blocking probability of jobs in the system. In particular, we consider a scheme that assigns an incoming job to the server having maximum available vacancy or unused resource among randomly sampled servers. We consider the system in the limit where both the number of servers and the arrival rates of jobs are scaled by a large factor. This gives rise to a mean field analysis. We show that in the limiting system the servers behave independently—a property termed as propagation of chaos. Stationary tail probabilities of server occupancies are obtained from the stationary solution of the mean field which is shown to be unique and globally attractive. We further characterize the rate of decay of the stationary tail probabilities. Numerical results suggest that the proposed scheme significantly reduces the average blocking probability of jobs as compared to static schemes that probabilistically route jobs to servers independently of their states