8,303 research outputs found
An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems
We show a methodology for the computation of the probability of deadline miss
for a periodic real-time task scheduled by a resource reservation algorithm. We
propose a modelling technique for the system that reduces the computation of
such a probability to that of the steady state probability of an infinite state
Discrete Time Markov Chain with a periodic structure. This structure is
exploited to develop an efficient numeric solution where different
accuracy/computation time trade-offs can be obtained by operating on the
granularity of the model. More importantly we offer a closed form conservative
bound for the probability of a deadline miss. Our experiments reveal that the
bound remains reasonably close to the experimental probability in one real-time
application of practical interest. When this bound is used for the optimisation
of the overall Quality of Service for a set of tasks sharing the CPU, it
produces a good sub-optimal solution in a small amount of time.Comment: IEEE Transactions on Parallel and Distributed Systems, Volume:27,
Issue: 3, March 201
Theory and evidence on pricing by asymmetric oligopolies
We present an analysis of markets with many asymmetrically positioned retailers that compete for the business of both informed and uninformed customers for a homogenous good, such as software, music, book or a brand-name appliance. We show that two forms of asymmetry, one related to loyal segment sizes of retailers and one related to the positioning of firms, completely explain the observed price dispersion in such markets and the multitude of asymmetrical strategies adopted by retailers. The stochastic dominance of empirical mixed strategy measures is used to test the theory with data on 968 books from 10 online retailers
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
ERA: A Framework for Economic Resource Allocation for the Cloud
Cloud computing has reached significant maturity from a systems perspective,
but currently deployed solutions rely on rather basic economics mechanisms that
yield suboptimal allocation of the costly hardware resources. In this paper we
present Economic Resource Allocation (ERA), a complete framework for scheduling
and pricing cloud resources, aimed at increasing the efficiency of cloud
resources usage by allocating resources according to economic principles. The
ERA architecture carefully abstracts the underlying cloud infrastructure,
enabling the development of scheduling and pricing algorithms independently of
the concrete lower-level cloud infrastructure and independently of its
concerns. Specifically, ERA is designed as a flexible layer that can sit on top
of any cloud system and interfaces with both the cloud resource manager and
with the users who reserve resources to run their jobs. The jobs are scheduled
based on prices that are dynamically calculated according to the predicted
demand. Additionally, ERA provides a key internal API to pluggable algorithmic
modules that include scheduling, pricing and demand prediction. We provide a
proof-of-concept software and demonstrate the effectiveness of the architecture
by testing ERA over both public and private cloud systems -- Azure Batch of
Microsoft and Hadoop/YARN. A broader intent of our work is to foster
collaborations between economics and system communities. To that end, we have
developed a simulation platform via which economics and system experts can test
their algorithmic implementations
Are adverse selection models of debt robust to changes in market structure?
Many adverse selection models of standard one-period debt contracts are based on the following seemingly innocuous assumptions. First, entrepreneurs have private information about the quality of their return distributions. Second, return distributions are ordered by the monotone likelihood-ratio property. Third, financiersā payoff functions are restricted to be monotonically non-decreasing in firm profits. Fourth, financial markets are competitive. We argue that debt is not an optimal contract in these models if there is only one (monopoly) financier rather than an infinite number of competitive financiers.security design; adverse selection; monotonic contracts; monotone likelihood ratio; first-order stochastic dominance
Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS
We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making
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