Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation

The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario

A Decision-Theoretic Approach to Resource Allocation in Wireless Multimedia Networks

The allocation of scarce spectral resources to support as many user applications as possible while maintaining reasonable quality of service is a fundamental problem in wireless communication. We argue that the problem is best formulated in terms of decision theory. We propose a scheme that takes decision-theoretic concerns (like preferences) into account and discuss the difficulties and subtleties involved in applying standard techniques from the theory of Markov Decision Processes (MDPs) in constructing an algorithm that is decision-theoretically optimal. As an example of the proposed framework, we construct such an algorithm under some simplifying assumptions. Additionally, we present analysis and simulation results that show that our algorithm meets its design goals. Finally, we investigate how far from optimal one well-known heuristic is. The main contribution of our results is in providing insight and guidance for the design of near-optimal admission-control policies.Comment: To appear, Dial M for Mobility, 200

Labour Supply, Work Effort and Contract Choice: Theory and Evidence on Physicians

We develop and estimate a generalized labour supply model that incorporates work effort into the standard consumption-leisure trade-off. We allow workers a choice between two contracts: a piece rate contract, wherein he is paid per unit of service provided, and a mixed contract, wherein he receives an hourly wage and a reduced piece rate. This setting gives rise to a non-convex budget set and an efficient budget constraint (the upper envelope of contract-specific budget sets). We apply our model to data collected on specialist physicians working in the Province of Quebec (Canada). Our data set contains information on each physician's labour supply and their work effort (clinical services provided per hour worked). It also covers a period of policy reform under which physicians could choose between two compensation systems: the traditional fee-for-service, under which physicians receive a fee for each service provided, and mixed remuneration, under which physicians receive a per diem as well as a reduced fee-for-service. We estimate the model using a discrete choice approach. We use our estimates to simulate elasticities and the effects of ex ante reforms on physician contracts. Our results show that physician services and effort are much more sensitive to contractual changes than is their time spent at work. Our results also suggest that a mandatory reform, forcing all physicians to adopt the mixed remuneration system, would have had substantially larger effects on physician behaviour than those observed under the voluntary reform.labour supply, effort, contracts, practice patterns of physicians, discrete choice econometric models, mixed logit

Labour Supply, Work Effort and Contract Choice: Theory and Evidence on Physicians

We develop and estimate a generalized labour supply model that incorporates work effort into the standard consumption-leisure trade-off. We allow workers a choice between two contracts: a piece rate contract, wherein he is paid per unit of service provided, and a mixed contract, wherein he receives an hourly wage and a reduced piece rate. This setting gives rise to a nonconvex budget set and an efficient budget constraint (the upper envelope of contract-specific budget sets). We apply our model to data collected on specialist physicians working in the Province of Quebec (Canada). Our data set contains information on each physician’s labour supply and their work effort (clinical services provided per hour worked). It also covers a period of policy reform under which physicians could choose between two compensation systems: the traditional fee-for-service, under which physicians receive a fee for each service provided, and mixed remuneration, under which physicians receive a per diem as well as a reduced fee-for-service. We estimate the model using a discrete choice approach. We use our estimates to simulate elasticities and the effects of ex ante reforms on physician contracts. Our results show that physician services and effort are much more sensitive to contractual changes than is their time spent at work. Our results also suggest that a mandatory reform, forcing all physicians to adopt the mixed remuneration system, would have had substantially larger effects on physician behaviour than those observed under the voluntary reform.labour supply, effort, contracts, practice patterns of physicians, discrete choice econometric models, mixed logit

Cloud-based Content Distribution on a Budget

To leverage the elastic nature of cloud computing, a solution provider must be able to accurately gauge demand for its offering. For applications that involve swarm-to-cloud interactions, gauging such demand is not straightforward. In this paper, we propose a general framework, analyze a mathematical model, and present a prototype implementation of a canonical swarm-to-cloud application, namely peer-assisted content delivery. Our system – called Cyclops – dynamically adjusts the off-cloud bandwidth consumed by content servers (which represents the bulk of the provider's cost) to feed a set of swarming clients, based on a feedback signal that gauges the real-time health of the swarm. Our extensive evaluation of Cyclops in a variety of settings – including controlled PlanetLab and live Internet experiments involving thousands of users – show significant reduction in content distribution costs (by as much as two orders of magnitude) when compared to non-feedback-based swarming solutions, with minor impact on content delivery times

U.S. Agricultural Labor Out-migration Determinants, 1939-2004

Replaced with revised version of paper 06/01/06.Labor and Human Capital,

DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.Comment: Submitted to Springer Computin