A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Improving Routing Efficiency, Fairness, Differentiated Servises And Throughput In Optical Networks

Wavelength division multiplexed (WDM) optical networks are rapidly becoming the technology of choice in next-generation Internet architectures. This dissertation addresses the important issues of improving four aspects of optical networks, namely, routing efficiency, fairness, differentiated quality of service (QoS) and throughput. A new approach for implementing efficient routing and wavelength assignment in WDM networks is proposed and evaluated. In this approach, the state of a multiple-fiber link is represented by a compact bitmap computed as the logical union of the bitmaps of the free wavelengths in the fibers of this link. A modified Dijkstra\u27s shortest path algorithm and a wavelength assignment algorithm are developed using fast logical operations on the bitmap representation. In optical burst switched (OBS) networks, the burst dropping probability increases as the number of hops in the lightpath of the burst increases. Two schemes are proposed and evaluated to alleviate this unfairness. The two schemes have simple logic, and alleviate the beat-down unfairness problem without negatively impacting the overall throughput of the system. Two similar schemes to provide differentiated services in OBS networks are introduced. A new scheme to improve the fairness of OBS networks based on burst preemption is presented. The scheme uses carefully designed constraints to avoid excessive wasted channel reservations, reduce cascaded useless preemptions, and maintain healthy throughput levels. A new scheme to improve the throughput of OBS networks based on burst preemption is presented. An analytical model is developed to compute the throughput of the network for the special case when the network has a ring topology and the preemption weight is based solely on burst size. The analytical model is quite accurate and gives results close to those obtained by simulation. Finally, a preemption-based scheme for the concurrent improvement of throughput and burst fairness in OBS networks is proposed and evaluated. The scheme uses a preemption weight consisting of two terms: the first term is a function of the size of the burst and the second term is the product of the hop count times the length of the lightpath of the burst

Healthcare queueing models.

Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing model of a general class of healthcare systems. We develop new expressions to assess the impact of service outages and use the resulting model to approximate patient flow times and to evaluate a number of practical applications. We illustrate the devastating impact of service interruptions on patient flow times and show the potential gains obtained by pooling hospital resources. In addition, we present an optimization model to determine the optimal number of patients to be treated during a service session.Operations research; Health care evaluation mechanisms; Organizational efficiency; Management decision support systems; Time management; Queueing theory;

Predicting Real-Time Safety of the National Airspace System

To gain the situational awareness necessary for informed decision making regarding avoidance of airspace hazards, each operator must consolidate operations-relevant information from disparate sources and apply extensive domain knowledge to correctly interpret not just the current state of the NAS but forecast its (combined) evolution over the duration of the operation. This time- and workload-intensive process is periodically repeated throughout the operation so that changes can be managed in a timely manner.The imprecision, inaccuracies, inconsistency, and incompleteness of the incoming data further challenges the process. To facilitate informed decision making, this paper presents a model-based framework for the textitautomated real-time monitoring and prediction of possible effects of airspace hazards on the safety of the National Airspace System (NAS). First, hazards to flight are identified and transformed into sms, that is, quantities of interest that could be evaluated based on available data and are predictive of an unsafe event. The sms and associated thresholds that specify when an event transitions from emphsafe to emphunsafe are combined with models of airspace operations and aircraft dynamics. The framework can include any hazard to flight that can be modeled quantitatively. Models can be detailed and complex, or they can be considerably simplifed, as appropriate to the application. Real-time NAS safety monitoring and prediction begins with an estimate of the state of the NAS using the dynamic models. Given the state estimate and a probability distribution of future inputs to the NAS, we can then predict the evolution of the NAS - the future state - and the occurrence of hazards and unsafe events. The entire probability distribution of airspace sms is computed, not just point estimates, without significant assumptions regarding the distribution type andor parameters. We demonstrate our overall approach through a simulated scenario in which we predict the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress. Predictions accounting for common sources of uncertainty are included and it is shown how the predictions improve in time, become more confident, and change dynamically as new information is made available to the prediction algorithm

Dynamic Reserve and Transmission Capacity Allocation in Wind-Dominated Power Systems

The large shares of wind power generation in electricity markets motivate higher levels of operating reserves. However, current reserve sizing practices fail to account for important topological aspects that might hinder their deployment, thus resulting in high operating costs. Zonal reserve procurement mitigates such inefficiencies, however, the way the zones are defined is still open to interpretation. This paper challenges the efficiency of predetermined zonal setups that neglect the location of stochastic power production in the system, as well as the availability, cost and accessibility of flexible generating units. To this end, we propose a novel reserve procurement approach, formulated as a two-stage stochastic bilevel model, in which the upper level identifies a number of contiguous reserve zones using dynamic grid partitioning and sets zonal requirements based on the total expected operating costs. Using two standard IEEE reliability test cases, we show how the efficient partitioning of reserve zones can reduce expected system cost and promote the integration of stochastic renewables.Comment: Submitted to IEEE Transactions on Power Systems on the 20th of March 202

A STUDY OF QUEUING THEORY IN LOW TO HIGH REWORK ENVIRONMENTS WITH PROCESS AVAILABILITY

In manufacturing systems subject to machine and operator resource constraints the effects of rework can be profound. High levels of rework burden the resources unnecessarily and as the utilization of these resources increases the expected queuing time of work in process increases exponentially. Queuing models can help managers to understand and control the effects of rework, but often this tool is overlooked in part because of concerns over accuracy in complex environments and/or the need for limiting assumptions. One aim of this work is to increase understanding of system variables on the accuracy of simple queuing models. A queuing model is proposed that combines G/G/1 modeling techniques for rework with effective processing time techniques for machine availability and the accuracy of this model is tested under varying levels of rework, external arrival variability, and machine availability. Results show that the model performs best under exponential arrival patterns and can perform well even under high rework conditions. Generalizations are made with regards to the use of this tool for allocation of jobs to specific workers and/or machines based on known rework rates with the ultimate aim of queue time minimization

Proceedings of the first international workshop on Investigating dataflow in embedded computing architectures (IDEA 2015), January 21, 2015, Amsterdam, The Netherlands

IDEA '15 held at HiPEAC 2015, Amsterdam, The Netherlands on January 21st, 2015 is the rst workshop on Investigating Data ow in Embedded computing Architectures. This technical report comprises of the proceedings of IDEA '15. Over the years, data ow has been gaining popularity among Embedded Systems researchers around Europe and the world. However, research on data ow is limited to small pockets in dierent communities without a common forum for discussion. The goal of the workshop was to provide a platform to researchers and practitioners to present work on modelling and analysis of present and future high performance embedded computing architectures using data ow. Despite being the rst edition of the workshop, it was very pleasant to see a total of 14 submissions, out of which 6 papers were selected following a thorough reviewing process. All the papers were reviewed by at least 5 reviewers. This workshop could not have become a reality without the help of a Technical Program Committee (TPC). The TPC members not only did the hard work to give helpful reviews in time, but also participated in extensive discussion following the reviewing process, leading to an excellent workshop program and very valuable feedback to authors. Likewise, the Organisation Committee also deserves acknowledgment to make this workshop a successful event. We take this opportunity to thank everyone who contributed in making this workshop a success