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proceedings of the Winter Simulation conference

作者： Mahdi Darvishi Abdolreza Abhari Fac. of Eng. and Architectural Scie. Toronto Metropolitan University Toronto ON CANADA Dept. of Computer Science Eng . Toronto Metropolitan University Toronto ON CANADA

ISBN: (纸本)9798331534202

Photovoltaic (PV) systems are pivotal in the global energy transition, where accurate solar power forecasting is critical. Traditional forecasting has leaned heavily on solar irradiance data, yet such reliance carries inherent uncertainties and measurement complexities, presenting significant forecasting challenges. This paper introduces a novel hybrid/ensemble model that reduces dependence on solar irradiance data, utilizing geographic, meteorological, and temporal data to predict solar power output. Combining the strengths of XGBoost and LightGBM algorithms through a linear regression meta-model, our approach demonstrates improved prediction accuracy, evidenced by a mean absolute error (MAE) of 0.033, and an R-squared value of 0.693. This study advances solar power forecasting, enhancing PV system efficiency, and reliability, and promoting sustainable energy investments.

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Adaptive Generative modeling in Resource-Constrained Environments

Adaptive Generative Modeling in Resource-Constrained Environ...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Kim, Jung-Eun Bradford, Richard Del Giudice, Max Shao, Zhong Yale Univ Comp Sci New Haven CT 06520 USA Collins Aerosp Commercial Av Engn Cedar Rapids IA USA

ISBN: (纸本)9783981926354

Modern generative techniques, deriving realistic data from incomplete or noisy inputs, require massive computation for rigorous results. These limitations hinder generative techniques from being incorporated in systems in resource-constrained environment, thus motivating methods that grant users control over the time-quality trade-offs for a reasonable "payoff" of execution cost. Hence, as a new paradigm for adaptively organizing and employing recurrent networks, we propose an architectural design for generative modeling achieving flexible quality. We boost the overall efficiency by introducing non-recurrent layers into stacked recurrent architectures. Accordingly, we design the architecture with no redundant recurrent cells so we avoid unnecessary overhead.

关键词： Knowledge engineering Adaptation models Adaptive systems Recurrent neural networks computer architecture Noise measurement

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Generalized Sketch Families for Network Traffic measurement 20

Generalized Sketch Families for Network Traffic Measurement

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2020 sigmetrics/Performance Joint International conference on measurement and modeling of computer systems, sigmetrics 2020

作者： Zhou, You Zhang, Youlin Ma, Chaoyi Chen, Shigang Odegbile, Olufemi O. University of Florida Gainesville United States

ISBN: (纸本)9781450379854

Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals. © 2019 Owner/Author.

关键词： Information management

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POMACS V8, N2, June 2024 Editorial

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proceedings OF THE acm ON measurement AND ANALYSIS OF COMPUTING systems 2024年第2期8卷 1-1页

作者： Ciucu, Florin Fanti, Giulia Righter, Rhonda Univ Warwick Coventry England Carnegie Mellon Univ Pittsburgh PA USA Univ Calif Berkeley Berkeley CA USA

The proceedings of the acm on measurement and Analysis of Computing systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the acm Special Interest Group sigmetrics. All papers in this issue of POMACS will be presented at the acm sigmetrics/IFIP Performance 2024 conference on June 10-14, 2024, in Venice, Italy. These papers were selected during the Winter submission round by the 93 members of the acm sigmetrics/IFIP Performance 2024 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two sigmetrics/Performance deadlines), or rejected (with re-submission allowed after a year). For this issue, which represents the Winter deadline, POMACS is publishing 10 papers out of 127 submissions, of which 2 had previously received a one-shot revision decision. All submissions received at least 3 reviews and borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 28% of the submissions were in the Theory track, 55% were in the measurement & Applied modeling track, 29% were in the systems track, and 22% were in the Learning track (papers could be part of more than one track). Many individuals contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to sigmetrics/Performance/POMACS. Second, we would like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. We also thank the several external reviewers who provided their expert opinions on specific submissions that required additional input. We are also grateful to the sigmetrics Board Chair, Mor Harchol-Balter, the IFIP Working Group 7.3 Chair, Mark S. Squillante, the previ

关键词： analysis measurement performance modeling

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Understanding power variation and its implications on performance optimization on the Cori supercomputer 12

Understanding power variation and its implications on perfor...

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12th International Workshop on Performance modeling, Benchmarking and Simulation of High Performance computer systems held as part of the 33rd acm/IEEE International conference for High Performance Computing, Networking, Storage and Analysis

作者： Bhalachandra, Sridutt Austin, Brian Wright, Nicholas J. Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

ISBN: (纸本)9781665411189

Power is increasingly becoming a limiting factor in supercomputing. The performance and scale of future high-performance computing systems will be determined by how efficiently they manage their power budgets. Therefore any amount of unused power is forsaken performance. Regardless of the processors chosen for a future system, it will be necessary to understand power variation and its implications on performance optimization. In this paper, we identify and quantify the factors that affect the power consumption of the NERSC Cori supercomputer at different levels of the system hierarchy. Our study presents node-level power-performance trade-offs for fundamental computational patterns. We show that I/O activity and load imbalance are common causes of job-level power variation among jobs in Cori's production workload. We quantitatively attribute system-level power variation to three sources and find that 86% of the variation is due to temporal variation in application behavior over the duration of a job. Furthermore, our analysis reveals that under typical workload conditions, the Cori system's power budget could accommodate up to 60% more nodes.

关键词： Schedules Power measurement Power demand Heuristic algorithms Computational modeling Production Supercomputers

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ICPE 2020 - proceedings of the acm/SPEC International conference on Performance Engineering

ICPE 2020 - Proceedings of the ACM/SPEC International Confer...

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11th acm/SPEC International conference on Performance Engineering, ICPE 2020

ISBN: (纸本)9781450369916

The proceedings contain 32 papers. The topics discussed include: the use of change point detection to identify software performance regressions in a continuous integration system;out of band performance monitoring of server workloads: leveraging restful API to monitor compute resource utilization and performance related metrics for server performance analysis;transferring Pareto frontiers across heterogeneous hardware environments;modeling of request cloning in cloud server systems using processor sharing;taming energy consumption variations in systems benchmarking;an automated forecasting framework based on method recommendation for seasonal time series;learning queuing networks by recurrent neural networks;the use of change point detection to identify software performance regressions in a continuous integration system;and duet benchmarking: improving measurement accuracy in the cloud.

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ICPE 2020 - Companion of the acm/SPEC International conference on Performance Engineering

ICPE 2020 - Companion of the ACM/SPEC International Conferen...

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11th acm/SPEC International conference on Performance Engineering, ICPE 2020

ISBN: (纸本)9781450371094

The proceedings contain 16 papers. The topics discussed include: performance anomaly and change point detection for large-scale system management;extended abstract of performance analysis and prediction of model transformation;issues arising in using kernel traces to make a performance model;how to apply modeling to compare options and select the appropriate cloud platform;towards performance modeling of speculative execution for cloud applications;kubernetes: towards deployment of distributed IoT applications in fog computing;acceleration opportunities in linear algebra applications via idiom recognition;and tutorial on benchmarking big data analytics systems.

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Characterizing Policies with Optimal Response Time Tails under Heavy-Tailed Job Sizes 20

Characterizing Policies with Optimal Response Time Tails und...

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2020 sigmetrics/Performance Joint International conference on measurement and modeling of computer systems, sigmetrics 2020

作者： Scully, Ziv Van Kreveld, Lucas Boxma, Onno Dorsman, Jan-Pieter Wierman, Adam Carnegie Mellon University Pittsburgh United States University of Amsterdam Amsterdam Netherlands Eindhoven University of Technology Eindhoven Netherlands California Institute of Technology Pasadena United States

ISBN: (纸本)9781450379854

We consider the tail behavior of the response time distribution in an M/G/1 queue with heavy-tailed job sizes, specifically those with intermediately regularly varying tails. In this setting, the response time tail of many individual policies has been characterized, and it is known that policies such as Shortest Remaining Processing Time (SRPT) and Foreground-Background (FB) have response time tails of the same order as the job size tail, and thus such policies are tail-optimal. Our goal in this work is to move beyond individual policies and characterize the set of policies that are tail-optimal. Toward that end, we use the recently introduced SOAP framework to derive sufficient conditions on the form of prioritization used by a scheduling policy that ensure the policy is tail-optimal. These conditions are general and lead to new results for important policies that have previously resisted analysis, including the Gittins policy, which minimizes mean response time among policies that do not have access to job size information. As a by-product of our analysis, we derive a general upper bound for fractional moments of M/G/1 busy periods, which is of independent interest. © 2019 Owner/Author.

关键词： Response time (computer systems)

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Using Deep Learning for Simulation of Real Time Video Streaming Applications 22

Using Deep Learning for Simulation of Real Time Video Stream...

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proceedings of the Winter Simulation conference

作者： Abdolreza Abhari Dipak Pudasaini Distributed Systems & Multimedia Processing Laboratory (DSMP lab) Department of Computer Science Ryerson University Toronto Ontario CANADA

The traditional approaches for simulation of video analytics applications suffer from the lack of real-data generated by employed machine learning techniques. Machine learning methods need huge data that causes network congestion and high latency in cloud-based networks. This paper proposes a novel method for performance measurement and simulation of video analytics applications to evaluate the solutions addressing the cloud congestion problem. The proposed simulation is achieved by building a model prototype called Video Analytic Data Reduction Model (VADRM) that divides video analytic jobs into smaller tasks with fewer processing requirements to run on edge networking. Real data generated from VADRM prototype is characterized and tested by curve fitting to find the distribution models for generating the larger number of artificial data for resource management simulation. Distribution models based on real data of CNN-based VADRM prototype are used to build a queueing model and comprehensive simulation of real-time video analytics applications.

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sigmetrics 2018 - Abstracts of the 2018 acm International conference on measurement and modeling of computer systems

SIGMETRICS 2018 - Abstracts of the 2018 ACM International Co...

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2018 acm International conference on measurement and modeling of computer systems, sigmetrics 2018

ISBN: (纸本)9781450358460

The proceedings contain 45 papers. The topics discussed include: state dependent control of closed queueing networks;Dandelion++: lightweight cryptocurrency networking with formal anonymity guarantees: extended abstract;bootstrapped graph diffusions: exposing the power of nonlinearity;the cost of uncertainty in curing epidemics;the price of fragmentation in mobility-on-demand services;new metrics and models for a post-ISA era: managing complexity;delay scaling in many-sources wireless networks without queue state information;practical bounds on optimal caching with variable object sizes;on resource pooling and separation for LRU caching;an optimal randomized online algorithm for QoS buffer management;minimizing queue length regret under adversarial network models;dynamic proportional sharing: a game-theoretic approach;SOAP: one clean analysis of all age-based scheduling policies;a Whittle's index based approach for QoE optimization in wireless networks;an optimal algorithm for online non-convex learning;asymptotic optimal control of Markov-modulated restless bandits;online learning of optimally diverse rankings;learning proportionally fair allocations with low regret;multi-armed bandit with additional observations;online learning in weakly coupled Markov decision processes: a convergence time study;working set size estimation techniques in virtualized environments: one size does not fit all;PreFix: switch failure prediction in datacenter networks;on non-preemptive VM scheduling in the cloud;and why some like it loud: timing power attacks in multi-tenant data centers using an acoustic side channel.

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