Distributed Systems

2022


  1. Network Testing Utilizing ProgrammableNetworking Hardware. (, , , and ), In IEEE Communications Magazine, IEEE, .

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    url
  2. Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand (, , and ), In Energies 2022, Vol. 15, Page 3425, Multidisciplinary Digital Publishing Institute, volume 15, .

    Abstract

    This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1 6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73 2.98%; Summer: 8.41 14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.


    Keywords: Bayesian optimization algorithm, NARX, SARIMAX, electricity demand, short, term forecast


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    urldoi
  3. On the Incremental Reconfiguration of Time-sensitive Networks at Runtime (, , , , and ), In Proceedings of the IFIP Networking Conference., IFIP, .

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  4. Enhancing Flexibility for Dynamic Time-Sensitive Network Configurations (, , , , and ), In Proceedings of the 3rd KuVS Fachgespräch on Network Softwarization, Universität Tübingen, .

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    doi
  5. Travel light: state shedding for efficient operator migration (, , and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS'22), ACM press, .

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    doi
  6. FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees (, , , and ), In Proceedings of the 28th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2022), IEEE, .

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  7. PANDA: performance prediction for parallel and dynamic stream processing (, , and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, ACM press, .

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    doi
  8. Window-based Parallel Operator Execution with In-Network Computing: Proceedings (, and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-based Systems (DEBS '22), ACM New York, NY, USA, .

    Abstract

    Data parallel processing is a key concept to increase the scalability and elasticity in event streaming systems. Often data parallelism is accomplished in a splitter-merger architecture where the splitter divides incoming streams into partitions and forwards them to parallel operator instances. The splitter performance is a limiting factor to the system throughput and the parallelization degree.This work studies how to leverage novel methods of in-network computing to accelerate the splitter functionality by implementing it as an in-network function. While dedicated hardware for in-network computing has a high potential to enhance the splitter performance, in-network programming models like the P4 language are also highly limited in their expressiveness to support corresponding parallelization models. We propose P4SS which supports overlapping and non-overlapping count-based windows for multiple independent data streams and parallelizes them to a dynamically configurable number of operator instances. We validate in the context of a prototypical implementation our splitting strategy and its scalability in terms of switch resource consumption.


    Keywords: Data Parallelism, In-network Computing, Load Balancing, Complex Event Processing (CEP), P4 Language, Data Plane Programming


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    doi
  9. On the Use of the Conformance and Compliance Keywords During Verification of Business Processes (, and ), In BPM 2022 Forum, Springer International Publishing, .

    Abstract

    A wealth of techniques have been developed over the past decades to help organizations understand their processes, verify correctness against requirements and diagnose potential problems. In general, these techniques for verification allow us to check whether a business process conforms or complies with some specification, and each of them is specifically designed to solve a particular business problem at each stage of the BPM lifecycle. However, the terms conformance and compliance are often used as synonyms and their distinct differences in verification goals is blurring. As a result, the terminology used to describe the techniques or the corresponding verification activity does not always match with the precise meaning of the terms as they are defined in the area of verification. Consequently, the confusion of these terms may hamper the application of the different techniques and the correct positioning of research. In this position paper, we aim to provide comprehensive definitions and a unified terminology throughout the BPM lifecycle and the artifacts they apply to. Moreover, we explore the consequences when these terms are used incorrectly. In doing so, we aim to improve transfer from research to practical applications and increase adoption of relevant approaches and new advances in the field by clarifying the relation between available techniques and the intended verification goals.


    Keywords: Conformance, Compliance, Verification, Review


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