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1. NoStop: A Novel Configuration Optimization Scheme for Spark Streaming

   https://doi.org/10.1145/3472456.3472515  

   Ye, Qianwen; Liu, Wuji; Wu, Chase Q. (August 2021, Proceedings of the 50th International Conference on Parallel Processing)

   null (Ed.)

   An increasing number of big data applications in various domains generate datasets continuously, which must be processed for various purposes in a timely manner. As one of the most popular streaming data processing systems, Spark Streaming applies a batch-based mechanism, which receives real-time input data streams and divides the data into multiple batches before passing them to Spark processing engine. As such, inappropriate system configurations including batch interval and executor count may lead to unstable states, hence undermining the capability and efficiency of real-time computing. Hence, determining suitable configurations is crucial to the performance of such systems. Many machine learning- and search-based algorithms have been proposed to provide configuration recommendations for streaming applications where input data streams are fed at a constant speed, which, however, is extremely rare in practice. Most real-life streaming applications process data streams arriving at a time-varying rate and hence require real-time system monitoring and continuous configuration adjustment, which still remains largely unexplored. We propose a novel streaming optimization scheme based on Simultaneous Perturbation Stochastic Approximation (SPSA), referred to as NoStop, which dynamically tunes system configurations to optimize real-time system performance with negligible overhead and proved convergence. The performance superiority of NoStop is illustrated by real-life experiments in comparison with Bayesian Optimization and Spark Back Pressure solutions. Extensive experimental results show that NoStop is able to keep track of the changing pattern of input data in real time and provide optimal configuration settings to achieve the best system performance. This optimization scheme could also be applied to other streaming data processing engines with tunable parameters. 

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2. GreenABR: energy-aware adaptive bitrate streaming with deep reinforcement learning

   https://doi.org/10.1145/3524273.3528188  

   Turkkan, Bekir Oguzhan; Dai, Ting; Raman, Adithya; Kosar, Tevfik; Chen, Changyou; Bulut, Muhammed Fatih; Zola, Jaroslaw; Sow, Daby (June 2022, Proceedings of the 13th ACM Multimedia Systems Conference)

   Adaptive bitrate (ABR) algorithms aim to make optimal bitrate de- cisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video stream- ing. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming pa- rameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR’s deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations. 

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3. GreenABR: Energy-aware Adaptive Bitrate Streaming with Deep Reinforcement Learning

   Turkkan, B.; Dai, T.; Raman, A.; Kosar, T.; Chen, C.; Bulut, F.; Zola, Z.; Sow, D. (June 2022, Proceedings of the 13th ACM Multimedia Systems Conference (MMSys) 2022)

   Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video streaming. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming parameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR's deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations. 

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4. TWIN-ADAPT: Continuous Learning for Digital Twin-Enabled Online Anomaly Classification in IoT-Driven Smart Labs

   https://doi.org/10.3390/fi16070239  

   Gupta, Ragini; Tian, Beitong; Wang, Yaohui; Nahrstedt, Klara (July 2024, Future Internet)

   Lu, Xin; Wang, Wei; Wu, Dehao; Li, Xiaoxia (Ed.)

   In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as “Cleanrooms”): Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT’s continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT’s adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT’s robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings. 

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5. StreamQRE: modular specification and efficient evaluation of quantitative queries over streaming data

   https://doi.org/10.1145/3062341.3062369  

   Mamouras, Konstantinos; Raghothaman, Mukund; Alur, Rajeev; Ives, Zachary G.; Khanna, Sanjeev (January 2017, Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation)

   Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a novel integration of linguistic constructs from two distinct programming paradigms: streaming extensions of relational query languages and quantitative extensions of regular expressions. The former allows the programmer to employ relational constructs to partition the input data by keys and to integrate data streams from different sources, while the latter can be used to exploit the logical hierarchy in the input stream for modular specifications. We first present the core language with a small set of combinators, formal semantics, and a decidable type system. We then show how to express a number of common patterns with illustrative examples. Our compilation algorithm translates the high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. We also show how to integrate approximation algorithms into our framework. We report on an implementation in Java, and evaluate it with respect to existing high-performance engines for processing streaming data. Our experimental evaluation shows that (1) StreamQRE allows more natural and succinct specification of queries compared to existing frameworks, (2) the throughput of our implementation is higher than comparable systems (for example, two-to-four times greater than RxJava), and (3) the approximation algorithms supported by our implementation can lead to substantial memory savings. 

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