Adnan El Moussawi, Ricardo Rojas Ruiz, Nacéra Bennacer Seghouani
{"title":"Sampling-based Label Propagation for Balanced Graph Partitioning","authors":"Adnan El Moussawi, Ricardo Rojas Ruiz, Nacéra Bennacer Seghouani","doi":"10.1145/3489525.3511698","DOIUrl":"https://doi.org/10.1145/3489525.3511698","url":null,"abstract":"","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"75 1","pages":"223-230"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74651113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ICPE '22: ACM/SPEC International Conference on Performance Engineering, Bejing, China, April 9 - 13, 2022","authors":"","doi":"10.1145/3489525","DOIUrl":"https://doi.org/10.1145/3489525","url":null,"abstract":"","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89085481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Containerized environments introduce a set of performance challenges that require extensive measurements and benchmarking to identify and model application behavior regarding a variety of parameters. Databases present extra challenges given their extensive need for synchronization and orchestration of a benchmark run, especially in microservice-oriented technologies (such as container platforms) and dynamic business models such as DBaaS. In this work we describe the adaptation of our open source, baseline load injection as a service tool, Flexibench, in order to enable the automated, parametric launching and measurement of containerized and distributed databases as a service. Adaptation and synchronization needs are described for ensuring test sequence and applied through a case study on MySQL. Therefore a performance engineer can directly test selected configuration and performance of a database in a given target workload with simple REST invocations. Experimentation starts from adapting the official MySQL docker images as well as OLTP Bench Client ones and investigates scenarios such as parameter sweep experiments and co-allocation scenarios where multiple DB instances are sharing physical nodes, as expected in the DBaaS paradigm.
{"title":"Enabling Containerized, Parametric and Distributed Database Deployment and Benchmarking as a Service","authors":"George Kousiouris, D. Kyriazis","doi":"10.1145/3447545.3451188","DOIUrl":"https://doi.org/10.1145/3447545.3451188","url":null,"abstract":"Containerized environments introduce a set of performance challenges that require extensive measurements and benchmarking to identify and model application behavior regarding a variety of parameters. Databases present extra challenges given their extensive need for synchronization and orchestration of a benchmark run, especially in microservice-oriented technologies (such as container platforms) and dynamic business models such as DBaaS. In this work we describe the adaptation of our open source, baseline load injection as a service tool, Flexibench, in order to enable the automated, parametric launching and measurement of containerized and distributed databases as a service. Adaptation and synchronization needs are described for ensuring test sequence and applied through a case study on MySQL. Therefore a performance engineer can directly test selected configuration and performance of a database in a given target workload with simple REST invocations. Experimentation starts from adapting the official MySQL docker images as well as OLTP Bench Client ones and investigates scenarios such as parameter sweep experiments and co-allocation scenarios where multiple DB instances are sharing physical nodes, as expected in the DBaaS paradigm.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87156672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scalability is promoted as a key quality feature of modern big data stream processing engines. However, even though research made huge efforts to provide precise definitions and corresponding metrics for the term scalability, experimental scalability evaluations or benchmarks of stream processing engines apply different and inconsistent metrics. With this paper, we aim to establish general metrics for scalability of stream processing engines. Derived from common definitions of scalability in cloud computing, we propose two metrics: a load capacity function and a resource demand function. Both metrics relate provisioned resources and load intensities, while requiring specific service level objectives to be fulfilled. We show how these metrics can be employed for scalability benchmarking and discuss their advantages in comparison to other metrics, used for stream processing engines and other software systems.
{"title":"How to Measure Scalability of Distributed Stream Processing Engines?","authors":"S. Henning, W. Hasselbring","doi":"10.1145/3447545.3451190","DOIUrl":"https://doi.org/10.1145/3447545.3451190","url":null,"abstract":"Scalability is promoted as a key quality feature of modern big data stream processing engines. However, even though research made huge efforts to provide precise definitions and corresponding metrics for the term scalability, experimental scalability evaluations or benchmarks of stream processing engines apply different and inconsistent metrics. With this paper, we aim to establish general metrics for scalability of stream processing engines. Derived from common definitions of scalability in cloud computing, we propose two metrics: a load capacity function and a resource demand function. Both metrics relate provisioned resources and load intensities, while requiring specific service level objectives to be fulfilled. We show how these metrics can be employed for scalability benchmarking and discuss their advantages in comparison to other metrics, used for stream processing engines and other software systems.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82732307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Norbert Schmitt, Richard Vobl, Andreas Brunnert, Samuel Kounev
Data centers already account for over 250TWh of energy usage every year and their energy demand will grow above 1PWh until 2030 even in the best-case scenarios of some studies. As this demand cannot be met with renewable sources as of today, this growth will lead to a further increase of CO2 emissions. The data center growth is mainly driven by software resource usage but most of the energy efficiency improvements are nowadays done on hardware level that cannot compensate the demand. To reduce the resource demand of software in data centers one needs to be able to quantify its resource usage. Therefore, we propose a benchmark to assess the resource consumption of data center software (i.e., cloud applications) and make the resource usage of standard application types comparable between vendors. This benchmark aims to support three main goals (i) software vendors should be able to get an understanding of the resource consumption of their software; (ii) software buyers should be able to compare the software of different vendors; and (iii) spark competition between the software vendors to make their software more efficient and thus, in the long term, reduce the data center growth as the software systems require less resources.
{"title":"Towards a Benchmark for Software Resource Efficiency","authors":"Norbert Schmitt, Richard Vobl, Andreas Brunnert, Samuel Kounev","doi":"10.1145/3447545.3451176","DOIUrl":"https://doi.org/10.1145/3447545.3451176","url":null,"abstract":"Data centers already account for over 250TWh of energy usage every year and their energy demand will grow above 1PWh until 2030 even in the best-case scenarios of some studies. As this demand cannot be met with renewable sources as of today, this growth will lead to a further increase of CO2 emissions. The data center growth is mainly driven by software resource usage but most of the energy efficiency improvements are nowadays done on hardware level that cannot compensate the demand. To reduce the resource demand of software in data centers one needs to be able to quantify its resource usage. Therefore, we propose a benchmark to assess the resource consumption of data center software (i.e., cloud applications) and make the resource usage of standard application types comparable between vendors. This benchmark aims to support three main goals (i) software vendors should be able to get an understanding of the resource consumption of their software; (ii) software buyers should be able to compare the software of different vendors; and (iii) spark competition between the software vendors to make their software more efficient and thus, in the long term, reduce the data center growth as the software systems require less resources.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88838393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advances in deep neural networks have provided a significant improvement in accuracy and speed across a large range of Computer Vision (CV) applications. However, our ability to perform real-time CV on edge devices is severely restricted by their limited computing capabilities. In this paper we employ Vega, a parallel graph-based framework, to study the performance limitations of four heterogeneous edge-computing platforms, while running 12 popular deep learning CV applications. We expand the framework's capabilities, introducing two new performance enhancements: 1) an adaptive stage instance controller (ASI-C) that can improve performance by dynamically selecting the number of instances for a given stage of the pipeline; and 2) an adaptive input resolution controller (AIR-C) to improve responsiveness and enable real-time performance. These two solutions are integrated together to provide a robust real-time solution. Our experimental results show that ASI-C improves run-time performance by 1.4x on average across all heterogeneous platforms, achieving a maximum speedup of 4.3x while running face detection executed on a high-end edge device. We demonstrate that our integrated optimization framework improves performance of applications and is robust to changing execution patterns.
{"title":"Performance Evaluation and Improvement of Real-Time Computer Vision Applications for Edge Computing Devices","authors":"Julian Gutierrez, Nicolas Bohm Agostini, D. Kaeli","doi":"10.1145/3447545.3451202","DOIUrl":"https://doi.org/10.1145/3447545.3451202","url":null,"abstract":"Advances in deep neural networks have provided a significant improvement in accuracy and speed across a large range of Computer Vision (CV) applications. However, our ability to perform real-time CV on edge devices is severely restricted by their limited computing capabilities. In this paper we employ Vega, a parallel graph-based framework, to study the performance limitations of four heterogeneous edge-computing platforms, while running 12 popular deep learning CV applications. We expand the framework's capabilities, introducing two new performance enhancements: 1) an adaptive stage instance controller (ASI-C) that can improve performance by dynamically selecting the number of instances for a given stage of the pipeline; and 2) an adaptive input resolution controller (AIR-C) to improve responsiveness and enable real-time performance. These two solutions are integrated together to provide a robust real-time solution. Our experimental results show that ASI-C improves run-time performance by 1.4x on average across all heterogeneous platforms, achieving a maximum speedup of 4.3x while running face detection executed on a high-end edge device. We demonstrate that our integrated optimization framework improves performance of applications and is robust to changing execution patterns.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79593254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bowen Li, Nat Shineman, Jayson G. Boubin, Christopher Stewart
Unmanned aerial vehicles (UAVs) are gaining popularity in many governmental and civilian sectors. The combination of aerial mobility and data sensing capabilities facilitates previously impossible workloads. UAVs are normally piloted by remote operators who determine where to fly and when to sense data, but operations over large areas put a heavy burden on human pilots. Fully autonomous aerial systems (FAAS) have emerged as an alternative to human piloting by using software combined with edge and cloud hardware to execute autonomous UAV missions. The compute and networking infrastructure required for autonomy has significant power and performance demands. FAAS deployed in remote environments, such as crop fields, must manage limited power and networking capabilities. To facilitate widespread adoption of FAAS, middleware must support heterogeneous compute and networking resources at the edge while ensuring that the workloads quickly produce effective and efficient autonomous flight paths. Object detectors are a vital component of FAAS. FAAS flight mission goals and flight path generation are often focused on locating and photographing phenomena identified using object detectors. Given the importance of object detection to FAAS, it is paramount that object detectors produce accurate results as quickly and efficiently as possible to elongate FAAS missions and save precious energy. In this poster, we analyze the performance of different object detection techniques for facial recognition, a core FAAS workload. We analyzed the accuracy and performance of three facial recognition techniques provided in SoftwarePilot, an FAAS middleware, on two architectural configurations for FAAS edge systems. These findings can be used when selecting an object detector for any FAAS mission type and hardware configuration.
{"title":"Comparison of Object Detectors for Fully Autonomous Aerial Systems Performance","authors":"Bowen Li, Nat Shineman, Jayson G. Boubin, Christopher Stewart","doi":"10.1145/3447545.3451170","DOIUrl":"https://doi.org/10.1145/3447545.3451170","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are gaining popularity in many governmental and civilian sectors. The combination of aerial mobility and data sensing capabilities facilitates previously impossible workloads. UAVs are normally piloted by remote operators who determine where to fly and when to sense data, but operations over large areas put a heavy burden on human pilots. Fully autonomous aerial systems (FAAS) have emerged as an alternative to human piloting by using software combined with edge and cloud hardware to execute autonomous UAV missions. The compute and networking infrastructure required for autonomy has significant power and performance demands. FAAS deployed in remote environments, such as crop fields, must manage limited power and networking capabilities. To facilitate widespread adoption of FAAS, middleware must support heterogeneous compute and networking resources at the edge while ensuring that the workloads quickly produce effective and efficient autonomous flight paths. Object detectors are a vital component of FAAS. FAAS flight mission goals and flight path generation are often focused on locating and photographing phenomena identified using object detectors. Given the importance of object detection to FAAS, it is paramount that object detectors produce accurate results as quickly and efficiently as possible to elongate FAAS missions and save precious energy. In this poster, we analyze the performance of different object detection techniques for facial recognition, a core FAAS workload. We analyzed the accuracy and performance of three facial recognition techniques provided in SoftwarePilot, an FAAS middleware, on two architectural configurations for FAAS edge systems. These findings can be used when selecting an object detector for any FAAS mission type and hardware configuration.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75640394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Much of the data produced today is processed as it is generated by data stream processing systems. Although the cloud is often the target infrastructure for deploying data stream processing applications, resources located at the edges of the Internet have increasingly been used to offload some of the processing performed in the cloud and hence reduce the end-to-end latency when handling data events. In this work, I highlight some of the challenges in executing data stream processing applications on edge computing infrastructure and discuss directions for future research on making such applications more elastic and sustainable.
{"title":"Towards Elastic and Sustainable Data Stream Processing on Edge Infrastructure","authors":"Marcos Dias de Assunção","doi":"10.1145/3447545.3451902","DOIUrl":"https://doi.org/10.1145/3447545.3451902","url":null,"abstract":"Much of the data produced today is processed as it is generated by data stream processing systems. Although the cloud is often the target infrastructure for deploying data stream processing applications, resources located at the edges of the Internet have increasingly been used to offload some of the processing performed in the cloud and hence reduce the end-to-end latency when handling data events. In this work, I highlight some of the challenges in executing data stream processing applications on edge computing infrastructure and discuss directions for future research on making such applications more elastic and sustainable.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73323130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Event-driven architecture (EDAs) improves scalability by combining stateless servers and asynchronous interactions. Models to predict the performance of pure EDA systems are relatively easy to make, systems with a combination of event-driven components and legacy components with blocking service requests (synchronous interactions) require special treatment. Layered queueing was developed for such systems, and this work describes a method for combining event-driven behaviour and synchronous behaviour in a layered queueing model. The performance constraints created by the legacy components can be explored to guide decisions regarding converting them, or reconfiguring them, when the system is scaled.
{"title":"Performance Models of Event-Driven Architectures","authors":"C. Woodside","doi":"10.1145/3447545.3451203","DOIUrl":"https://doi.org/10.1145/3447545.3451203","url":null,"abstract":"Event-driven architecture (EDAs) improves scalability by combining stateless servers and asynchronous interactions. Models to predict the performance of pure EDA systems are relatively easy to make, systems with a combination of event-driven components and legacy components with blocking service requests (synchronous interactions) require special treatment. Layered queueing was developed for such systems, and this work describes a method for combining event-driven behaviour and synchronous behaviour in a layered queueing model. The performance constraints created by the legacy components can be explored to guide decisions regarding converting them, or reconfiguring them, when the system is scaled.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90053527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lorenzo Pagliari, Mirko D'Angelo, M. Caporuscio, R. Mirandola, Catia Trubiani
Modern information systems connecting software, physical systems and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of systems and lead to incomplete, inaccurate, and unreliable results. To deal with this issue, in this paper we report our incremental experience on the usage of different performance modelling notations while analyzing Intelligent Transportation Systems. More specifically, Queueing Networks and Petri Nets have been adopted and interesting insights are derived.
{"title":"Performance Modelling of Intelligent Transportation Systems: Experience Report","authors":"Lorenzo Pagliari, Mirko D'Angelo, M. Caporuscio, R. Mirandola, Catia Trubiani","doi":"10.1145/3447545.3451205","DOIUrl":"https://doi.org/10.1145/3447545.3451205","url":null,"abstract":"Modern information systems connecting software, physical systems and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of systems and lead to incomplete, inaccurate, and unreliable results. To deal with this issue, in this paper we report our incremental experience on the usage of different performance modelling notations while analyzing Intelligent Transportation Systems. More specifically, Queueing Networks and Petri Nets have been adopted and interesting insights are derived.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89550605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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