Companion of the 2023 ACM/SPEC International Conference on Performance Engineering最新文献

{"title":"Verifying Transient Behavior Specifications in Chaos Engineering Using Metric Temporal Logic and Property Specification Patterns","authors":"Sebastian Frank, Alireza Hakamian, Denis Zahariev, André van Hoorn","doi":"10.1145/3578245.3584314","DOIUrl":"https://doi.org/10.1145/3578245.3584314","url":null,"abstract":"Chaos Engineering is an approach for assessing the resilience of software systems, i.e., their ability to withstand unexpected events, adapt accordingly, and return to a steady state. The traditional Chaos Engineering approach only verifies whether the system is in a steady state and considers no statements about state changes over time and timing. Thus, Chaos Engineering conceptually does not consider transient behavior hypotheses, i.e., specifications regarding the system behavior during the transition between steady states after a failure has been injected. We aim to extend the Chaos Engineering approach and tooling to support the specification of transient behavior hypotheses and their verification. We interviewed three Chaos Engineering practitioners to elicit requirements for extending the Chaos Engineering process. Our concept uses Metric Temporal Logic and Property Specification Patterns to specify transient behavior hypotheses. We then developed a prototype that can be used stand-alone or to complement the established Chaos Engineering framework Chaos Toolkit. We successfully conducted a correctness evaluation comprising 160 test cases from the Timescales benchmark and demonstrate the prototype's applicability in Chaos Experiment settings by executing three chaos experiments.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116190895","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":"WEPPE'23 Workshop Chairs' Welcome","authors":"Alberto Avritzer, Matteo Camilli","doi":"10.1145/3578245.3584349","DOIUrl":"https://doi.org/10.1145/3578245.3584349","url":null,"abstract":"It is our great pleasure to welcome you to the 4th International Workshop on Education and Practice of Performance Engineering - WEPPE 2023. The goal of the Workshop on Education and Practice of Performance Engineering is to bring together University researchers and Industry Performance Engineers to share education and practice experiences. This year's symposium continues its tradition of being a forum for performance engineering educators. We are interested in creating opportunities to share valuable experience between researchers that are actively teaching performance engineering and Performance Engineers that are applying Performance Engineering techniques in industry.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127465906","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":"FAIR Enabling Re-Use of Data-Intensive Workflows and Scientific Reproducibility","authors":"Line C. Pouchard","doi":"10.1145/3578245.3586012","DOIUrl":"https://doi.org/10.1145/3578245.3586012","url":null,"abstract":"Scientific computing communities often run their experiments using complex data- and compute-intensive workflows that utilize high performance computing (HPC), distributed clusters and specialized architectures targeting machine learning and artificial intelligence. FAIR principles for data and software can be useful enablers for the reproducibility of performance (a key HPC metric) and that of scientific results (a crucial tenet of the scientific method) that are based in part on re-use, the R of FAIR principles. FAIR principles are under-used by HPC and data-intensive communities who have been slow to adopt them. This is due in part to the complexity of workflow life cycles, the numerous workflow management systems, the lack of integration of FAIR within existing technologies, and the specificity of managed systems that include rapidly evolving architectures and software stacks, and execution models that require resource managers and batch schedulers. Numerous challenges emerge for scientists attempting to publish FAIR datasets and software for the purpose of re-use and reproducibility, e.g. what data to publish and where due to sizes, how to \"FAIRify\" data subsetting, at what level of granularity to attribute persistent identifiers to software, what is the minimal amount of metadata needed to guarantee a certain level of reproducibility, what does reproducible AI actually mean? This talk will focus on such challenges and illustrate the negative impact of not applying FAIR on the reproducibility of experiments. We will introduce the notion of FAIR Digital Objects and present RECUP, a framework for data and metadata services for high performance workflows that proposes micro-solutions for adapting FAIR principles to HPC.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126454732","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":"Extreme and Sustainable Graph Processing for Green Finance Investment and Trading","authors":"Laurentiu A. Vasiliu, D. Roman, R.-C. Prodan","doi":"10.1145/3578245.3585337","DOIUrl":"https://doi.org/10.1145/3578245.3585337","url":null,"abstract":"In this paper we present a case addressing the drawbacks of financial market data, its limited volumes, history, and sometimes the incomplete and erroneous datasets with variable quality, limited availability, and price barriers. The case aims to enable fast, semi-automated creation of realistic and affordable synthetic (extreme) financial datasets, unlimited in size and accessibility, ready to be commercialized. Peracton Ltd. intends to apply the resulting extreme financial data multiverse for testing and improving artificial intelligence (AI)-enhanced financial algorithms (e.g., using machine learning) focused on green investment and trading. Using synthetic data for testing financial algorithms removes critical biases, such as prior knowledge, overfitting, and indirect contamination due to real-world data scarcity, and ensures data completeness at an affordable cost. The availability of extreme (volumes) of synthetic data will consolidate further financial algorithms and provide a statistically relevant sample size for advanced back-testing.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130394983","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":"Let's Take Back Control: A Journey through a Novel Generation of Control Techniques for Performance Engineering","authors":"Daniele Masti","doi":"10.1145/3578245.3585426","DOIUrl":"https://doi.org/10.1145/3578245.3585426","url":null,"abstract":"Optimizing the performance of complex systems has always been a central issue for the control theory community. However, ideas and tools from this field often require very precise assumptions and extensive tuning to perform well, making them unsuited for a non-specialist practitioner. In recent times, however, the influx of the machine learning community has brought a wave of renewal in the field, making many of these powerful methods finally applicable outside academic examples. In this talk, I will discuss my journey at the border between control theory and machine learning, from classical system identification and model-based control to modern autotuning data-driven techniques. I will also shed light on how this novel generation of much more user-friendly techniques can easily be applied to improve the performance of a large class of systems, including software ones.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"10 45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132245321","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":"Non-Volatile Hardware Transactional Memory: Challenges, Advancements, and Future Directions","authors":"P. Romano","doi":"10.1145/3578245.3584848","DOIUrl":"https://doi.org/10.1145/3578245.3584848","url":null,"abstract":"Transactional memory (TM) has emerged as a powerful paradigm to simplify concurrent programming. Nowadays, hardware-based TM (HTM) implementations are available in several mainstream CPUs (e.g., by ARM, Intel and IBM). Due to their hardware nature, HTM implementations spare the cost of software instrumentation and can efficiently detect conflicts by extending existing cache- coherency protocols. However, their cache-centric approach also imposes a number of limitations that impact how effectively such systems can be used in practice. This talk investigates the challenges that arise when leveraging existing HTM systems in conjunction with another recent disrup- tive hardware technology, namely Non-Volatile Memory (NVM). NVM, such as Intel Optane DC, provide much higher density than existing DRAM, while attaining competitive performance and pre- serving DRAM's byte addressability. However, the cache-centric approach adopted by existing HTM implementations raises a crucial problem when these are used in conjunction with NVM: since CPU caches are volatile, existing HTM fail to guarantee that data updated by committed transactions are atomically persisted to NVM. I will overview how this problem has been so far tackled in the literature, with a focus on solutions that do not assume ad-hoc hard- ware mechanisms not provided by current HTM implementations, but that rather rely on hardware-software co-design techniques to ensure consistency on unmodified existing HTM systems. I will conclude by presenting ongoing research directions that depart from state of the art approaches in a twofold way: i) they assume the availability of durable caches, i.e., systems equipped with addi- tional power sources that ensure that cache contents can be safely persisted to NVM upon crashes; ii) they assume a weaker isolation levels at the TM level, namely Snapshot Isolation, which despite being more relaxed than the reference consistency model for TM systems (e.g., opacity), can still ensure correct execution of a wide range of applications while enabling new optimizations to boost the efficiency HTM applications operating on NVM.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134254891","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":"8th Workshop on Challenges in Performance Methods for Software Development: WOSP-C'23 Chairs' Welcome","authors":"Daniele Di Pompeo, Michele Tucci","doi":"10.1145/3578245.3584847","DOIUrl":"https://doi.org/10.1145/3578245.3584847","url":null,"abstract":"It is our great pleasure to welcome you to the 8th Workshop on Challenges in Performance Methods for Software Development - WOSP-C 2023. This year's workshop continues its tradition of being the forum for the discussion of emerging or unaddressed challenges in software and performance, including challenges in developing software to be performant, concurrent programming issues, performance and architecture, performance measurement, cloud performance, and testing. Its purpose is to open new avenues for research on methods to address continuously emerging performance challenges. The software world is changing, and new challenges are to be expected. We also encourage attendees to attend the keynote and talk presentations. These valuable and insightful talks can and will guide us to a better understanding of the future: Non-Volatile Hardware Transactional Memory: Advancements, Challenges, and Future Directions, Paolo Romano (who is currently at IST, Lisbon University & INESC-ID) Putting together WOSP-C'23 was a team effort. We first thank the authors for providing the content of the program. We are grateful to the program committee, who worked very hard in reviewing papers and providing feedback for authors. Finally, we thank the hosting organization or university, our sponsor, ACM SIGs. We hope that you will find this program interesting and thought-provoking and that the workshop will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126085439","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":"Incremental Change Detection Method For Data Center Power Efficiency Metrics (Work In Progress Paper)","authors":"J. Backhus, Yasutaka Kono","doi":"10.1145/3578245.3585027","DOIUrl":"https://doi.org/10.1145/3578245.3585027","url":null,"abstract":"We propose an incremental change detection method for data center (DC) energy efficiency metrics and consider its application to the power usage efficiency (PUE) metric. In recent years, there is an increasing focus on the sustainability of DCs and PUE is playing an important role to evaluate the DC's energy efficiency. Publicly reported PUE values are mostly calculated over a whole year as there are many fluctuations caused by outside influences as outdoor air temperature (OAT). In this paper, we propose a method to detect short-term changes in the DC energy efficiency (e.g., PUE) , while considering outside influences (e.g., OAT) observing related daily aggregated DC data. We also conduct a few preliminary experiments for PUE change detection based on real-world DC data, where we have manually labeled changes in the PUE using visualization tools. The experimental results show that the method can detect important major and minor changes in the PUE with a very low false positive rate. However, due to the small number of positive labels, the recall rate is currently between 57% and 70%. Further investigation is necessary to see how representative the current recall rates are and what kind of improvements are necessary to make the change detection method more stable.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128828928","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":"Using Cloud Native Technologies to Understand the Performance of Cloud Native Technologies","authors":"C. Klein","doi":"10.1145/3578245.3584921","DOIUrl":"https://doi.org/10.1145/3578245.3584921","url":null,"abstract":"The ambition of this talk is to seed discussions around how cloud native technologies can help research on performance engineering, but also what are the interesting performance engineering challenges to solve with cloud native technologies. Cloud native technologies are building blocks for creating a modern environment for hosting containerized applications. Amongst others, great focus is placed on observability, which allows engineers to collect and analyze massive amounts of performance data in near real-time. Take as an example service meshes, which are a layer 7 network platform for containerized applications. Service meshes not only allow traffic engineering, but also add observability on top of a microservice application. Amongst other, this allows understanding traffic patterns between microservices, including upstream-downstream relationships, request rate, etc. without writing a single line of code. This talk discusses how cloud native technologies may help researchers in performance engineering. The benefits are three-fold. They allow researchers - e.g., PhD students - to be more productive, by getting the mechanism of collecting performance data out of the way. They improve collaboration because the effects of changing a parameter can be visualized in near real time. Finally, experiments are based on proven technologies with skills more widely available, which helps reproducibility. These benefits are illustrated through our research on adaptive service meshes. Indeed, service meshes have many parameters which impact performance. Discussions with practitioners revealed a gap in understanding on how to effectively choose these parameters. We therefore proposed an adaptive controller that configures a service mesh so as to maintain a target tail response time.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130030232","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":"Graph Greenifier: Towards Sustainable and Energy-Aware Massive Graph Processing in the Computing Continuum","authors":"A. Iosup, R.-C. Prodan, A. Varbanescu, Sacheendra Talluri, Gilles Magalhaes, Kailhan Hokstam, Hugo Zwaan, Vincent van Beek, Reza Farahani, Dragi Kimovski","doi":"10.1145/3578245.3585329","DOIUrl":"https://doi.org/10.1145/3578245.3585329","url":null,"abstract":"Our society is increasingly digital, and its processes are increasingly digitalized. As an emerging technology for the digital society, graphs provide a universal abstraction to represent concepts and objects, and the relationships between them. However, processing graphs at a massive scale raises numerous sustainability challenges; becoming energy-aware could help graph-processing infrastructure alleviate its climate impact. Graph Greenifier aims to address this challenge in the conceptual framework offered by the Graph Massivizer architecture. We present an early vision of how Graph Greenifier could provide sustainability analysis and decision-making capabilities for extreme graph-processing workloads. Graph Greenifier leverages an advanced digital twin for data center operations, based on the OpenDC open-source simulator, a novel toolchain for workload-driven simulation of graph processing at scale, and a sustainability predictor. The input to the digital twin combines monitoring of the information and communication technology infrastructure used for graph processing with data collected from the power grid. Graph Greenifier thus informs providers and consumers on operational sustainability aspects, requiring mutual information sharing, reducing energy consumption for graph analytics, and increasing the use of electricity from renewable sources.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129608642","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}