Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion最新文献

{"title":"Model-driven Engineering IDE for Quality Assessment of Data-intensive Applications","authors":"M. Gil, Christophe Joubert, Ismael Torres","doi":"10.1145/3053600.3053633","DOIUrl":"https://doi.org/10.1145/3053600.3053633","url":null,"abstract":"This article introduces a model-driven engineering (MDE) integrated development environment (IDE) for Data-Intensive Cloud Applications (DIA) with iterative quality enhancements. As part of the H2020 DICE project (ICT-9-2014, id 644869), a framework is being constructed and it is composed of a set of tools developed to support a new MDE methodology. One of these tools is the IDE which acts as the front-end of the methodology and plays a pivotal role in integrating the other tools of the framework. The IDE enables designers to produce from the architectural structure of the general application along with their properties and QoS/QoD annotations up to the deployment model. Administrators, quality assurance engineers or software architects may also run and examine the output of the design and analysis tools in addition to the designer in order to assess the DIA quality in an iterative process.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116951092","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":"Session details: Workshop on Education and Practice of Performance Engineering (WEPPE'17)","authors":"Alberto Avritzer, A. Iosup","doi":"10.1145/3254605","DOIUrl":"https://doi.org/10.1145/3254605","url":null,"abstract":"","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133901762","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":"DevOps Performance Engineering: A Quasi-Ethnographical Study","authors":"Giuseppe Vergori, D. Tamburri, Diego Perez-Palacin, R. Mirandola","doi":"10.1145/3053600.3053628","DOIUrl":"https://doi.org/10.1145/3053600.3053628","url":null,"abstract":"DevOps is a software engineering strategy to reduce soft- ware changes' rollout times by adopting any set of tactics that reduce friction in software lifecycles and their organisational variables, for example: coordination, communication, product evolution, deployment, operation, continuous architecting, continuous integration and more. Going DevOps is increasingly demanding that software engineering disciplines which were typically product-oriented such as software performance engineering to rethink their typical comfort zone, enlarging their scope from product, to process or even further to ecosystem and organisational levels of abstraction. This article makes an attempt at understanding what are the dimensions in DevOps organisational scenarios that can be addressed with a performance engineering lens. To do this, we performed a quasi-ethnographical study featuring a real-life industrial DevOps scenario. Discussing our results we conclude that many synergies exist between DevOps and performance engineering each with peculiarities, limitations and challenges - more research is needed to offer a full-spectrum performance-engineering support for DevOps practitioners.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131656138","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":"Towards Omnia: A Monitoring Factory for Quality-Aware DevOps","authors":"Marco Miglierina, D. Tamburri","doi":"10.1145/3053600.3053629","DOIUrl":"https://doi.org/10.1145/3053600.3053629","url":null,"abstract":"Modern DevOps pipelines entail extreme automation and speed as paramount assets for continuous application improvement. Likewise, monitoring is required to assess the quality of service and user-experience such that applications can continuously evolve towards use-centric excellence. In this scenario however, it is increasingly difficult to pull up and maintain efficient monitoring infrastructures which are frictionless, i.e., they do not introduce any slowdown neither in the DevOps pipeline nor in the DevOps organizational and social structure comprising multiple roles and responsibilities. Using an experimental prototype, this paper elaborates Omnia an approach for structured monitoring configuration and rollout based around a monitoring factory, i.e., a re-interpretation of the factory design-pattern for building and managing ad-hoc monitoring platforms. Comparing with practitioner surveys and the state of the art, we observed that Omnia shows the promise of delivering an effective solution that tackles the steep learning curve and entry costs needed to embrace cloud monitoring and monitoring-based DevOps continuous improvement.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122941025","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":"Session details: Third International Workshop on Energy-aware Simulation (ENERGY-SIM'17)","authors":"S. McGough, M. Forshaw","doi":"10.1145/3254600","DOIUrl":"https://doi.org/10.1145/3254600","url":null,"abstract":"","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908660","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":"Teaching Performance Modeling in the Era of 140characters Information","authors":"V. D. N. Persone","doi":"10.1145/3053600.3053641","DOIUrl":"https://doi.org/10.1145/3053600.3053641","url":null,"abstract":"It is not easy to state the birthdate of Performance Modeling (PM). On April 1971, a workshop on System Performance Evaluation was held at Harvard University. Richard Muntz was the chairman of the session \"Queueing Theoretic Models\". In that session, Jeffrey Buzen presented \"Analysis of system bottlenecks using a queueing network model\". In the 70s, some groups were founded to work on the computer performance modeling. The National Bureau of Standards organized several task groups and the Computer Performance Evaluation Users Group collected people \"from many United States Governmental agencies involved in various phases of this field -- a number of academicians as well as analysts from business and industry working in this area, and this gave rise to the formation within the ACM of SIGME [Special Interest Group in Measurement and Evaluation] which is currently known as SIGMETRICS.\" In 1973 the International Federation for Information Processing founded the Working Group 7.3 Computer System Modelling and its International Symposium on Computer Performance Modeling, Measurement, and Evaluation started to take place. More difficult is to go back to the first courses in general Performance modeling and prediction. Definitely, in the 80s the PM area reached its peak and relative courses were taught in some universities for some decades. In the first years of 2000, some of these general PM courses started to disappear while specific contents still remained in courses relative to applications as \"tools\" for that particular area. A question naturally arises: is it no more time to teach the modelling principles and basic methodologies? Is it time to just use the techniques in specific domains? The author has not sure answers, but some doubts. Starting from a close examination of the state of the art of PM courses in the main Universities, we try to give some food for thought about the role of the education, the meaning of knowledge and information, their difference and the importance of criticism to face with incoming changing challenges.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124640172","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":"Performance Engineering for Microservices: Research Challenges and Directions","authors":"R. Heinrich, A. Hoorn, H. Knoche, Fei Li, Lucy Ellen Lwakatare, C. Pahl, Stefan Schulte, Johannes Wettinger","doi":"10.1145/3053600.3053653","DOIUrl":"https://doi.org/10.1145/3053600.3053653","url":null,"abstract":"Microservices complement approaches like DevOps and continuous delivery in terms of software architecture. Along with this architectural style, several important deployment technologies, such as container-based virtualization and container orchestration solutions, have emerged. These technologies allow to efficiently exploit cloud platforms, providing a high degree of scalability, availability, and portability for microservices. Despite the obvious importance of a sufficient level of performance, there is still a lack of performance engineering approaches explicitly taking into account the particularities of microservices. In this paper, we argue why new solutions to performance engineering for microservices are needed. Furthermore, we identify open issues and outline possible research directions with regard to performance-aware testing, monitoring, and modeling of microservices.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126371692","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":"Towards Holistic Continuous Software Performance Assessment","authors":"Vincenzo Ferme, C. Pautasso","doi":"10.1145/3053600.3053636","DOIUrl":"https://doi.org/10.1145/3053600.3053636","url":null,"abstract":"In agile, fast and continuous development lifecycles, software performance analysis is fundamental to confidently release continuously improved software versions. Researchers and industry practitioners have identified the importance of integrating performance testing in agile development processes in a timely and efficient way. However, existing techniques are fragmented and not integrated taking into account the heterogeneous skills of the users developing polyglot distributed software, and their need to automate performance practices as they are integrated in the whole lifecycle without breaking its intrinsic velocity. In this paper we present our vision for holistic continuous software performance assessment, which is being implemented in the BenchFlow tool. BenchFlow enables performance testing and analysis practices to be pervasively integrated in continuous development lifecycle activities. Users can specify performance activities (e.g., standard performance tests) by relying on an expressive Domain Specific Language for objective-driven performance analysis. Collected performance knowledge can be thus reused to speed up performance activities throughout the entire process.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130559776","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":"An Expandable Extraction Framework for Architectural Performance Models","authors":"J. Walter, Christian Stier, H. Koziolek, Samuel Kounev","doi":"10.1145/3053600.3053634","DOIUrl":"https://doi.org/10.1145/3053600.3053634","url":null,"abstract":"Providing users with Quality of Service (QoS) guarantees and the prevention of performance problems are challenging tasks for software systems. Architectural performance models can be applied to explore performance properties of a software system at design time and run time. At design time, architectural performance models support reasoning on effects of design decisions. At run time, they enable automatic reconfigurations by reasoning on the effects of changing user behavior. In this paper, we present a framework for the extraction of architectural performance models based on monitoring log files generalizing over the targeted architectural modeling language. Using the presented framework, the creation of a performance model extraction tool for a specific modeling formalism requires only the implementation of a key set of object creation routines specific to the formalism. Our framework integrates them with extraction techniques that apply to many architectural performance models, e.g., resource demand estimation techniques. This lowers the effort to implement performance model extraction tools tremendously through a high level of reuse. We evaluate our framework presenting builders for the Descartes Modeling Language (DML) and the Palladio Component Model(PCM). For the extracted models we compare simulation results with measurements receiving accurate results.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127854025","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":"What Does I(o)T Cost?","authors":"Edua Eszter Kalmar, A. Kertész","doi":"10.1145/3053600.3053601","DOIUrl":"https://doi.org/10.1145/3053600.3053601","url":null,"abstract":"Though in recent years cloud-based solutions already started to dominate the Internet of Services, with the appearance of the Internet of Things (IoT) paradigm, more complex systems have been formed that still need a significant amount of research. The large amount of data produced by these devices requires cloud services to be efficiently processed and meaningfully visualized. In this paper, we perform cost-based investigations of two popular IoT application groups to help users to better understand IoT cloud ecosystems. We also compare existing IoT cloud providers by estimating on-demand service costs considering different circumstances, e.g. diverse messages sizes, the number of messages, the frequency of message sending and different virtual machine or application service configurations. We also implemented the defined scenarios with the IBM Bluemix IoT Platform and measure the exact data usage and prices.","PeriodicalId":115833,"journal":{"name":"Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114765875","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}