Simon Spinner, G. Casale, Xiaoyun Zhu, Samuel Kounev
When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.
{"title":"LibReDE: a library for resource demand estimation","authors":"Simon Spinner, G. Casale, Xiaoyun Zhu, Samuel Kounev","doi":"10.1145/2568088.2576093","DOIUrl":"https://doi.org/10.1145/2568088.2576093","url":null,"abstract":"When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130324742","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}
This paper focuses on devising efficient resource management techniques used by the resource management middleware in clouds that handle MapReduce jobs with end-to-end service level agreements (SLAs) comprising an earliest start time, execution time, and a deadline. This research and development work, performed in collaboration with our industrial partner, presents the formulation of the matchmaking and scheduling problem for MapReduce jobs as an optimization problem using: Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques. In addition to the formulations devised, our experience in implementing the MILP and CP models using various open source as well as commercial software packages is described. Furthermore, a performance evaluation of the different approaches used to implement the formulations is conducted using a variety of different workloads.
{"title":"Engineering resource management middleware for optimizing the performance of clouds processing mapreduce jobs with deadlines","authors":"Norman Lim, S. Majumdar, P. Ashwood-Smith","doi":"10.1145/2568088.2576796","DOIUrl":"https://doi.org/10.1145/2568088.2576796","url":null,"abstract":"This paper focuses on devising efficient resource management techniques used by the resource management middleware in clouds that handle MapReduce jobs with end-to-end service level agreements (SLAs) comprising an earliest start time, execution time, and a deadline. This research and development work, performed in collaboration with our industrial partner, presents the formulation of the matchmaking and scheduling problem for MapReduce jobs as an optimization problem using: Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques. In addition to the formulations devised, our experience in implementing the MILP and CP models using various open source as well as commercial software packages is described. Furthermore, a performance evaluation of the different approaches used to implement the formulations is conducted using a variety of different workloads.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129003014","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}
Databases are the origin of many performance problems found in transactional information systems. Performance suffers especially when databases employ locking to isolate concurrent transactions. Software performance models therefore need to reflect lock contention in order to be a credible source for guiding design decisions. We propose a hybrid simulation approach that integrates a novel locking model into the Palladio software architecture performance simulator. Our model operates on a row level and is tailored to be used with architecture-level performance models. An experimental evaluation leads to promising results close to the measured performance.
{"title":"Modelling database lock-contention in architecture-level performance simulation","authors":"Philipp Merkle, Christian Stier","doi":"10.1145/2568088.2576762","DOIUrl":"https://doi.org/10.1145/2568088.2576762","url":null,"abstract":"Databases are the origin of many performance problems found in transactional information systems. Performance suffers especially when databases employ locking to isolate concurrent transactions. Software performance models therefore need to reflect lock contention in order to be a credible source for guiding design decisions. We propose a hybrid simulation approach that integrates a novel locking model into the Palladio software architecture performance simulator. Our model operates on a row level and is tailored to be used with architecture-level performance models. An experimental evaluation leads to promising results close to the measured performance.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130988706","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}
Catia Trubiani, A. Marco, V. Cortellessa, Nariman Mani, D. Petriu
The problem of interpreting the results of performance analysis is quite critical, mostly because the analysis results (i.e. mean values, variances, and probability distributions) are hard to transform into feedback for software engineers that allows to remove performance problems. Approaches aimed at identifying and removing the causes of poor performance in software systems commonly fall in two categories: (i) bottleneck analysis, aimed at identifying overloaded software components and/or hardware resources that affect the whole system performance, and (ii) performance antipatterns, aimed at detecting and removing common design mistakes that notably induce performance degradation. In this paper, we look for possible synergies between these two categories of approaches in order to empower the performance investigation capabilities. In particular, we aim at showing that the approach combination allows to provide software engineers with broader sets of alternative solutions leading to better performance results. We have explored this research direction in the context of Layered Queueing Network models, and we have considered a case study in the e-commerce domain. After comparing the results achievable with each approach separately, we quantitatively show the benefits of merging bottleneck analysis and performance antipatterns.
{"title":"Exploring synergies between bottleneck analysis and performance antipatterns","authors":"Catia Trubiani, A. Marco, V. Cortellessa, Nariman Mani, D. Petriu","doi":"10.1145/2568088.2568092","DOIUrl":"https://doi.org/10.1145/2568088.2568092","url":null,"abstract":"The problem of interpreting the results of performance analysis is quite critical, mostly because the analysis results (i.e. mean values, variances, and probability distributions) are hard to transform into feedback for software engineers that allows to remove performance problems. Approaches aimed at identifying and removing the causes of poor performance in software systems commonly fall in two categories: (i) bottleneck analysis, aimed at identifying overloaded software components and/or hardware resources that affect the whole system performance, and (ii) performance antipatterns, aimed at detecting and removing common design mistakes that notably induce performance degradation. In this paper, we look for possible synergies between these two categories of approaches in order to empower the performance investigation capabilities. In particular, we aim at showing that the approach combination allows to provide software engineers with broader sets of alternative solutions leading to better performance results. We have explored this research direction in the context of Layered Queueing Network models, and we have considered a case study in the e-commerce domain. After comparing the results achievable with each approach separately, we quantitatively show the benefits of merging bottleneck analysis and performance antipatterns.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132719150","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}
Feifei Chen, J. Grundy, Jean-Guy Schneider, Yun Yang, Qiang He
In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.
{"title":"Automated analysis of performance and energy consumption for cloud applications","authors":"Feifei Chen, J. Grundy, Jean-Guy Schneider, Yun Yang, Qiang He","doi":"10.1145/2568088.2568093","DOIUrl":"https://doi.org/10.1145/2568088.2568093","url":null,"abstract":"In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235217","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}
Xi Chen, C. Ho, Rasha Osman, P. Harrison, W. Knottenbelt
As the computing industry enters the Cloud era, multicore architectures and virtualisation technologies are replacing traditional IT infrastructures. However, the complex relationship between applications and system resources in multicore virtualised environments is not well understood. Workloads such as web services and on-line financial applications have the requirement of high performance but benchmark analysis suggests that these applications do not optimally benefit from a higher number of cores. In this paper, we try to understand the scalability behaviour of network/CPU intensive applications running on multicore architectures. We begin by benchmarking the Petstore web application, noting the systematic imbalance that arises with respect to per-core workload. Having identified the reason for this phenomenon, we propose a queueing model which, when appropriately parametrised, reflects the trend in our benchmark results for up to 8 cores. Key to our approach is providing a fine-grained model which incorporates the idiosyncrasies of the operating system and the multiple CPU cores. Analysis of the model suggests a straightforward way to mitigate the observed bottleneck, which can be practically realised by the deployment of multiple virtual NICs within our VM. Next we make blind predictions to forecast performance with multiple virtual NICs. The validation results show that the model is able to predict the expected performance with relative errors ranging between 8 and 26 per cent.
{"title":"Understanding, modelling, and improving the performance of web applications in multicore virtualised environments","authors":"Xi Chen, C. Ho, Rasha Osman, P. Harrison, W. Knottenbelt","doi":"10.1145/2568088.2568102","DOIUrl":"https://doi.org/10.1145/2568088.2568102","url":null,"abstract":"As the computing industry enters the Cloud era, multicore architectures and virtualisation technologies are replacing traditional IT infrastructures. However, the complex relationship between applications and system resources in multicore virtualised environments is not well understood. Workloads such as web services and on-line financial applications have the requirement of high performance but benchmark analysis suggests that these applications do not optimally benefit from a higher number of cores. In this paper, we try to understand the scalability behaviour of network/CPU intensive applications running on multicore architectures. We begin by benchmarking the Petstore web application, noting the systematic imbalance that arises with respect to per-core workload. Having identified the reason for this phenomenon, we propose a queueing model which, when appropriately parametrised, reflects the trend in our benchmark results for up to 8 cores. Key to our approach is providing a fine-grained model which incorporates the idiosyncrasies of the operating system and the multiple CPU cores. Analysis of the model suggests a straightforward way to mitigate the observed bottleneck, which can be practically realised by the deployment of multiple virtual NICs within our VM. Next we make blind predictions to forecast performance with multiple virtual NICs. The validation results show that the model is able to predict the expected performance with relative errors ranging between 8 and 26 per cent.","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127643284","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":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","authors":"","doi":"10.1145/2568088","DOIUrl":"https://doi.org/10.1145/2568088","url":null,"abstract":"","PeriodicalId":243233,"journal":{"name":"Proceedings of the 5th ACM/SPEC international conference on Performance engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123854314","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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