An increasing number of data-driven applications rely on the ability of processing data flows in a timely manner, exploiting for this purpose Data Stream Processing~(DSP) systems. Elasticity is an essential feature for DSP systems, as workload variability calls for automatic scaling of the application processing capacity, to avoid both overload and resource wastage. In this work, we implement auto-scaling in Pulsar Functions, a function-based streaming framework built on top of Apache Pulsar. The latter is is a distributed publish-subscribe messaging platform that natively supports serverless functions. Considering various state-of-the-art policies, we show that the proposed solution is able to scale application parallelism with minimal overhead.
{"title":"Elastic Pulsar Functions for Distributed Stream Processing","authors":"G. Russo, Antonio Schiazza, V. Cardellini","doi":"10.1145/3447545.3451901","DOIUrl":"https://doi.org/10.1145/3447545.3451901","url":null,"abstract":"An increasing number of data-driven applications rely on the ability of processing data flows in a timely manner, exploiting for this purpose Data Stream Processing~(DSP) systems. Elasticity is an essential feature for DSP systems, as workload variability calls for automatic scaling of the application processing capacity, to avoid both overload and resource wastage. In this work, we implement auto-scaling in Pulsar Functions, a function-based streaming framework built on top of Apache Pulsar. The latter is is a distributed publish-subscribe messaging platform that natively supports serverless functions. Considering various state-of-the-art policies, we show that the proposed solution is able to scale application parallelism with minimal overhead.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83892982","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}
C. Elm, T. Ilsche, R. Schöne, Mario Bielert, Markus Schmidl
This paper discusses an architectural anomaly observed on server processors of the AMD Zen microarchitecture: At a specific operating point, increasing the number of active cores reduces system power consumption while increasing performance more than proportionally to the additional cores. The occurrence of the anomaly is rooted in the hardware control loop for energy management and software-independent. Experiments show a connection to the AMD turbo frequency feature Max Core Boost Frequency (MCBF). In less efficient configurations, this feature could be employed from a processor's perspective, even though it is not necessarily used on any core. Voltage measurements indicate that the availability of MCBF leads to a higher voltage from mainboard voltage regulators, subsequently raising power consumption unnecessarily. We describe the impact of this anomaly on the performance and energy-efficiency of several micro-benchmarks. The reduced power consumption when additional cores are enabled can lead to higher core frequencies and increased per-core-performance. The presented findings can be used to avoid inefficient core configurations and reduce the overall energy-to-solution.
本文讨论了在AMD Zen微架构的服务器处理器上观察到的架构异常:在特定的操作点上，增加活动内核的数量可以降低系统功耗，同时提高性能，而不是与额外的内核成比例。异常发生的根源在于硬件控制回路的能源管理和软件无关。实验显示了与AMD turbo频率特征Max Core Boost frequency (MCBF)的连接。在效率较低的配置中，可以从处理器的角度使用此功能，即使它不一定在任何核心上使用。电压测量表明，MCBF的可用性导致主板电压调节器产生更高的电压，随后不必要地增加功耗。我们描述了这种异常对几个微型基准的性能和能效的影响。当启用额外的核心时，降低的功耗可以导致更高的核心频率和提高的每核性能。本文的研究结果可用于避免低效率的堆芯配置，并减少解决方案的总能量。
{"title":"Investigating the Cause and Effect of an AMD Zen Energy Management Anomaly","authors":"C. Elm, T. Ilsche, R. Schöne, Mario Bielert, Markus Schmidl","doi":"10.1145/3447545.3451193","DOIUrl":"https://doi.org/10.1145/3447545.3451193","url":null,"abstract":"This paper discusses an architectural anomaly observed on server processors of the AMD Zen microarchitecture: At a specific operating point, increasing the number of active cores reduces system power consumption while increasing performance more than proportionally to the additional cores. The occurrence of the anomaly is rooted in the hardware control loop for energy management and software-independent. Experiments show a connection to the AMD turbo frequency feature Max Core Boost Frequency (MCBF). In less efficient configurations, this feature could be employed from a processor's perspective, even though it is not necessarily used on any core. Voltage measurements indicate that the availability of MCBF leads to a higher voltage from mainboard voltage regulators, subsequently raising power consumption unnecessarily. We describe the impact of this anomaly on the performance and energy-efficiency of several micro-benchmarks. The reduced power consumption when additional cores are enabled can lead to higher core frequencies and increased per-core-performance. The presented findings can be used to avoid inefficient core configurations and reduce the overall energy-to-solution.","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":"88251687","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}
R. Cordingly, Navid Heydari, Hanfei Yu, Varik Hoang, Zohreh Sadeghi, W. Lloyd
To improve the observability of workload performance, resource utilization, and infrastructure underlying serverless Function-as-a-Service (FaaS) platforms, we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can leverage to inspect performance, resource utilization, scalability, and infrastructure metrics of function deployments to commercial and open-source FaaS platforms. To automate reproducible FaaS performance experiments, we provide the FaaS Runner as a multithreaded FaaS client. FaaS Runner provides a programmable client that can orchestrate over one thousand concurrent FaaS function calls. The ReportGenerator is then used to aggregate experiment output into CSV files for consumption by popular data analytics tools. SAAF and its supporting tools combined can assess forty-eight distinct metrics to enhance observability of serverless software deployments. In this tutorial paper, we describe SAAF and its supporting tools and provide examples of observability insights that can be derived.
{"title":"Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework","authors":"R. Cordingly, Navid Heydari, Hanfei Yu, Varik Hoang, Zohreh Sadeghi, W. Lloyd","doi":"10.1145/3447545.3451173","DOIUrl":"https://doi.org/10.1145/3447545.3451173","url":null,"abstract":"To improve the observability of workload performance, resource utilization, and infrastructure underlying serverless Function-as-a-Service (FaaS) platforms, we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can leverage to inspect performance, resource utilization, scalability, and infrastructure metrics of function deployments to commercial and open-source FaaS platforms. To automate reproducible FaaS performance experiments, we provide the FaaS Runner as a multithreaded FaaS client. FaaS Runner provides a programmable client that can orchestrate over one thousand concurrent FaaS function calls. The ReportGenerator is then used to aggregate experiment output into CSV files for consumption by popular data analytics tools. SAAF and its supporting tools combined can assess forty-eight distinct metrics to enhance observability of serverless software deployments. In this tutorial paper, we describe SAAF and its supporting tools and provide examples of observability insights that can be derived.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73055646","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}
Real time stream processing demands processed outcomes in minimal latency. Massive streams are generated in real time where linear relationship is determined using correlation. Existing approaches are used for correlating static data sets such as, Kandell, Pearson, Spearman etc. These approaches are insufficient to solve noise free online correlation. In this paper, we propose an online ordinal correlation approach having functionalities such as single pass, avoiding recalculation from scratch, removing outliers, and low memory requirements. In this approach, Compare Reduce Aggregate (CRA) algorithm is used for determining association between two feature vectors in real time using single scanning technique. Time and space complexities in CRA algorithm are measured as O(n) and O(1), respectively. This algorithm is used for reducing noise or error in a stream and used as a replacement of rank based correlation. It is recommended to have distinct elements and less variability in the streams for gaining maximum performance of this algorithm.
{"title":"An Online Approach to Determine Correlation between Data Streams","authors":"Devesh Kumar Lal, U. Suman","doi":"10.1145/3447545.3451900","DOIUrl":"https://doi.org/10.1145/3447545.3451900","url":null,"abstract":"Real time stream processing demands processed outcomes in minimal latency. Massive streams are generated in real time where linear relationship is determined using correlation. Existing approaches are used for correlating static data sets such as, Kandell, Pearson, Spearman etc. These approaches are insufficient to solve noise free online correlation. In this paper, we propose an online ordinal correlation approach having functionalities such as single pass, avoiding recalculation from scratch, removing outliers, and low memory requirements. In this approach, Compare Reduce Aggregate (CRA) algorithm is used for determining association between two feature vectors in real time using single scanning technique. Time and space complexities in CRA algorithm are measured as O(n) and O(1), respectively. This algorithm is used for reducing noise or error in a stream and used as a replacement of rank based correlation. It is recommended to have distinct elements and less variability in the streams for gaining maximum performance of this algorithm.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73393418","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 presentation considers elements of Software Performance Engineering (SPE) and how they have evolved. It addresses both skills needed by practitioners and areas of research. Which topics should be covered? How can the education cover realistic systems and problems? What is the history of SPE and is it relevant today? Are these topics unique to SPE education? How should SPE education be integrated with other specialties in Computer Science and Engineering?
{"title":"Software Performance Engineering Education: What Topics Should be Covered?","authors":"C. U. Smith","doi":"10.1145/3447545.3451200","DOIUrl":"https://doi.org/10.1145/3447545.3451200","url":null,"abstract":"This presentation considers elements of Software Performance Engineering (SPE) and how they have evolved. It addresses both skills needed by practitioners and areas of research. Which topics should be covered? How can the education cover realistic systems and problems? What is the history of SPE and is it relevant today? Are these topics unique to SPE education? How should SPE education be integrated with other specialties in Computer Science and Engineering?","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82569037","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}
The 2030 Agenda for Sustainable Development of the United Nations General Assembly defines 17 development goals to be met for a sustainable future. Goals such as Industry, Innovation and Infrastructure and Sustainable Cities and Communities depend on digital systems. As a matter of fact, billions of Euros are invested into digital transformation within the European Union, and many researchers are actively working to push state-of-the-art boundaries for techniques/tools able to extract value and insights from the large amounts of raw data sensed in digital systems. Edge computing aims at supporting such data-to-value transformation. In digital systems that traditionally rely on central data gathering, edge computing proposes to push the analysis towards the devices and data sources, thus leveraging the large cumulative computational power found in modern distributed systems. Some of the ideas promoted in edge computing are not new, though. Continuous and distributed data analysis paradigms such as stream processing have argued about the need for smart distributed analysis for basically 20 years. Starting from this observation, this talk covers a set of standing challenges for smart, distributed, and continuous stream processing in edge computing, with real-world examples and use-cases from smart grids and vehicular networks.
{"title":"Motivations and Challenges for Stream Processing in Edge Computing","authors":"Vincenzo Gulisano","doi":"10.1145/3447545.3451899","DOIUrl":"https://doi.org/10.1145/3447545.3451899","url":null,"abstract":"The 2030 Agenda for Sustainable Development of the United Nations General Assembly defines 17 development goals to be met for a sustainable future. Goals such as Industry, Innovation and Infrastructure and Sustainable Cities and Communities depend on digital systems. As a matter of fact, billions of Euros are invested into digital transformation within the European Union, and many researchers are actively working to push state-of-the-art boundaries for techniques/tools able to extract value and insights from the large amounts of raw data sensed in digital systems. Edge computing aims at supporting such data-to-value transformation. In digital systems that traditionally rely on central data gathering, edge computing proposes to push the analysis towards the devices and data sources, thus leveraging the large cumulative computational power found in modern distributed systems. Some of the ideas promoted in edge computing are not new, though. Continuous and distributed data analysis paradigms such as stream processing have argued about the need for smart distributed analysis for basically 20 years. Starting from this observation, this talk covers a set of standing challenges for smart, distributed, and continuous stream processing in edge computing, with real-world examples and use-cases from smart grids and vehicular networks.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80264332","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}
Key-value stores are currently used by major cloud computing vendors, such as Google, Facebook, and LinkedIn, to support large-scale applications with concurrent read and write operations. Based on very simple data access APIs, the key-value stores can deliver outstanding throughput, which have been hooked up to high-performance solid-state drives (SSDs) to boost this performance even further. However, measuring performance interference on SSDs while sharing cloud computing resources is complex and not well covered by current benchmarks and tools. Different applications can access these resources concurrently until becoming overloaded without notice either by the benchmark or the cloud application. In this paper, we define a methodology to measure the problem of performance interference. Depending on the block size and the proportion of concurrent write operations, we show how a key-value store may quickly degrade throughput until becoming almost inoperative while sharing persistent storage resources with other tenants.
{"title":"Performance Interference on Key-Value Stores in Multi-tenant Environments: When Block Size and Write Requests Matter","authors":"Adriano Lange, T. R. Kepe, M. Sunyé","doi":"10.1145/3447545.3451191","DOIUrl":"https://doi.org/10.1145/3447545.3451191","url":null,"abstract":"Key-value stores are currently used by major cloud computing vendors, such as Google, Facebook, and LinkedIn, to support large-scale applications with concurrent read and write operations. Based on very simple data access APIs, the key-value stores can deliver outstanding throughput, which have been hooked up to high-performance solid-state drives (SSDs) to boost this performance even further. However, measuring performance interference on SSDs while sharing cloud computing resources is complex and not well covered by current benchmarks and tools. Different applications can access these resources concurrently until becoming overloaded without notice either by the benchmark or the cloud application. In this paper, we define a methodology to measure the problem of performance interference. Depending on the block size and the proportion of concurrent write operations, we show how a key-value store may quickly degrade throughput until becoming almost inoperative while sharing persistent storage resources with other tenants.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"192 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89234528","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}
2021 is the 50th anniversary of SIGMETRICS, the ACM Special Interest Group on Performance Evaluation. For this occasion, I wrote a review1 of the role played by analytical modeling - a major topic in SIGMETRICS in the engineering and science of computer systems. This talk is a summary of that review.
{"title":"The Role of Analytical Models in the Engineering and Science of Computer Systems","authors":"Y. Tay","doi":"10.1145/3447545.3451194","DOIUrl":"https://doi.org/10.1145/3447545.3451194","url":null,"abstract":"2021 is the 50th anniversary of SIGMETRICS, the ACM Special Interest Group on Performance Evaluation. For this occasion, I wrote a review1 of the role played by analytical modeling - a major topic in SIGMETRICS in the engineering and science of computer systems. This talk is a summary of that review.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72638023","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}
Stevens Institute of Technology offers a graduate course on functional software testing that addresses test planning driven by use cases, the use of software tools, and the derivation of test cases to achieve coverage with minimal effort. The course also contains material on performance testing. Teaching performance testing and measurement in a university setting was challenging because giving the students access to a target system would have required more time, resources, and planning than were available. %neither the students nor the university typically have access to a system that can be tested and measured. We addressed these challenges (a) by showing the students how resource usage could be measured in a controlled way with the instrumentation that comes with most modern laptops by default, and (b) by having the students use JMeter to measure the response times of existing websites . We describe how students were introduced to the concept of a controlled performance test by playing recordings of the same musical piece with and without video. We make recommendations for the future avoidance of the emergent ethical issue that one should not subject one does not own to anything but the most trivial loads. We also describe some successes and pitfalls in this effort.
{"title":"Experience with Teaching Performance Measurement and Testing in a Course on Functional Testing","authors":"A. Bondi, R. Saremi","doi":"10.1145/3447545.3451196","DOIUrl":"https://doi.org/10.1145/3447545.3451196","url":null,"abstract":"Stevens Institute of Technology offers a graduate course on functional software testing that addresses test planning driven by use cases, the use of software tools, and the derivation of test cases to achieve coverage with minimal effort. The course also contains material on performance testing. Teaching performance testing and measurement in a university setting was challenging because giving the students access to a target system would have required more time, resources, and planning than were available. %neither the students nor the university typically have access to a system that can be tested and measured. We addressed these challenges (a) by showing the students how resource usage could be measured in a controlled way with the instrumentation that comes with most modern laptops by default, and (b) by having the students use JMeter to measure the response times of existing websites . We describe how students were introduced to the concept of a controlled performance test by playing recordings of the same musical piece with and without video. We make recommendations for the future avoidance of the emergent ethical issue that one should not subject one does not own to anything but the most trivial loads. We also describe some successes and pitfalls in this effort.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89160386","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}
Luuk Klaver, T. Knaap, J. V. Geest, E. Harmsma, B. D. Waaij, P. Pileggi
Cloud computing services are integral to the digital transformation. They deliver greater connectivity, tremendous savings, and lower total cost of ownership. Despite such benefits and benchmarking advances, costs are still quite unpredictable, performance is unclear, security is inconsistent, and there is minimal control over aspects like data and service locality. Estimating performance of cloud environments is very hard for cloud consumers. They would like to make informed decisions about which provider better suits their needs using specialized evaluation mechanisms. Providers have their own tools reporting specific metrics, but they are potentially biased and often incomparable across providers. Current benchmarking tools allow comparison but consumers need more flexibility to evaluate environments under actual operating conditions for specialized applications. Ours is early stage work and a step towards a monitoring solution that enables independent evaluation of clouds for very specific application needs. In this paper, we present our initial architecture of the Cloud Monitor that aims to integrate existing and new benchmarks in a flexible and extensible way. By way of a simplistic demonstrator, we illustrate the concept. We report some preliminary monitoring results after a brief time of monitoring and are able to observe unexpected anomalies. The results suggest an independent monitoring solution is a powerful enabler of next generation cloud computing, not only for the consumer but potentially the whole ecosystem.
{"title":"Towards Independent Run-Time Cloud Monitoring","authors":"Luuk Klaver, T. Knaap, J. V. Geest, E. Harmsma, B. D. Waaij, P. Pileggi","doi":"10.1145/3447545.3451180","DOIUrl":"https://doi.org/10.1145/3447545.3451180","url":null,"abstract":"Cloud computing services are integral to the digital transformation. They deliver greater connectivity, tremendous savings, and lower total cost of ownership. Despite such benefits and benchmarking advances, costs are still quite unpredictable, performance is unclear, security is inconsistent, and there is minimal control over aspects like data and service locality. Estimating performance of cloud environments is very hard for cloud consumers. They would like to make informed decisions about which provider better suits their needs using specialized evaluation mechanisms. Providers have their own tools reporting specific metrics, but they are potentially biased and often incomparable across providers. Current benchmarking tools allow comparison but consumers need more flexibility to evaluate environments under actual operating conditions for specialized applications. Ours is early stage work and a step towards a monitoring solution that enables independent evaluation of clouds for very specific application needs. In this paper, we present our initial architecture of the Cloud Monitor that aims to integrate existing and new benchmarks in a flexible and extensible way. By way of a simplistic demonstrator, we illustrate the concept. We report some preliminary monitoring results after a brief time of monitoring and are able to observe unexpected anomalies. The results suggest an independent monitoring solution is a powerful enabler of next generation cloud computing, not only for the consumer but potentially the whole ecosystem.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90035655","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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