The document discusses using machine learning techniques like Gaussian processes (GPs) to optimize the configuration of software systems. It notes that software performance landscapes are often complex, with non-linear interactions between parameters and non-convex response surfaces. Measurements are also subject to noise. The document introduces an approach called TL4CO that uses multi-task Gaussian processes to model software performance across different versions/deployments, allowing it to leverage data from other versions to improve optimization. This helps address challenges in DevOps where new versions are continuously delivered.
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Pooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
Transfer Learning for Improving Model Predictions in Robotic SystemsPooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Pooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
Transfer Learning for Improving Model Predictions in Robotic SystemsPooyan Jamshidi
Modern software systems are now being built to be used in dynamic environments utilizing configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and, therefore, we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesMLAI2
Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fundamental problem of machine learning. Regularization techniques are versatile, as they are task- and architecture-agnostic, but they do not exploit a large amount of data available. Transfer learning methods learn to transfer knowledge from one domain to another, but may not generalize across tasks and architectures, and may introduce new training cost for adapting to the target task. To bridge the gap between the two, we propose a transferable perturbation, MetaPerturb, which is meta-learned to improve generalization performance on unseen data. MetaPerturb is implemented as a set-based lightweight network that is agnostic to the size and the order of the input, which is shared across the layers. Then, we propose a meta-learning framework, to jointly train the perturbation function over heterogeneous tasks in parallel. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures. We validate the efficacy and generality of MetaPerturb trained on a specific source domain and architecture, by applying it to the training of diverse neural architectures on heterogeneous target datasets against various regularizers and fine-tuning. The results show that the networks trained with MetaPerturb significantly outperform the baselines on most of the tasks and architectures, with a negligible increase in the parameter size and no hyperparameters to tune.
AI optimizing HPC simulations (presentation from 6th EULAG Workshop)byteLAKE
See our presentation from the 6th International EULAG Users Workshop. We talked about taking HPC to the "Industry 4.0" by implementing smart techniques to optimize the codes in terms of performance and energy consumption. It explains how Machine Learning can dynamically optimize HPC simulations and byteLAKE's software autotuning solution.
Find out more about byteLAKE at: www.byteLAKE.com
논문 제목부터 재미있어 보이는 주제 입니다. 오늘 딥러닝 논문읽기 모임에서 소개드릴 논문은 DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems, 강화학습을 이용한 온라인 추천 시스템 입니다. 비공개 된 정보들이 몇가지가 있지만, 아이디어면에서 여러분들이 충분히 재밌게 들으실수 있습니다. 강화학습의 기본적인 개념부터,
논문에 대한 디테일하고 깊이 있는 리뷰를
펀디멘탈팀 김창연 님이 도와주셨습니다!
오늘도 많은 관심 미리 감사드립니다!
추가로 .. 딥러닝 논문읽기 모임은 청강방 오픈채팅 방을 운영하고 있습니다. 최근 악성 홍보 봇 계정이 늘어나 방을 비밀번호를 걸어두게 되었습니다
딥러닝 청강방도 많은 관심 부탁드립니다!
청강방 링크 : https://open.kakao.com/o/gp6GHMMc
청강방 비밀번호 : 0501
Buffer Allocation Problem is an important research issue in manufacturing system design.
Objective of this paper is to find optimum buffer allocation for closed queuing network with
multi servers at each node. Sum of buffers in closed queuing network is constant. Attempt is
made to find optimum number of pallets required to maximize throughput of manufacturing
system which has pre specified space for allocating pallets. Expanded Mean Value Analysis is
used to evaluate the performance of closed queuing network. Particle Swarm Optimization is
used as generative technique to optimize the buffer allocation. Numerical experiments are
shown to explain effectiveness of procedure
Towards a Unified Data Analytics Optimizer with Yanlei DiaoDatabricks
Today’s big data analytics systems are best effort only: despite the wide adoption, they still lack the ability to take user monetary constraints and performance goals, and automatically configure an analytic job to achieve those goals. Our work aims to take a step further towards building a new data analytics optimizer that works for arbitrary dataflow programs and determines the job configuration in an automated manner based on user objectives regarding latency, throughput, monetary cost, etc.
At the core of the optimizer are a principled multi-objective optimization framework that enables one to explore the tradeoffs between different objectives, and a deep learning-based modeling approach that can learn a model for each user objective as complex as necessary for the user computing environment. Using both SQL-like and machine learning jobs in Spark, we show that our techniques can learn a model of each objective with high accuracy, and the multi-objective optimizer can automatically recommend new configurations that significantly improve performance from the configurations manually set by engineers.
Sergei Vassilvitskii, Research Scientist, Google at MLconf NYC - 4/15/16MLconf
Teaching K-Means New Tricks: Over 50 years old, the k-means algorithm remains one of the most popular clustering algorithms. In this talk we’ll cover some recent developments, including better initialization, the notion of coresets, clustering at scale, and clustering with outliers.
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...Sunny Kr
Cardinality estimation has a wide range of applications and
is of particular importance in database systems. Various
algorithms have been proposed in the past, and the HyperLogLog algorithm is one of them
Adversarial Reinforced Learning for Unsupervised Domain Adaptationtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임입니다 오늘 업로드된 논문 리뷰 영상은 2021 WACB 에서 발표된 Adversarial Reinforced Learning for Unsupervised Domain Adaptation 라는 제목의 논문입니다.
데이터 분류의 자동화를 위해서는 많은양의 학습데이터가 필요합니다. 그렇기에 레이블이 존재하는 데이터로 학습이 끝난 모델을 재활용해서 새로운 도메인에 적용하는 연구인 도메인 어뎁션 분야는 많은 각광을 받고 있습니다.
논문의 특징으로는 크게 세가지를 둘 수 있습니다.
첫 번째로 본 논문에서는 GAN을 이용하여 비지도 방식으로 도메인 어뎁션이 가능한 프레임워크를 제안하였습니다 여기서 이제 강화학습 모델은 소스와 타겟
도메인간 가장 최적의 피처쌍을 선택하는데 사용됩니다
두 번째로 레이블링 되지않은 타겟 도메인에서 가장 적합한 피처를 찾아내기 위해
소스와 타겟간 상관관계를 보상으로 적용하는 정책을 개발하였습니다
마지막으로 제안된 적대적 강화학습 모델을 소스와 타겟 도메인간
최소화하는 피처쌍의 탐색과 각 도메인의 거리 분포상태의
Alignment 학습을 통해 소타대비 이제 성능을 향상 하였습니다
논문에 대한 디테일한 리뷰를 펀디멘탈팀 이근배님이 많은 도움 주셨습니다!
Gradient Boosted Regression Trees in scikit-learnDataRobot
Slides of the talk "Gradient Boosted Regression Trees in scikit-learn" by Peter Prettenhofer and Gilles Louppe held at PyData London 2014.
Abstract:
This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling. GBRT is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price.
I will give a brief introduction to the GBRT model and regression trees -- focusing on intuition rather than mathematical formulas. The majority of the talk will be dedicated to an in depth discussion how to apply GBRT in practice using scikit-learn. We will cover important topics such as regularization, model tuning and model interpretation that should significantly improve your score on Kaggle.
201907 AutoML and Neural Architecture SearchDaeJin Kim
Brief introduction of NAS
Review of EfficientNet (Google Brain), RandWire (FAIR) papers
NAS flow slide from KihoSuh's slideshare (https://www.slideshare.net/KihoSuh/neural-architecture-search-with-reinforcement-learning-76883153)
[References]
[1] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (https://arxiv.org/abs/1905.11946)
[2] Exploring Randomly Wired Neural Networks for Image Recognition (https://arxiv.org/abs/1904.01569)
Microservices Architecture Enables DevOps: Migration to a Cloud-Native Archit...Pooyan Jamshidi
A look at the searches related to the term “microservices” on Google Trends revealed that the top searches are now technology driven. This implies that the time of general search terms such as “What is microservices?” has now long passed. Not only are software vendors (for example, IBM and Microsoft) using microservices and DevOps practices, but also content providers (for example, Netflix and the BBC) have adopted and are using them.
I report on experiences and lessons learned during incremental migration and architectural refactoring of a commercial mobile back end as a service to microservices architecture. I explain how we adopted DevOps and how this facilitated a smooth migration towards Microservices architecture.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesMLAI2
Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fundamental problem of machine learning. Regularization techniques are versatile, as they are task- and architecture-agnostic, but they do not exploit a large amount of data available. Transfer learning methods learn to transfer knowledge from one domain to another, but may not generalize across tasks and architectures, and may introduce new training cost for adapting to the target task. To bridge the gap between the two, we propose a transferable perturbation, MetaPerturb, which is meta-learned to improve generalization performance on unseen data. MetaPerturb is implemented as a set-based lightweight network that is agnostic to the size and the order of the input, which is shared across the layers. Then, we propose a meta-learning framework, to jointly train the perturbation function over heterogeneous tasks in parallel. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures. We validate the efficacy and generality of MetaPerturb trained on a specific source domain and architecture, by applying it to the training of diverse neural architectures on heterogeneous target datasets against various regularizers and fine-tuning. The results show that the networks trained with MetaPerturb significantly outperform the baselines on most of the tasks and architectures, with a negligible increase in the parameter size and no hyperparameters to tune.
AI optimizing HPC simulations (presentation from 6th EULAG Workshop)byteLAKE
See our presentation from the 6th International EULAG Users Workshop. We talked about taking HPC to the "Industry 4.0" by implementing smart techniques to optimize the codes in terms of performance and energy consumption. It explains how Machine Learning can dynamically optimize HPC simulations and byteLAKE's software autotuning solution.
Find out more about byteLAKE at: www.byteLAKE.com
논문 제목부터 재미있어 보이는 주제 입니다. 오늘 딥러닝 논문읽기 모임에서 소개드릴 논문은 DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems, 강화학습을 이용한 온라인 추천 시스템 입니다. 비공개 된 정보들이 몇가지가 있지만, 아이디어면에서 여러분들이 충분히 재밌게 들으실수 있습니다. 강화학습의 기본적인 개념부터,
논문에 대한 디테일하고 깊이 있는 리뷰를
펀디멘탈팀 김창연 님이 도와주셨습니다!
오늘도 많은 관심 미리 감사드립니다!
추가로 .. 딥러닝 논문읽기 모임은 청강방 오픈채팅 방을 운영하고 있습니다. 최근 악성 홍보 봇 계정이 늘어나 방을 비밀번호를 걸어두게 되었습니다
딥러닝 청강방도 많은 관심 부탁드립니다!
청강방 링크 : https://open.kakao.com/o/gp6GHMMc
청강방 비밀번호 : 0501
Buffer Allocation Problem is an important research issue in manufacturing system design.
Objective of this paper is to find optimum buffer allocation for closed queuing network with
multi servers at each node. Sum of buffers in closed queuing network is constant. Attempt is
made to find optimum number of pallets required to maximize throughput of manufacturing
system which has pre specified space for allocating pallets. Expanded Mean Value Analysis is
used to evaluate the performance of closed queuing network. Particle Swarm Optimization is
used as generative technique to optimize the buffer allocation. Numerical experiments are
shown to explain effectiveness of procedure
Towards a Unified Data Analytics Optimizer with Yanlei DiaoDatabricks
Today’s big data analytics systems are best effort only: despite the wide adoption, they still lack the ability to take user monetary constraints and performance goals, and automatically configure an analytic job to achieve those goals. Our work aims to take a step further towards building a new data analytics optimizer that works for arbitrary dataflow programs and determines the job configuration in an automated manner based on user objectives regarding latency, throughput, monetary cost, etc.
At the core of the optimizer are a principled multi-objective optimization framework that enables one to explore the tradeoffs between different objectives, and a deep learning-based modeling approach that can learn a model for each user objective as complex as necessary for the user computing environment. Using both SQL-like and machine learning jobs in Spark, we show that our techniques can learn a model of each objective with high accuracy, and the multi-objective optimizer can automatically recommend new configurations that significantly improve performance from the configurations manually set by engineers.
Sergei Vassilvitskii, Research Scientist, Google at MLconf NYC - 4/15/16MLconf
Teaching K-Means New Tricks: Over 50 years old, the k-means algorithm remains one of the most popular clustering algorithms. In this talk we’ll cover some recent developments, including better initialization, the notion of coresets, clustering at scale, and clustering with outliers.
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...Sunny Kr
Cardinality estimation has a wide range of applications and
is of particular importance in database systems. Various
algorithms have been proposed in the past, and the HyperLogLog algorithm is one of them
Adversarial Reinforced Learning for Unsupervised Domain Adaptationtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임입니다 오늘 업로드된 논문 리뷰 영상은 2021 WACB 에서 발표된 Adversarial Reinforced Learning for Unsupervised Domain Adaptation 라는 제목의 논문입니다.
데이터 분류의 자동화를 위해서는 많은양의 학습데이터가 필요합니다. 그렇기에 레이블이 존재하는 데이터로 학습이 끝난 모델을 재활용해서 새로운 도메인에 적용하는 연구인 도메인 어뎁션 분야는 많은 각광을 받고 있습니다.
논문의 특징으로는 크게 세가지를 둘 수 있습니다.
첫 번째로 본 논문에서는 GAN을 이용하여 비지도 방식으로 도메인 어뎁션이 가능한 프레임워크를 제안하였습니다 여기서 이제 강화학습 모델은 소스와 타겟
도메인간 가장 최적의 피처쌍을 선택하는데 사용됩니다
두 번째로 레이블링 되지않은 타겟 도메인에서 가장 적합한 피처를 찾아내기 위해
소스와 타겟간 상관관계를 보상으로 적용하는 정책을 개발하였습니다
마지막으로 제안된 적대적 강화학습 모델을 소스와 타겟 도메인간
최소화하는 피처쌍의 탐색과 각 도메인의 거리 분포상태의
Alignment 학습을 통해 소타대비 이제 성능을 향상 하였습니다
논문에 대한 디테일한 리뷰를 펀디멘탈팀 이근배님이 많은 도움 주셨습니다!
Gradient Boosted Regression Trees in scikit-learnDataRobot
Slides of the talk "Gradient Boosted Regression Trees in scikit-learn" by Peter Prettenhofer and Gilles Louppe held at PyData London 2014.
Abstract:
This talk describes Gradient Boosted Regression Trees (GBRT), a powerful statistical learning technique with applications in a variety of areas, ranging from web page ranking to environmental niche modeling. GBRT is a key ingredient of many winning solutions in data-mining competitions such as the Netflix Prize, the GE Flight Quest, or the Heritage Health Price.
I will give a brief introduction to the GBRT model and regression trees -- focusing on intuition rather than mathematical formulas. The majority of the talk will be dedicated to an in depth discussion how to apply GBRT in practice using scikit-learn. We will cover important topics such as regularization, model tuning and model interpretation that should significantly improve your score on Kaggle.
201907 AutoML and Neural Architecture SearchDaeJin Kim
Brief introduction of NAS
Review of EfficientNet (Google Brain), RandWire (FAIR) papers
NAS flow slide from KihoSuh's slideshare (https://www.slideshare.net/KihoSuh/neural-architecture-search-with-reinforcement-learning-76883153)
[References]
[1] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (https://arxiv.org/abs/1905.11946)
[2] Exploring Randomly Wired Neural Networks for Image Recognition (https://arxiv.org/abs/1904.01569)
Microservices Architecture Enables DevOps: Migration to a Cloud-Native Archit...Pooyan Jamshidi
A look at the searches related to the term “microservices” on Google Trends revealed that the top searches are now technology driven. This implies that the time of general search terms such as “What is microservices?” has now long passed. Not only are software vendors (for example, IBM and Microsoft) using microservices and DevOps practices, but also content providers (for example, Netflix and the BBC) have adopted and are using them.
I report on experiences and lessons learned during incremental migration and architectural refactoring of a commercial mobile back end as a service to microservices architecture. I explain how we adopted DevOps and how this facilitated a smooth migration towards Microservices architecture.
Cloud Migration Patterns: A Multi-Cloud Architectural PerspectivePooyan Jamshidi
Cloud migration requires an engineering, verifiable, measurable, transparent and repeatable approach rather than an ad-hoc approach based on trial and error.
We describe a comprehensive set of (multi-)cloud migration patterns from an architectural perspective. In this work, we focus on application components and their migration to the multi-cloud environments. We define and characterize the patterns with concrete usage scenario. We also describe the process for migration pattern selection, composition and extension.
Autonomic Resource Provisioning for Cloud-Based SoftwarePooyan Jamshidi
The Third National Conference on Cloud Computing and Commerce (NC4), for more information please refer to: http://computing.dcu.ie/~pjamshidi/PDF/SEAMS2014.pdf
Sensitivity Analysis for Building Adaptive Robotic SoftwarePooyan Jamshidi
P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar. Transfer learning for improving model predictions in highly configurable software. Int’l Symp. Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2017.
Microxchg Analyzing Response Time Distributions for MicroservicesAdrian Cockcroft
Research oriented presentation @Microxchg Berlin Feb 5th 2016. New code to collect histograms of response time and export them to monte-carlo simulation spreadsheet via getguesstimate.com
A microservices architecture is not a new style of building large scale enterprise applications. Companies like Netflix and Amazon have implemented a microservices architecture to deliver successful products over the last few years.
But is a microservices architecture right for your organization? What should you focus on when getting started? How do microservices affect your business model?
In this presentation you will learn:
• What Microservices are
• Why use Microservices
• How to model Microservices
• How to automatically create your Microservice EA repository
• How to plan the Microservice roadmap
How can we develop websites where the different parts of the pages are developed by different teams? If you work in a large enough organization which has its content and services on the web, this is probably a question you have asked yourself several times.
With this talk I want to show that server-side rendered websites integrated on content (using transclusion) allow for high long-term evolvability compared to client-side rendering integrated with shared code. In other words, if you want a system with high long-term evolvability, you should not develop websites using only client-side JavaScript and integrate them using a shared components approach.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1SbjUWM.
Aviran Mordo talks about how microservices and DevOps go hand in hand, and what it takes to operate and build a successful microservices architecture from development to production. Filmed at qconlondon.com.
Aviran Mordo is the head of back-end engineering at Wix. He has over 20 years of experience in the software industry and has filled many engineering roles and leading positions, from designing and building the US national Electronic Records Archives prototype to building search engine infrastructures.
Discussion of how microservices are being applied across both web scale and enterprise/government use cases to help speed up development.
Video available at http://www.ustream.tv/recorded/86151804
Microservices, containers, and machine learningPaco Nathan
http://www.oscon.com/open-source-2015/public/schedule/detail/41579
In this presentation, an open source developer community considers itself algorithmically. This shows how to surface data insights from the developer email forums for just about any Apache open source project. It leverages advanced techniques for natural language processing, machine learning, graph algorithms, time series analysis, etc. As an example, we use data from the Apache Spark email list archives to help understand its community better; however, the code can be applied to many other communities.
Exsto is an open source project that demonstrates Apache Spark workflow examples for SQL-based ETL (Spark SQL), machine learning (MLlib), and graph algorithms (GraphX). It surfaces insights about developer communities from their email forums. Natural language processing services in Python (based on NLTK, TextBlob, WordNet, etc.), gets containerized and used to crawl and parse email archives. These produce JSON data sets, then we run machine learning on a Spark cluster to find out insights such as:
* What are the trending topic summaries?
* Who are the leaders in the community for various topics?
* Who discusses most frequently with whom?
This talk shows how to use cloud-based notebooks for organizing and running the analytics and visualizations. It reviews the background for how and why the graph analytics and machine learning algorithms generalize patterns within the data — based on open source implementations for two advanced approaches, Word2Vec and TextRank The talk also illustrates best practices for leveraging functional programming for big data.
Continuous Architecting of Stream-Based SystemsCHOOSE
Pooyan Jamshidi CHOOSE Talk 2016-11-01
Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors making it a difficult task for software architects to refactor the initial designs. As an aid to designers and developers, we developed OSTIA (On-the-fly Static Topology Inference Analysis) that allows: (a) visualizing big data architectures for the purpose of design-time refactoring while maintaining constraints that would only be evaluated at later stages such as deployment and run-time; (b) detecting the occurrence of common anti-patterns across big data architectures; (c) exploiting software verification techniques on the elicited architectural models. In the lecture, OSTIA will be shown on three industrial-scale case studies.
See: http://www.choose.s-i.ch/events/jamshidi-2016/
Transfer Learning for Performance Analysis of Highly-Configurable SoftwarePooyan Jamshidi
A wide range of modern software-intensive systems (e.g., autonomous systems, big data analytics, robotics, deep neural architectures) are built configurable. These systems offer a rich space for adaptation to different domains and tasks. Developers and users often need to reason about the performance of such systems, making tradeoffs to change specific quality attributes or detecting performance anomalies. For instance, developers of image recognition mobile apps are not only interested in learning which deep neural architectures are accurate enough to classify their images correctly, but also which architectures consume the least power on the mobile devices on which they are deployed. Recent research has focused on models built from performance measurements obtained by instrumenting the system. However, the fundamental problem is that the learning techniques for building a reliable performance model do not scale well, simply because the configuration space is exponentially large that is impossible to exhaustively explore. For example, it will take over 60 years to explore the whole configuration space of a system with 25 binary options.
In this talk, I will start motivating the configuration space explosion problem based on my previous experience with large-scale big data systems in industry. I will then present my transfer learning solution to tackle the scalability challenge: instead of taking the measurements from the real system, we learn the performance model using samples from cheap sources, such as simulators that approximate the performance of the real system, with a fair fidelity and at a low cost. Results show that despite the high cost of measurement on the real system, learning performance models can become surprisingly cheap as long as certain properties are reused across environments. In the second half of the talk, I will present empirical evidence, which lays a foundation for a theory explaining why and when transfer learning works by showing the similarities of performance behavior across environments. I will present observations of environmental changes‘ impacts (such as changes to hardware, workload, and software versions) for a selected set of configurable systems from different domains to identify the key elements that can be exploited for transfer learning. These observations demonstrate a promising path for building efficient, reliable, and dependable software systems. Finally, I will share my research vision for the next five years and outline my immediate plans to further explore the opportunities of transfer learning.
Related Papers:
https://arxiv.org/pdf/1709.02280
https://arxiv.org/pdf/1704.00234
https://arxiv.org/pdf/1606.06543
A wide variety of combinatorial problems can be viewed as Weighted Constraint Satisfaction Problems (WCSPs). All resolution methods have an exponential time complexity for big instances. Moreover, they combine several techniques, use a wide variety of concepts and notations that are difficult to understand and implement. In this paper, we model this problem in terms of an original 0-1 quadratic programming subject to linear constraints. This model is validated by the proposed and demonstrated theorem. View its performance, we use the Hopfield neural network to solve the obtained model basing on original energy function. To validate our model, we solve several instances of benchmarking WCSP. Our approach has the same memory complexity as the HNN and the same time complexity as Euler-Cauchy method. In this regard, our approach recognizes the optimal solution of the said instances.
Transfer Learning for Performance Analysis of Configurable Systems:A Causal ...Pooyan Jamshidi
Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different configurations. The fundamental challenge is that one cannot simply measure all configurations due to the sheer size of the configuration space. Transfer learning has been used to reduce the measurement efforts by transferring knowledge about performance behavior of systems across environments. Previously, research has shown that statistical models are indeed transferable across environments. In this work, we investigate identifiability and transportability of causal effects and statistical relations in highly-configurable systems. Our causal analysis agrees with previous exploratory analysis~\cite{Jamshidi17} and confirms that the causal effects of configuration options can be carried over across environments with high confidence. We expect that the ability to carry over causal relations will enable effective performance analysis of highly-configurable systems.
Learning LWF Chain Graphs: A Markov Blanket Discovery ApproachPooyan Jamshidi
LWF Chain graphs were introduced by Lauritzen, Wermuth, and Frydenberg as a generalization of graphical models based on undirected graphs and DAGs. From the causality point of view, in an LWF CG: Directed edges represent direct causal effects. Undirected edges represent causal effects due to interference, which occurs when an individual’s outcome is influenced by their social interaction with other population members, e.g., in situations that involve contagious agents, educational programs, or social networks. The construction of chain graph models is a challenging task that would be greatly facilitated by automation.
Markov blanket discovery has an important role in structure learning of Bayesian network. It is surprising, however, how little attention it has attracted in the context of learning LWF chain graphs. In this work, we provide a graphical characterization of Markov blankets in chain graphs. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF chain graphs. We also provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data. With the use of our algorithm, the problem of structure learning is reduced to finding an efficient algorithm for Markov blanket discovery in LWF chain graphs. This greatly simplifies the structure-learning task and makes a wide range of inference/learning problems computationally tractable because our approach exploits locality.
A Framework for Robust Control of Uncertainty in Self-Adaptive Software Conn...Pooyan Jamshidi
We enable reliable and dependable self‐adaptations of component connectors in unreliable environments with imperfect monitoring facilities and conflicting user opinions about adaptation policies by developing a framework which comprises: (a) mechanisms for robust model evolution, (b) a method for adaptation reasoning, and (c) tool support that allows an end‐to‐end application of the developed techniques in real‐world domains.
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Aut...Pooyan Jamshidi
Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time. Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades). Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies. Planners can be used to find and enact optimal reconfigurations in such an evolving context. However, for systems that are highly configurable, such planning becomes intractable due to the size of the adaptation space. To overcome this challenge, in this paper we explore an approach that (a) uses machine learning to find Pareto-optimal configurations without needing to explore every configuration and (b) restricts the search space to such configurations to make planning tractable. We explore this in the context of robot missions that need to consider task timeliness and energy consumption. An independent evaluation shows that our approach results in high-quality adaptation plans in uncertain and adversarial environments.
Paper: https://arxiv.org/abs/1903.03920
Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural ...Pooyan Jamshidi
Despite achieving state-of-the-art performance across many domains, machine learning systems are highly vulnerable to subtle adversarial perturbations. Although defense approaches have been proposed in recent years, many have been bypassed by even weak adversarial attacks. Previous studies showed that ensembles created by combining multiple weak defenses (i.e., input data transformations) are still weak. In this talk, I will show that it is indeed possible to construct effective ensembles using weak defenses to block adversarial attacks. However, to do so requires a diverse set of such weak defenses. Based on this motivation, I will present Athena, an extensible framework for building effective defenses to adversarial attacks against machine learning systems. I will talk about the effectiveness of ensemble strategies with a diverse set of many weak defenses that comprise transforming the inputs (e.g., rotation, shifting, noising, denoising, and many more) before feeding them to target deep neural network classifiers. I will also discuss the effectiveness of the ensembles with adversarial examples generated by various adversaries in different threat models. In the second half of the talk, I will explain why building defenses based on the idea of many diverse weak defenses works, when it is most effective, and what its inherent limitations and overhead are.
Integrated Model Discovery and Self-Adaptation of RobotsPooyan Jamshidi
Machine learn models efficiently under budget constraints to adapt to perturbations such as environmental changes or changes in the internal resources.
Modern software-intensive systems are composed of components that are likely to change their behaviour over time (e.g., adding/removing components).
For software to continue to operate under such changes, the assumptions about parts of the system made at design time may not hold at runtime due to uncertainty.
Mechanisms must be put in place that can dynamically learn new models of these assumptions and use them to make decisions about missions, configurations, etc.
Architectural Tradeoff in Learning-Based SoftwarePooyan Jamshidi
In classical software development, developers write explicit instructions in a programming language to hardcode the explicit behavior of software systems. By writing each line of code, the programmer instructs the software to have the desirable behavior by exploring a specific point in program space.
Recently, however, software systems are adding learning components that, instead of hardcoding an explicit behavior, learn a behavior through data. The learning-intensive software systems are written in terms of models and their parameters that need to be adjusted based on data. In learning-enabled systems, we specify some constraints on the behavior of a desirable program (e.g., a data set of input–output pairs of examples) and use the computational resources to search through the program space to find a program that satisfies the constraints. In neural networks, we restrict the search to a continuous subset of the program space.
This talk provides experimental evidence of making tradeoffs for deep neural network models, using the Deep Neural Network Architecture system as a case study. Concrete experimental results are presented; also featured are additional case studies in big data (Storm, Cassandra), data analytics (configurable boosting algorithms), and robotics applications.
In this Dagstuhl talk, I presented my current research on cloud auto-scaling and component connector self-adaptation and how I employed type-2 fuzzy control to tame the uncertainty regarding knowledge specification.
Autonomic Resource Provisioning for Cloud-Based SoftwarePooyan Jamshidi
9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'14) @ ICSE 2014, for more information please refer to: http://computing.dcu.ie/~pjamshidi/PDF/SEAMS2014.pdf
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
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Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
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(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
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Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
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We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
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5. Motivation (throughput)
-500 0 500 1000 1500
throughput (ops/sec)
0
10
20
30
40
50
60
observations
Configuration
that generate
low throughput
Configurations that
generate high
throughput
6. Motivation (Apache Storm)
number of counters
number of splitters
latency(ms)
100
150
1
200
250
2
300
Cubic Interpolation Over Finer Grid
243 684 10125 14166 18
In our experiments we
observed improvement
up to 100%
7. Goal
is denoted by f(x). Throughout, we assume
ncy, however, other metrics for response may
re consider the problem of finding an optimal
⇤
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to the accuracy of the learned model
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2.4 Motivation
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benchmark [12]. WordCount features
(Xi). In general, Xi may either indicate (i) integer vari-
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as messaging frameworks or Boolean variable such as
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assume that each configuration x 2 X in the configura-
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is denoted by f(x). Throughout, we assume
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⇤
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, i.e., yi = f(xi), xi ⇢ X. In practice, such
may contain noise, i.e., yi = f(xi) + ✏. The
of the optimal configuration is thus a black-
n program subject to noise [27, 33], which
harder than deterministic optimization. A
n is based on sampling that starts with a
pled configurations. The performance of the
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tanding of f(·) and guide the generation of
of samples. If properly guided, the process
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erge and the optimal configuration will be
in DevOps di↵erent versions of a system is delivered
tinuously, (ii) Big Data systems are developed using s
frameworks (e.g., Apache Hadoop, Spark, Kafka) an
on similar platforms (e.g., cloud clusters), (iii) and di↵
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the possibility of transfer learning in system configur
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of a system and reuse this model for tuning other s
systems. However, the learning is limited to the struct
the Bayesian network. In this paper, we introduce a m
that not only reuse a model that has been learned prev
but also the valuable raw data. Therefore, we are not li
to the accuracy of the learned model. Moreover, we d
consider Bayesian networks and instead focus on MTG
2.4 Motivation
A motivating example. We now illustrate the pre
points on an example. WordCount (cf. Figure 1) is a po
benchmark [12]. WordCount features a three-layer arc
ture that counts the number of words in the incoming s
A Processing Element (PE) of type Spout reads the
havior. The response with
). Throughout, we assume
metrics for response may
blem of finding an optimal
nimizes f(·) over X:
f(x) (1)
(·) is usually unknown or
xi ⇢ X. In practice, such
i.e., yi = f(xi) + ✏. The
figuration is thus a black-
t to noise [27, 33], which
ministic optimization. A
mpling that starts with a
. The performance of the
itial samples can deliver
d guide the generation of
perly guided, the process
in DevOps di↵erent versions of a system is delivered con
tinuously, (ii) Big Data systems are developed using simila
frameworks (e.g., Apache Hadoop, Spark, Kafka) and ru
on similar platforms (e.g., cloud clusters), (iii) and di↵eren
versions of a system often share a similar business logic.
To the best of our knowledge, only one study [9] explore
the possibility of transfer learning in system configuration
The authors learn a Bayesian network in the tuning proces
of a system and reuse this model for tuning other simila
systems. However, the learning is limited to the structure o
the Bayesian network. In this paper, we introduce a metho
that not only reuse a model that has been learned previousl
but also the valuable raw data. Therefore, we are not limite
to the accuracy of the learned model. Moreover, we do no
consider Bayesian networks and instead focus on MTGPs.
2.4 Motivation
A motivating example. We now illustrate the previou
points on an example. WordCount (cf. Figure 1) is a popula
benchmark [12]. WordCount features a three-layer archite
Partially known
Measurements subject to noise
Configuration space
8. Non-linear interactions
0 5 10 15 20
Number of counters
100
120
140
160
180
200
220
240
Latency(ms)
splitters=2
splitters=3
number of counters
number of splitters
latency(ms)
100
150
1
200
250
2
300
Cubic Interpolation Over Finer Grid
243 684 10125 14166 18
Response surface is:
- Non-linear
- Non convex
- Multi-modal
9. The measurements are subject to variability
wc wc+rs wc+sol 2wc 2wc+rs+sol
10
1
10
2
Latency(ms)
The scale of
measurement variability
is different in different
deployments
(heteroscedastic noise)
y at points x that has been
here consider the problem
x⇤
that minimizes f over
w experiments as possible:
f(x) (1)
) is usually unknown or
xi ⇢ X. In practice, such
.e., yi = f(xi) + ✏i. Note
ly partially-known, finding
kbox optimization problem
noise. In fact, the problem
on-convex and multi-modal
P-hard [36]. Therefore, on
locate a global optimum,
st possible local optimum
udget.
It shows the non-convexity, multi-modality and the substantial
performance difference between different configurations.
0 5 10 15 20
Number of counters
100
120
140
160
180
200
220
240
Latency(ms)
splitters=2
splitters=3
Fig. 3: WordCount latency, cut though Figure 2.
demonstrates that if one tries to minimize latency by acting
just on one of these parameters at the time, the resulting
12. GP for modeling black box response function
true function
GP mean
GP variance
observation
selected point
true
minimum
mposed by its prior mean (µ(·) : X ! R) and a covariance
nction (k(·, ·) : X ⇥ X ! R) [41]:
y = f(x) ⇠ GP(µ(x), k(x, x0
)), (2)
here covariance k(x, x0
) defines the distance between x
d x0
. Let us assume S1:t = {(x1:t, y1:t)|yi := f(xi)} be
e collection of t experimental data (observations). In this
mework, we treat f(x) as a random variable, conditioned
observations S1:t, which is normally distributed with the
lowing posterior mean and variance functions [41]:
µt(x) = µ(x) + k(x)|
(K + 2
I) 1
(y µ) (3)
2
t (x) = k(x, x) + 2
I k(x)|
(K + 2
I) 1
k(x) (4)
here y := y1:t, k(x)|
= [k(x, x1) k(x, x2) . . . k(x, xt)],
:= µ(x1:t), K := k(xi, xj) and I is identity matrix. The
ortcoming of BO4CO is that it cannot exploit the observa-
ns regarding other versions of the system and as therefore
nnot be applied in DevOps.
2 TL4CO: an extension to multi-tasks
TL4CO 1
uses MTGPs that exploit observations from other
evious versions of the system under test. Algorithm 1
fines the internal details of TL4CO. As Figure 4 shows,
4CO is an iterative algorithm that uses the learning from
her system versions. In a high-level overview, TL4CO: (i)
ects the most informative past observations (details in
ction 3.3); (ii) fits a model to existing data based on kernel
arning (details in Section 3.4), and (iii) selects the next
ork are based on tractable linear algebra.
evious work [21], we proposed BO4CO that ex-
task GPs (no transfer learning) for prediction of
tribution of response functions. A GP model is
y its prior mean (µ(·) : X ! R) and a covariance
·, ·) : X ⇥ X ! R) [41]:
y = f(x) ⇠ GP(µ(x), k(x, x0
)), (2)
iance k(x, x0
) defines the distance between x
us assume S1:t = {(x1:t, y1:t)|yi := f(xi)} be
n of t experimental data (observations). In this
we treat f(x) as a random variable, conditioned
ons S1:t, which is normally distributed with the
sterior mean and variance functions [41]:
µ(x) + k(x)|
(K + 2
I) 1
(y µ) (3)
k(x, x) + 2
I k(x)|
(K + 2
I) 1
k(x) (4)
1:t, k(x)|
= [k(x, x1) k(x, x2) . . . k(x, xt)],
, K := k(xi, xj) and I is identity matrix. The
of BO4CO is that it cannot exploit the observa-
ng other versions of the system and as therefore
pplied in DevOps.
CO: an extension to multi-tasks
uses MTGPs that exploit observations from other
Motivations:
1- mean estimates + variance
2- all computations are linear algebra
3- good estimations when few data
13. Sparsity of Effects
• Correlation-based
feature selector
• Merit is used to select
subsets that are highly
correlated with the
response variable
• At most 2-3 parameters
were strongly interacting
with each other
TABLE I: Sparsity of effects on 5 experiments where we have varied
different subsets of parameters and used different testbeds. Note that
these are the datasets we experimentally measured on the benchmark
systems and we use them for the evaluation, more details including
the results for 6 more experiments are in the appendix.
Topol. Parameters Main factors Merit Size Testbed
1 wc(6D)
1-spouts, 2-max spout,
3-spout wait, 4-splitters,
5-counters, 6-netty min wait
{1, 2, 5} 0.787 2880 C1
2 sol(6D)
1-spouts, 2-max spout,
3-top level, 4-netty min wait,
5-message size, 6-bolts
{1, 2, 3} 0.447 2866 C2
3 rs(6D)
1-spouts, 2-max spout,
3-sorters, 4-emit freq,
5-chunk size, 6-message size
{3} 0.385 3840 C3
4 wc(3D)
1-max spout, 2-splitters,
3-counters {1, 2} 0.480 756 C4
5 wc(5D)
1-spouts, 2-splitters,
3-counters,
4-buffer-size, 5-heap
{1} 0.851 1080 C5
102
s)
Experiments on:
1. C1: OpenNebula (X)
2. C2: Amazon EC2 (Y)
3. C3: OpenNebula (3X)
4. C4: Amazon EC2 (2Y)
5. C5: Microsoft Azure (X)
14. -1.5 -1 -0.5 0 0.5 1 1.5
-1.5
-1
-0.5
0
0.5
1
x1 x2 x3 x4
true function
GP surrogate
mean estimate
observation
Fig. 5: An example of 1D GP model: GPs provide mean esti-
mates as well as the uncertainty in estimations, i.e., variance.
Configuration
Optimisation Tool
performance
repository
Monitoring
Deployment Service
Data Preparation
configuration
parameters
values
configuration
parameters
values
Experimental Suite
Testbed
Doc
Data Broker
Tester
experiment time
polling interval
configuration
parameters
GP model
Kafka
System Under Test
Workload
Generator
Technology Interface
Storm
Cassandra
Spark
Algorithm 1 : BO4CO
Input: Configuration space X, Maximum budget Nmax, Re-
sponse function f, Kernel function K✓, Hyper-parameters
✓, Design sample size n, learning cycle Nl
Output: Optimal configurations x⇤
and learned model M
1: choose an initial sparse design (lhd) to find an initial
design samples D = {x1, . . . , xn}
2: obtain performance measurements of the initial design,
yi f(xi) + ✏i, 8xi 2 D
3: S1:n {(xi, yi)}n
i=1; t n + 1
4: M(x|S1:n, ✓) fit a GP model to the design . Eq.(3)
5: while t Nmax do
6: if (t mod Nl = 0) ✓ learn the kernel hyper-
parameters by maximizing the likelihood
7: find next configuration xt by optimizing the selection
criteria over the estimated response surface given the data,
xt arg maxxu(x|M, S1:t 1) . Eq.(9)
8: obtain performance for the new configuration xt, yt
f(xt) + ✏t
9: Augment the configuration S1:t = {S1:t 1, (xt, yt)}
10: M(x|S1:t, ✓) re-fit a new GP model . Eq.(7)
11: t t + 1
12: end while
13: (x⇤
, y⇤
) = min S1:Nmax
14: M(x)
-1.5 -1 -0.5 0 0.5 1 1.5
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Configuration
Space
Empirical
Model
2
4
6
8
10
12
1
2
3
4
5
6
160
140
120
100
80
60
180
Experiment
(exhastive)
Experiment
Experiment
0 20 40 60 80 100 120 140 160 180 200
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Selection Criteria
(b) Sequential Design
(a) Design of Experiment
P. Jamshidi, G. Casale, “An Uncertainty-Aware Approach to Optimal
Configuration of Stream Processing Systems”, MASCOTS 2016.
15. -1.5 -1 -0.5 0 0.5 1 1.5
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
configuration domain
responsevalue
-1.5 -1 -0.5 0 0.5 1 1.5
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
true response function
GP fit
-1.5 -1 -0.5 0 0.5 1 1.5
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
criteria evaluation
new selected point
-1.5 -1 -0.5 0 0.5 1 1.5
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
new GP fit
Acquisition function:
btaining the measurements
O then fits a GP model to
elief about the underlying
rithm 1). The while loop in
belief until the budget runs
:t = {(xi, yi)}t
i=1, where
a prior distribution Pr(f)
1:t|f) form the posterior
) Pr(f).
ions [37], specified by its
iance (see Section III-E1):
), k(x, x0
)), (3)
where
µt(x) = µ(x) + k(x)|
(K + 2
I) 1
(y µ) (7)
2
t (x) = k(x, x) + 2
I k(x)|
(K + 2
I) 1
k(x) (8)
These posterior functions are used to select the next point xt+1
as detailed in Section III-C.
C. Configuration selection criteria
The selection criteria is defined as u : X ! R that selects
xt+1 2 X, should f(·) be evaluated next (step 7):
xt+1 = argmax
x2X
u(x|M, S1:t) (9)
20. The case where we learn from correlated responses
-1.5 -1 -0.5 0 0.5 1 1.5
-4
-3
-2
-1
0
1
2
3
(a) 3 sample response functions
configuration domain
responsevalue
(1)
(2)
(3)
observations
(b) GP fit for (1) ignoring observations for (2),(3)
LCB
not informative
(c) multi-task GP fit for (1) by transfer learning from (2),(3)
highly informative
GP prediction mean
GP prediction variance
probability distribution
of the minimizers
21. Comparison with default and expert prescription
0 500 1000 1500
Throughput (ops/sec)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Averagereadlatency(µs)
×10
4
TL4CO
BO4CO
BO4CO after
20 iterations TL4CO after
20 iterations
TL4CO after
100 iterations
0 500 1000 1500
Throughput (ops/sec)
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Averagewritelatency(µs)
TL4CO
BO4CO
Default configuration
Configuration
recommended
by expert
TL4CO after
100 iterations
BO4CO after
100 iterations
Default configuration
Configuration
recommended
by expert
23. Entropy of the density function of the minimizers
0 20 40 60 80 100
0
1
2
3
4
5
6
7
8
9
10
Entropy
T=1(BO4CO)
T=2,m=100
T=2,m=200
T=2,m=300
T=2,m=400
T=3,m=100
1 2 3 4 5 6 7 8 9
0
2
4
6
8
10
BO4CO
TL4CO
Entropy
Iteration
Branin Hartmann WC(3D) SOL(6D) WC(5D)Dixon WC(6D) RS(6D) cass-20
he knowledge about the location of optimum configura-
is summarized by the approximation of the conditional
bability density function of the response function mini-
ers, i.e., X⇤
= Pr(x⇤
|f(x)), where f(·) is drawn from
MTGP model (cf. solid red line in Figure 5(b,c)). The
opy of the density functions in Figure 5(b,c) are 6.39,
so we know more information about the latter.
he results in Figure 19 confirm that the entropy measure
e minimizers with the models provided by TL4CO for all
datasets (synthetic and real) significantly contains more
mation. The results demonstrate that the main reason
finding quick convergence comparing with the baselines
at TL4CO employs a more e↵ective model. The results
igure 19(b) show the change of entropy of X⇤
over time
WC(5D) dataset. First, it shows that in TL4CO, the
opy decreases sharply. However, the overall decrease of
opy for BO4CO is slow. The second observation is that
TL4CO
variance,
storing K
making th
5. DIS
5.1 Be
TL4CO
experimen
practice. A
than thre
the system
our appro
Knowledge about the location of the minimizer
24. Lets discuss
Ø Be aware of Uncertainty
- By quantifying the uncertainty (look at Catia’s work)
- Make decisions taking into account the right level of uncertainty (homoscedastic vs
heteroscedastic)
- Uncertainty sometimes helps (models that provide an estimation of the uncertainty
are typically more informative)
- By exploiting this knowledge you can only explore interesting zones rather than
learning the whole performance function
Ø You can learn from operational data
- Not only from the current version, but from previous measurements as well
- Use the learning from past measurements as prior knowledge
- Too much data can be also harmful, it would slow down or blur the proper learning
25. Submit to SEAMS 2017
- Any work on Self-*
- For Performance-Aware DevOps community:
- DevOps for adaptive systems?
- Self-adaptive DevOps pipeline?
- Abstract Submission: 6 Jan, 2017 (firm)
- Paper Submission: 13 Jan, 2017 (firm)
- Page limit:
- Long: 10+2,
- New ideas and tools: 6+1
- More info: https://wp.doc.ic.ac.uk/seams2017/
- Symposium: 22-23 May, 2017
- We accept artifacts submissions (tool, data, model)
12th
International Symposium on Software Engineering for Adaptive and Self-Managing Systems
http://wp.doc.ic.ac.uk/seams2017
Call for Papers
Self-adaptation and self-management are key objectives in many modern and emerging software systems, including
the industrial internet of things, cyber-physical systems, cloud computing, and mobile computing. These systems must
be able to adapt themselves at run time to preserve and optimize their operation in the presence of uncertain changes
in their operating environment, resource variability, new user needs, attacks, intrusions, and faults.
Approaches to complement software-based systems with self-managing and self-adaptive capabilities are an important
area of research and development, offering solutions that leverage advances in fields such as software architecture,
fault-tolerant computing, programming languages, robotics, and run-time program analysis and verification.
Additionally, research in this field is informed by related areas like biologically-inspired computing, artificial
intelligence, machine learning, control systems, and agent-based systems. The SEAMS symposium focuses on applying
software engineering to these approaches, including methods, techniques, and tools that can be used to support self-*
properties like self-adaptation, self-management, self-healing, self-optimization, and self-configuration.
The objective of SEAMS is to bring together researchers and practitioners from diverse areas to investigate, discuss,
and examine the fundamental principles, state of the art, and critical challenges of engineering self-adaptive and self-
managing systems.
Topics of Interest: All topics related to engineering self-adaptive and self-managing systems, including:
Foundational Concepts
• self-* properties
• control theory
• algorithms
• decision-making and planning
• managing uncertainty
• mixed-initiative and human-in-the-loop systems
Languages
• formal notations for modeling and analyzing self-*
properties
• programming language support for self-adaptation
Constructive methods
• requirements elicitation techniques
• reuse support (e.g., patterns, designs, code)
• architectural techniques
• legacy systems
Analytical Methods for Self-Adaptation and -Management
• evaluation and assurance
• verification and validation
• analysis and testing frameworks
Application Areas
• Industrial internet of things
• Cyber-physical systems
• Cloud computing
• Mobile computing
• Robotics
• Smart user interfaces
• Security and privacy
• Wearables and ubiquitous/pervasive systems
Artifacts* and Evaluations
• model problems and exemplars
• resources, metrics, or software that can be used to
compare self-adaptive approaches
• experiences in applying tools to real problems
*There will be a specific session to be dedicated to artifacts that may be useful for the community as a
whole. Please see http://wp.doc.ic.ac.uk/seams2017/call-for-artifacts/ for more details.
Selected papers will be invited to submit to the ACM Transactions on Autonomous and Adaptive Systems (TAAS).
Paper Submission Details
Further Information
Symposia-related email should be addressed to:
seams17-org@lists.andrew.cmu.edu
Important Dates:
Abstract Submission: 6 January, 2017 (firm)
Paper Submission: 13 January, 2017 (firm)
Notification: 21 February, 2017
Camera ready: 6 Mar, 2017
SEAMS solicits three types of papers: long papers (10 pages for the main text, inclusive of figures, tables, appendices,
etc.; references may be included on up to two additional pages), short papers for new ideas and early results (6 pages +
1 references) and artifact papers (6 pages + 1 reference). Long papers should clearly describe innovative and original
research or explain how existing techniques have been applied to real-world examples. Short papers should describe
novel and promising ideas and/or techniques that are in an early stage of development. Artifact papers must describe
why and how the accompanying artifact may be useful for the broader community. Papers must not have been
previously published or concurrently submitted elsewhere. Papers must conform to IEEE formatting guidelines (see
ICSE 2017 style guidelines), and submitted via EasyChair. Accepted papers will appear in the symposium proceedings
that will be published in the ACM and IEEE digital libraries.
General Chair
David Garlan, USA
Program Chair
Bashar Nuseibeh, UK
Artifact Chair
Javier Cámara, US
Publicity Chair
Pooyan Jamshidi, UK
Local Chair
Nicolás D’Ippolito, AR
Program Committee
TBD
Steering Committee
Luciano Baresi, Italy
Nelly Bencomo, UK
Gregor Engels, Germany
Rogério de Lemos, UK
David Garlan, USA
Paola Inverardi, Italy
Marin Litoiu (Chair), Canada
John Mylopoulos, Italy
Hausi A. Müller, Canada
Bashar Nuseibeh, UK
Bradley Schmerl, USA
Co-locatedwith
26. Acknowledgement / IC4 activities
- My participation to the Dagstuhl seminar is fully supported by IC4.
- We are working on a machine learning work for predicting the
performance (job completion time, utilizations, throughput,
performance regressions) of big data (Apache Hadoop and Spark), the
results will be soon published (PIs: Theo Lynn, Brian Lee, and other
colleagues Saul Gill, Binesh Nair, David O’Shea, Yuansong Qiao)
- We are also working on cloud/microservices migration for IC4 industry
members (PIs: Theo Lynn, Claus Pahl)
- And a self-configuration tool for highly configurable systems (with
Theo Lynn)