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
New Clustering-based Forecasting Method for Disaggregated End-consumer Electr...Peter Laurinec
This paper presents a new method for forecasting the load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of representations of time series using a multiple linear regression model. Final centroid-based forecasts are scaled by saved normalisation parameters to create forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on two large smart meter datasets from residences of Ireland and factories of Slovakia.
The achieved results proved that our clustering-based method improves forecasting accuracy and decreases high rates of errors (maximum). It is also more scalable since it is not necessary to train the model for every consumer.
Cost-Aware Virtual Machine Placement acrossDistributed Data Centers using Ba...Soodeh Farokhi
In recent years, cloud computing providers have been working to provide highly available and scalable cloud services to keep themselves alive in the competitive market of various cloud services. The difficulty is that to provide such high quality services, they need to enlarge data centers (DCs), and consequently, to increase operating costs. Hence, leveraging cost-aware solutions to manage resources is necessary for cloud providers to decrease the total energy consumption, while keeping their customers satisfied with high quality services. In this paper, we consider the cost-aware virtual machine (VM) placement across geographically distributed DCs as a multi-criteria decision making problem and propose a novel approach to solve it by utilizing Bayesian Networks and two algorithms for VM allocation and consolidation. The novelty of our work lays in building the Bayesian Network according to the extracted expert knowledge and the probabilistic dependencies among parameters to make decisions regarding cost-aware VM placement across distributed DCs, which can face power outages. Moreover, to evaluate the proposed approach we design a novel simulation framework that provides the required features for simulating distributed DCs. The performance evaluation results reveal that using the proposed approach can reduce operating costs by up to 45% in comparison with First-Fit-Decreasing heuristic method as a baseline algorithm.
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
New Clustering-based Forecasting Method for Disaggregated End-consumer Electr...Peter Laurinec
This paper presents a new method for forecasting the load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of representations of time series using a multiple linear regression model. Final centroid-based forecasts are scaled by saved normalisation parameters to create forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on two large smart meter datasets from residences of Ireland and factories of Slovakia.
The achieved results proved that our clustering-based method improves forecasting accuracy and decreases high rates of errors (maximum). It is also more scalable since it is not necessary to train the model for every consumer.
Cost-Aware Virtual Machine Placement acrossDistributed Data Centers using Ba...Soodeh Farokhi
In recent years, cloud computing providers have been working to provide highly available and scalable cloud services to keep themselves alive in the competitive market of various cloud services. The difficulty is that to provide such high quality services, they need to enlarge data centers (DCs), and consequently, to increase operating costs. Hence, leveraging cost-aware solutions to manage resources is necessary for cloud providers to decrease the total energy consumption, while keeping their customers satisfied with high quality services. In this paper, we consider the cost-aware virtual machine (VM) placement across geographically distributed DCs as a multi-criteria decision making problem and propose a novel approach to solve it by utilizing Bayesian Networks and two algorithms for VM allocation and consolidation. The novelty of our work lays in building the Bayesian Network according to the extracted expert knowledge and the probabilistic dependencies among parameters to make decisions regarding cost-aware VM placement across distributed DCs, which can face power outages. Moreover, to evaluate the proposed approach we design a novel simulation framework that provides the required features for simulating distributed DCs. The performance evaluation results reveal that using the proposed approach can reduce operating costs by up to 45% in comparison with First-Fit-Decreasing heuristic method as a baseline algorithm.
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.
Microservices have emerged as an architectural style for developing maintainable and scalable applications. Understanding the performance of alternative deployment configurations is challenging and must be aligned with the system usage in the production environment. In this talk I present an approach for automatically assessing scalability of microservice configuration alternatives. The talk with briefly introduce the concept of microservices, present the deployment approach and the evaluation approach based on the open source tool locust.io; it will present the tool PPTAM used to conduct the experiments and the performed data analysis.
The Machine Learning behind the Autonomous Database ILOUG Feb 2020 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. Some of the other use cases is to use convolution filters...
Alex Smola, Professor in the Machine Learning Department, Carnegie Mellon Uni...MLconf
Fast, Cheap and Deep – Scaling Machine Learning: Distributed high throughput machine learning is both a challenge and a key enabling technology. Using a Parameter Server template we are able to distribute algorithms efficiently over multiple GPUs and in the cloud. This allows us to design very fast recommender systems, factorization machines, classifiers, and deep networks. This degree of scalability allows us to tackle computationally expensive problems efficiently, yielding excellent results e.g. in visual question answering.
In this analysis, we establish an economic and mathematical (queueing theory) framework to calculate how much it would cost to build the dev/test lab of your dreams. One in which developers never wait for testing resources, and every commit is tested on replicas of the production environment. Then, we contrast several different options to get there and evaluate all of them from an economic perspective.
After the presentation, in order to illustrate that this is not only theory, I also gave a quick demonstration of how we at Ravello use our own technology to develop our own application. Each engineer can spin up as many instances of the production replica app as needed on demand for dev/test. We showed how we have integrated Ravello with Jenkins so that on every commit, we spin up the production replica application and run integration tests in parallel.
If you have any questions, feel free to reach out. We are more than happy to discuss how this may be relevant to your development process. www.ravellosystems.com
When Agile is a Quality Game Changer Webinar - Michael Mah, Philip LewXBOSoft
Accelerate your Agile success with in-depth research and smarter decisions. Michael Mah of QSM Associates shows you what it takes to find and utilize patterns of successful Agile development in this quarterly XBOSoft webinar.
This talk was given at the Online Kubernetes Meetup July 2020 as well as DevOps Fusion 2020. The talk discusses 3 major problems in current delivery and operations: too much time spent in delivery, hard to maintain monolithic delivery pipelines and a lack of auto-remediation of production problems
The talk focuses on new approaches to solve these problems inspired by SRE practices and event-driven architectures.
As an implementation for a new approach we use Keptn (www.keptn.sh) - a CNCF Open Source project.
Solve the colocation conundrum: Performance and density at scale with KubernetesNiklas Quarfot Nielsen
As we move from monolithic applications to microservices, the ability to colocate workloads offers a tremendous opportunity to realize greater development velocity, robustness, and resource utilization. But workload colocation can also introduce performance variability and affect service levels. Google describes the problem as the “tail at scale”—the amplification of negative results observed at the tail of the latency curve when many systems are involved.
With its latest tooling capabilities, Intel has an experiments framework to calculate the trade-offs between low latency and higher density. Niklas Nielsen discusses the challenges and complexities of workload colocation, why solving these challenges matters to your business no matter the size, and how Intel intends to help smarter resource allocations with its latest tooling capabilities and Kubernetes.
This presentation was first given at INFORMS in November 2013. It presents an analysis of the features that had the most impact on MIP solver performance during the last 12 years.
More presentations are available at https://www.ibm.com/developerworks/community/groups/community/DecisionOptimization
Keynote: Building and Operating A Serverless Streaming Runtime for Apache Bea...Flink Forward
Apache Beam is Flink’s sibling in the Apache family of streaming processing frameworks. The Beam and Flink teams work closely together on advancing what is possible in streaming processing, including Streaming SQL extensions and code interoperability on both platforms.
Beam was originally developed at Google as the amalgamation of its internal batch and streaming frameworks to power the exabyte-scale data processing for Gmail, YouTube and Ads. It now powers a fully-managed, serverless service Google Cloud Dataflow, as well as is available to run in other Public Clouds and on-premises when deployed in portability mode on Apache Flink, Spark, Samza and other runners. Users regularly run distributed data processing jobs on Beam spanning tens of thousands of CPU cores and processing millions of events per second.
In this session, Sergei Sokolenko, Cloud Dataflow product manager, and Reuven Lax, the founding member of the Dataflow and Beam team, will share Google’s learnings from building and operating a global streaming processing infrastructure shared by thousands of customers, including:
safe deployment to dozens of geographic locations,
resource autoscaling to minimize processing costs,
separating compute and state storage for better scaling behavior,
dynamic work rebalancing of work items away from overutilized worker nodes,
offering a throughput-optimized batch processing capability with the same API as streaming,
grouping and joining of 100s of Terabytes in a hybrid in-memory/on-desk file system,
integrating with the Google Cloud security ecosystem, and other lessons.
Customers benefit from these advances through faster execution of jobs, resource savings, and a fully managed data processing environment that runs in the Cloud and removes the need to manage infrastructure.
Production Readiness Strategies in an Automated WorldSean Chittenden
Production Ready. What does it mean? And to whom? Does that term factor in post-launch concerns such as debugability and ownership? What are the lifecycle phases for moving an idea into a hardened production system?
As the world continues its furious adoption of automation, Foo-as-a-Service, and ever changing tools, what are the baseline assumptions, risks, checklists, and processes required to support the evolving landscape of "production ready." In this talk we will deploy a sample application and build both a checklist and scorecard to evaluate the readiness of a system and an organization's practices.
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.
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
More Related Content
Similar to Fuzzy Control meets Software Engineering
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.
Microservices have emerged as an architectural style for developing maintainable and scalable applications. Understanding the performance of alternative deployment configurations is challenging and must be aligned with the system usage in the production environment. In this talk I present an approach for automatically assessing scalability of microservice configuration alternatives. The talk with briefly introduce the concept of microservices, present the deployment approach and the evaluation approach based on the open source tool locust.io; it will present the tool PPTAM used to conduct the experiments and the performed data analysis.
The Machine Learning behind the Autonomous Database ILOUG Feb 2020 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. Some of the other use cases is to use convolution filters...
Alex Smola, Professor in the Machine Learning Department, Carnegie Mellon Uni...MLconf
Fast, Cheap and Deep – Scaling Machine Learning: Distributed high throughput machine learning is both a challenge and a key enabling technology. Using a Parameter Server template we are able to distribute algorithms efficiently over multiple GPUs and in the cloud. This allows us to design very fast recommender systems, factorization machines, classifiers, and deep networks. This degree of scalability allows us to tackle computationally expensive problems efficiently, yielding excellent results e.g. in visual question answering.
In this analysis, we establish an economic and mathematical (queueing theory) framework to calculate how much it would cost to build the dev/test lab of your dreams. One in which developers never wait for testing resources, and every commit is tested on replicas of the production environment. Then, we contrast several different options to get there and evaluate all of them from an economic perspective.
After the presentation, in order to illustrate that this is not only theory, I also gave a quick demonstration of how we at Ravello use our own technology to develop our own application. Each engineer can spin up as many instances of the production replica app as needed on demand for dev/test. We showed how we have integrated Ravello with Jenkins so that on every commit, we spin up the production replica application and run integration tests in parallel.
If you have any questions, feel free to reach out. We are more than happy to discuss how this may be relevant to your development process. www.ravellosystems.com
When Agile is a Quality Game Changer Webinar - Michael Mah, Philip LewXBOSoft
Accelerate your Agile success with in-depth research and smarter decisions. Michael Mah of QSM Associates shows you what it takes to find and utilize patterns of successful Agile development in this quarterly XBOSoft webinar.
This talk was given at the Online Kubernetes Meetup July 2020 as well as DevOps Fusion 2020. The talk discusses 3 major problems in current delivery and operations: too much time spent in delivery, hard to maintain monolithic delivery pipelines and a lack of auto-remediation of production problems
The talk focuses on new approaches to solve these problems inspired by SRE practices and event-driven architectures.
As an implementation for a new approach we use Keptn (www.keptn.sh) - a CNCF Open Source project.
Solve the colocation conundrum: Performance and density at scale with KubernetesNiklas Quarfot Nielsen
As we move from monolithic applications to microservices, the ability to colocate workloads offers a tremendous opportunity to realize greater development velocity, robustness, and resource utilization. But workload colocation can also introduce performance variability and affect service levels. Google describes the problem as the “tail at scale”—the amplification of negative results observed at the tail of the latency curve when many systems are involved.
With its latest tooling capabilities, Intel has an experiments framework to calculate the trade-offs between low latency and higher density. Niklas Nielsen discusses the challenges and complexities of workload colocation, why solving these challenges matters to your business no matter the size, and how Intel intends to help smarter resource allocations with its latest tooling capabilities and Kubernetes.
This presentation was first given at INFORMS in November 2013. It presents an analysis of the features that had the most impact on MIP solver performance during the last 12 years.
More presentations are available at https://www.ibm.com/developerworks/community/groups/community/DecisionOptimization
Keynote: Building and Operating A Serverless Streaming Runtime for Apache Bea...Flink Forward
Apache Beam is Flink’s sibling in the Apache family of streaming processing frameworks. The Beam and Flink teams work closely together on advancing what is possible in streaming processing, including Streaming SQL extensions and code interoperability on both platforms.
Beam was originally developed at Google as the amalgamation of its internal batch and streaming frameworks to power the exabyte-scale data processing for Gmail, YouTube and Ads. It now powers a fully-managed, serverless service Google Cloud Dataflow, as well as is available to run in other Public Clouds and on-premises when deployed in portability mode on Apache Flink, Spark, Samza and other runners. Users regularly run distributed data processing jobs on Beam spanning tens of thousands of CPU cores and processing millions of events per second.
In this session, Sergei Sokolenko, Cloud Dataflow product manager, and Reuven Lax, the founding member of the Dataflow and Beam team, will share Google’s learnings from building and operating a global streaming processing infrastructure shared by thousands of customers, including:
safe deployment to dozens of geographic locations,
resource autoscaling to minimize processing costs,
separating compute and state storage for better scaling behavior,
dynamic work rebalancing of work items away from overutilized worker nodes,
offering a throughput-optimized batch processing capability with the same API as streaming,
grouping and joining of 100s of Terabytes in a hybrid in-memory/on-desk file system,
integrating with the Google Cloud security ecosystem, and other lessons.
Customers benefit from these advances through faster execution of jobs, resource savings, and a fully managed data processing environment that runs in the Cloud and removes the need to manage infrastructure.
Production Readiness Strategies in an Automated WorldSean Chittenden
Production Ready. What does it mean? And to whom? Does that term factor in post-launch concerns such as debugability and ownership? What are the lifecycle phases for moving an idea into a hardened production system?
As the world continues its furious adoption of automation, Foo-as-a-Service, and ever changing tools, what are the baseline assumptions, risks, checklists, and processes required to support the evolving landscape of "production ready." In this talk we will deploy a sample application and build both a checklist and scorecard to evaluate the readiness of a system and an organization's practices.
Similar to Fuzzy Control meets Software Engineering (20)
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
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.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
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.
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Understanding Nidhi Software Pricing: A Quick Guide 🌟
Choosing the right software is vital for Nidhi companies to streamline operations. Our latest presentation covers Nidhi software pricing, key factors, costs, and negotiation tips.
📊 What You’ll Learn:
Key factors influencing Nidhi software price
Understanding the true cost beyond the initial price
Tips for negotiating the best deal
Affordable and customizable pricing options with Vector Nidhi Software
🔗 Learn more at: www.vectornidhisoftware.com/software-for-nidhi-company/
#NidhiSoftwarePrice #NidhiSoftware #VectorNidhi
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/
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Fuzzy Control meets Software Engineering
1. Fuzzy Control meets Software Engineering
Pooyan Jamshidi
IC4-Irish Centre for Cloud Computing and Commerce
School of Computing,Dublin City University
Pooyan.jamshidi@computing.dcu.ie
DagstuhlSeminar
Control Theory meets Software Engineering
September, 2014
2. Naeem Esfahani and Sam Malek,
“Uncertainty in Self-Adaptive
Software Systems”
Knowledge
Specification
Uncertainty
2
6. Problem 1: ~75% wasted capacityActual demandProblem 2: customer lostTraffic in an unexpected burst in requests (e.g. end of year traffic to Amazon.com)
6
7. Really like this?? Auto-scaling enables you to realize this ideal on-demand provisioning
Time
Demand
? Enacting change in the Cloud resources are notreal-time
7
8. Capacity we can provision with Auto-ScalingA realistic figure of dynamic provisioning
8
12. These quantitative values are required to be determined by the user requires deep knowledge of application (CPU, memory, thresholds) requires performance modeling expertise (when and how to scale) A unified opinion of user(s) is required
Amazon auto scaling
Microsoft Azure Watch
12
Microsoft Azure Auto- scaling Application Block
12
14. Uncertainty related to enactment latency:
The same scaling action (adding/removing
a VM with precisely the same size) took
different time to be enacted on the
cloud platform (here is Microsoft Azure)
at different points and
this difference were significant
(up to couple of minutes).
The enactment latency would be also different
on different cloud platforms. 14
15. Offline benchmarking
Trial-and-error
Expert knowledge
Costly and
not systematic
A. Gandhi, P. Dube, A. Karve, A. Kochut, L. Zhang, Adaptive, “Model-driven Autoscalingfor Cloud Applications”, ICAC’14
arrival rate (req/s)
95% Resp. time (ms)
400 ms
60 req/s 15
17. 0 0.5 1 1.5 2 2.5 3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Region of
definite
satisfaction
Region of
definite
Region of dissatisfaction
uncertain
satisfaction
Performance Index
Possibility
Performance Index
Possibility
words can mean different
things to different people
Different users often
recommend
different elasticity policies
0 0.5 1 1.5 2 2.5 3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Type-2 MF
Type-1 MF
17
20. Rule
(풍)
Antecedents Consequent
풄풂풗품
풍
Workload
Response-time
Normal
(-2)
Effort
(-1)
Medium
Effort
(0)
High
Effort
(+1)
Maximum
Effort (+2)
1 Very low Instantaneous 7 2 1 0 0 -1.6
2 Very low Fast 5 4 1 0 0 -1.4
3 Very low Medium 0 2 6 2 0 0
4 Very low Slow 0 0 4 6 0 0.6
5 Very low Very slow 0 0 0 6 4 1.4
6 Low Instantaneous 5 3 2 0 0 -1.3
7 Low Fast 2 7 1 0 0 -1.1
8 Low Medium 0 1 5 3 1 0.4
9 Low Slow 0 0 1 8 1 1
10 Low Very slow 0 0 0 4 6 1.6
11 Medium Instantaneous 6 4 0 0 0 -1.6
12 Medium Fast 2 5 3 0 0 -0.9
13 Medium Medium 0 0 5 4 1 0.6
14 Medium Slow 0 0 1 7 2 1.1
15 Medium Very slow 0 0 1 3 6 1.5
16 High Instantaneous 8 2 0 0 0 -1.8
17 High Fast 4 6 0 0 0 -1.4
18 High Medium 0 1 5 3 1 0.4
19 High Slow 0 0 1 7 2 1.1
20 High Very slow 0 0 0 6 4 1.4
21 Very high Instantaneous 9 1 0 0 0 -1.9
22 Very high Fast 3 6 1 0 0 -1.2
23 Very high Medium 0 1 4 4 1 0.5
24 Very high Slow 0 0 1 8 1 1
25 Very high Very slow 0 0 0 4 6 1.6
Rule
(퐥)
Antecedents Consequent
풄풂풗품
Work 풍
load
Response
-time
-2 -1 0 +1 +2
12 Medium Fast 2 5 3 0 0 -0.9
10 experts’ responses
푅푙 : IF (the workload (푥1) is 퐹 푖1, AND the response-time
(푥2) is 퐺 푖2), THEN (add/remove 푐푎푣푔
푙 instances).
푐푎푣푔
푙 =
푢=1
푁푙 푤푢푙
× 퐶
푢=1
푁푙 푤푢푙
Goal: pre-computations of costly calculations
to make a runtime efficient elasticity
reasoning based on fuzzy inference 20
21. Liang, Q., Mendel, J. M. (2000). Interval type-2 fuzzy
logic systems: theory and design. Fuzzy Systems, IEEE
Transactions on, 8(5), 535-550.
Scaling Actions
Monitoring Data
21
30. SUT
Criteria
Big spike
Dual phase
Large variations
Quickly varying
Slowly varying
Steep tri phase
RobusT2Scale
푟푡95%
973ms
537ms
509ms
451ms
423ms
498ms 푣푚
3.2
3.8
5.1
5.3
3.7
3.9
Overprovisioning
푟푡95%
354ms
411ms
395ms
446ms
371ms
491ms 푣푚
6
6
6
6
6
6
Under provisioning
푟푡95%
1465ms
1832ms
1789ms
1594ms
1898ms
2194ms 푣푚
2
2
2
2
2
2
SLA: 풓풕ퟗퟓ≤ퟔퟎퟎ풎풔
For every 10s control interval•RobusT2Scale is superior to under-provisioning in terms of guaranteeing the SLA and does not require excessive resources•RobusT2Scale is superior to over-provisioning in terms of guaranteeing required resources while guaranteeing the SLA 30
37. 0.05
0.1
0.15
0.2
0.25
0.3
0.35
Type-1 FLS Type-2 FLS
RMSE
• The rule reduction reduced the rules
quite considerably.
• IT2 FLCs are more robust due to less
mean error and less variation in the
estimation error.
• T1 FLCs in some realization drop more
rules in comparison with the IT2 FLCs.
• IT2 FLC original designs can be designed
with less rules.
37