I Bet You Did Not Know That Software Can Adapt Itself On-the-fly?
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Software has the ability to adapt itself automatically based on new data and situations without needing to be reprogrammed. Pooyan Jamshidi developed a technique for software to learn and change how it works based on its own experiences. His research allows software to become smarter over time and better address the dynamic needs of users.
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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.
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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
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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.
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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.
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Integrated Model Discovery and Self-Adaptation of Robots
Integrated Model Discovery and Self-Adaptation of Robots
Transfer Learning for Performance Analysis of Highly-Configurable Software
Transfer Learning for Performance Analysis of Highly-Configurable Software
Production-Ready Machine Learning for the Software Architect
Production-Ready Machine Learning for the Software Architect
Transfer Learning for Software Performance Analysis: An Exploratory Analysis
Transfer Learning for Software Performance Analysis: An Exploratory Analysis
Learning Software Performance Models for Dynamic and Uncertain Environments
Learning Software Performance Models for Dynamic and Uncertain Environments
Sensitivity Analysis for Building Adaptive Robotic Software
Sensitivity Analysis for Building Adaptive Robotic Software
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Transfer Learning for Improving Model Predictions in Highly Configurable Soft...
Transfer Learning for Improving Model Predictions in Robotic Systems
Transfer Learning for Improving Model Predictions in Robotic Systems
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An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...
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Ukraine Euro Cup 2024 Squad Sergiy Rebrov's Selections and Prospects.docx
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Belgium vs Romania Injuries and Patience in Belgium’s Euro Cup Germany Squad....
Belgium coach Domenico Tedesco will wait for several key players to recover from injury. Even if it means they miss the opening Euro Cup Germany stages of the European Championship in Germany this month. Veteran defender Jan Vertonghen, midfielder Youri Tielemans and defender Arthur. Theate are being given time to play in the tournament because they are considered vital to Belgium’s cause, Tedesco said on Tuesday.
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"Of course, you prefer to take players who are fully fit, but that's okay. We want to wait and be patient for some players even if they cannot play in those first matches," he told a press conference. The 37-year-old Vertonghen, Belgium’s Euro Cup 2024 most-capped international with 154 appearances, is struggling to shake off a groin injury.
"He will be there normally. This also applies to Youri Tielemans and Arthur Theate. The latter's position is very sensitive. We don't have many choices at left back. "It will only change if it turns out that they will only be available when, say, the final of the Euro 2024 Championship comes around. That's too long to wait. "However, I am confident that the injured boys are on track for the Euros.
Belgium vs Romania: Radu Dragusin Prepares for Crucial Role in Euro Cup Germany
Some of them have taken not one but two steps forward in their rehabilitation," he said. None of the injured players will feature in this week’s warm-up friendlies against Montenegro and Luxembourg. Romania centre-back Radu Dragusin found chances limited at Tottenham Hotspur in the second half of the 2023-24 season.
But is crucial to his country's cause at UEFA Euro 2024 where his aerial ability, physicality and hard graft make him a standout player. The 22-year-old moved to North London from Italian side Genoa in January but was kept on the sidelines by the form of another new arrival for the season, Mickey van de Ven, something Romania coach Edward Iordanescu admitted was a concern.
It will mean limited game-time going into the finals, but Dragusin, who cites Netherlands defender Virgil van Dijk as a role model, started every Euro Cup Germany qualifier as Romania went through the campaign unbeaten in their 10 games. He will be among their most important players in their first game in Germany against Ukraine in Munich on June 17, taking the right centre-back role in what is likely to be a back four.
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Narrated Business Proposal for the Philadelphia Eagles
Slide 1:
Welcome, and thank you for joining me today. We will explore a strategic proposal to enhance parking and traffic management at Lincoln Financial Field, aiming to improve the overall fan experience and operational efficiency. This comprehensive plan addresses existing challenges and leverages innovative solutions to create a smoother and more enjoyable experience for our fans.
Slide 2:
Picture this: It’s a crisp fall afternoon, driving towards Lincoln Financial Field. The atmosphere is electric—tailgaters grilling, fans in Eagles jerseys creating a sea of green and white. The air buzzes with camaraderie and anticipation. You park, join the throng, and make your way to your seat. The stadium roars as the Eagles take the field, sending chills down your spine. Each play is a thrilling dance of strategy and skill. This is what being an Eagles fan is all about—the joy, the pride, and the shared experience.
Slide 3:
But now, the day is marred by frustration. The excitement wanes as you struggle to find a parking spot. The congestion is overwhelming, and tempers flare. The delays mean you miss the pre-game excitement, the tailgate camaraderie, and even the opening kick-off. After the game, the joy of victory or the shared solace of defeat is overshadowed by the stress of navigating out of the parking lot. The gridlock, honking horns, and endless waiting drain the energy and joy from what should have been an unforgettable experience.
Our proposal aims to eliminate these frustrations, ensuring that from arrival to departure, your experience is extraordinary. Efficient parking and smooth traffic flow are key to maintaining the high spirits and excitement that make game days special.
Slide 4:
The Philadelphia Eagles are not just a premier NFL team; they are an integral part of the community, hosting games, concerts, and various events at Lincoln Financial Field. Our state-of-the-art stadium is designed to provide a world-class experience for every attendee. Whether it's the thrill of game day, the excitement of a live concert, or the camaraderie of community events, we pride ourselves on delivering a fan-first experience and maintaining operational excellence across all our activities. Our commitment to our fans and community is unwavering, and we continuously strive to enhance every aspect of their experience, ensuring they leave with unforgettable memories.
Slide 5:
Recent trends show an increasing demand for efficient event logistics. Our customer feedback has consistently highlighted frustrations with parking and traffic. Surveys indicate that a significant number of fans are dissatisfied with the current parking situation. Comparisons with other venues like Citizens Bank Park and Wells Fargo Center reveal that we lag in terms of parking efficiency and convenience. These insights underscore the urgent need for innovation to meet and exceed fan expectations.
Slide 6:
As we delve into the intricacies of our operations, one glaring issue emer
Understanding Golf Simulator Equipment A Beginner's Guide.pdf
Dive into golf simulation with our beginner's guide, perfect for anyone new to the concept. Understand the critical components like sturdy frames, high-quality impact screens, and side netting that ensure your safety and enrich your practice sessions. Learn the benefits of proper projector mounts and compatibility with your existing setup. This guide helps you make informed choices, transforming your home into a realistic and effective golfing practice environment.
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Gabriel Kalembo A Rising Star in the World of Football Coaching
Gabriel Kalembo is a player's coach who connects with his teams on a deep level. With a strong background in sports science and a passion for the game, Kalembo has developed a unique coaching philosophy that emphasizes player development and tactical flexibility. His ability to connect with players and create a positive team culture has led to success at every level he has coached.
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I Bet You Did Not Know That Software Can Adapt Itself On-the-fly?
- 1. POOYAN JAMSHIDI I BET YOU DID NOT KNOW THAT SOFTWARE CAN ADAPT ITSELF ON-THE-FLY?
- 3. Solution
- 5. Research Challenge Results and Benefits Solution
Editor's Notes
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