MDBF Bülteni 5. Sayı

All for Computer System Analytics

Today’s large-scale computer systems that serve high performance computing and cloud face challenges in delivering predictable performance, while maintaining efficiency, resilience, and security. Much of computer system management has traditionally relied on (manual) expert analysis and policies that rely on heuristics derived based on such analysis. This talk will discuss a new path on designing “automated analytics” methods for large-scale computer systems and how to make strides towards a longer term vision where computing systems are able to self-manage and improve. Specifically, the talk will first cover how to systematically diagnose root causes of performance “anomalies”, which cause substantial efficiency losses and higher cost. Second, it will discuss how to identify applications running on computing systems and discuss how such discoveries can help reduce vulnerabilities and avoid unwanted applications. The talk will also highlight how to apply machine learning in a practical and scalable way to help understand complex systems, demonstrate methods to help standardize study of performance anomalies, discuss explainability of applied ML methods in the context of computer system analytics, and point out future directions in automating computer system management.

‘’AI-on-chip’’ with Bio-inspired Memory Technologies

Today’s large-scale computer systems that serve high performance computing and cloud face challenges in delivering predictable performance, while maintaining efficiency, resilience, and security. Much of computer system management has traditionally relied on (manual) expert analysis and policies that rely on heuristics derived based on such analysis. This talk will discuss a new path on designing “automated analytics” methods for large-scale computer systems and how to make strides towards a longer term vision where computing systems are able to self-manage and improve. Specifically, the talk will first cover how to systematically diagnose root causes of performance “anomalies”, which cause substantial efficiency losses and higher cost. Second, it will discuss how to identify applications running on computing systems and discuss how such discoveries can help reduce vulnerabilities and avoid unwanted applications. The talk will also highlight how to apply machine learning in a practical and scalable way to help understand complex systems, demonstrate methods to help standardize study of performance anomalies, discuss explainability of applied ML methods in the context of computer system analytics, and point out future directions in automating computer system management.

All for Computer System Analytics

Today’s large-scale computer systems that serve high performance computing and cloud face challenges in delivering predictable performance, while maintaining efficiency, resilience, and security. Much of computer system management has traditionally relied on (manual) expert analysis and policies that rely on heuristics derived based on such analysis. This talk will discuss a new path on designing “automated analytics” methods for large-scale computer systems and how to make strides towards a longer term vision where computing systems are able to self-manage and improve. Specifically, the talk will first cover how to systematically diagnose root causes of performance “anomalies”, which cause substantial efficiency losses and higher cost. Second, it will discuss how to identify applications running on computing systems and discuss how such discoveries can help reduce vulnerabilities and avoid unwanted applications. The talk will also highlight how to apply machine learning in a practical and scalable way to help understand complex systems, demonstrate methods to help standardize study of performance anomalies, discuss explainability of applied ML methods in the context of computer system analytics, and point out future directions in automating computer system management.