Dr. Dongmei Zhang is the Assistant Managing Director of Microsoft Research Asia (MSRA), leading the research of Data, Knowledge and Intelligence (DKI) which is an interdisciplinary area across data intelligence, knowledge computing, information visualization, and software engineering. Dr. Zhang founded the Software Analytics Group in MSRA in 2009. Since then she has been leading the group to research software analytics technologies. In addition to conducting state-of-the-art research, her group collaborates closely with multiple product teams in Microsoft, and has developed and deployed software analytics tools that have created high business impact. In recent years, Dr. Zhang and her teams have expanded the impact into the business intelligence area, and helped Microsoft establish technology leadership in the direction of Smart Data Discovery.

Dr. Dan Pei is a tenured Associate Professor in Computer Science Department at Tsinghua University in Beijing, China. Before joining Tsinghua, Dr. Pei was a Principal Researcher at AT&T Research in the U.S. He received his PhD degree (with honor) from UCLA in 2005, and his Bachelor's and Master's degrees from Tsinghua University in 1997 and 2000. His current research interests are in AIOps (Artificial Intelligence for Operations). He is an ACM Senior Member and an IEEE Senior Member.

Title: Towards Autonomous IT Operations through Artificial Intelligence
IT operations are important techniques for ensuring our increasingly digitalized world run reliably, efficiently and safely, yet current IT Operations are labor-intensive, very stressful, and ineffective. In this talk, I will first explain why AI is necessary for IT Operations. I will then present a suite of unsupervised anomaly detection algorithms for time series, log, and trace data, followed by the lessons learned. I will conclude my talk by envisioning that future IT Operations will become more and more autonomous, which will enable an even better digitalized world.

Xin Peng received the bachelor’s and PhD degrees in computer science from Fudan University, in 2001 and 2006, respectively. He is a professor of the School of Computer Science, Fudan University, China. His research interests include data-driven intelligent software development, cloud-native software and AIOps, software engineering for AI and cyber-physical-social Systems. His work won the ICSM 2011 Best Paper Award, the ACM SIGSOFT Distinguished Paper Award at ASE 2018, the IEEE TCSE Distinguished Paper Awards at ICSME 2018/2019/2020, and the IEEE Transactions on Software Engineering 2018 Best Paper Award. He was a steering committee member of International Conference on Software Maintenance and Evolution (ICSME) during 2017-2020. Now he is a co-editor of Journal of Software: Evolution and Process (JSEP), an editorial board member of ACM Transactions on Software Engineering and Methodology (TOSEM), Empirical Software Engineering (EMSE), and Chinese Journal of Software.

Title: Large-Scale Trace Analysis for Microservice Architecture Understanding and Fault Analysis
Operation engineers and developers highly rely on trace analysis to understand architectures and diagnose various problems such as service failures and quality degradation. However, the huge number of traces produced at runtime makes it challenging to capture the required information in real-time. In this talk, I will present two recent works collaborating with industrial partners, GMTA and MicroHECL, on large-scale trace analysis for microservice architecture understanding and fault analysis. Built on a graph-based representation, GMTA abstracts traces into different paths and further groups them into business flows and supports various analytical applications based on an efficient storage and access mechanism. MicroHECL is a high-efficient root cause localization approach for availability issues of microservice systems. It analyzes possible anomaly propagation chains, and ranks candidate root causes based on correlation analysis. Both the two works have been applied in the production systems of our industrial partners.

Christina Delimitrou is an Assistant Professor and the John and Norma Balen Sesquicentennial Faculty Fellow at Cornell University, where she works on computer architecture and computer systems. She specifically focuses on improving the performance predictability and resource efficiency of large-scale cloud infrastructures by revisiting the way these systems are designed and managed. Christina is the recipient of the 2020 TCCA Young Computer Architect Award, an Intel Rising Star Award, a Microsoft Research Faculty Fellowship, an NSF CAREER Award, a Sloan Research Scholarship, two Google Research Awards, and a Facebook Faculty Research Award. Her work has also received 4 IEEE Micro Top Picks awards and several best paper awards. Before joining Cornell, Christina received her PhD from Stanford University. She had previously earned an MS also from Stanford, and a diploma in Electrical and Computer Engineering from the National Technical University of Athens. More information can be found at: http://www.csl.cornell.edu/~delimitrou/

Title: Leveraging ML to Handle the Increasing Complexity of the Cloud
Cloud services are increasingly adopting new programming models, such as microservices and serverless compute. While these frameworks offer several advantages, such as better modularity, ease of maintenance and deployment, they also introduce new hardware and software challenges.

In this talk, I will briefly discuss the challenges that these new cloud models introduce in hardware and software, and present some of our work on employing ML to improve the cloud’s performance predictability and resource efficiency. I will first discuss Seer, a performance debugging system that identifies root causes of unpredictable performance in multi-tier interactive microservices, and Sage, which improves on Seer by taking a completely unsupervised learning approach to data-driven performance debugging, making it both practical and scalable.

Rama Akkiraju is an IBM Fellow, Master Inventor, and IBM Academy Member at IBM’s Automation Division where she is the CTO of AI Operations. AI Operations is about optimizing information technology (IT) operations management using Artificial Intelligence (AI). Prior to this role, Rama led the AI mission of enabling natural, personalized, and compassionate conversations between computers and humans where she and her team developed and delivered several differentiating AI Services such as Personality Insights, Tone Analyzer, Emotion Analysis, and Sentiment Analysis services to the IBM Watson platform. Before this, Rama led various projects and Research teams at IBM Watson Research Center and IBM Almaden Research Center in the areas of AI, analytics, business process optimization and delivered innovative analytical assets to IBM’s Global Business Services (GBS), and Global Technology Services (GTS) organizations. Rama has been named by Forbes as one of the ‘Top 20 Women in AI Research’ in May 2017, has been featured in ‘A-Team in AI’ by Fortune magazine in July 2018, and named ‘Top 10 pioneering women in AI and Machine Learning’ by Enterprise Management 360 in April 2019. Rama is also the recipient of the University of California, Berkeley’s Athena award for Technical and Executive Leadership for 2020.

In her career, Rama has worked on agent-based decision support systems, business process management, electronic market places, and semantic Web services, for which she led a World-Wide-Web (W3C) standard. Rama has co-authored 4 book chapters and over 100 technical papers. Rama has 30+ issued patents and 25+ pending. She is the recipient of 4 best paper awards in AI and Operations Research. Rama served as the President for ISSIP, a Service Science professional society for 2018, and continues to actively drive AI projects through this professional society. Rama holds a Master’s degree in Computer Science and has received a gold medal from New York University for her MBA for the highest academic excellence.

Talk Abstract
The vision of self-aware, self-healing and self-managing Information Technology (IT) systems has remained elusive till recently. Recent advancements in Cloud computing, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) in general, are all making it possible to realize this vision now. AI can optimize IT operations management processes by increasing application availability, predicting and detecting problems early, reducing the time it takes to resolve problems, proactively avoiding problems, and optimizing the resources and cost of running business applications on hybrid Clouds. In this talk, I will discuss the opportunity for AI to optimize IT Operations Management. I will describe how semi-structured application and infrastructure logs can be analyzed to predict anomalies early, how entities can be extracted and linked from logs, alerts and events to reduce alert noise for IT operations admins, how NLP can be put to use on unstructured content in prior incident tickets to extract next-best-action recommendations to resolve problems, and how deployment change request descriptions can be analyzed in combination with past incident root cause information to predict risks of deployment changes to prevent issues from happening in the first place.