#High tail hall 2 full rip manual#
Therefore, our contributions aim to solve the above challenges to deliver automated performance models with minimal computational overhead and no manual intervention. Unlike previous work, our focus is on cloud-hosted and continuously evolving microservice architectures, so-called cloud-native applications. We achieve this by combining machine learning with traditional performance modeling techniques.
#High tail hall 2 full rip software#
In this thesis, we present five techniques for automated learning of performance models for cloud-native software systems. This violates common assumptions of the state of the art and opens a research gap for our work. These include the amount, type, and structure of monitoring data, frequent behavioral changes, or infrastructure variabilities.
![high tail hall 2 full rip high tail hall 2 full rip](http://4.bp.blogspot.com/_Q5MYmbdm7Eg/So8pyKz2fwI/AAAAAAAAAkg/zRL8JF-ypuA/s320/Foto.png)
![high tail hall 2 full rip high tail hall 2 full rip](http://heathcaldwell.com/yahoo_site_admin/assets/images/Linley_Wood_Staffordshire.12621930_std.jpg)
In summary, the increasing use of ever-changing cloud-hosted microservice applications introduces a number of unique challenges for modeling the performance of modern applications. Such applications are often referred to as cloud-native applications. In addition, the microservices paradigm is typically paired with a DevOps culture, resulting in frequent application and deployment changes. Microservice architectures offer massive benefits but also have broad implications for the performance characteristics of the respective systems. Modern cloud applications are often deployed as a collection of fine-grained and interconnected components called microservices. However, the emergence of modern software paradigms makes it increasingly difficult to derive such models and renders previous performance modeling techniques infeasible.
![high tail hall 2 full rip high tail hall 2 full rip](http://3.bp.blogspot.com/-MXyuhqj2UUo/T92aU2smVnI/AAAAAAAAATE/CTTGyLfWziI/s1600/aakFvHQe.png)
This requires the research and development of new modeling approaches to understand the behavior of running applications with minimal information. Simultaneously, meeting the increasing and changing resource and performance requirements is only possible by optimizing resource management without introducing additional overhead. With data centers already responsible for about one percent of the world's power consumption, optimizing resource usage is of paramount importance. These increased performance and availability requirements, coupled with the unpredictable usage growth, are driving an increasing proportion of applications to run on public cloud platforms as they promise better scalability and reliability. At the same time, performance requirements for modern technologies are becoming more stringent as users become accustomed to higher standards. One consequence of the recent coronavirus pandemic is increased demand and use of online services around the globe.