Predictable Performance in the Cloud

Predictable Performance in the Cloud

Internship Description

The position will be in the context of a project whose goal is to enable cloud-based applications to achieve more predictable performance. Our main focus is on modern, highly distributed applications that are realized as service-oriented architectures. In particular, we focus on how to enable consistent low latency, which is critical for many cloud applications yet difficult to achieve due to many complex sources that skew the tail of latency distribution even in well-provisioned systems. Execution in multi-tenant environments further exacerbates things as it is known that performance degrades due to contention for shared resources in face of imperfect resource isolation.

The internship work will upon our recent EuroSys '17 results and improve our Rein approach in regard to some of the open questions such as accurate bottleneck estimation and using server-based feedback. Candidates should be motivated to work on research-oriented problems in a fast-paced and tightly-night team. They should have a strong computing or engineering background with a good background in computer systems, networking, and distributed systems. Ideally, they would have experience in building and working with large software systems and tools, and proven knowledge of Java.


The students are expected to study the existing Rein solution and devise theoretically-sound approaches (with the assistance of the supervisor) to improve its performance. The students will be able to also collaborate with the lead PhD student of Rein and to evaluate the mechanisms on real-world datasets on a state-of-the-art testbed. The above results, if completed, are considered novel and can result into a publication (with the agreement of the supervisor). ​

Faculty Name

Marco Canini

Field of Study

Computer Science