Kubernetes scheduling: Taxonomy, ongoing issues and challenges

C Carrión - ACM Computing Surveys, 2022 - dl.acm.org
Continuous integration enables the development of microservices-based applications using
container virtualization technology. Container orchestration systems such as Kubernetes …

Serverless computing: state-of-the-art, challenges and opportunities

Y Li, Y Lin, Y Wang, K Ye, C Xu - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Serverless computing is growing in popularity by virtue of its lightweight and simplicity of
management. It achieves these merits by reducing the granularity of the computing unit to …

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

E Afgan, D Baker, B Batut, M Van Den Beek… - Nucleic acids …, 2018 - academic.oup.com
Abstract Galaxy (homepage: https://galaxyproject. org, main public server: https://usegalaxy.
org) is a web-based scientific analysis platform used by tens of thousands of scientists …

{FIRM}: An intelligent fine-grained resource management framework for {SLO-Oriented} microservices

H Qiu, SS Banerjee, S Jha, ZT Kalbarczyk… - 14th USENIX symposium …, 2020 - usenix.org
User-facing latency-sensitive web services include numerous distributed,
intercommunicating microservices that promise to simplify software development and …

Learning scheduling algorithms for data processing clusters

H Mao, M Schwarzkopf, SB Venkatakrishnan… - Proceedings of the …, 2019 - dl.acm.org
Efficiently scheduling data processing jobs on distributed compute clusters requires complex
algorithms. Current systems use simple, generalized heuristics and ignore workload …

An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems

Y Gan, Y Zhang, D Cheng, A Shetty, P Rathi… - Proceedings of the …, 2019 - dl.acm.org
Cloud services have recently started undergoing a major shift from monolithic applications,
to graphs of hundreds or thousands of loosely-coupled microservices. Microservices …

Borg: the next generation

M Tirmazi, A Barker, N Deng, ME Haque… - Proceedings of the …, 2020 - dl.acm.org
This paper analyzes a newly-published trace that covers 8 different Borg [35] clusters for the
month of May 2019. The trace enables researchers to explore how scheduling works in …

Ray: A distributed framework for emerging {AI} applications

P Moritz, R Nishihara, S Wang, A Tumanov… - … USENIX symposium on …, 2018 - usenix.org
The next generation of AI applications will continuously interact with the environment and
learn from these interactions. These applications impose new and demanding systems …

{INFaaS}: Automated model-less inference serving

F Romero, Q Li, NJ Yadwadkar… - 2021 USENIX Annual …, 2021 - usenix.org
Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

Gandiva: Introspective cluster scheduling for deep learning

W Xiao, R Bhardwaj, R Ramjee, M Sivathanu… - … USENIX Symposium on …, 2018 - usenix.org
We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific
knowledge to improve latency and efficiency of training deep learning models in a GPU …