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 …

Pond: Cxl-based memory pooling systems for cloud platforms

H Li, DS Berger, L Hsu, D Ernst, P Zardoshti… - Proceedings of the 28th …, 2023 - dl.acm.org
Public cloud providers seek to meet stringent performance requirements and low hardware
cost. A key driver of performance and cost is main memory. Memory pooling promises to …

{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 …

Autopilot: workload autoscaling at google

K Rzadca, P Findeisen, J Swiderski, P Zych… - Proceedings of the …, 2020 - dl.acm.org
In many public and private Cloud systems, users need to specify a limit for the amount of
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …

{Heterogeneity-Aware} cluster scheduling policies for deep learning workloads

D Narayanan, K Santhanam, F Kazhamiaka… - … USENIX Symposium on …, 2020 - usenix.org
Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been
increasingly deployed to train deep learning models. These accelerators exhibit …

Carbon-aware computing for datacenters

A Radovanović, R Koningstein… - … on Power Systems, 2022 - ieeexplore.ieee.org
The amount of CO emitted per kilowatt-hour on an electricity grid varies by time of day and
substantially varies by location due to the types of generation. Networked collections of …