Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey

TL Duc, RG Leiva, P Casari, PO Östberg - ACM Computing Surveys …, 2019 - dl.acm.org
Large-scale software systems are currently designed as distributed entities and deployed in
cloud data centers. To overcome the limitations inherent to this type of deployment …

Machine learning (ML)-centric resource management in cloud computing: A review and future directions

T Khan, W Tian, G Zhou, S Ilager, M Gong… - Journal of Network and …, 2022 - Elsevier
Cloud computing has rapidly emerged as a model for delivering Internet-based utility
computing services. Infrastructure as a Service (IaaS) is one of the most important and …

Machine learning based workload prediction in cloud computing

J Gao, H Wang, H Shen - 2020 29th international conference …, 2020 - ieeexplore.ieee.org
As a widely used IT service, more and more companies shift their services to cloud
datacenters. It is important for cloud service providers (CSPs) to provide cloud service …

A large-scale analysis of hundreds of in-memory key-value cache clusters at twitter

J Yang, Y Yue, KV Rashmi - ACM Transactions on Storage (TOS), 2021 - dl.acm.org
Modern web services use in-memory caching extensively to increase throughput and reduce
latency. There have been several workload analyses of production systems that have fueled …

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 …

Smartly handling renewable energy instability in supporting a cloud datacenter

J Gao, H Wang, H Shen - 2020 IEEE international parallel and …, 2020 - ieeexplore.ieee.org
The size and energy consumption of datacenters have been increasing significantly over the
past years. As a result, datacenters' increasing electricity monetary cost, energy …

Who limits the resource efficiency of my datacenter: An analysis of alibaba datacenter traces

J Guo, Z Chang, S Wang, H Ding, Y Feng… - Proceedings of the …, 2019 - dl.acm.org
Cloud platform provides great flexibility and cost-efficiency for end-users and cloud
operators. However, low resource utilization in modern datacenters brings huge wastes of …

Optimal VNF placement via deep reinforcement learning in SDN/NFV-enabled networks

J Pei, P Hong, M Pan, J Liu… - IEEE Journal on Selected …, 2019 - ieeexplore.ieee.org
The emerging paradigm-Software-Defined Networking (SDN) and Network Function
Virtualization (NFV)-makes it feasible and scalable to run Virtual Network Functions (VNFs) …

Online learning for offloading and autoscaling in energy harvesting mobile edge computing

J Xu, L Chen, S Ren - IEEE Transactions on Cognitive …, 2017 - ieeexplore.ieee.org
Mobile edge computing (also known as fog computing) has recently emerged to enable in-
situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid …