Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey
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 …
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
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 …
computing services. Infrastructure as a Service (IaaS) is one of the most important and …
Machine learning based workload prediction in cloud computing
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 …
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
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 …
latency. There have been several workload analyses of production systems that have fueled …
Borg: the next generation
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 …
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 …
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
The size and energy consumption of datacenters have been increasing significantly over the
past years. As a result, datacenters' increasing electricity monetary cost, energy …
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
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 …
operators. However, low resource utilization in modern datacenters brings huge wastes of …
Optimal VNF placement via deep reinforcement learning in SDN/NFV-enabled networks
The emerging paradigm-Software-Defined Networking (SDN) and Network Function
Virtualization (NFV)-makes it feasible and scalable to run Virtual Network Functions (VNFs) …
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
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 …
situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid …