Resource management in a cloud environment is a hard problem, due to: the scale of
modern data centers; the heterogeneity of resource types and their interdependencies; the …

Developers use Machine Learning (ML) platforms to train ML models and then deploy these
ML models as web services for inference (prediction). A key challenge for platform providers …

To meet the challenges of consistent performance, low communication latency, and a high
degree of user mobility, cloud and Telecom infrastructure vendors and operators foresee a …

Worldwide interest in the delivery of computing and storage capacity as a service continues
to grow at a rapid pace. The complexities of such cloud computing centers require advanced …

Many Internet applications can benefit from an automatic scaling property where their
resource usage can be scaled up and down automatically by the cloud service provider. We …

We address the problem of dynamic resource management for a large-scale cloud
environment. Our contribution includes outlining a distributed middleware architecture and …

In cloud environments, resources can be requested on-demand when they are needed. A
cloud management system is responsible for determining which physical machines are …

We address the problem of resource management for a large-scale cloud environment that
hosts sites. Our contribution centers around outlining a distributed middleware architecture …

Recently, the autonomic communication networks paradigm has been introduced as a
solution to the increasing management complexity of communication networks in the Future …

Cloud computing technologies can be used to more flexibly provision application resources.
By exploiting multi-tenancy, instances can be shared between users, lowering the cost of …