Containerization in a multi-cloud environment facilitates workload portability and optimized
resource utilization. Containerization in multi-cloud environments has received significant …

Serverless Function-as-a-Service (FaaS) offers improved programmability for customers, yet
it is not server-" less" and comes at the cost of more complex infrastructure management (eg …

The emergence of ML in various cloud system management tasks (eg, workload autoscaling
and job scheduling) has become a core driver of ML-centric cloud platforms. However, there …

Public cloud service vendors provide a surplus of computing resources at a cheaper price as
a spot instance. Despite the cheaper price, the spot instance can be forced to be shutdown …

The article revealed an in-depth outcome of the multi-cloud method, meaning what it is, why
it is crucial, the advantages, drawbacks, best practices, future forecasts, and practical cases …

Foundation models (FMs) are machine learning models that are trained broadly on large-
scale data and can be adapted to a set of downstream tasks via fine-tuning, few-shot …

Internet Clouds are essentially service factories that offer various networked services
through different service models, viz., Infrastructure, Platform, Software, and Functions as a …

Multi-cloud services developed by microservices-based applications have become
increasingly popular using container virtualization. Here, managing multi-cloud services is …

Many complex workflows consist of multiple tasks represented as a directed acyclic graph
(DAG). Optimal deployment of such workflows on a cloud using multiple services requires a …

As machine learning (ML) models have grown in complexity, so too have the expenses they
incur when deployed in the cloud. In order to reduce the costs associated with ML serving, it …