User-facing applications often experience excessive loads and are shifting towards the
microservice architecture. To fully utilize heterogeneous resources, current datacenters have …

Current microservice applications always meet with load and call graph dynamics. These
dynamics can easily lead to inappropriate resource allocation for microservices, and further …

Cloud providers often build a geo-distributed cloud from multiple datacenters in different
geographic regions, to serve tenants at different locations. The tenants that run large scale …

Latency-critical applications directly interact with end users and often experience the diurnal
load pattern. In production, best-effort applications are often co-located with them to utilize …

The dispersed node locations and complex topologies of edge networks, combined with
intricate dynamic microservice dependencies, render traditional centralized microservice …

Modern user-facing services are progressively evolving from large monolithic applications to
complex graphs of loosely-coupled microservices. While this shift provides opportunities to …

Graph processing is widely used in cloud services; however, current frameworks face
challenges in efficiency and cost-effectiveness when deployed under the Infrastructure-as-a …

Serverless computing that runs functions with auto-scaling is a popular task execution
pattern in the cloud-native era. By connecting serverless functions into workflows, tenants …

Containerized deployment of microservices has been becoming prevalent, as it provides
flexible deployment and elastic resource configuration. For high concurrency and fault …

Nowadays, the rise of various network applications makes network traffic become
increasingly complex, which brings more stringent requirements to traffic engineering (TE) …