Continuous integration enables the development of microservices-based applications using
container virtualization technology. Container orchestration systems such as Kubernetes …

Containerization is a lightweight application virtualization technology, providing high
environmental consistency, operating system distribution portability, and resource isolation …

Deep Neural Networks (DNNs) have reinvigorated real-world applications that rely on
learning patterns of data and are permeating into different industries and markets. Cloud …

The growing popularity of microservices has led to the proliferation of online cloud service-
based applications, which are typically modelled as Directed Acyclic Graphs (DAGs) …

With a growing demand for adopting ML models for a variety of application services, it is vital
that the frameworks serving these models are capable of delivering highly accurate …

Datacenters are witnessing a rapid surge in the adoption of serverless functions for
microservices-based applications. A vast majority of these microservices typically span less …

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped
with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of …

While user-facing services experience diurnal load patterns, co-locating services improve
hardware utilization. Prior work on co-locating services on GPUs run queries sequentially …

As cloud services expand, the need to improve the performance of data center infrastructure
becomes more important. High-performance computing, advanced networking solutions …

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …