The rapid development of cloud computing with virtualization technology has benefited both
academia and industry. For any cloud data center at scale, one of the primary challenges is …

Most cloud applications use a large number of smaller sub-components (called
microservices) that interact with each other in the form of a complex graph to provide the …

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous
demand for hosting application services on the cloud. Further, contemporary data-intensive …

Modern micro-service and container-based cloud-native applications have leveraged multi-
tenancy as a first class system design concern. The increasing number of co-located …

Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape
of cloud computing has been undergoing remarkable changes with the emergence of many …

Many IoT applications found in cyber-physical systems, such as smart grids, must take
control actions in response to critical events, such as supply-demand mismatch, which …

The fog/edge computing paradigm is increasingly being adopted to support a range of
latency-sensitive IoT services due to its ability to assure the latency requirements of these …

Smart Internet of Things (IoT) applications require real-time and robust predictive analytics,
which are based on Machine Learning (ML) models. Building ML models from Big Data is …

Random forests use ensembles of decision trees to boost accuracy for machine learning
tasks. However, large ensembles slow down inference on platforms that process each tree …

Multi-access Edge Computing (MEC), also known as Mobile Edge Computing, has gained
significant momentum as a key facilitator of the stringent Quality of Service (QoS) …