Recent demands for storing and querying big data have revealed various shortcomings of
traditional relational database systems. This, in turn, has led to the emergence of a new kind …

The virtualization concept and elasticity feature of cloud computing enable users to request
resources on-demand and in the pay-as-you-go model. However, the high flexibility of the …

Data-driven recommender systems that can help to predict users' preferences are deployed
in many real online service platforms. Several studies show that they are vulnerable to data …

The dynamic nature of the cloud environment has made the distributed resource
management process a challenge for cloud service providers. The importance of …

Discretization-based approaches to solving online reinforcement learning problems are
studied extensively on applications such as resource allocation and cache management …

Autoscaling is a critical component for efficient resource utilization with satisfactory quality of
service (QoS) in cloud computing. This paper investigates proactive autoscaling for widely …

We introduce the technique of adaptive discretization to design an efficient model-based
episodic reinforcement learning algorithm in large (potentially continuous) state-action …

Abstract The Light Detection and Ranging (LiDAR) sensor is utilized to track each sensed
obstructions at their respective locations with their relative distance, speed, and direction; …

Contagion models are ubiquitous in epidemiology, social sciences, engineering, and
management. This paper formulates a prescriptive contagion analytics model where a …

SLAOrchestrator is a new system designed to reduce the price increases necessary to
support performance SLAs in cloud analytics systems. SLAOrchestrator is designed for SLAs …