Cloud Computing

The Reflection Server Tuning dataset contains HiPerConTracer latency measurements performed in a lab setup. The purpose of the dataset is to measure the latency and jitter effects of  firewalls and Linux kernel tuning.

The advent of the Internet of Things has increased the interest in automating mission-critical processes from domains such as smart cities. These applications' stringent Quality of Service (QoS) requirements motivate their deployment through the Cloud-IoT Continuum, which requires solving the NP-hard problem of placing the application's services onto the infrastructure's devices. Moreover, as the infrastructure and application change over time, the placement needs to continuously adapt to these changes to maintain an acceptable QoS.

When designing task scheduling algorithms in mobile edge computing (MEC), the mobile device (MD)'s mobility becomes an important concern, since the change in MD's location would affect the data transmission rate, leading to fluctuations in task transmission duration and completion time. In this paper, we study a mobility-aware task off-and-downloading scheduling problem in MEC, considering both the communication delay and energy consumption caused by the data offloading and the result downloading.

Predicting the data transfer throughput of cloud networks plays an important role in several resource optimization applications, such as auto-scaling, replica selection, and load balancing. However, constant short-term variations in cloud networks make the prediction of end-to-end data transfer throughput a very challenging task.