Large-scale software systems are currently designed as distributed entities and deployed in
cloud data centers. To overcome the limitations inherent to this type of deployment …

With the fast development of big data systems in recent years, a variety of open-source
benchmarks have been built to evaluate and compare the workloads on these systems, and …

Due to increasing amounts of data and compute resources, the deep learning achieves
many successes in various domains. Recently, researchers and engineers make effort to …

The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approachto modelling and characterizing big …

The design of solutions that are able to exploit the available data collected in smart cities
environments can lead to insights that can guide the implementation of approaches that …

The increasing importance of GPUs as high-performance accelerators and the power and
energy constraints of computing systems, make it fundamental to develop techniques for …

In last decade, data analytics have rapidly progressed from traditional disk-based
processing to modern in-memory processing. However, little effort has been devoted at …

With the rapid growth of artificial intelligence (AI), the Internet of Things (IoT) and big data,
emerging applications that cross stacks with different techniques bring new challenges to …

Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital for efficient
application deployment and resource provisioning. However, the heterogeneous application …

The long I/O latency posts significant challenges for many data-intensive applications, such
as the emerging big data workloads. Recently, the NVDIMM (Non-Volatile Dual In-line …