To better understand the challenges in developing effective cloud-based resource
schedulers, we analyze the first publicly available trace data from a sizable multi-purpose …

Limited data access is a longstanding barrier to data-driven research and development in
the networked systems community. In this work, we explore if and how generative …

We present a multilayer study of the Facebook Messages stack, which is based on HBase
and HDFS. We collect and analyze HDFS traces to identify potential improvements, which …

With the emergence of large, heterogeneous, shared computing clusters, their efficient use
by mixed distributed workloads and tenants remains an important challenge. Unfortunately …

Edge computing services are exposed to infrastructural failures due to geographical
dispersion, ad hoc deployment, and rudimentary support systems. Two unique …

Cloud computing (CC) systems are the most popular computational environments for
providing elastic and scalable services on a massive scale. The nature of such systems …

Achieving efficiency both in terms of resource utilisation and energy consumption is a
complex challenge, especially in large-scale wide-purpose data centers that serve cloud …

The development of cloud computing has facilitate the organizations with its services. This
makes the security and privacy of the cloud even more significant. Diversification and …

Realistic, relevant, and reproducible experiments often need input traces collected from real-
world environments. In this work, we focus on traces of workflows—common in datacenters …

Edge computing paradigm is prone to failures as it trades reliability against other quality of
service properties such as low latency and geographical prevalence. Therefore, software …