Related papers: Design and Implementation of a Serverless MapReduc…
In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision making and applications. Scaling data analysis, possibly…
Serverless computing has revolutionized cloud architectures by enabling developers to deploy event-driven applications via lightweight, self-contained virtualized containers. However, serverless frameworks face critical cold-start…
Thanks to the latest advances in containerization, the serverless edge computing model is becoming close to reality. Serverless at the edge is expected to enable low latency applications with fast autoscaling mechanisms, all running on…
Datacenters are witnessing a rapid surge in the adoption of serverless functions for microservices-based applications. A vast majority of these microservices typically span less than a second, have strict SLO requirements, and are chained…
MapReduce (MR) is the most popular solution to build applications for large-scale data processing. These applications are often deployed on large clusters of commodity machines, where failures happen constantly due to bugs, hardware…
Serverless applications are typically composed of function workflows in which multiple short-lived functions are triggered to exchange data in response to events or state changes. Current serverless platforms coordinate and trigger…
The growth of compute-intensive AI tasks highlights the need to mitigate the processing costs and improve performance and energy efficiency. This necessitates the integration of intelligent agents as architectural adaptation supervisors…
Recent advancements in the cloud computing domain have resulted in huge strides toward simplifying the procurement of hardware and software for diverse needs. By moving enterprise workloads to managed cloud offerings (private, public,…
In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and robustness. Due to the complex service invocation paths and…
One of the major challenges in large-scale data processing with MapReduce is the smart computation of joins. Since Semantic Web datasets published in RDF have increased rapidly over the last few years, scalable join techniques become an…
Apache Flink is an open-source system for scalable processing of batch and streaming data. Flink does not natively support efficient processing of spatial data streams, which is a requirement of many applications dealing with spatial data.…
Data processing frameworks such as Apache Beam and Apache Spark are used for a wide range of applications, from logs analysis to data preparation for DNN training. It is thus unsurprising that there has been a large amount of work on…
In a context of ever-growing worldwide communication traffic, cloud service providers aim at deploying scalable infrastructures to address heterogeneous needs. Part of the network infrastructure, FPGAs are tailored to guarantee low-latency…
Edge computing is an emerging paradigm to enable low-latency applications, like mobile augmented reality, because it takes the computation on processing devices that are closer to the users. On the other hand, the need for highly scalable…
We present CASE, an open-source framework for adaptive participatory research and disease surveillance. Unlike traditional survey platforms with static branching logic, CASE uses an event-driven architecture that adjusts survey workflows in…
We present the first open-source implementation and evaluation of Fast Raft, a hierarchical consensus protocol designed for dynamic, distributed environments. Fast Raft reduces the number of message rounds needed to commit log entries…
Autoscaling GPU inference workloads in Kubernetes remains challenging due to the reactive and threshold-based nature of default mechanisms such as the Horizontal Pod Autoscaler (HPA), which struggle under dynamic and bursty traffic patterns…
In Complex Event Processing, handling out-of-order, late, and duplicate events is critical for real-time analytics, especially on resource-constrained devices that process heterogeneous data from multiple sources. We present LimeCEP, a…
Serverless computing, also referred to as Function-as-a-Service (FaaS), is a cloud computing model that has attracted significant attention and has been widely adopted in recent years. The serverless computing model offers an intuitive,…
The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation Systems (ITS) have emerged as a promising solution in this context. However,…