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With recent increasing computational and data requirements of scientific applications, the use of large clustered systems as well as distributed resources is inevitable. Although executing large applications in these environments brings…

Recent advances in large language models (LLMs) and dense retrievers have driven significant progress in retrieval-augmented generation (RAG). However, existing approaches face significant challenges in complex reasoning-oriented multi-hop…

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness…

Function as a Service (FaaS) permits cloud customers to deploy to cloud individual functions, in contrast to complete virtual machines or Linux containers. All major cloud providers offer FaaS products (Amazon Lambda, Google Cloud…

Application autotuning is a promising path investigated in literature to improve computation efficiency. In this context, the end-users define high-level requirements and an autonomic manager is able to identify and seize optimization…

Cloud computing infrastructures increasingly rely on geographically distributed data centers to meet the growing demand for low latency, high availability, and cost-efficient service delivery. In this context, load balancing plays a…

Query Optimization remains an open problem for Big Data Management Systems. Traditional optimizers are cost-based and use statistical estimates of intermediate result cardinalities to assign costs and pick the best plan. However, such…

As cloud providers push multi-tenancy to new levels to meet growing scalability demands, ensuring that externally developed untrusted microservices will preserve tenant isolation has become a high priority. Developers, in turn, lack a means…

A novel modeling framework is proposed for dynamic scheduling of projects and workforce assignment in open source software development (OSSD). The goal is to help project managers in OSSD distribute workforce to multiple projects to achieve…

We explore the problem of efficiently implementing shared data structures in an asynchronous computing environment. We start with a traditional FIFO queue, showing that full replication is possible with a delay of only a single round-trip…

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with its own restrictive syntax. We introduce an…

Distributed software systems that are designed to run over workstation machines within organisations are termed workstation-based. Workstation-based systems are characterised by dynamically changing sets of machines that are used primarily…

A huge amount of data is everyday managed in large organizations in many critical business sectors with the support of spreadsheet applications. The process of elaborating spreadsheet data is often performed in a distributed, collaborative…

We envisage future context-aware applications will dynamically adapt their behaviors to various context data from sources in wide-area networks, such as the Internet. Facing the changing context and the sheer number of context sources, a…

Vehicles are becoming increasingly automated and interconnected, enabling the formation of cooperative intelligent transport systems (C-ITS) and the use of offboard services. As a result, cloud-native techniques, such as microservices and…

Graph processing systems are essential for analyzing large-scale data with complex relationships, yet most existing frameworks rely on statically provisioned clusters, resulting in poor elasticity and inefficient resource utilization under…

Due to the highly variable execution context in which edge services run, adapting their behavior to the execution context is crucial to comply with their requirements. However, adapting service behavior is a challenging task because it is…

The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation. Compared to central architectures, however, heterogeneous…

Cloud native systems are processing large amounts of personal data through numerous and possibly multi-paradigmatic data stores (e.g., relational and non-relational databases). From a privacy engineering perspective, a core challenge is to…

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime…