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Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynamic, multi-modal approach that integrates the strengths of…
Choreographic Programming is a paradigm for developing correct-by-construction concurrent programs, by writing high-level descriptions of the desired communications and then synthesising process implementations automatically. So far,…
Microservice architecture is widely adopted in modern systems, where auto-scaling is critical for satisfying service-level objectives (SLOs). However, determining optimal scaling for microservices is difficult, and reactive resource…
Emerging ad hoc networks are infrastructure-less networks consisting of wireless devices with various power constraints, capabilities and mobility characteristics. An essential capability in future ad hoc networks is the ability to provide…
Actual applications (mostly component based) requirements cannot be expressed without a ubiquitous and mobile part for end-users as well as for M2M applications (Machine to Machine). Such an evolution implies context management in order to…
Training deep learning (DL) models on petascale datasets is essential for achieving competitive and state-of-the-art performance in applications such as speech, video analytics, and object recognition. However, existing distributed…
Advances in networks, accelerators, and cloud services encourage programmers to reconsider where to compute -- such as when fast networks make it cost-effective to compute on remote accelerators despite added latency. Workflow and…
With the growing reliance on the ubiquitous availability of IT systems and services, these systems become more global, scaled, and complex to operate. To maintain business viability, IT service providers must put in place reliable and cost…
Adaptive scheduling is crucial for ensuring the reliability and safety of time-triggered systems (TTS) in dynamic operational environments. Scheduling frameworks face significant challenges, including message collisions, locked loops from…
This study targets cloud native environments where microservice invocation relations are complex, load fluctuations are multi-scale and superimposed, and cross-service impacts are significant. We propose a structured temporal joint load…
A new approach to software design based on an agent-oriented architecture is presented. Unlike current research, we consider software to be designed and implemented with this methodology in mind. In this approach agents are considered…
The design and implementation of efficient concurrent data structures have seen significant attention. However, most of this work has focused on concurrent data structures providing good \emph{worst-case} guarantees. In real workloads,…
In nonlinear dynamical systems, tipping refers to a critical transition from one steady state to another, typically catastrophic, steady state, often resulting from a saddle-node bifurcation. Recently, the machine-learning framework of…
The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous sources, but information about the source…
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices,…
Currently, organizations are transforming their business processes into e-services and service-oriented architectures to improve coordination across sales, marketing, and partner channels, to build flexible and scalable systems, and to…
In this paper, we introduce Choreographer, a simulation framework that enables a holistic system-level evaluation of fine-grained accelerators designed for latency-sensitive tasks. Unlike existing frameworks, Choreographer captures all…
Scaling language models to larger and deeper sizes has led to significant boosts in performance. Even though the size of these models limits their application in compute-constrained environments, the race to continually develop ever larger…
Advances in federated learning (FL) algorithms,along with technologies like differential privacy and homomorphic encryption, have led to FL being increasingly adopted and used in many application domains. This increasing adoption has led to…
Nowadays, many companies design and develop their software systems as a set of loosely coupled microservices that communicate via their Application Programming Interfaces (APIs). While the loose coupling improves maintainability,…