Related papers: Artificial Intelligence (AI)-Centric Management of…
In cloud computing resource management plays a significant role in data centres and it is directly dependent on the application workload. Various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and…
Nowadays cloud computing adoption as a form of hosted application and services is widespread due to decreasing costs of hardware, software, and maintenance. Cloud enables access to a shared pool of virtual resources hosted in large…
As AI-based clinical decision support (AI-CDS) is introduced in more and more aspects of healthcare services, HCI research plays an increasingly important role in designing for complementarity between AI and clinicians. However, current…
Data-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. Here software engineering needs to be re-thought where data…
Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However,…
Distributed AI systems face critical memory management challenges across computation, communication, and deployment layers. RRAM based in memory computing suffers from scalability limitations due to device non idealities and fixed array…
As artificial intelligence (AI) becomes increasingly embedded in healthcare delivery, this chapter explores the critical aspects of developing reliable and ethical Clinical Decision Support Systems (CDSS). Beginning with the fundamental…
The advances in data, computing and networking over the last two decades led to a shift in many application domains that includes machine learning on big data as a part of the scientific process, requiring new capabilities for integrated…
The landscape of maintenance in distributed systems is rapidly evolving with the integration of Artificial Intelligence (AI). Also, as the complexity of computing continuum systems intensifies, the role of AI in predictive maintenance…
Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which…
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities. Further, widespread Internet availability enables communication among sensing devices. As a result, vast amounts of data are generated on…
Cloud computing is an increasingly popular computing paradigm, now proving a necessity for utility computing services. Each provider offers a unique service portfolio with a range of resource configurations. Resource provisioning for cloud…
In the edge-cloud continuum, datacenters provide microservices (MSs) to mobile users, with each MS having specific latency constraints and computational requirements. Deploying such a variety of MSs matching their requirements with the…
In the past few years, we have envisioned an increasing number of businesses start driving by big data analytics, such as Amazon recommendations and Google Advertisements. At the back-end side, the businesses are powered by big data…
The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability.…
Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era.…
Centralized clouds processing the large amount of data generated by Internet-of-Things (IoT) can lead to unacceptable latencies for the end user. Against this backdrop, Edge Computing (EC) is an emerging paradigm that can address the…
Resource-aware machine learning has been a trending topic in recent years, focusing on making ML computational aspects more exploitable by the edge devices in the Internet of Things. This paper attempts to review a conceptually and…
As applications become more distributed to improve user experience and offer higher availability, businesses rely on geographically dispersed datacenters that host such applications more than ever. Dedicated inter-datacenter networks have…
Limited computing resources of internet-of-things (IoT) nodes incur prohibitive latency in processing input data. This triggers new research opportunities toward task offloading systems where edge servers handle intensive computations of…