Related papers: Brame: Hierarchical Data Management Framework for …
Multi-cloud computing systems face significant challenges in ensuring acceptable performance while adhering to tenant budget requirements. This paper proposes a tenant budget-aware (tenant-centric) data replication framework for Multi-Cloud…
A large amount of data and applications are migrated by researchers, stakeholders, academia, and business organizations to the cloud environment due to its large variety of services, which involve the least maintenance cost, maximum…
In recent years with the advent of high bandwidth internet access availability, the cloud computing applications have boomed. With more and more applications being run over the cloud and an increase in the overall user base of the different…
The transformation of smart mobility is unprecedented--Autonomous, shared and electric connected vehicles, along with the urgent need to meet ambitious net-zero targets by shifting to low-carbon transport modalities result in new traffic…
In modern mobile applications, users frequently encounter various new contexts, necessitating on-device continual learning (CL) to ensure consistent model performance. While existing research predominantly focused on developing lightweight…
Nowadays, a significant focus within the research community on the intelligent management of data at the confluence of the Internet of Things (IoT) and Edge Computing (EC) is observed. In this manuscript, we propose a scheme to be…
Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at…
Existing memory management mechanisms used in commodity computing machines typically adopt hardware based address interleaving and OS directed random memory allocation to service generic application requests. These conventional memory…
Scientific research increasingly depends on robust and scalable IT infrastructures to support complex computational workflows. With the proliferation of services provided by research infrastructures, NRENs, and commercial cloud providers,…
Scientific applications in HPC environment are more com-plex and more data-intensive nowadays. Scientists usually rely on workflow system to manage the complexity: simply define multiple processing steps into a single script and let the…
Channel knowledge maps (CKMs) provide a site-specific, location-indexed knowledge base that supports environment-aware communications and sensing in 6G networks. In practical deployments, CKM observations are often noisy and irregular due…
We introduce HCLM, a hierarchical framework for general-purpose cooperative loco-manipulation with dual quadrupedal systems. Coordinating multi-robot collaborative manipulation across floating bases is highly challenging due to the…
Edge computing enables data processing and storage closer to where the data are created. Given the largely distributed compute environment and the significantly dispersed data distribution, there are increasing demands of data sharing and…
Mobile edge computing (MEC) is a promising technology for enhancing the computation capacities and features of mobile users by offloading complex computation tasks to the edge servers. However, mobility poses great challenges on delivering…
Traditional memory management suffers from metadata overhead, architectural complexity, and stability degradation, problems intensified in cloud environments. Existing software/hardware optimizations are insufficient for cloud computing's…
As machine learning (ML) models become increasingly deployed through cloud infrastructures, the confidentiality of user data during inference poses a significant security challenge. Homomorphic Encryption (HE) has emerged as a compelling…
Edge Computing (EC) offers an infrastructure that acts as the mediator between the Cloud and the Internet of Things (IoT). The goal is to reduce the latency that we enjoy when relying on Cloud. IoT devices interact with their environment to…
Cooperative computation is a promising approach for localized data processing at the edge, e.g. for Internet of Things (IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into…
Over the past ten years, many different approaches have been proposed for different aspects of the problem of resources management for long running, dynamic and diverse workloads such as processing query streams or distributed deep…
We study general techniques for implementing distributed data structures on top of future many-core architectures with non cache-coherent or partially cache-coherent memory. With the goal of contributing towards what might become, in the…