Related papers: Optimal Deployment of Geographically Distributed W…
This paper presents a theoretical discussion for environmentally-conscious job deployment and migration in cloud environments, aiming to minimize the environmental impact of resource provisioning while incorporating sustainability…
Function offloading is a promising solution to address limitations concerning computational capacity and available energy of Connected Automated Vehicles~(CAVs) or other autonomous robots by distributing computational tasks between local…
Training Graph Neural Networks (GNN) on large graphs is resource-intensive and time-consuming, mainly due to the large graph data that cannot be fit into the memory of a single machine, but have to be fetched from distributed graph storage…
In this paper, we study a network slicing problem for edge-cloud orchestrated vehicular networks, in which the edge and cloud servers are orchestrated to process computation tasks for reducing network slicing cost while satisfying the…
Pick-and-place is an important manipulation task in domestic or manufacturing applications. There exist many works focusing on grasp detection with high picking success rate but lacking consideration of downstream manipulation tasks (e.g.,…
As novel applications spring up in future network scenarios, the requirements on network service capabilities for differentiated services or burst services are diverse. Aiming at the research of collaborative computing and resource…
The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the…
Worldwide, Geo-distributed Data Centers (GDCs) provide computing and storage services for massive workflow applications, resulting in high electricity costs that vary depending on geographical locations and time. How to reduce electricity…
With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly…
Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of…
When deploying machine learning (ML) applications, the automated allocation of computing resources-commonly referred to as autoscaling-is crucial for maintaining a consistent inference time under fluctuating workloads. The objective is to…
A current trend in networking and cloud computing is to provide compute resources over widely dispersed places exemplified by initiatives like Network Function Virtualisation. This paves the way for a widespread service deployment and can…
Static (offline) techniques for mapping applications given by task graphs to MPSoC systems often deliver overly pessimistic and thus suboptimal results w.r.t. exploiting time slack in order to minimize the energy consumption. This holds…
Python-written data analytics applications can be modeled as and compiled into a directed acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges are task dependencies. Such analytics workflow jobs are…
Many scientific workflows can be modeled as a Directed Acyclic Graph (henceforth mentioned as DAG) where the nodes represent individual tasks, and the directed edges represent data and control flow dependency between two tasks. Due to the…
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…
Typical schedulers in multi-tenancy environments make use of reactive, feedback-oriented mechanisms based on performance counters to avoid resource contention but suffer from detection lag and loss of performance. In this paper, we address…
As the number of services and the size of data involved in workflows increases, centralised orchestration techniques are reaching the limits of scalability. In the classic orchestration model, all data passes through a centralised engine,…
A fundamental challenge in large-scale cloud networks and data centers is to achieve highly efficient server utilization and limit energy consumption, while providing excellent user-perceived performance in the presence of uncertain and…
Distributed cloud networking enables the deployment of a wide range of services in the form of interconnected software functions instantiated over general purpose hardware at multiple cloud locations distributed throughout the network. We…