Related papers: QoS-SLA-Aware Adaptive Genetic Algorithm for Multi…
Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services.…
Edge computing decentralizes computing resources, allowing for novel applications in domains such as the Internet of Things (IoT) in healthcare and agriculture by reducing latency and improving performance. This decentralization is achieved…
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…
Unmanned aerial vehicles (UAVs) often collaborate by collecting and offloading sensing streams to an edge server, where a deep neural network (DNN) model performs cross-stream alignment, fusion, and inference. However, the coupling between…
Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while…
Modern-day cars are equipped with numerous cameras and sensors, typically integrated with advanced decision-control systems that enable the vehicle to perceive its surroundings and navigate autonomously. Efficient processing of data from…
In this paper, we explore adaptive offloading and enhancement strategies for video analytics tasks on computing-constrained mobile devices in low-light conditions. We observe that the accuracy of low-light video analytics varies from…
The smart vehicles construct Vehicle of Internet which can execute various intelligent services. Although the computation capability of the vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for vehicular…
An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitting and processing large data volumes at distant…
IEEE 802.11be (Wi-Fi 7) introduces Multi-Link Operation (MLO) as a While MLO offers significant parallelism and capacity, realizing its full potential in guaranteeing strict delay bounds and optimizing Quality of Service (QoS) for diverse,…
Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle's cost, quickly drains its battery,…
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…
Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems, enabling vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing.…
Cloud computing environments demand dynamic and efficient resource management to ensure optimal performance, reduced energy consumption, and adherence to Service Level Agreements (SLAs). This paper presents a Genetic Algorithm (GA)-based…
Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based…
The integration of the Industrial Internet of Things (IIoT) with Artificial Intelligence-Generated Content (AIGC) offers new opportunities for smart manufacturing, but it also introduces challenges related to computation-intensive tasks and…
Cloud computing environments often have to deal with random-arrival computational workloads that vary in resource requirements and demand high Quality of Service (QoS) obligations. It is typical that a Service-Level-Agreement (SLA) is…
In modern SD-WAN networks, a global controller is able to steer traffic on different paths based on application requirements and global intents. However, existing solutions cannot dynamically tune the way bandwidth is shared between flows…
Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via…
The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the…