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2,356 papers
The proposed design uses adaptive sampling, data aggregation, compression, a workload-aware offloading mechanism, dynamic voltage/frequency scaling where available, and container consolidation and renewable-energy awareness, and carbon-aware workload placement, all of which are aligned with the direction of current Mul...
Edge server placement (ESP) is critical in mobile edge computing (MEC) by enabling low-latency services and cost-efficient operation through effective resource allocation. However, with the rapid growth of candidate nodes and the integration of heterogeneous server types, ESP becomes a complex large-scale multi-objecti...
The Internet of Things (IoT) faces critical challenges in supporting computation-intensive applications under resource-constrained edge networks, particularly in high-precision manufacturing scenarios requiring high-concurrency and low-latency task offloading. This paper proposes Joint Communication and Computation Co-...
Unmanned aerial vehicles (UAVs) equipped with edge computing capabilities offer a promising solution for the coverage and flexibility of terrestrial networks, but they also face challenges in low-latency, security-aware data transmission. To address this, a UAV-assisted, security-aware vehicular edge computing system i...
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With the rapid advancement of Uncrewed Aerial Vehicle (UAV) technology, fleets are increasingly deployed in disaster relief, logistics, and environmental monitoring, necessitating real-time task execution and efficient UAV resource allocation. However, UAVs’ constrained computational and communication capabilities, par...
Collaboration among edge nodes via task migration is a promising solution for mobile edge computing (MEC) to efficiently meet time-varying and spatially unbalanced computing demands with limited resources. However, existing task migration strategies are primarily confined to a discrete-time paradigm, which suffers from...
The proliferation of Multi-Access Edge Computing (MEC) has led to massive data generation. This imposes complex and dynamic requirements on task scheduling. Conventional Uncrewed Aerial Vehicle (UAV) scheduling methods struggle with task diversity. They lack adaptability to stochastic environments. Large Language Model...
Edge Computing (EC) provides low-latency environment for delay-sensitive network services by deploying limited cloud infrastructure at the edge of the network. Network Function Virtualization (NFV) further enhances flexibility by replacing hardware with Virtual Network Functions (VNFs) that can run on general servers....
To ensure continuous low-latency service in Mobile Edge Computing (MEC), edge applications must migrate statefully along the user’s mobility path. However, existing migration schemes fail to meet the strict Quality of Service (QoS) requirements of live-serving, time-sensitive MEC applications due to excessive downtime...
With the advent of the 6G era, Low Earth Orbit (LEO) satellite networks deliver low latency, high throughput in space edge computing services, yet they face severe energy constraints and battery aging. This challenge worsens when periodic solar power harvesting fails to match fluctuating task demand, driving deep disch...
With the swift evolution of intelligent transportation systems, Vehicular Edge Computing (VEC) has developed rapidly. In VEC environments, the lack of dynamic awareness of spatiotemporal-varying heterogeneous resources results in high task offloading latency in high mobility scenarios. Therefore, this paper proposes an...
Considering the limitations of a single uncrewed aerial vehicle (UAV) in capacity and coverage, cooperative UAVs assisted mobile edge computing (MEC) architecture has been popular for large-scale mobile terminals (MTs). However, the high mobility of MTs and the limited resources of UAVs lead to challenges such as imbal...
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
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