Vincent Lannurien
d822cdaa6a
3.6 KiB
title, venue, year, date, tags, paper_count, draft
| title | venue | year | date | tags | paper_count | draft |
|---|---|---|---|---|---|---|
| SEC 2024 Digest | SEC | 2024 | 2024-01-01 | 12 | false |
12 papers selected.
EdgeCore: Resource Dependency-Aware Multi-Tenant Orchestration for Mobile Edge Clouds
Amran Haroon
TL;DR — Introduces a multi-tenant edge orchestration system that captures resource dependencies across co-located workloads, demonstrating significant improvements in task completion latency and resource utilization.
Righteous: Automatic Right-Sizing for Complex Edge Deployments
Aniruddha Rakshit
TL;DR — Presents an automated right-sizing framework for edge deployments that dynamically adjusts resource allocations to match workload demands without manual intervention.
Colibri: Efficient Collection of Fine-Grained Resource Metrics Necessary for Mobile Edge Computing
Ke-Jou Hsu
TL;DR — Proposes a low-overhead monitoring system for collecting fine-grained resource metrics at the edge, enabling more accurate profiling for MEC scheduling decisions.
HyperDrive: Scheduling Serverless Functions in the Edge-Cloud-Space 3D Continuum
Thomas W. Pusztai
TL;DR — Extends serverless scheduling across a three-dimensional edge-cloud-space continuum, addressing latency and resource constraints introduced by satellite and terrestrial tiers.
Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge
Pritish Mishra
TL;DR — Enables live, low-disruption reconfiguration of stateful stream processing pipelines at the edge, minimizing downtime during topology changes.
FusedInf: Efficient Swapping of DNN Models for On-Demand Serverless Inference Services on the Edge
Sifat Ut Taki
TL;DR — Reduces cold-start latency for serverless DNN inference at the edge by fusing model loading with active inference through selective layer swapping.
EcoEdgeInfer: Dynamically Optimizing Latency and Sustainability for Inference on Edge Devices
Sri Pramodh Rachuri
TL;DR — Co-optimizes inference latency and energy sustainability on edge devices by dynamically trading off accuracy and hardware utilization under carbon-aware constraints.
Elastic Execution of Multi-Tenant DNNs on Heterogeneous Edge MPSoCs
Soroush Heidari
TL;DR — Demonstrates elastic, interference-aware co-execution of multiple DNNs across heterogeneous processing elements in edge MPSoCs to maximize throughput and fairness.
Optimizing Edge Offloading Decisions for Object Detection
Jiaming Qiu
TL;DR — Formulates and solves an online offloading decision problem for object detection that jointly minimizes latency and energy consumption under variable network conditions.
VideoJam: Self-Balancing Architecture for Live Video Analytics
Youssouph Faye
TL;DR — Proposes a self-balancing edge architecture for live video analytics that dynamically redistributes pipeline stages to prevent bottlenecks under fluctuating camera workloads.
OVIDA: Orchestrator for Video Analytics on Disaggregated Architecture
Manavjeet Singh
TL;DR — Designs an orchestration layer for disaggregated edge hardware that places and migrates video analytics microservices to exploit spatial locality and heterogeneous accelerators.
TA-ASF: Attention-Sensitive Token Sampling and Fusing for Visual Transformer Models on the Edge
Junquan Chen
TL;DR — Accelerates Vision Transformer inference at the edge by pruning and fusing attention tokens based on saliency, achieving accuracy-efficiency trade-offs suitable for resource-constrained devices.