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