85 citations · 273 across the 9 of their papers we have counts for
9 papers
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs
Pooria Namyar, Behnaz Arzani, Srikanth Kandula +5
We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cas…
Enhancing Network Failure Mitigation with Performance-Aware Ranking
Pooria Namyar, Arvin Ghavidel, Daniel Crankshaw +5
Cloud providers install mitigations to reduce the impact of network failures within their datacenters. Existing network mitigation systems rely on simple local criteria or global p…
SOL: Safe On-Node Learning in Cloud Platforms
Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar +3
Cloud platforms run many software agents on each server node. These agents manage all aspects of node operation, and in some cases frequently collect data and make decisions. Unfor…
InferLine: ML Prediction Pipeline Provisioning and Management for Tight Latency Objectives
Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar +4
Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet…
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
Richard Liaw, Sanjay Krishnan, Animesh Garg +3
Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This pape…
IDK Cascades: Fast Deep Learning by Learning not to Overthink
Xin Wang, Yujia Luo, Daniel Crankshaw +3
Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that f…
Clipper: A Low-Latency Online Prediction Serving System
Daniel Crankshaw, Xin Wang, Giulio Zhou +3
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learni…
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez +5
To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavi…
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics
Reynold S. Xin, Daniel Crankshaw, Ankur Dave +3
From social networks to language modeling, the growing scale and importance of graph data has driven the development of numerous new graph-parallel systems (e.g., Pregel, GraphLab)…