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Methods and Apparatus for Application-agnostic Load Partition in Cloud

IP.com Disclosure Number: IPCOM000247438D
Publication Date: 2016-Sep-08
Document File: 7 page(s) / 390K

Publishing Venue

The IP.com Prior Art Database

Abstract

To strike a tradeoff between a strong performance guarantee and resource provisions, we propose a tail-driven anomaly avoidance policy for boxes, TailGuard, which allows a small fraction of AIs, e.g., 5%of usages can be above the target value, and still avoid severe performance degradation, typically caused by a burst of continuous AI. Specifically, TailGuard first introduces a novel usage tail prediction that explores the similarity patterns across a great number of boxes within a very recent history, and then redistributes the server load in an online fashion by proactive VM cloning and reactive load balancing.

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