Microsoft Technology Licensing, LLC

Yuval Peres

Eric J Horvitz

Andrey Kolobov

Cheng Lu

Dahlia Malkhi

Reid Andersen

Po-Shen Loh

Eyal Lubetzky

The technology described herein builds an optimal refresh schedule by minimizing a cost function constrained by an available refresh bandwidth. The cost function receives an importance score for a content item and a change rate for the content item as input in order to optimize the schedule. The cost function is considered optimized when a refresh schedule is found that minimizes the cost while using the available bandwidth and no more. The technology can build an optimized schedule to refresh content with incomplete change data, content with complete change data, or a mixture of content with and without complete change data. It can also re-learn content item change rates from its own schedule execution history and re-compute the refresh schedule, ensuring that this schedule takes into account the latest trends in content item updates.

Efficient freshness crawl scheduling

A 'Stochastic Clustering-Based Network Generator' enables rapid formation of an interconnected hierarchical network structure from an arbitrary number of agents via an iterative turn-based coalescence process. Given N agents wishing to coalesce into one hierarchical network, a turn-based process allows each agent (or the head of each hierarchical cluster of agents), to randomly decide whether to issue or listen for merge requests in each round. Issuing a request amounts to contacting a randomly chosen agent with a merge request. Given multiple received requests, a cluster head will randomly accept one request for a merge received by any agent in that cluster. The requesting cluster then merges as a hierarchical child of the accepting cluster. In a related embodiment, given multiple merge requests, the request from the smallest cluster is accepted. In further embodiments, ties of the smallest cluster size are broken based on various options.

Distributed stochastic clustering for automated formation of connected networks of agents

Providing for local graph partitioning using an evolving set process is disclosed herein. By way of example, a computer processor can be configured to execute local partitioning based on evolving set instructions. The instructions can be employed to transition a set of analyzed vertices of a graph until a segment of the graph with small conductance is identified. A transitioning algorithm can expand or contract the analyzed set of vertices based on characteristics of vertices at a boundary of the analyzed set. Accordingly, as the set of analyzed vertices becomes large, significant processing efficiency is gained by employing the characteristics of boundary vertices to transition the set or determine conductance, rather than all vertices of the analyzed set.

Local graph partitioning using evolving sets

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Redmond, WA, United States of America

Yuval Peres