English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In a computing center with a huge amount of machines, when a job arrives, adispatcher need to decide which machine to route this job to based on limitedinformation. A classical method, called the power-of-$d$ choices algorithm isto pick $d$ servers independently at random and dispatch the job to the leastloaded server among the $d$ servers. In this paper, we analyze a low-randomnessvariant of this dispatching scheme, where $d$ queues are sampled through $d$independent non-backtracking random walks on a $k$-regular graph $G$. Undercertain assumptions of the graph $G$ we show that under this scheme, thedynamics of the queuing system converges to the same deterministic ordinarydifferential equation (ODE) for the power-of-$d$ choices scheme. We also showthat the system is stable under the proposed scheme, and the stationarydistribution of the system converges to the fixed point of the ODE.

Derandomized Load Balancing using Random Walks on Expander Graphs