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

MapReduce framework is the de facto standard in Hadoop. Considering the datalocality in data centers, the load balancing problem of map tasks is a specialcase of affinity scheduling problem. There is a huge body of work on affinityscheduling, proposing heuristic algorithms which try to increase data localityin data centers like Delay Scheduling and Quincy. However, not enough attentionhas been put on theoretical guarantees on throughput and delay optimality ofsuch algorithms. In this work, we present and compare different algorithms anddiscuss their shortcoming and strengths. To the best of our knowledge, mostdata centers are using static load balancing algorithms which are not efficientin any ways and results in wasting the resources and causing unnecessary delaysfor users.

Affinity Scheduling and the Applications on Data Center Scheduling with Data Locality