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The performance of data-intensive parallel jobs is often constrained by the cluster's hard-to-scale network bisection bandwidth. Previous solutions do not consider inter-job data dependencies and schedule jobs independently from one another. In this work, we find that aggregating and co-locating the data and tasks of dependent jobs offers an extra opportunity of data locality that can help to greatly...
The emerging deployment of geographically distributed data centers (DCs) incurs a significant amount of data transfers over the Internet. Such transfers are typically charged by Internet Service Providers (ISPs) with the widely adopted q-th percentile charging model. In such charging model, the time slots with top 100-q percent of data transmission do not affect the total transmission cost, and can...
In the era of global-scale services, analytical queries are performed on datasets that span multiple data centers (DCs). Due to the scarce and expensive inter-DC bandwidth, various methods have been proposed to reduce either the traffic cost or the completion time for those analytics queries. However, current methods make no attempt to maximize the number of successfully served query requests. Moreover,...
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