Databases in Networked Information Systems.. 6th by Shinji Kikuchi, Shelly Sachdeva, Subhash Bhalla

By Shinji Kikuchi, Shelly Sachdeva, Subhash Bhalla

This e-book constitutes the refereed complaints of the sixth overseas Workshop on Databases in Networked details platforms, DNIS 2010, held in Aizu-Wakamatsu, Japan in October 2010. The thirteen revised complete papers offered including nine invited talks and 1 keynote lecture have been rigorously reviewed and chosen for inclusion within the booklet. The workshop normally places the main target on info semantics and infrastructure for info administration and interchange. The papers are equipped in topical sections on networked info platforms: infrastructure, entry to info assets, info and data administration platforms, details extraction from info assets, and geo-spatial selection making

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613 (2002) 23. : Dynamic Memory Allocation for Multiple-Query Workload. In: Proc. of the 19th Intl. Conf. on Very Large Data Bases (VLDB) (August 1993) 24. : Data Center Workload Monitoring, Analysis, and Emulation (2005) 25. : Achieving Class-Based QoS for Transactional Workloads. In: Proc. of the 22nd Intl. Conf. on Data Engineering (ICDE), p. 153 (2006) 26. : LEO - DB2’s LEarning Optimizer. In: Proc. of the 27th Intl. Conf. on Very Large Data Bases (VLDB), pp. 19–28 (2001) 27. : The COMFORT Automatic Tuning Project.

Currently, Jumbo does not yet contain any load balancing features beyond what Hadoop provides. However, Jumbo’s more flexible design means that it will be much better suited for future experiments with load balancing than what we would be able to do with Hadoop. Jumbo’s design allows us to implement complex algorithms such as the PFP algorithm from [5] as a single job so task scheduling decisions for load balancing can consider the entire job structure 30 S. Groot, K. Goda, and M. Kitsuregawa rather than just a part.

Since each task reads data from a single block, the task scheduler can attempt to schedule that task to run on a node that has a local replica of that block, reducing network load. When a stage reads input from another stage, the data from that input stage is partitioned by using a partitioning function. Every task in the input stage creates the same partitions, and each task in the stage reading that data will read all the pieces of just one partition from all the tasks in the input stage. Unlike in MapReduce, it is not required for the intermediate data to be sorted.

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