Modern implementations of DBMS software are intended to take advantage of high core counts that are becoming common in high-end servers. However, we have observed that several database platforms, including MySQL, Shore-MT, and a commercial system, exhibit throughput collapse as load increases into oversaturation (where there are more request threads than cores), even for a workload with little or no logical contention for locks, such as a read-only workload. Our analysis of MySQL identifies latch contention within the lock manager as the bottleneck responsible for this collapse.We design a lock manager with reduced latching, implement it in MySQL, and show that it avoids the collapse and generally improves performance. Our efficient implementation of a lock manager is enabled by a staged allocation and deallocation of locks. Locks are preallocated in bulk, so that the lock manager only has to perform simple list manipulation operations during the acquire and release phases of a transaction. Deallocation of the lock data structures is also performed in bulk, which enables the use of fast implementations of lock acquisition and release as well as concurrent deadlock checking.
The most significant performance factors in Mobile Agent Planning (MAP) are; (a) the number of mobile agents, (b) the total routing time consumed by the participated agents, and (c) the time constraints, (such as information ready time and deadline triggered unto each nodes to be visited).We propose the Timed Mobile Agent Planning (TMAP), a time constrained mobile agent planning method for finding the minimal number of agents and the best scheduled agents' itineraries for information retrieval from a distributed computing environment processed under the time-constraints while keeping the completion and the total routing time minimal.Experimental results show that this method is required and highly applicable directly to the time-constrained distributed information retrieval environment which has a time window which consists of a "ready-line" (information ready time) and a "deadline" (the end of the information's lifetime).
A data-parallel framework is very attractive for large-scale data processing since it enables such an application to easily process a huge amount of data on commodity machines. MapReduce, a popular data-parallel framework, is used in various fields such as web search, data mining and data warehouses; it is proven to be very practical for such a data-parallel application. A star-join query is a popular query in data warehouses that are a current target domain of data-parallel frameworks. This article proposes a new algorithm that efficiently processes star-join queries in data-parallel frameworks such as MapReduce and Dryad. Our star-join algorithm for general data-parallel frameworks is called Scatter-Gather-Merge, and it processes star-join queries in a constant number of computation steps, although the number of participating dimension tables increases. By adopting bloom filters, Scatter-Gather-Merge reduces a nontrivial amount of IO. We also show that Scatter-GatherMerge can be easily applied to MapReduce. Our experimental results in both cluster and cloud environments show that Scatter-Gather-Merge outperforms existing approaches.
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