Database Concurrency Control: Methods, Performance, and by Alexander Thomasian (auth.)

By Alexander Thomasian (auth.)

Database Concurrency keep an eye on: tools, functionality and Analysis is a evaluation of advancements in concurrency keep an eye on equipment for centralized database platforms, with a brief digression into disbursed databases and multicomputers, the emphasis being on functionality.
the most ambitions of Database Concurrency regulate: tools, Performanceand Analysis are to succinctly specify quite a few concurrency keep an eye on equipment; to explain versions for comparing the relative functionality of concurrency keep watch over tools; to indicate areas of difficulty in prior functionality analyses; to introduce queuing community versions to judge the baseline functionality of transaction processing platforms; to supply insights into the relative functionality of transaction processing structures; to demonstrate the applying of uncomplicated analytic the way to the functionality research of varied concurrency keep an eye on tools; to study transaction versions that are meant to alleviate the impact of lock competition; to supply directions for bettering the functionality of transaction processing structures as a result of concurrency keep watch over; and to show components for additional research.
This monograph will be of direct curiosity to desktop scientists doing study on concurrency keep an eye on equipment for prime functionality transaction processing platforms, designers of such structures, and execs inquisitive about enhancing (tuning) the functionality of transaction processing systems.

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Additional info for Database Concurrency Control: Methods, Performance, and Analysis

Example text

These throughputs then characterize the aggregate server. Exact aggregation is possible when the overall queueing network model is product-form, such that a subnetwork can be substituted by an aggregate server. , the maximum number of transactions that can be activated in the computer system is limited. Examples of applying aggregation in queueing networks appear in [ThNa81],[Lave83],[LZGS84]. Hierarchical analytic solutions typically consist of a lower level closed queueing network model, which is analyzed to obtain the throughput characteristic of the aggregate server which replaces the queueing network.

A careful validation requires experimentation with a larger set of input parameters, as weil as the validation of lower level performance measures. , [RyTh90a]. This is because validation may be carried out in a bottom-up manner, to ensure that errors at lower levels do not propagate to higher levels (refer to Figures 4 and 6 in [RyTh90a]). Assuming that it is known from simulation results that the effect of deadlocks on the performance of the standard locking system is negligibly small [ThRy91],[Thom93b], then the analysis should take into account only the mean transaction blocking time, which equals the product of the mean number of lock requests per transaction, the probability of lock conflict (Pe), and the mean waiting time per lock conflict (W).

11) [ThRy91]. 51X + 1)P - IX = O. 12) It is stated in [TayY90] that "As yet, there are no proposed measures for the resource requirements of a given concurrency control algorithm". Note that IX is a single measure, which determines the level of lock contention for standard locking. Two different systems will have the same lock contention level as long as they have the same value for IX. 1 that the value of IX for fixed size transactions is one sixth of the IX for transactions with a geometric transaction size distribution and the same mean size.

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