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The objective of work ow recovery is to enforce the failure atomicity of the work ows The recovery procedures must make sure that, if a failure occurs in any of the work ow-processing components (including the scheduler), the work ow will eventually reach an acceptable termination state (whether aborted or committed) For example, the scheduler could continue processing after failure and recovery, as though nothing happened, thus providing forward recoverability Otherwise, the scheduler could abort the whole work ow (that is, reach one of the global abort states) In either case, some subtransactions may need to be committed or even submitted for execution (for example, compensating subtransactions) We assume that the processing entities involved in the work ow have their own local recovery systems and handle their local failures To recover the executionenvironment context, the failure-recovery routines need to restore the state information of the scheduler at the time of failure, including the information about the execution states of each task Therefore, the appropriate status information must be logged on stable storage We also need to consider the contents of the message queues When one agent hands off a task to another, the handoff should be carried out exactly once: If the handoff happens twice a task may get executed twice; if the handoff does not occur, the task may get lost Persistent messaging (Section 1943) provides exactly the features to ensure positive, single handoff
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Work ows are often hand coded as part of application systems For instance, enterprise resource planning (ERP) systems, which help coordinate activities across an entire enterprise, have numerous work ows built into them The goal of work ow management systems is to simplify the construction of work ows and make them more reliable, by permitting them to be speci ed in a high-level manner and executed in accordance with the speci cation There are a large number of commercial work ow management systems; some, like FlowMark from IBM, are general-purpose work ow management systems, while others are speci c to particular work ows, such as order processing or bug/failure reporting systems In today s world of interconnected organizations, it is not suf cient to manage work ows only within an organization Work ows that cross organizational boundaries are becoming increasingly common For instance, consider an order placed by an organization and communicated to another organization that ful lls the order In each organization there may be a work ow associated with the order, and it is important that the work ows be able to interoperate, in order to minimize human intervention The Work ow Management Coalition has developed standards for interoperation between work ow systems Current standardization efforts use XML as the underlying language for communicating information about the work ow See the bibliographical notes for more information
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To allow a high rate of transaction processing (hundreds or thousands of transactions per second), we must use high-performance hardware, and must exploit parallelism These techniques alone, however, are insuf cient to obtain very low response times, since disk I/O remains a bottleneck about 10 milliseconds are required for each I/O and this number has not decreased at a rate comparable to the increase in processor speeds Disk I/O is often the bottleneck for reads, as well as for transaction commits The long disk latency (about 10 milliseconds average) increases not only the time to access a data item, but also limits the number of accesses per second We can make a database system less disk bound by increasing the size of the database buffer Advances in main-memory technology let us construct large main memories at relatively low cost Today, commercial 64-bit systems can support main memories of tens of gigabytes For some applications, such as real-time control, it is necessary to store data in main memory to meet performance requirements The memory size required for most such systems is not exceptionally large, although there are at least a few applications that require multiple gigabytes of data to be memory resident Since memory sizes have been growing at a very fast rate, an increasing number of applications can be expected to have data that t into main memory Large main memories allow faster processing of transactions, since data are memory resident However, there are still disk-related limitations:
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