http://www.workflowpatterns.com/documentation/documents/yawl_qut_report_FIT-TR-2002-06.pdf

YAWL: yet another workflow language

An Introduction to Probability Theory and its Applications, Volume I

Multiple applications request data from multiple storage units over a computer network. The data is divided into segments and each segment is distributed randomly on one of several storage units, independent of the storage units on which other segments of the media data are stored. At least one additional copy of each segment also is distributed randomly over the storage units, such that each segment is stored on at least two storage units. This random distribution of multiple copies of segments of data improves both scalability and reliability. When an application requests a selected segment of data, the request is processed by the storage unit with the shortest queue of requests. Random fluctuations in the load applied by multiple applications on multiple storage units are balanced nearly equally over all of the storage units. This combination of techniques results in a system which can transfer multiple, independent high-bandwidth streams of data in a scalable manner in both directions between multiple applications and multiple storage units.

Computer system and process for transferring multiple high bandwidth streams of data between multiple storage units and multiple applications in a scalable and reliable manner

DB2TM, IMS, and TandemTM systems. ARIES is applicable not only to database management systems but also to persistent object-oriented languages, recoverable file systems and transaction-based operating systems. ARIES has been implemented, to varying degrees, in IBM's OS/2TM Extended Edition Database Manager, DB2, Workstation Data Save Facility/VM, Starburst and QuickSilver, and in the University of Wisconsin's EXODUS and Gamma database machine.

/pdf/aries-a-transaction-recovery-method-supporting-fine-27kfd8jref.pdf

ARIES: a transaction recovery method supporting fine-granularity locking and partial rollbacks using write-ahead logging

Multiple applications request data from multiple storage units over a computer network. The data is divided into segments and each segment is distributed randomly on one of several storage units, independent of the storage units on which other segments of the media data are stored. Redundancy information corresponding to each segment also is distributed randomly over the storage units. The redundancy information for a segment may be a copy of the segment, such that each segment is stored on at least two storage units. The redundancy information also may be based on two or more segments. This random distribution of segments of data and corresponding redundancy information improves both scalability and reliability. When a storage unit fails, its load is distributed evenly over to remaining storage units and its lost data may be recovered because of the redundancy information. When an application requests a selected segment of data, the request may be processed by the storage unit with the shortest queue of requests. Random fluctuations in the load applied by multiple applications on multiple storage units are balanced nearly equally over all of the storage units. Small data files also may be stored on storage units that combine small files into larger segments of data using a log structured file system. This combination of techniques results in a system which can transfer both multiple, independent high-bandwidth streams of data and small data files in a scalable manner in both directions between multiple applications and multiple storage units.

Computer system and process for transferring streams of data between multiple storage units and multiple applications in a scalable and reliable manner

Current workflow management systems fall short of supporting large-scale distributed, enterprise-wide applications. We present a scalable, rigorously founded approach to enterprise-wide workflow management, based on the distributed execution of state and activity charts. By exploiting the formal semantics of state and activity charts, we develop an algorithm for transforming a centralized state and activity chart into a provably equivalent partitioned one, suitable for distributed execution. A synchronization scheme is developed that guarantees an execution equivalent to a non-distributed one. This basic solution is further refined in order to reduce communication overhead and exploit parallelism between partitions whenever possible. The developed synchronization schemes are compared in terms of the number and size of synchronization messages.

From Centralized Workflow Specification to Distributed WorkflowExecution

Numerous applications such as stock market or medical information systems require that both historical and current data be logically integrated into a temporal database. The underlying access method must support different forms of “time-travel” queries, the migration of old record versions onto inexpensive archive media, and high insertion and update rates. This paper presents an access method for transaction-time temporal data, called the log-structured history data access method (LHAM) that meets these demands. The basic principle of LHAM is to partition the data into successive components based on the timestamps of the record versions. Components are assigned to different levels of a storage hierarchy, and incoming data is continuously migrated through the hierarchy. The paper discusses the LHAM concepts, including concurrency control and recovery, our full-fledged LHAM implementation, and experimental performance results based on this implementation. A detailed comparison with the TSB-tree, both analytically and based on experiments with real implementations, shows that LHAM is highly superior in terms of insert performance, while query performance is in almost all cases at least as good as for the TSB-tree; in many cases it is much better.

https://domino.mpi-inf.mpg.de/intranet/ag5/ag5publ.nsf/80255f02006a559a80255ef20056fc02/14008bbd691c7183c125713f00489b29/$FILE/MuthOPW00.pdf

The LHAM log-structured history data access method

This paper presents an approach towards the specification, verification, and distributed execution of workflows based on state and activity charts. The formal foundation of state and activity charts is exploited at three levels. At the specification level, the formalism enforces precise descriptions of business processes while also allowing subsequent refinements. In addition, precise specifications based on other methods can be automatically converted into state and activity charts. At the level of verification, state charts are amenable to the efficient method of model checking, in order to verify particularly critical workflow properties. Finally, at the execution level, a state chart specification forms the basis for the automatic generation of modules that can be directly executed in a distributed manner. Within the MENTOR project, a coherent prototype system has been built that comprises all three levels: specification, verification, and distributed execution.

Enterprise-wide workflow management based on state and activity charts

Numerous applications such as stock market or medical information systems require that both historical and current data be logically integrated into a temporal database. The underlying access method must support different forms of “time-travel” queries, the migration of old record versions onto inexpensive archive media, and high insert and update rates. This paper introduces a new access method for transaction-time temporal data, called the Logstructured History Data Access Method (LHAM) that meets these demands. The basic principle of LHAM is to partition the data into successive components based on the timestamps of the record versions. Components are assigned to different levels of a storage hierarchy, and incoming data is continuously migrated through the hierarchy. The paper discusses the LHAM concepts, including concurrency control and recovery, our full-fledged LHAM implementation, and experimental performance results based on this implementation. A detailed comparison with the TSB-tree, both analytically and based on experiments with real implementations, shows that LHAM is highly superior in terms of insert performance while query performance is in almost all cases at least as good as for the TSB-tree; in many cases it is much better,

/pdf/design-implementation-and-performance-of-the-lham-log-cynq8e16pf.pdf

Design, Implementation, and Performance of the LHAM Log-Structured History Data Access Method

Most multimedia applications require storage and retrieval of large amounts of continuous and discrete data at very high rates. Disk drives should be servicing such mixed workloads achieving low response times for discrete requests, while guaranteeing the uninterrupted delivery of continuous data. Disk scheduling algorithms for mixed workloads, although they play a central role in this task, have been overlooked by related multimedia research efforts, which so far have mostly concentrated on the scheduling of continuous requests only. The focus of this paper is on efficient disk I/O scheduling algorithms for mixed workloads in a multimedia storage server. We propose novel algorithms, a taxonomy of relevant algorithms, and study their performance through experimentation. Our results show that our proposed algorithms offer drastic improvements in discrete request average response times, low response-time variability, while serving continuous requests without interruptions.

Disk Scheduling for Mixed-Media Workloads in a Multimedia Server

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