Workflow management systems (WfMSs) have been used to support various types of business processes for more than a decade now. In workflows for e-commerce and Web service applications, suppliers and customers define a binding agreement or contract between the two parties, specifying Quality of Service (QoS) items such as products or services to be delivered, deadlines, quality of products, and cost of services. The management of QoS metrics directly impacts the success of organizations participating in e-commerce. Therefore, when services or products are created or managed using workflows, the underlying workflow system must accept the specifications and be able to estimate, monitor, and control the QoS rendered to customers. In this paper, we present a predictive QoS model that makes it possible to compute the quality of service for workflows automatically based on atomic task QoS attributes. To this end, we present a model that specifies QoS and describe an algorithm and a simulation system in order to compute, analyze and monitor workflow QoS metrics.

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Modeling Quality of Service for Workflows and Web Service Processes

The enormous growth in the number of documents in the World Wide Web increases the need for improved link navigation and path analysis models. Link prediction and path analysis are important problems with a wide range of applications ranging from personalization to Web server request prediction. The sheer size of the World Wide Web coupled with the variation in users' navigation patterns makes this a very difficult sequence modelling problem. In this paper, the notion of probabilistic link prediction and path analysis using Markov chains is proposed and evaluated. Markov chains allow the system to dynamically model the URL access patterns that are observed in navigation logs based on the previous state. Furthermore, the Markov chain model can also be used in a generative mode to automatically obtain tours. The Markov transition matrix can be analysed further using eigenvector decomposition to obtain `personalized hubs/authorities'. The utility of the Markov chain approach is demonstrated in many domains: HTTP request prediction, system-driven adaptive Web navigation, tour generation, and detection of `personalized hubs/authorities' from user navigation profiles. The generality and power of Markov chains is a first step towards the application of powerful probabilistic models to Web path analysis and link prediction.

Link prediction and path analysis using Markov chains

A system and method for remote asynchronous replication or mirroring of changes in a source file system snapshot in a destination replica file system using a scan (via a scanner) of the blocks that make up two versions of a snapshot of the source file system, which identifies changed blocks in the respective snapshot files based upon differences in volume block numbers identified in a scan of the logical file block index of each snapshot. Trees of blocks associated with the files are traversed, bypassing unchanged pointers between versions and walking down to identify the changes in the hierarchy of the tree. These changes are transmitted to the destination mirror or replicated snapshot. This technique allows regular files, directories, inodes and any other hierarchical structure to be efficiently scanned to determine differences between versions thereof. The changes in the files and directories are transmitted over the network for update of the replicated destination snapshot in an asynchronous (lazy write) manner. The changes are described in an extensible, system-independent data stream format layered under a network transport protocol. At the destination, source changes are used to update the destination snapshot. Any deleted or modified inodes already on the destination are moved to a temporary or “purgatory” directory and, if reused, are relinked to the rebuilt replicated snapshot directory. The source file system snapshots can be representative of a volume sub-organization, such as a qtree.

System and method for determining changes in two snapshots and for transmitting changes to destination snapshot

This paper compares different indexing techniques proposed for supporting efficient access to temporal data. The comparison is based on a collection of important performance criteria, including the space consumed, update processing, and query time for representative queries. The comparison is based on worst-case analysis, hence no assumptions on data distribution or query frequencies are made. When a number of methods have the same asymptotic worst-case behavior, features in the methods that affect average case behavior are discussed. Additional criteria examined are the pagination of an index, the ability to cluster related data together, and the ability to efficiently separate old from current data (so that larger archival storage media such as write-once optical disks can be used). The purpose of the paper is to identify the difficult problems in accessing temporal data and describe how the different methods aim to solve them. A general lower bound for answering basic temporal queries is also introduced.

Comparison of access methods for time-evolving data

A multi-protocol storage appliance serves file and block protocol access to information stored on storage devices in an integrated manner for both network attached storage (NAS) and storage area network (SAN) deployments. A storage operating system of the appliance implements a file system (320) that cooperates with novel virtualization modules to provide a virtualization system (300) that “virtualizes” the storage space provided by the devices. The file system provides volume management capabilities for use in block-based access to the information stored on the devices. The virtualization system (300) allows the file system to logically organize the information as named file (324), directory (326) and virtual disk storage objects (322, 328) to thereby provide an integrated NAS and SAN appliance approach to storage by enabling file-based access to the files and directories while further enabling block-based access to the virtual disks.

Multi-protocol storage appliance that provides integrated support for file and block access protocols

Although today’s computers provide huge amounts of main memory, the ever-increasing load of large data servers, imposed by resource-intensive decision-support queries and accesses to multimedia and other complex data, often leads to memory contention and may result in severe performance degradation. Therefore, careful tuning of memory mangement is crucial for heavy-load data servers. This paper gives an overview of self-tuning methods for a spectrum of memory management issues, ranging from traditional caching to exploiting distributed memory in a server cluster and speculative prefetching in a Web-based system. The common, fundamental elements in these methods include on-line load tracking, near-future access prediction based on stochastic models and the available on-line statistics, and dynamic and automatic adjustment of control parameters in a feedback loop. 1 The Need for Memory Tuning Although memory is relatively inexpensive and modern computer systems are amply equipped with it, memory contention on heavily loaded data servers is a common cause of performance problems. The reasons are threefold: Servers are operating with a multitude of complex software, ranging from the operating system to database systems, object request brokers, and application services. Much of this software has been written so as to quickly penetrate the market rather than optimizing memory usage and other resource consumption. The distinctive characteristic and key problem of a data server is that it operates in multi-user mode, serving many clients concurrently or in parallel. Therefore, a server needs to divide up its resources among the simultaneously active threads for executing queries, transactions, stored procedures, Web applications, etc. Often, multiple data-intensive decision-support queries compete for memory. The data volumes that need to be managed by a server seem to be growing without limits. One part of this trend is that multimedia data types such as images, speech, or video have become more popular and are being merged into conventional-data applications (e.g., images or videos for insurance claims). The other Copyright 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering

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Towards Self-Tuning Memory Management for Data Servers

Large multimedia document archives may hold a major fraction of their data in tertiary storage libraries for cost reasons. This paper develops an integrated approach to the vertical data migration between the tertiary, secondary, and primary storage in that it reconciles speculative prefetching, to mask the high latency of the tertiary storage, with the replacement policy of the document caches at the secondary and primary storage level, and also considers the interaction of these policies with the tertiary and secondary storage request scheduling. The integrated migration policy is based on a continuous-time Markov chain model for predicting the expected number of accesses to a document within a specified time horizon. Prefetching is initiated only if that expectation is higher than those of the documents that need to be dropped from secondary storage to free up the necessary space. In addition, the possible resource contention at the tertiary and secondary storage is taken into account by dynamically assessing the response-time benefit of prefetching a document versus the penalty that it would incur on the response time of the pending document requests. The parameters of the continuous-time Markov chain model, the probabilities of co-accessing certain documents and the interaction times between successive accesses, are dynamically estimated and adjusted to evolving workload patterns by keeping online statistics. The integrated policy for vertical data migration has been implemented in a prototype system. The system makes profitable use of the Markov chain model also for the scheduling of volume exchanges in the tertiary storage library. Detailed simulation experiments with Web-server-like synthetic workloads indicate significant gains in terms of client response time. The experiments also show that the overhead of the statistical bookkeeping and the computations for the access predictions is affordable.

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Integrated document caching and prefetching in storage hierarchies based on Markov-chain predictions

Multimedia applications demand specific support from database management systems due to the characteristics of multimedia data and their interactive usage. This includes integrated support for high-volume and time-dependent (continuous) data types like audio and video. One critical issue is to provide handling of continuous data streams including buffer management as needed for multimedia presentations. Buffer management strategies for continuous data have to consider specific requirements like providing for continuity of presentations, for immediate continuation of presentations after frequent user interactions by appropriate buffer resource consumption. Existing buffer management strategies do not sufficiently support the handling of continuous data streams in highly interactive multimedia presentations. In this paper we present the “least/most relevant for presentation” (LJMRP) buffer management strategy whichconsiderspresentationspecificinformationin order to provide an optimized behavior with respect to the requirements mentioned above. WMRP is a framework to formulate specific interaction models and is therefore adaptable to individual multimedia applications. We present a simulation study showing that an instantiated L/MRP outperforms existing approachesforgiventypesofinteractivemultimedia applications. It is shown that UMRP is especially suitable to support highly interactive multimedia presentations. + The new address of the author is Department of Computer Science, University of the Saarland, D-66041 Saarbriicken, Germany. kraiss@cs.uni-sb.de. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributedfor direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. Tocopyotherwise, ortorepublish, requiresafeeantiorspecial permission from the Endowment. Proceedings of the 21th VLDB Conference Zurich, Switzerland, 1995

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L/MRP: A Buffer Management Strategy for Interactive Continuous Data Flows in a Multimedia DBMS

Workflow management systems (WFMSs) that are geared for the orchestration of enterprise-wide or even "virtual-enterprise"-style business processes across multiple organizations are complex distributed systems. They consist of multiple workflow engines, application servers, and ORB-style communication servers. Thus, deriving a suitable configuration of an entire distributed WFMS for a given application workload is a difficult task. 
 
This paper presents a mathematically based method for configuring a distributed WFMS such that the application's demands regarding performance and availability can be met while aiming to minimize the total system costs. The major degree of freedom that the configuration method considers is the replication of the underlying software components, workflow engines and application servers of different types as well as the communication server, on multiple computers for load partitioning and enhanced availability. The mathematical core of the method consists of Markov-chain models, derived from the application's workflow specifications, that allow assessing the overall system's performance, availability, and also its performability in the degraded mode when some server replicas are offline, for given degrees of replication. By iterating over the space of feasible system configurations and assessing the quality of candidate configurations, the developed method determines a configuration with near-minimum costs.

Performance and Availability Assessment for the Configuration of Distributed Workflow Management Systems

Large multimedia document archives hold most of their data in near-line tertiary storage libraries for cost reasons This paper develops an integrated approach to the vertical data migration hetween the tertiary and secondary storage in that it reconciles speculative preloading, to mask the high latency of the tertiary storage, with the replacement policy of the secondary storage In addition, it considers the interaction of these policies with the tertiary storage scheduling and controls preloading aggressiveness by taking contention for tertiary storage drives into account The integrated migration policy is based on a continuous-time Markov-chain (CTMC) model,fijr predicting the expected number of accesses to a document within a specified time horizon The parameters of the CTMC model, the probabilities of co-accessing certain documents and the interaction times between successive accesses, are dynamically estimated and adjusted to evolving workload patterns by keeping online statistics The integrated policy for vertical data migration has been implemented in a prototype system Detailed simulation studies with Web-server-like synthetic workloads indicate sign$cant gains in terms of client response time The studies also show that the overhead of the statistical bookkeeping and the computations for the access predictions is affordable

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Achim Kraiss

Papers

Towards Self-Tuning Memory Management for Data Servers

Integrated document caching and prefetching in storage hierarchies based on Markov-chain predictions

L/MRP: A Buffer Management Strategy for Interactive Continuous Data Flows in a Multimedia DBMS

Performance and Availability Assessment for the Configuration of Distributed Workflow Management Systems

Vertical Data Migration in Large Near-Line Document Archives Based on Markov-Chain Predictions