7291217
Merging workflows: A new perspective on connecting business processes
This paper describes the concept of workflow merge and methods for merging business processes. We grouped merges in four categories according to the type of merge: sequential, parallel, conditional, and iterative, and describe the corresponding algorithms for performing these operations. We give results that allow us to determine whether a merge operation is sound. It is shown that to avoid invalid merges, one should choose merge points between which a sub-workflow, called a merge region, is well structured. These findings can provide useful guidance for future workflow merge research. We also raise issues of more complex merge problems, such as merge conflicts, semantic ambiguities and workflow splits.
45847616
EROICA: A Rule-Based Approach to Organizational Policy Management in Workflow Systems
Conventional workflow management systems have been criticized for being too rigid in dealing with changes in organizational policies, in particular for the lack of flexible mechanisms for handling complex role resolution. In this paper we describe a rule-based approach (EROICA) for managing organizational policies in workflow systems. Our approach extends the well-known ECA (event-condition-action) approach used in database systems by elevating roles, objects and time intervals to first class citizens, hence the term EROICA (event-role-object-interval-condition-action). In this paper, we focus on the development of a flexible role resolution mechanism that operates under various business constraints specified with EROICA rules. We further discuss the issues of architecture and implementation and demonstrate that the EROICA framework is promising in making next generation of workflow systems more powerful and flexible.
14897390
Meta workflows as a control and coordination mechanism for exception handling in workflow systems
A higher level control and coordination mechanism is required for exception handling in workflow systems. This paper describes such a framework based on events, states, and a new kind of process called a meta workflow. Meta workflows have five kinds of meta activities and facilitate control over base workflows. We describe the framework and illustrate it with examples to show its features. The paper gives an architecture for incorporating it into existing workflows and also provides a formal semantics of execution. This framework can be used in Web services, supply chains, and inter-organizational applications where coordination requirements are complex, and flexible and adaptable workflows are needed. It is also useful for handling not just failure recovery but also various kinds of special situations, which arise frequently in web-based applications.
206778468
A performance study of general grid structures for replicated data
Recently, there has been considerable interest in the study of replica-control protocols which are based on organizing several copies of an object into logical structures, such as rectangular grids. In addition to high availability, another objective in exploiting such structures is to improve the degree of load sharing in a system. The authors extend the scope of grid structures to general grids, which allow holes in various positions of a rectangular structure and are useful to consider because they often produce availabilities that are higher than solid grids, where every position must be occupied by a node. In addition to proposing an improvement to the existing grid protocol, new insights are offered into the performance of the grids, from both availability and load sharing points of view. Algorithms for designing grids to maximize availability independently and also in conjunction with a load sharing constraint are given.<<ETX>>
16012186
A fine-grained, controllable, user-to-user delegation method in RBAC
This paper addresses the issues surrounding user-to-user delegation in RBAC. We show how delegations can be incorporated into the RBAC model in a simple and straightforward manner. A special feature of the model is that it allows fine-grained control over what rights a user wishes to delegate as opposed to delegation at the role level where all the rights of a role must be delegated. In addition, the model provides a rich set of controls regarding further delegations of a right, generic constraints that further control delegations, and an innovative model for revocations. Properties of both delegation and revocation are discussed, and our work is compared with other related research.
59757838
Operating System Support for Data Management.
16105375
Stochastic models for throughput analysis of randomly arriving elastic flows in the Internet
This paper is about analytical models for calculating the average bandwidth shares obtained by TCP controlled finite file transfers that arrive randomly and share a single (bottleneck) link. Owing to the complex nature of the TCP congestion control algorithm, a single model does not work well for all combinations of network parameters (i.e., mean file size, link capacity, and propagation delay). We propose two models, develop their analyses, and identify the regions of their applicability. One model is obtained from a detailed analysis of TCP's AIMD adaptive window mechanism; the analysis accounts for session arrivals and departures, and finite link buffers. It is essentially a processor sharing (PS) model with time varying service rate; hence we call it TCP-PS. The other model is a simple modification of the PS model that accounts for large propagation delays; we call this model rate limited-PS (RL-PS). The TCP-PS model analysis accommodates a general file size distribution by approximating it with a mixture of exponentials. The RL-PS model can be used for general file size distributions. We show that the TCP-PS model converges to the standard PS model as the propagation delay approaches zero. We also observe that the PS model provides very poor estimates of throughput unless the propagation delay is very small. We observe that the key parameters affecting the throughput are the bandwidth delay product (BDP), file size distribution, the link buffer and the traffic intensity. Several numerical comparisons between analytical and simulation results are provided. We observe that the TCP-PS model is accurate when the BDP is small compared to the mean file size, and the RL-PS model works well when the BDP is large compared to the mean file size.
208013096
New insights from a fixed point analysis of single cell IEEE 802.11 WLANs
We study a fixed point formalisation of the well known analysis of Bianchi. We provide a significant simplification and generalisation of the analysis. In this more general framework, the fixed point solution and performance measures resulting from it are studied. Uniqueness of the fixed point is established. Simple and general throughput formulas are provided. It is shown that the throughput of any flow will be bounded by the one with the smallest transmission rate. The aggregate throughput is bounded by the reciprocal of the harmonic mean of the transmission rates. In an asymptotic regime with a large number of nodes, explicit formulas for the collision probability, the aggregate attempt rate and the aggregate throughput are provided. The results from the analysis are compared with ns2 simulations, and also with an exact Markov model of the back-off process. It is shown how the saturated network analysis can be used to obtain TCP transfer throughputs in some cases.
7308298
Data streaming algorithms for accurate and efficient measurement of traffic and flow matrices
The traffic volume between origin/destination (OD) pairs in a network, known as traffic matrix, is essential for efficient network provisioning and traffic engineering. Existing approaches of estimating the traffic matrix, based on statistical inference and/or packet sampling, usually cannot achieve very high estimation accuracy. In this work, we take a brand new approach in attacking this problem. We propose a novel data streaming algorithm that can process traffic stream at very high speed (e.g., 40 Gbps) and produce traffic digests that are orders of magnitude smaller than the traffic stream. By correlating the digests collected at any OD pair using Bayesian statistics, the volume of traffic flowing between the OD pair can be accurately determined. We also establish principles and techniques for optimally combining this streaming method with sampling, when sampling is necessary due to stringent resource constraints. In addition, we propose another data streaming algorithm that estimates flow matrix, a finer-grained characterization than traffic matrix. Flow matrix is concerned with not only the total traffic between an OD pair (traffic matrix), but also how it splits into flows of various sizes. Through rigorous theoretical analysis and extensive synthetic experiments on real Internet traffic, we demonstrate that these two algorithms can produce very accurate estimation of traffic matrix and flow matrix respectively.
7719957
Connectivity and inference problems for temporal networks
Many network problems are based on fundamental relationships involving time. Consider, for example, the problems of modeling the flow of information through a distributed network, studying the spread of a disease through a population, or analyzing the reachability properties of an airline timetable. In such settings, a natural model is that of a graph in which each edge is annotated with a time label specifying the time at which its endpoints "communicated." We will call such a graph a temporal network. To model the notion that information in such a network "flows" only on paths whose labels respect the ordering of time, we call a path time-respecting if the time labels on its edges are non-decreasing. The central motivation for our work is the following question: how do the basic combinatorial and algorithmic properties of graphs change when we impose this additional temporal condition? The notion of a path is intrinsic to many of the most fundamental algorithmic problems on graphs; spanning trees, connectivity, flows, and cuts are some examples. When we focus on time-respecting paths in place of arbitrary paths, many of these problems acquire a character that is different from the traditional setting, but very rich in its own right. We provide results on two types of problems for temporal networks. First, we consider connectivity problems, in which we seek disjoint time-respecting paths between pairs of nodes. The natural analogue of Menger's Theorem for node-disjoint paths fails in general for time-respecting paths; we give a non-trivial characterization of those graphs for which the theorem does hold in terms of an excluded subdivision theorem, and provide a polynomial-time algorithm for connectivity on this class of graphs. (The problem on general graphs is NP-complete.) We then define and study the class of inference problems, in which we seek to reconstruct a partially specified time labeling of a network in a manner consistent with an observed history of information flow.
2987975
Data streaming algorithms for efficient and accurate estimation of flow size distribution
Knowing the distribution of the sizes of traffic flows passing through a network link helps a network operator to characterize network resource usage, infer traffic demands, detect traffic anomalies, and accommodate new traffic demands through better traffic engineering. Previous work on estimating the flow size distribution has been focused on making inferences from sampled network traffic. Its accuracy is limited by the (typically) low sampling rate required to make the sampling operation affordable. In this paper we present a novel data streaming algorithm to provide much more accurate estimates of flow distribution, using a "lossy data structure" which consists of an array of counters fitted well into SRAM. For each incoming packet, our algorithm only needs to increment one underlying counter, making the algorithm fast enough even for 40 Gbps (OC-768) links. The data structure is lossy in the sense that sizes of multiple flows may collide into the same counter. Our algorithm uses Bayesian statistical methods such as Expectation Maximization to infer the most likely flow size distribution that results in the observed counter values after collision. Evaluations of this algorithm on large Internet traces obtained from several sources (including a tier-1 ISP) demonstrate that it has very high measurement accuracy (within 2%). Our algorithm not only dramatically improves the accuracy of flow distribution measurement, but also contributes to the field of data streaming by formalizing an existing methodology and applying it to the context of estimating the flow-distribution.