Gianfranco Ciardo

A physical, genetic and functional sequence assembly of the barley genome

Barley (Hordeum vulgare L.) is among the world’s earliest domesticated and most important crop plants. It is diploid with a large haploid genome of 5.1 gigabases (Gb). Here we present an integrated and ordered physical, genetic and functional sequence resource that describes the barley gene-space in a structured whole-genome context. We developed a physical map of 4.98 Gb, with more than 3.90 Gb anchored to a high-resolution genetic map. Projecting a deep whole-genome shotgun assembly, complementary DNA and deep RNA sequence data onto this framework supports 79,379 transcript clusters, including 26,159 ‘high-confidence’ genes with homology support from other plant genomes. Abundant alternative splicing, premature termination codons and novel transcriptionally active regions suggest that post-transcriptional processing forms an important regulatory layer. Survey sequences from diverse accessions reveal a landscape of extensive single-nucleotide variation. Our data provide a platform for both genome-assisted research and enabling contemporary crop improvement.

SPNP: stochastic Petri net package

SPNP, a powerful GSPN package that allows the modeling of complex system behaviors, is presented. Advanced constructs are available in SPNP such as marking-dependent arc multiplicities, enabling functions, arrays of places or transitions, and subnets; the full expressive power of the C programming language is also available to increase the flexibility of the net description. Sophisticated steady-state and transient solvers are available including cumulative and up-to-absorption measures. The user is not limited to a predefined set of measures; detailed expressions reflecting exactly the measures sought can be easily specified. The authors conclude by comparing SPNP with two other SPN-based packages, Great SPN and METASAN.<<ETX>>

A decomposition approach for stochastic reward net models

Abstract We present a decomposition approach for the solution of large stochastic reward nets (SRNs) based on the concept of near-independence. The overall model consists of a set of submodels whose interactions are described by an import graph. Each node of the graph corresponds to a parametric SRN submodel and an arc from submodel A to submodel B corresponds to a parameter value that B must receive from A. The quantities exchanged between submodels are based on only three primitives. The import graph normally contains cycles, so the solution method is based on fixed point iteration. Any SRN containing one or more of the nearly-independent structures we present, commonly encountered in practice, can be analyzed using our approach. No other restriction on the SRN is required. We apply our technique to the analysis of a flexible manufacturing system.

Automated Generation and Analysis of Markov Reward Models Using Stochastic Reward Nets

Markov and Markov reward models are widely used for the performance and reliability analysis of computer and communication systems. Models of real systems often contain thousands or even millions of states. We propose the use of Stochastic Reward Nets (SRNs) for the automatic generation of these large Markov reward models. SRNs do allow the concise specification of practical performance, reliability and per-formability models.

Applications and Theory of Petri Nets 2005

Invited Papers.- Expressiveness and Efficient Analysis of Stochastic Well-Formed Nets.- Applications of Craig Interpolation to Model Checking.- Towards an Algebra for Security Policies.- Continuization of Timed Petri Nets: From Performance Evaluation to Observation and Control.- Full Papers.- Genetic Process Mining.- The (True) Concurrent Markov Property and Some Applications to Markov Nets.- On the Equivalence Between Liveness and Deadlock-Freeness in Petri Nets.- Extremal Throughputs in Free-Choice Nets.- A Framework to Decompose GSPN Models.- Modeling Dynamic Architectures Using Nets-Within-Nets.- A High Level Language for Structural Relations in Well-Formed Nets.- Derivation of Non-structural Invariants of Petri Nets Using Abstract Interpretation.- Modeling Multi-valued Genetic Regulatory Networks Using High-Level Petri Nets.- Termination Properties of TCPs Connection Management Procedures.- Soundness of Resource-Constrained Workflow Nets.- High-Level Nets with Nets and Rules as Tokens.- Can I Execute My Scenario in Your Net?.- Reference and Value Semantics Are Equivalent for Ordinary Object Petri Nets.- Particle Petri Nets for Aircraft Procedure Monitoring Under Uncertainty.- On the Expressive Power of Petri Net Schemata.- Determinate STG Decomposition of Marked Graphs.- Timed-Arc Petri Nets vs. Networks of Timed Automata.- Specifying and Analyzing Software Safety Requirements of a Frequency Converter Using Coloured Petri Nets.- Achieving a General, Formal and Decidable Approach to the OR-Join in Workflow Using Reset Nets.- Tool Papers.- The ProM Framework: A New Era in Process Mining Tool Support.- High Level Petri Nets Analysis with Helena.- Protos 7.0: Simulation Made Accessible.

Efficient Reachability Set Generation and Storage Using Decision Diagrams

We present a new technique for the generation and storage of the reachability set of a Petri net. Our approach is inspired by previous work on Binary and Multi-valued Decision Diagrams but exploits a concept of locality for the effect of a transitions firing to vastly improve algorithmic performance. The result is a data structure and a set of manipulation routines that can be used to generate and store enormous sets extremely efficiently in terms of both memory and execution time.

Workload-aware load balancing for clustered Web servers

We focus on load balancing policies for homogeneous clustered Web servers that tune their parameters on-the-fly to adapt to changes in the arrival rates and service times of incoming requests. The proposed scheduling policy, ADAPTLOAD, monitors the incoming workload and self-adjusts its balancing parameters according to changes in the operational environment such as rapid fluctuations in the arrival rates or document popularity. Using actual traces from the 1998 World Cup Web site, we conduct a detailed characterization of the workload demands and demonstrate how online workload monitoring can play a significant part in meeting the performance challenges of robust policy design. We show that the proposed load, balancing policy based on statistical information derived from recent workload history provides similar performance benefits as locality-aware allocation schemes, without requiring locality data. Extensive experimentation indicates that ADAPTLOAD results in an effective scheme, even when servers must support both static and dynamic Web pages.

A data structure for the efficient Kronecker solution of GSPNs

Kronecker-based approaches have been proposed for the solution of structured GSPNs with extremely large state spaces. Representing the transition rate matrix using Kronecker sums and products of smaller matrices virtually eliminates its storage requirements, but introduces various sources of overhead. We show how, by using a new data structure which we call matrix diagrams, we are able to greatly reduce or eliminate many of these overheads, resulting in a very efficient overall solution process.

Performability analysis using semi-Markov reward processes

M.D. Beaudry (1978) proposed a simple method of computing the distribution of performability in a Markov reward process. Two extensions of Beaudrys approach are presented. The authors generalize the method to a semi-Markov reward process by removing the restriction requiring the association of zero reward to absorbing states only. The algorithm proceeds by replacing zero reward nonabsorbing states by a probabilistic switch; it is therefore related to the elimination of vanishing states from the reachability graph of a generalized stochastic Petri net and to the elimination of fast transient states in a decomposition approach to stiff Markov chains. The use of the approach is illustrated with three applications. >

Logic and stochastic modeling with SMART

We describe the main features of SMART, a software package providing a seamless environment for the logic and probabilistic analysis of complex systems. SMART can combine different formalisms in the same modeling study. For the analysis of logical behavior, both explicit and symbolic state-space generation techniques, as well as symbolic CTL model-checking algorithms, are available. For the study of stochastic and timing behavior, both sparse-storage and Kronecker-based numerical solution approaches are available when the underlying process is a Markov chain, while discrete-event simulation is always applicable regardless of the stochastic nature of the process, and certain classes of non-Markov models can also be solved numerically. Finally, since SMART targets both the classroom and realistic industrial settings as a learning, research, and application tool, it is written in a modular way that allows for easy integration of new formalisms and solution algorithms.