Bringing Model Checking Closer To Practical Software Engineering

Bal, H.E. [Promotor]Templon, J.A. [Copromotor]Willemse, T.A.C. [Copromotor

Terrace Standard, March, 01, 1995

Observation has lead to a conclusion that the physics analysis jobs run by LHCb physicists on a local computing farm (i.e. non-grid) require more efficient access to the data which resides on the Grid. Our experiments have shown that the I/O bound nature of the analysis jobs in combination with the latency due to the remote access protocols (e.g. rfio, dcap) cause a low CPU efficiency of these jobs. In addition to causing a low CPU efficiency, the remote access protocols give rise to high overhead (in terms of amount of data transferred). This paper gives an overview of the concept of pre-fetching and caching of input files in the proximity of the processing resources, which is exploited to cope with the I/O bound analysis jobs. The files are copied from Grid storage elements (using GridFTP), while concurrently performing computations, inspired from a similar idea used in the ATLAS experiment. The results illustrate that this file staging approach is relatively insensitive to the original location of the data, and a significant improvement can be achieved in terms of the CPU efficiency of an analysis job. Dealing with scalability of such a solution on the Grid environment is discussed briefly

Verifying system-wide properties of industrial component-based software

\u3cp\u3eAnalytical Software Design (ASD) enables model-based development of component software systems. Until now, functional verification of ASD systems is only possible on a per-component basis. There is no functional verification engine for ASD itself, so this verification relies on a translation of individual components to mCRL2, a process-algebraic model checker. We show how to extend the ASD-mCRL2 translation to support multiple components in order to enable checking of system wide functional properties. With our extended translation, we perform a case-study on a newly developed industrial system consisting of 26 communicating components. The results indicate that it is feasible to model check functional properties on this scale.\u3c/p\u3

On the importance of predictor choice, modelling technique, and number of pseudo-absences for bioclimatic envelope model performance

Contains fulltext : 226343.pdf (publisher's version ) (Open Access)6 november 202011 p

Using model checking to analyze the system behavior of the LHC production grid

DIRAC (Distributed Infrastructure with Remote Agent Control) is the grid solution designed to support production activities as well as user data analysis for the Large Hadron Collider beauty experiment. It consists of cooperating distributed services and a plethora of light-weight agents delivering the workload to the grid resources. Services accept requests from agents and running jobs, while agents actively fulfill specific goals. Services maintain database back-ends to store dynamic state information of entities such as jobs, queues, or requests for data transfer. Agents continuously check for changes in the service states, and react to these accordingly. The logic of each agent is rather simple; the main source of complexity lies in their cooperation. These agents run concurrently, and communicate using the services’ databases as a shared memory for synchronizing the state transitions. Despite the effort invested in making DIRAC reliable, entities occasionally get into inconsistent states. Tracing and fixing such behaviors is difficult, given the inherent parallelism among the distributed components and the size of the implementation. In this paper we present an analysis of DIRAC with mCRL2, process algebra with data. We have reverse engineered two critical and related DIRAC subsystems, and subsequently modeled their behavior with the mCRL2 toolset. This enabled us to easily locate race conditions and livelocks which were confirmed to occur in the real system. We further formalized and verified several behavioral properties of the two modeled subsystems. Keywords: Model checking; Process algebra; Grid; LHC; Distributed system; Workflo

Using model checking to analyze the system behavior of the LHC production grid

DIRAC (Distributed Infrastructure with Remote Agent Control) is the grid solution designed to support production activities as well as user data analysis for the Large Hadron Collider "beauty" experiment. It consists of cooperating distributed services and a plethora of light-weight agents delivering the workload to the grid resources. Services accept requests from agents and running jobs, while agents actively fulfill specific goals. Services maintain database back-ends to store dynamic state information of entities such as jobs, queues, or requests for data transfer. Agents continuously check for changes in the service states, and react to these accordingly. The logic of each agent is rather simple, the main source of complexity lies in their cooperation. These agents run concurrently, and communicate using the services' databases as a shared memory for synchronizing the state transitions. Despite the effort invested in making DIRAC reliable, entities occasionally get into inconsistent states. Tracing and fixing such behaviors is difficult, given the inherent parallelism among the distributed components and the size of the implementation. In this paper we present an analysis of DIRAC with mCRL2, process algebra with data. We have reverse engineered two critical and related DIRAC subsystems, and subsequently modeled their behavior with the mCRL2 toolset. This enabled us to easily locate race conditions and live locks which were confirmed to occur in the real system. We further formalized and verified several behavioral properties of the two modeled subsystems

Property specification made easy : harnessing the power of model checking in UML designs

Developing correct concurrent software is challenging. Design errors can result in deadlocks, race conditions and livelocks, and discovering these is difficult. A serious obstacle for an industrial uptake of rigorous analysis techniques such as model checking is the learning curve associated to the languages — typically temporal logics — used for specifying the application-specific properties to be checked. To bring the process of correctly eliciting functional properties closer to software engineers, we introduce PASS, a Property ASSistant wizard as part of a UML-based front-end to the mCRL2 toolset. PASS instantiates pattern templates using three notations: a natural language summary, a µ-calculus formula and a UML sequence diagram depicting the desired behavior. Most approaches to date have focused on LTL, which is a state-based formalism. Conversely, µ-calculus is event-based, making it a good match for sequence diagrams, where communication between components is depicted. We revisit a case study from the Grid domain, using PASS to obtain the formula and monitor for checking the property

The LHCb Data Management System

The LHCb Data Management System is based on the DIRAC Grid Community Solution. LHCbDirac provides extensions to the basic DMS such as a Bookkeeping System. Datasets are defined as sets of files corresponding to a given query in the Bookkeeping system. Datasets can be manipulated by CLI tools as well as by automatic transformations (removal, replication, processing). A dynamic handling of dataset replication is performed, based on disk space usage at the sites and dataset popularity. For custodial storage, an on-demand recall of files from tape is performed, driven by the requests of the jobs, including disk cache handling. We shall describe the tools that are available for Data Management, from handling of large datasets to basic tools for users as well as for monitoring the dynamic behavior of LHCb Storage capacity