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  • 1.
    Cai, Simin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Modeling Real-time Transactions in UPPAAL2015Report (Other academic)
    Abstract [en]

    During the development of an Real-time Database Management System (RTDBMS) one needs to trade-off between data consistency and timeliness. To achieve a systematic method for such trade-off, we must find a way to model the behaviors and properties of real-time transactions. In this report, we experiment with the modeling of transactions and verification of transaction properties in UPPAAL. We build a model for an exemplary transaction with the optimistic concurrency control mechanisms, and model-check the timeliness property.

  • 2.
    Cai, Simin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Systematic Design of Data Management for Real-Time Data-Intensive Applications2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Modern real-time data-intensive systems generate large amounts of data that are processed using complex data-related computations such as data aggregation. In order to maintain the consistency of data, such computations must be both logically correct (producing correct and consistent results) and temporally correct (completing before specified deadlines). One solution to ensure logical and temporal correctness is to model these computations as transactions and manage them using a Real-Time Database Management System (RTDBMS). Ideally, depending on the particular system, the transactions are customized with the desired logical and temporal correctness properties, which are achieved by the customized RTDBMS with appropriate run-time mechanisms. However, developing such a data management solution with provided guarantees is not easy, partly due to inadequate support for systematic analysis during the design. Firstly, designers do not have means to identify the characteristics of the computations, especially data aggregation, and to reason about their implications. Design flaws might not be discovered, and thus they may be propagated to the implementation. Secondly, trade-off analysis of conflicting properties, such as conflicts between transaction isolation and temporal correctness, is mainly performed ad-hoc, which increases the risk of unpredictable behavior.

    In this thesis, we propose a systematic approach to develop transaction-based data management with data aggregation support for real-time systems. Our approach includes the following contributions: (i) a taxonomy of data aggregation, (ii) a process for customizing transaction models and RTDBMS, and (iii) a pattern-based method of modeling transactions in the timed automata framework, which we show how to verify with respect to transaction isolation and temporal correctness. Our proposed taxonomy of data aggregation processes helps in identifying their common and variable characteristics, based on which their implications can be reasoned about. Our proposed process allows designers to derive transaction models with desired properties for the data-related computations from system requirements, and decide the appropriate run-time mechanisms for the customized RTDBMS to achieve the desired properties. To perform systematic trade-off analysis between transaction isolation and temporal correctness specifically, we propose a method to create formal models of transactions with concurrency control, based on which the isolation and temporal correctness properties can be verified by model checking, using the UPPAAL tool. By applying the proposed approach to the development of an industrial demonstrator, we validate the applicability of our approach.

  • 3.
    Cai, Simin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gallina, Barbara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nyström, Dag
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Seceleanu, Cristina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Customized Real-Time Data Management for Automotive Systems: A Case Study2017In: IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2017, p. 8397-8404Conference paper (Refereed)
    Abstract [en]

    Real-time DataBase Management Systems (RTDBMS) have been considered as a promising means to manage data for data-centric automotive systems. During the design of an RTDBMS, one must carefully trade off data consistency and timeliness, in order to achieve an acceptable level of both properties. Previously, we have proposed a design process called DAGGERS to facilitate a systematic customization of transaction models and decision on the run-time mechanisms. In this paper, we evaluate the applicability of DAGGERS via an industrially relevant case study that aims to design the transaction management for an on-board diagnostic system, which should guarantee both timeliness and data consistency under concurrent access. To achieve this, we apply the pattern-based approach of DAGGERS to formalize the transactions, and derive the appropriate isolation level and concurrency control algorithm guided by model checking. We show by simulation that the implementation of our designed system satisfies the desired timeliness and derived isolation, and demonstrate that DAGGERS helps to customize desired real-time transaction management prior to implementation.

  • 4.
    Cai, Simin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gallina, Barbara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nyström, Dag
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Seceleanu, Cristina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    DAGGTAX: A Taxonomy of Data Aggregation Processes2017Report (Other academic)
    Abstract [en]

    Data aggregation processes are essential constituents in many data management applications. Due to their complexity, designing data aggregation processes often demands considerable efforts. A study on the features of data aggregation processes will provide a comprehensive view for the designers and ease the design process. Existing works either propose application-specific aggregation solutions, or focus on particular aspects of aggregation processes such as aggregate functions, hence they do not offer a high-level, generic description. In this paper, we propose a taxonomy of data aggregation processes called DAGGTAX, which builds on the results of an extensive survey within various application domains. Our work focuses on the features of aggregation processes and their implications, especially on the temporal data consistency and the process timeliness. We present our taxonomy as a feature diagram, which is a visual notation with formal semantics. The taxonomy can then serve as the foundation of a design tool that enables designers to build an aggregation process by selecting and composing desired features. Based on the implications of the features, we formulate three design rules that eliminate infeasible feature combinations. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. 

  • 5.
    Cai, Simin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gallina, Barbara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nyström, Dag
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Seceleanu, Cristina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    DAGGTAX: A taxonomy of data aggregation processes2017In: Lecture Notes in Computer Science, vol. 10563, Springer Verlag , 2017, p. 324-339Conference paper (Refereed)
    Abstract [en]

    Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things (IoT) systems. Due to the heterogeneity and real-time constraints in such systems, designing appropriate data aggregation processes often demands considerable effort. A study on the characteristics of data aggregation processes is then desirable, as it provides a comprehensive view of such processes, potentially facilitating their design, as well as the development of tool support to aid designers. In this paper, we propose a taxonomy called DAGGTAX, which is a feature diagram that models the common and variable characteristics of data aggregation processes, with a special focus on the real-time aspect. The taxonomy can serve as the foundation of a design tool, which we also introduce, enabling designers to build an aggregation process by selecting and composing desired features, and to reason about the feasibility of the design. We apply DAGGTAX on industrial case studies, showing that DAGGTAX not only strengthens the understanding, but also facilitates the model-driven design of data aggregation processes. © 2017, Springer International Publishing AG.

  • 6.
    Cai, Simin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gallina, Barbara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nyström, Dag
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Seceleanu, Cristina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Larsson, Alf
    Ericsson AB, Stockholm, Sweden.
    Design of Cloud Monitoring Systems via DAGGTAX: A Case Study2017In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 109, p. 424-431Article in journal (Refereed)
    Abstract [en]

    Efficient auto-scaling of cloud resources relies on the monitoring of the cloud, which involves multiple aggregation processes and large amounts of data with various and interdependent requirements. A systematic way of describing the data together with the possible aggregations is beneficial for designers to reason about the properties of these aspects as well as their implications on the design, thus improving quality and lowering development costs. In this paper, we propose to apply DAGGTAX, a feature-oriented taxonomy for organizing common and variable data and aggregation process properties, to the design of cloud monitoring systems. We demonstrate the effectiveness of DAGGTAX via a case study provided by industry, which aims to design a cloud monitoring system that serves auto-scaling for a video streaming system. We design the cloud monitoring system by selecting and composing DAGGTAX features, and reason about the feasibility of the selected features. The case study shows that the application of DAGGTAX can help designers to identify reusable features, analyze trade-offs between selected features, and derive crucial system parameters.

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