Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future

A QoS registry for adaptive real-time service-oriented applications

Real-time service-oriented applications are charac- terized by Quality of Service (QoS) requirements that cannot be properly managed by using classical real-time systems methodologies. In dynamic environments the QoS management can effectively leverage adaptive techniques, that provide flexibility and do not require a complex offline analysis. In turn, such techniques make a massive use of on-line collected data regarding the application performance and the resource requirements. Moreover, a common issue for adaptive systems is the one of deciding the initial configuration of the application and/or the run-time environment at the time of service instantiation. In this paper, we propose a QoS registry for coping with these issues and supporting the configuration of proper scheduling parameters in real-time Service Oriented Architectures (SOAs). The registry permits to gather QoS data related to different functional behaviors of applications, to predict the future trend based on data already collected and to permanently store such data for an effective reuse at the time of future re-instantiations. We have also built an implementation of such registry, computed its overhead costs and performed some experiments for showing the effectiveness in auto-tuning resource allocations for providing QoS guarantees in a real-time SOA

Integration of Data Distribution Service and distributed partitioned systems

[EN] Avionics systems are complex and time-critical systems that are progressively adopting more flexible (though equally robust) architectural designs. Although a number of current avionics systems follow federated architectures, the Integrated Modular Avionics (IMA) paradign is becoming the dominant style in the more modern developments. The reason is that the IMA concept promotes modular designs where applications with different levels of criticality can execute in an isolated manner in the same hardware. This approach complies with the requirements of cost, safety, and weight of the avionics systems. FACE standard (Future Airborne Capability Environment) defines the architectural baseline for easing integration in avionics systems, including the communication functions across distributed components. As specified in FACE, middleware will be integrated into avionics systems to ease development of portable components that can interoperate effectively. This paper describes the usage of publish-subscribe middleware (precisely, DDS - Data Distribution Service for real-time systems) into a fully distributed partitioned system. We describe, from a practical point of view, the integration of the middleware communication overhead into the hierarchical scheduling (as compliant with ARINC 653) to allow the usage of middleware in the partitions. We explain the design of a realiable communication setting, exemplified on a distributed monitoring application in a partitioned environment. The obtained implementation results show that, given the stable communication overhead of the middleware, it can be integrated in the time windows of partitions.This work has been partly supported by the Spanish Ministry of Economy and Competitiveness through projects REM4VSS (TIN 2011-28339) and M2C2 (TIN2014-56158-C4-3-P).Garcia-Valls, M.; Domínguez-Poblete, J.; Eddine Touahria, I.; Lu, C. (2018). Integration of Data Distribution Service and distributed partitioned systems. Journal of Systems Architecture. 83:23-31. https://doi.org/10.1016/j.sysarc.2017.11.00123318

Introduction to the Special Issue of the 16th ACM Workshop on Adaptive and Reflective Middleware (ARM)

Dubey, A.; Garcia-Valls, M. (2019). Introduction to the Special Issue of the 16th ACM Workshop on Adaptive and Reflective Middleware (ARM). Journal of Systems Architecture. 97. https://doi.org/10.1016/j.sysarc.2019.03.006S9

Accelerating smart eHealth services execution at the fog computing infrastructure

[EN] Fog computing improves the execution of computationally intensive services for remote client nodes as part of the data processing is performed close to the location where the results will be delivered. As opposed to other services running on smart cities, a major challenge of eHealth services on the fog is that they typically span multiple computational activities performing big data processing over sensible information that must be protected. Using the capacities of current processors can improve the servicing of remote patient nodes. This paper presents the design and validation of a framework that improves the service time of selected activities at the fog servers; precisely, of those activities requested by remote patients. It exploits the capacities of current processors to parallelize specific activities that can be run on reserved cores, and it relies on the quality of service guarantees of data distribution platforms to improve communication and response times to mobile patients. The proposed approach is validated on a prototype implementation of simulated computationally-intensive eHealth interactions, decreasing the response time by 4x when core reservation is activated. (C) 2018 Elsevier B.V. All rights reserved.This work has been primarily funded by the M2C2 (TIN201456158-C4-3-P) and PRECON-I4 (TIN2017-86520-C3-2-R), both funded by the Spanish Ministry of Economy and Competitiveness, Spain.Garcia Valls, M.; Calva-Urrego, C.; García-Fornes, A. (2020). Accelerating smart eHealth services execution at the fog computing infrastructure. Future Generation Computer Systems. 108:882-893. https://doi.org/10.1016/j.future.2018.07.001S882893108García-Valls, M., Cucinotta, T., & Lu, C. (2014). Challenges in real-time virtualization and predictable cloud computing. Journal of Systems Architecture, 60(9), 726-740. doi:10.1016/j.sysarc.2014.07.004García-Valls, M., Dubey, A., & Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture, 91, 83-102. doi:10.1016/j.sysarc.2018.05.007Bahtovski, A., & Gusev, M. (2014). Cloudlet Challenges. Procedia Engineering, 69, 704-711. doi:10.1016/j.proeng.2014.03.045Eze, B., Kuziemsky, C., & Peyton, L. (2017). Cloud-based performance management of community care services. Journal of Software: Evolution and Process, 30(7), e1897. doi:10.1002/smr.1897Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017). Advanced internet of things for personalised healthcare systems: A survey. Pervasive and Mobile Computing, 41, 132-149. doi:10.1016/j.pmcj.2017.06.018Ahmed, S. H., & Rani, S. (2018). A hybrid approach, Smart Street use case and future aspects for Internet of Things in smart cities. Future Generation Computer Systems, 79, 941-951. doi:10.1016/j.future.2017.08.054Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117-128. doi:10.1016/j.measurement.2018.01.022Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar, V. (2017). Security and Privacy in Fog Computing: Challenges. IEEE Access, 5, 19293-19304. doi:10.1109/access.2017.2749422Elhoseny, M., Ramirez-Gonzalez, G., Abu-Elnasr, O. M., Shawkat, S. A., Arunkumar, N., & Farouk, A. (2018). Secure Medical Data Transmission Model for IoT-Based Healthcare Systems. IEEE Access, 6, 20596-20608. doi:10.1109/access.2018.2817615Jadhav, A., Andrews, D., Fiksdal, A., Kumbamu, A., McCormick, J. B., Misitano, A., … Pathak, J. (2014). Comparative Analysis of Online Health Queries Originating From Personal Computers and Smart Devices on a Consumer Health Information Portal. Journal of Medical Internet Research, 16(7), e160. doi:10.2196/jmir.3186Golov, N., & Rönnbäck, L. (2017). Big Data normalization for massively parallel processing databases. Computer Standards & Interfaces, 54, 86-93. doi:10.1016/j.csi.2017.01.009Shehab, A., Elhoseny, M., Muhammad, K., Sangaiah, A. K., Yang, P., Huang, H., & Hou, G. (2018). Secure and Robust Fragile Watermarking Scheme for Medical Images. IEEE Access, 6, 10269-10278. doi:10.1109/access.2018.2799240Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383-1394. doi:10.1016/j.future.2018.03.005Garcia Valls, M., Lopez, I. R., & Villar, L. F. (2013). iLAND: An Enhanced Middleware for Real-Time Reconfiguration of Service Oriented Distributed Real-Time Systems. IEEE Transactions on Industrial Informatics, 9(1), 228-236. doi:10.1109/tii.2012.2198662García-Valls, M., Perez-Palacin, D., & Mirandola, R. (2018). Pragmatic cyber physical systems design based on parametric models. Journal of Systems and Software, 144, 559-572. doi:10.1016/j.jss.2018.06.044The OpenMP® API specification for parallel programming. http://www.openmp.org/ (Accessed June 2017).Message Passing Interface Forum. http://www.mpi-forum.org/ (Accessed June 2017).Kuhn, B., Petersen, P., & O’Toole, E. (2000). OpenMP versus threading in C/C++. Concurrency: Practice and Experience, 12(12), 1165-1176. doi:10.1002/1096-9128(200010)12:123.0.co;2-lMPI Intel, Benchmarks: Users Guide and Methodology Description, Intel GmbH, Germany.Object Management Group, A data distribution service for real-time systems version 1.4, 2015. http://www.omg.org/spec/DDS/1.4.Palanca, J., Navarro, M., García-Fornes, A., & Julian, V. (2013). Deadline prediction scheduling based on benefits. Future Generation Computer Systems, 29(1), 61-73. doi:10.1016/j.future.2012.05.007Palanca, J., Navarro, M., Julian, V., & García-Fornes, A. (2012). Distributed goal-oriented computing. Journal of Systems and Software, 85(7), 1540-1557. doi:10.1016/j.jss.2012.01.045Burdalo, L., Terrasa, A., Espinosa, A., & Garcia-Fornes, A. (2012). Analyzing the Effect of Gain Time on Soft-Task Scheduling Policies in Real-Time Systems. IEEE Transactions on Software Engineering, 38(6), 1305-1318. doi:10.1109/tse.2011.9

An extensible collaborative framework for monitoring software quality in critical systems

[EN] Context: Current practices on software quality monitoring for critical software systems development rely on the manual integration of the information provided by a number of independent commercial tools for code analysis; some external tools for code analysis are mandatory in some critical software projects that must comply with specific norms. However, there are no approaches to providing an integrated view over the analysis results of independent external tools into a unified software quality framework. Objective: This paper presents the design and development of ESQUF (Enhanced Software Quality Monitoring Framework) suitable for critical software systems. It provides the above enriched quality results presentation derived not only from multiple external tools but from the local analysis functions of the framework. Method: An analysis of the norms and standards that apply to critical software systems is provided. The detailed and modular design of ESQUF adjusts to the integration requirements for external tools. UML is used for designing the framework, and Java is used to provide the detailed design. The framework is validated with a prototype implementation that integrates two different external tools and their respective quality results over a real software project source code. Results: The integration of results files and data from external tools as well as from internal analysis functions is enabled. The analysis of critical software projects is made posible yielding a collaborative space where verification engineers share information about code analysis activities of specific projects; and single presentation space with rich static and dynamic analysis information of software projects that comply with the required development norms.Garcia Valls, M.; Escribano-Barreno, J.; García-Muñoz, J. (2019). An extensible collaborative framework for monitoring software quality in critical systems. Information and Software Technology. 107:3-17. https://doi.org/10.1016/j.infsof.2018.10.005S31710