Design and implementation of a system for incremental real-time visual object detection and autonomous recognition

In this work, a system for incremental real-time visual object detection and autonomous recognition is presented. The system is designed for indoor smart cameras and identifies objects appearing on the scene by detecting video changes in the video stream. Object detection is based on a novel interest point-based background subtraction method, which results in a more robust and informative background model with respect to typically color-based approaches. Objects are incrementally learnt by collecting observations in real-time. A similarity function between objects observations relying on local feature matching and geometric consistency checking is defined. The key idea of the system is to relate past and present object observations: clusters of similar observations are maintained exploiting transitivity of similarity between observations and are used to recognize a new observation of an already seen object. Since the system incrementally builds it up from observations during time, no training set for recognition is needed. Experiments have been performed on publicly available datasets to evaluate the detection task and the ability of the system to build good clusters of observations. The system has also been tested on the Raspberry Pi platform equipped with the Pi Camera module

Serverless Strategies and Tools in the Cloud Computing Continuum

Tesis por compendio[ES] En los últimos años, la popularidad de la computación en nube ha permitido a los usuarios acceder a recursos de cómputo, red y almacenamiento sin precedentes bajo un modelo de pago por uso. Esta popularidad ha propiciado la aparición de nuevos servicios para resolver determinados problemas informáticos a gran escala y simplificar el desarrollo y el despliegue de aplicaciones. Entre los servicios más destacados en los últimos años se encuentran las plataformas FaaS (Función como Servicio), cuyo principal atractivo es la facilidad de despliegue de pequeños fragmentos de código en determinados lenguajes de programación para realizar tareas específicas en respuesta a eventos. Estas funciones son ejecutadas en los servidores del proveedor Cloud sin que los usuarios se preocupen de su mantenimiento ni de la gestión de su elasticidad, manteniendo siempre un modelo de pago por uso de grano fino. Las plataformas FaaS pertenecen al paradigma informático conocido como Serverless, cuyo propósito es abstraer la gestión de servidores por parte de los usuarios, permitiéndoles centrar sus esfuerzos únicamente en el desarrollo de aplicaciones. El problema del modelo FaaS es que está enfocado principalmente en microservicios y tiende a tener limitaciones en el tiempo de ejecución y en las capacidades de computación (por ejemplo, carece de soporte para hardware de aceleración como GPUs). Sin embargo, se ha demostrado que la capacidad de autoaprovisionamiento y el alto grado de paralelismo de estos servicios pueden ser muy adecuados para una mayor variedad de aplicaciones. Además, su inherente ejecución dirigida por eventos hace que las funciones sean perfectamente adecuadas para ser definidas como pasos en flujos de trabajo de procesamiento de archivos (por ejemplo, flujos de trabajo de computación científica). Por otra parte, el auge de los dispositivos inteligentes e integrados (IoT), las innovaciones en las redes de comunicación y la necesidad de reducir la latencia en casos de uso complejos han dado lugar al concepto de Edge computing, o computación en el borde. El Edge computing consiste en el procesamiento en dispositivos cercanos a las fuentes de datos para mejorar los tiempos de respuesta. La combinación de este paradigma con la computación en nube, formando arquitecturas con dispositivos a distintos niveles en función de su proximidad a la fuente y su capacidad de cómputo, se ha acuñado como continuo de la computación en la nube (o continuo computacional). Esta tesis doctoral pretende, por lo tanto, aplicar diferentes estrategias Serverless para permitir el despliegue de aplicaciones generalistas, empaquetadas en contenedores de software, a través de los diferentes niveles del continuo computacional. Para ello, se han desarrollado múltiples herramientas con el fin de: i) adaptar servicios FaaS de proveedores Cloud públicos; ii) integrar diferentes componentes software para definir una plataforma Serverless en infraestructuras privadas y en el borde; iii) aprovechar dispositivos de aceleración en plataformas Serverless; y iv) facilitar el despliegue de aplicaciones y flujos de trabajo a través de interfaces de usuario. Además, se han creado y adaptado varios casos de uso para evaluar los desarrollos conseguidos.[CA] En els últims anys, la popularitat de la computació al núvol ha permès als usuaris accedir a recursos de còmput, xarxa i emmagatzematge sense precedents sota un model de pagament per ús. Aquesta popularitat ha propiciat l'aparició de nous serveis per resoldre determinats problemes informàtics a gran escala i simplificar el desenvolupament i desplegament d'aplicacions. Entre els serveis més destacats en els darrers anys hi ha les plataformes FaaS (Funcions com a Servei), el principal atractiu de les quals és la facilitat de desplegament de petits fragments de codi en determinats llenguatges de programació per realitzar tasques específiques en resposta a esdeveniments. Aquestes funcions són executades als servidors del proveïdor Cloud sense que els usuaris es preocupen del seu manteniment ni de la gestió de la seva elasticitat, mantenint sempre un model de pagament per ús de gra fi. Les plataformes FaaS pertanyen al paradigma informàtic conegut com a Serverless, el propòsit del qual és abstraure la gestió de servidors per part dels usuaris, permetent centrar els seus esforços únicament en el desenvolupament d'aplicacions. El problema del model FaaS és que està enfocat principalment a microserveis i tendeix a tenir limitacions en el temps d'execució i en les capacitats de computació (per exemple, no té suport per a maquinari d'acceleració com GPU). Tot i això, s'ha demostrat que la capacitat d'autoaprovisionament i l'alt grau de paral·lelisme d'aquests serveis poden ser molt adequats per a més aplicacions. A més, la seva inherent execució dirigida per esdeveniments fa que les funcions siguen perfectament adequades per ser definides com a passos en fluxos de treball de processament d'arxius (per exemple, fluxos de treball de computació científica). D'altra banda, l'auge dels dispositius intel·ligents i integrats (IoT), les innovacions a les xarxes de comunicació i la necessitat de reduir la latència en casos d'ús complexos han donat lloc al concepte d'Edge computing, o computació a la vora. L'Edge computing consisteix en el processament en dispositius propers a les fonts de dades per millorar els temps de resposta. La combinació d'aquest paradigma amb la computació en núvol, formant arquitectures amb dispositius a diferents nivells en funció de la proximitat a la font i la capacitat de còmput, s'ha encunyat com a continu de la computació al núvol (o continu computacional). Aquesta tesi doctoral pretén, doncs, aplicar diferents estratègies Serverless per permetre el desplegament d'aplicacions generalistes, empaquetades en contenidors de programari, a través dels diferents nivells del continu computacional. Per això, s'han desenvolupat múltiples eines per tal de: i) adaptar serveis FaaS de proveïdors Cloud públics; ii) integrar diferents components de programari per definir una plataforma Serverless en infraestructures privades i a la vora; iii) aprofitar dispositius d'acceleració a plataformes Serverless; i iv) facilitar el desplegament d'aplicacions i fluxos de treball mitjançant interfícies d'usuari. A més, s'han creat i s'han adaptat diversos casos d'ús per avaluar els desenvolupaments aconseguits.[EN] In recent years, the popularity of Cloud computing has allowed users to access unprecedented compute, network, and storage resources under a pay-per-use model. This popularity led to new services to solve specific large-scale computing challenges and simplify the development and deployment of applications. Among the most prominent services in recent years are FaaS (Function as a Service) platforms, whose primary appeal is the ease of deploying small pieces of code in certain programming languages to perform specific tasks on an event-driven basis. These functions are executed on the Cloud provider's servers without users worrying about their maintenance or elasticity management, always keeping a fine-grained pay-per-use model. FaaS platforms belong to the computing paradigm known as Serverless, which aims to abstract the management of servers from the users, allowing them to focus their efforts solely on the development of applications. The problem with FaaS is that it focuses on microservices and tends to have limitations regarding the execution time and the computing capabilities (e.g. lack of support for acceleration hardware such as GPUs). However, it has been demonstrated that the self-provisioning capability and high degree of parallelism of these services can be well suited to broader applications. In addition, their inherent event-driven triggering makes functions perfectly suitable to be defined as steps in file processing workflows (e.g. scientific computing workflows). Furthermore, the rise of smart and embedded devices (IoT), innovations in communication networks and the need to reduce latency in challenging use cases have led to the concept of Edge computing. Edge computing consists of conducting the processing on devices close to the data sources to improve response times. The coupling of this paradigm together with Cloud computing, involving architectures with devices at different levels depending on their proximity to the source and their compute capability, has been coined as Cloud Computing Continuum (or Computing Continuum). Therefore, this PhD thesis aims to apply different Serverless strategies to enable the deployment of generalist applications, packaged in software containers, across the different tiers of the Cloud Computing Continuum. To this end, multiple tools have been developed in order to: i) adapt FaaS services from public Cloud providers; ii) integrate different software components to define a Serverless platform on on-premises and Edge infrastructures; iii) leverage acceleration devices on Serverless platforms; and iv) facilitate the deployment of applications and workflows through user interfaces. Additionally, several use cases have been created and adapted to assess the developments achieved.Risco Gallardo, S. (2023). Serverless Strategies and Tools in the Cloud Computing Continuum [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202013Compendi

Serverless Workflows for Containerised Applications in the Cloud Continuum

[EN] This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.The authors would like to thank the European Union for the project "Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum" (AI-SPRINT), with code 101016577, funded under the H2020 Framework Programme and also the regional government of the Comunitat Valenciana (Spain) for the project IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020.Risco, S.; Moltó, G.; Naranjo-Delgado, DM.; Blanquer Espert, I. (2021). Serverless Workflows for Containerised Applications in the Cloud Continuum. Journal of Grid Computing. 19(3):1-18. https://doi.org/10.1007/s10723-021-09570-2S118193Agache, A., Brooker, M., Iordache, A., Liguori, A., Neugebauer, R., Piwonka, P., Popa, D.M.: Firecracker: lightweight virtualization for serverless applications. In: 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20), pp. 419–434. USENIX Association, Santa Clara, CA. https://www.usenix.org/conference/nsdi20/presentation/agache (2020)Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. Journal of Internet Services and Applications 6(1), 25 (2015). https://doi.org/10.1186/s13174-015-0041-5.de Alfonso, C., Caballer, M., Calatrava, A., Moltó, G., Blanquer, I.: Multi-elastic Datacenters: auto-scaled virtual clusters on energy-aware physical infrastructures. Journal of Grid Computing 17(1), 191–204 (2019). https://doi.org/10.1007/s10723-018-9449-z.Amazon Web Services: Amazon EC2. https://aws.amazon.com/ec2/Amazon Web Services: AWS Batch — Easy and Efficient Batch Computing Capabilities. https://aws.amazon.com/batch/Amazon Web Services: AWS Lambda. https://aws.amazon.com/lambdaApache: OpenWhisk - Open Source Serverless Cloud Platform. https://openwhisk.apache.org/Argo: Workflows & Pipelines. https://argoproj.github.io/projects/argo/Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., Mitchell, N., Muthusamy, V., Rabbah, R., Slominski, A., Suter, P.: Serverless computing: Current trends and open problems. In: Research Advances in Cloud Computing., pp 1–20. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-5026-8_1Baldini, I., Cheng, P., Fink, S.J., Mitchell, N., Muthusamy, V., Rabbah, R., Suter, P., Tardieu, O.: The serverless trilemma: function composition for serverless computing. In: Proceedings of the 2017 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software - Onward! 2017, pp 89–103. ACM Press, New York (2017). https://doi.org/10.1145/3133850.3133855. http://dl.acm.org/citation.cfm?doid=3133850.3133855Balouek-Thomert, D., Renart, E.G., Zamani, A.R., Simonet, A., Parashar, M.: Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows. International Journal of High Performance Computing Applications 33(6), 1159–1174 (2019). https://doi.org/10.1177/1094342019877383.Baresi, L., Mendonça, D.F., Garriga, M., Guinea, S., Quattrocchi, G.: A unified model for the mobile-edge-cloud continuum. ACM Transactions on Internet Technology 19(2), 1–21 (2019). https://doi.org/10.1145/3226644Beckman, P., Dongarra, J., Ferrier, N., Fox, G., Moore, T., Reed, D., Beck, M.: Harnessing the computing continuum for programming our world. In: Fog Computing., pp 215–230. Wiley (2020). https://doi.org/10.1002/9781119551713.ch7Bello, J.P., Mydlarz, C., Salamon, J.: Sound analysis in smart cities. In: Computational Analysis of Sound Scenes and Events, pp 373–397. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-63450-0_13Brewer, E.A.: Kubernetes and the path to cloud native. In: Proceedings of the Sixth ACM Symposium on Cloud Computing - SoCC ’15, pp 167–167. Association for Computing Machinery (ACM), New York (2015). https://doi.org/10.1145/2806777.2809955. http://dl.acm.org/citation.cfm?doid=2806777.2809955Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C.: Dynamic management of virtual infrastructures. Journal of Grid Computing 13(1), 53–70 (2015). https://doi.org/10.1007/s10723-014-9296-5Calatrava, A., Romero, E., Moltó, G., Caballer, M., Alonso, J.M.: Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures. Future Generation Computer Systems 61, 13–25 (2016). https://doi.org/10.1016/j.future.2016.01.018Camero, A., Alba, E.: Smart City and information technology: A review. Cities 93, 84–94 (2019). https://doi.org/10.1016/j.cities.2019.04.014Casalboni, A.: AWS Lambda Power Tuning. https://github.com/alexcasalboni/aws-lambda-power-tuningChard, R., Babuji, Y., Li, Z., Skluzacek, T., Woodard, A., Blaiszik, B., Foster, I., Chard, K.: funcX: a federated function serving fabric for science. In: Proceedings of the 29th International symposium on high-performance parallel and distributed computing, pp 65–76. ACM, New York (2020). https://doi.org/10.1145/3369583.3392683Chen, C.H., Favre, J., Kurillo, G., Andriacchi, T.P., Bajcsy, R., Chellappa, R.: Camera networks for healthcare, teleimmersion, and surveillance. Computer 47(5), 26–36 (2014). https://doi.org/10.1109/MC.2014.112. http://ieeexplore.ieee.org/document/6818909/Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., Huang, X.: A survey on an emerging area: deep learning for smart city data. IEEE Trans. Emerg. Topics Comput. Intell. 3(5), 392–410 (2019). https://doi.org/10.1109/TETCI.2019.2907718. https://ieeexplore.ieee.org/document/8704334/Christidis, A., Davies, R., Moschoyiannis, S.: Serving machine learning workloads in resource constrained environments: A serverless deployment example. In: Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019, pp. 55–63. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SOCA.2019.00016 (2019)Christidis, A., Moschoyiannis, S., Hsu, C. H., Davies, R.: Enabling Serverless Deployment of Large-Scale AI Workloads. IEEE Access 8, 70150–70161 (2020). https://doi.org/10.1109/ACCESS.2020.2985282CNCF: Serverless Workflow: A specification for defining declarative workflow models that orchestrate Event-driven, Serverless applications. https://serverlessworkflow.ioCouturier, R.: Designing scientific applications on GPUs. Chapman and Hall/CRC. https://doi.org/10.1201/b16051. https://www.taylorfrancis.com/books/designing-scientific-applications-gpus-raphael-couturier/e/10.1201/b16051 (2013)Docker: Enterprise Container Platform. https://www.docker.com/Docker: Docker hub. https://hub.docker.com/ (2019)Dutka, Ł., Wrzeszcz, M., Lichoń, T., Słota, R., Zemek, K., Trzepla, K., Opioła, Ł., Słota, R., Kitowski, J.: Onedata - A step forward towards globalization of data access for computing infrastructures, vol. 51, pp 2843–2847 (2015). https://doi.org/10.1016/j.procs.2015.05.445. https://www.sciencedirect.com/science/article/pii/S1877050915012533Fouladi, S., Romero, F., Iter, D., Li, Q., Chatterjee, S., Kozyrakis, C., Zaharia, M., Winstein, K.: From laptop to Lambda: Outsourcing everyday jobs to thousands of transient functional containers. In: Proceedings of the 2019 USENIX Annual Technical Conference, USENIX ATC 2019, pp 475–488 (2019). https://dl.acm.org/doi/10.5555/3358807.3358848Giménez-Alventosa, V., Moltó, G., Caballer, M.: A framework and a performance assessment for serverless MapReduce on AWS Lambda. Future Generation Computer Systems 97, 259–274 (2019). https://doi.org/10.1016/j.future.2019.02.057Gimėnez-Alventosa, V., Moltȯ, G., Segrelles, J. D.: RUPER-LB: Load balancing embarrasingly parallel applications in unpredictable cloud environments. In: International Symposium on Cloud Computing and Services for High Performance Computing Systems (as part of the 18th International Conference on High Performance Computing & Simulation (HPCS 2020) (2020)GRyCAP: minicon: minimization containers. https://github.com/grycap/miniconHeath, M.T.: Scientific computing: : an introductory survey, revised second edition. Society for Industrial and Applied Mathematics, Philadelphia, PA. https://doi.org/10.1137/1.9781611975581. (2018)Ishakian, V., Muthusamy, V., Slominski, A.: Serving deep learning models in a serverless platform. In: Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018, pp. 257–262. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IC2E.2018.00052 (2018)Ivie, P., Thain, D.: Reproducibility in scientific computing. https://doi.org/10.1145/3186266 (2018)Jonas, E., Pu, Q., Venkataraman, S., Stoica, I., Recht, B.: Occupy the cloud. In: Proceedings of the 2017 Symposium on Cloud Computing, pp 445–451. ACM, New York (2017). https://doi.org/10.1145/3127479.3128601. arXiv:1702.04024Knative: Kubernetes-based platform to deploy and manage modern serverless workloads. https://knative.dev/Linux Containers: LXC. https://linuxcontainers.org/lxc/introduction/Malawski, M., Gajek, A., Zima, A., Balis, B., Figiela, K.: Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud functions. Future Generation Computer Systems 110, 502–514 (2020). https://doi.org/10.1016/j.future.2017.10.029. https://linkinghub.elsevier.com/retrieve/pii/167739X1730047XMcCallister, E., Grance, T., Kent, K.: Guide to protecting the confidentiality of personally identifiable information (PII). Special Publication 800-122 Guide pp. 1–59. https://doi.org/10.5555/2206206 (2010)Microsoft Azure: Azure Functions—Develop Faster With Serverless Compute. https://azure.microsoft.com/en-us/services/functions/MinIO: High Performance, Kubernetes Native Object Storage. https://min.io/Mirkhan, A.: BlurryFaces: A tool to blur faces or other regions in photos and videos. https://github.com/asmaamirkhan/BlurryFacesMorris, K.: Infrastructure as code: managing servers in the cloud. O’Reilly Media, Inc. https://www.oreilly.com/library/view/infrastructure-as-code/9781491924334/ (2016)OASIS: TOSCA simple profile in YAML version 1.3. https://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.3/TOSCA-Simple-Profile-YAML-v1.3.htmlOpenFaaS: Serverless functions made simple. https://www.openfaas.com/OpenStack: Open Source Cloud Computing Infrastructure. https://www.openstack.orgPavlovic, M., Etsion, Y., Ramirez, A.: On the memory system requirements of future scientific applications: Four case-studies. In: Proceedings - 2011 IEEE International Symposium on Workload Characterization, IISWC - 2011, pp 159–170 (2011). https://doi.org/10.1109/IISWC.2011.6114176Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures. Future Generation Computer Systems 83, 50–59 (2018). https://doi.org/10.1016/j.future.2018.01.022. http://linkinghub.elsevier.com/retrieve/pii/S0167739X17316485Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures. Future Generation Computer Systems 83, 50–59 (2018). https://doi.org/10.1016/j.future.2018.01.022. http://www.sciencedirect.com/science/article/pii/S0167739X17316485Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: A programming model and middleware for high throughput serverless computing applications. In: Proceedings of the 34th ACM/SIGAPP symposium on applied Computing - SAC ’19, pp 106–113. ACM Press, New York (2019). https://doi.org/10.1145/3297280.3297292Perez, A., Risco, S., Naranjo, D.M., Caballer, M., Molto, G.: On-premises serverless computing for event-driven data processing applications. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 414–421. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/cloud.2019.00073. https://ieeexplore.ieee.org/document/8814513 (2019)Purohit, A.: face-mask-detector: Real-Time Face mask detection using deep learning with Alert system. https://github.com/adityap27/face-mask-detector/Reisslein, M., Rinner, B., Roy-Chowdhury, A.: Smart Camera Networks [Guest editors’ introduction]. Computer 47(5), 23–25 (2014). https://doi.org/10.1109/MC.2014.134Risco, S., Moltó, G.: GPU-enabled serverless workflows for efficient multimedia processing. Applied Sciences 11(4), 1438 (2021). https://doi.org/10.3390/app11041438. https://www.mdpi.com/2076-3417/11/4/1438Ristov, S., Pedratscher, S., Fahringer, T.: AFCL: An abstract function choreography language for serverless workflow specification. Future Generation Computer Systems 114, 368–382 (2021). https://doi.org/10.1016/j.future.2020.08.012. https://linkinghub.elsevier.com/retrieve/pii/S0167739X20302648Sengupta, S.: faas-flow: Function Composition for OpenFaaS. https://github.com/s8sg/faas-flowSewak, M., Singh, S.: Winning in the era of serverless computing and function as a service. In: 2018 3rd International Conference for Convergence in Technology, I2CT 2018. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/I2CT.2018.8529465 (2018)Shields, M.: Control-versus data-driven workflows. In: Workflows for e-Science, pp 167–173. Springer , London (2007). https://link.springer.com/chapter/10.1007/978-1-84628-757-2_11Spadini, T., Silva, D.L.d.O., Suyama, R.: Sound event recognition in a smart city surveillance context. arXiv:1910.12369 (2019)Vecchiola, C., Pandey, S., Buyya, R.: High-performance cloud computing: A view of scientific applications. In: I-SPAN 2009 - The 10th International Symposium on Pervasive Systems, Algorithms, and Networks, pp 4–16 (2009). https://doi.org/10.1109/I-SPAN.2009.15