Research Themes

Osmotic Computing principles

The background and core principles of osmotic computing are introduced here. Osmotic computing is a new paradigm that can realize the fluid and elastic management of complex service compositions deployed over heterogeneous, dynamic, and evolving network and compute infrastructures spanning the device to cloud continuum. Our ongoing research focuses both on the technology enablers for OC, but also on what OC can do towards achieving edge intelligence, end-to-end automated service coordination, and continuous elasticity.

Villari M., Fazio M., Dustdar S., Rana O., Ranjan R. (2016). Osmotic Computing: A New Paradigm for Edge/Cloud Integration. IEEE Cloud Computing, Volume 3, Issue 6, pp. 76-83
Villari M., Fazio M., Dustdar S., Rana O., Jha D. N., Ranjan R. (2019). Osmosis: The Osmotic Computing Platform for Microelements in the Cloud, Edge, and Internet of Things. IEEE Computer, Volume 52, Issue 8, pp. 14-26.

Osmotic Computing middleware and orchestration components

We have laid the design requirements for Osmotic Message-Oriented Middleware. Key aspects include how to model and implement osmotic pressure, how to provide the monitoring information necessary for it, and how to enable AI-driven management and orchestration of osmotic micro-elements (MELs).

Rausch T., Dustdar S., Ranjan R. (2018). Osmotic Message-Oriented Middleware for the Internet of Things. IEEE Cloud Computing, Volume 5, Issue 2, pp. 17-25.
Carnevale L., Celesti A., Galletta A., Dustdar S., Villari M. (2018). From the Cloud to Edge and IoT: a Smart Orchestration Architecture for Enabling Osmotic Computing. 6th International Workshop on Cloud Computing Project and Initiatives (CCPI 2018) in conjunction with IEEE AINA 2018, May 16-18, 2018, Krakow, Poland.

Edge computing system support, tools, and algorithmic aspects

Osmotic Computing builds on top and helps to efficiently manage diverse federated cloud/fog/edge computing resources. We have extensively addressed two key aspects: (i) how to build and operate edge computing fabrics which include low-cost but also specialized edge compute units (such as those tailored to AI workloads), and (ii) how to handle near-optimally and at run-time service component placement (e.g., MELs) and workload distribution. We are also carrying out work in the evaluation of such systems: We are studying how to generate realistic edge computing topologies and scenarios and how to simulate edge/cloud computing systems with a view to pervasive IoT systems.

Rausch T., Avasalcai C., Dustdar S. (2018). Portable Energy-Aware Cluster-Based Edge Computers. 3rd ACM/IEEE Symposium on Edge Computing (SEC 2018), October 25-27, 2018, Bellevue, WA, USA.
Rausch T., Rashed A., Dustdar S. (2020). Optimized container scheduling for data-intensive serverless edge computing. Future Generation Computer Systems, Volume 114, pp. 259-271.
Deng S., Xiang Z., Taheri J., Khoshkholghi M. A., Yin J., Zomaya A. Y. , Dustdar S. (2020). Optimal Application Deployment in Resource Constrained Distributed Edges. IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2020.2970698 (preprint).
Rausch, T., Lachner, C., Frangoudis, P. A., Raith, P., & Dustdar, S. (2020). Synthesizing Plausible Infrastructure Configurations for Evaluating Edge Computing Systems. In 3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20).
Rausch, T., Hummer, W., Muthusamy, V., Rashed, A., & Dustdar, S. (2019). Towards a Serverless Platform for Edge AI. In 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19).
Jha D. N., Alwasel K., Alshoshan A., Huang X., Naha R. K., Battula S. K., Garg S., Puthal D., James P., Zomaya A., Dustdar S., Ranjan R. (2020). IoTSim-Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments. Software: Practice and Experience, Volume 50, Issue 6, pp. 844-867.
Bittencourt, L. F., Diaz-Montes, J., Buyya, R., Rana, O. F., Parashar, M. (2017). Mobility-Aware Application Scheduling in Fog Computing. IEEE Cloud Computing, Volume 4, Issue 2, pp. 26-35.
Puliafito, C., Gonçalves, D. M., Lopes, M. M., Martins, L. L., Madeira, E., Mingozzi, E., Rana, O., Bittencourt, L. F. (2020). MobFogSim: Simulation of mobility and migration for fog computing. Simulation Modelling Practice and Theory, Volume 101, 102062.

Enabling edge intelligence and continuous elasticity

Our work is in the space of Edge Intelligence, which represents the confluence of AI and Edge Computing. Osmotic Computing can play a key role in two directions: Pushing intelligence on the edge, by decentralized, autonomous decision making on-device and/or on edge compute resources, but also, and equally importantly, bringing intelligence for the edge, by enabling the smart orchestration of AI services and workloads across the device to cloud continuum, by dynamically, optimally and continuously (re)deploying and (re)configuring elastic AI pipelines, such as those involved in federated learning or inference by means of Distributed Deep Neural Networks.

Deng S., Zhao H., Fang W., Yin J., Dustdar S, Zomaya A. Y. (2020). Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence. IEEE Internet of Things Journal, Volume 7, Issue 8, pp. 7457-7469.
Rausch, T., & Dustdar, S. (2019). Edge Intelligence: The Convergence of Humans, Things, and AI. In 2019 IEEE International Conference on Cloud Engineering (IC2E).
McNamee, F., Dustdar, S., Kilpatrick, P., Shi, W., Spence, I.T.A., Varghese, B. (2020). A Case For Adaptive Deep Neural Networks in Edge Computing. CoRR abs/2008.01814.
Ren P., Qiao X., Huang Y., Liu L., Dustdar S., Chen J. (2020). Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks. IEEE Network, Volume 34, Issue 2, pp. 254-261
Copil G., Moldovan D., Truong H.-L., Dustdar S. (2016). Continuous elasticity: Design and operation of elastic systems. it – Information Technology, Volume 58, Issue 6, pp. 329-348.
Copil G., Moldovan D., Truong H.-L., Dustdar S. (2016). rSYBL: A Framework for Specifying and Controlling Cloud Services Elasticity. ACM Transactions on Internet Technology, Volume 16, Issue 3, pp. 18:1 – 18:20
Vögler, M., Schleicher, J.M., Inzinger, C., Dustdar, S. (2018). Optimizing Elastic IoT Application Deployments. IEEE Trans. Serv. Comput. Volume 11, Issue 5, pp. 879-892.

Osmotic computing application scenarios and case studies

Osmotic Computing as a generic model for distributed computation has been studied in various application contexts, including e-Health in general, and body-area networking in particular, and IoT workflows.

Carnevale L., Celesti A., Galletta A., Dustdar S., Villari M. (2019). Osmotic computing as a distributed multi-agent system: The Body Area Network scenario. Internet of Things, Volume 5, pp.130-139.
Carnevale, L., Galletta, A., Celesti, A., Fazio, M., Paone, M., Bramanti, P., Villari, M. (2017). Big Data HIS of the IRCCS-ME Future: The Osmotic Computing Infrastructure. In: Longo A. et al. (eds) Cloud Infrastructures, Services, and IoT Systems for Smart Cities. (IISSC/CN4IoT 2017). Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 189. Springer, Cham. https://doi.org/10.1007/978-3-319-67636-4_21.
Nardelli M., Nastic S., Dustdar S., Villari M., Ranjan R. (2017). Osmotic Flow: Osmotic Computing+ IoT Workflow. IEEE Cloud Computing, Volume 4, Issue 2, pp. 68-75.

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