DA, Computer Systems (VT 2025)

 

Research Interests:

• Distributed Computing Continuum Systems
• Learning Techniques in IoT
• AI/ML for Computing Systems
• Congnition, Causality in Computing Systems
• Cyber-physical Continuum

Editorial Activities:

• IEEE Internet of Things Journal
• Computing, Springer
• Scientific Reports, Nature
• Transactions on Emerging Telecommunications Technologies (ETT), Wiley

PhD Students:

• Alfreds Lapkovskis (Supervisor)
• Shubham Vaishnav (Co-Supervisor)

 

A selection from Stockholm University publication database

• Equilibrium in the Computing Continuum through Active Inference

  2024. Boris Sedlak (et al.). Future Generation Computer Systems 160, 92-108

  Article

  Computing Continuum (CC) systems are challenged to ensure the intricate requirements of each computational tier. Given the system’s scale, the Service Level Objectives (SLOs), which are expressed as these requirements, must be disaggregated into smaller parts that can be decentralized. We present our framework for collaborative edge intelligence, enabling individual edge devices to (1) develop a causal understanding of how to enforce their SLOs and (2) transfer knowledge to speed up the onboarding of heterogeneous devices. Through collaboration, they (3) increase the scope of SLO fulfillment. We implemented the framework and evaluated a use case in which a CC system is responsible for ensuring Quality of Service (QoS) and Quality of Experience (QoE) during video streaming. Our results showed that edge devices required only ten training rounds to ensure four SLOs; furthermore, the underlying causal structures were also rationally explainable. The addition of new types of devices can be done a posteriori; the framework allowed them to reuse existing models, even though the device type had been unknown. Finally, rebalancing the load within a device cluster allowed individual edge devices to recover their SLO compliance after a network failure from 22% to 89%.

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• Edge Intelligence—Research Opportunities for Distributed Computing Continuum Systems

  2023. Victor Casamayor Pujol (et al.). IEEE Internet Computing 27 (4), 53-74

  Article

  Edge intelligence and, by extension, any distributed computing continuum system will bring to our future society a plethora of new and useful applications, which will certainly revolutionize our way of living. Nevertheless, managing these systems challenges all previously developed technologies for Internet-distributed systems. In this regard, this article presents a set of techniques and concepts that can help manage these systems; these are framed in the main paradigm for autonomic computing, the well-known monitor–analyze–plan–execute over shared knowledge, or MAPE-K. All in all, this article aims at unveiling research opportunities for these new systems, encouraging the community to work together toward new technologies to make edge intelligence a reality.

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• On Distributed Computing Continuum Systems

  2023. Schahram Dustdar, Victor Casamayor Pujol, Praveen Kumar Donta. IEEE Transactions on Knowledge and Data Engineering 35 (4), 4092-4105

  Article

  This article presents our vision on the need of developing new managing technologies to harness distributed “computing continuum” systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software. We present a new methodology to manage distributed “computing continuum” systems. This is based on a mathematical artifact called Markov Blanket, which sets these systems in a Markovian space, more suitable to cope with their complex characteristics. Furthermore, we develop the concept of equilibrium for these systems, providing a more flexible management framework compared with the one based on thresholds, currently in use for Internet-based distributed systems. Finally, we also link the equilibrium with the development of adaptive mechanisms. However, we are aware that developing the entire methodology requires a big effort and the use of learning techniques, therefore, we finish this article with an overview of the techniques required to develop this methodology.

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• Markov Blanket Composition of SLOs

  2024. Boris Sedlak (et al.). 2024 IEEE International Conference on Edge Computing and Communications (EDGE), 128-138

  Conference

  Smart environments use composable microservices pipelines to process Internet of Things (IoT) data, where each service is dependent on the outcome of its predecessor. To ensure Quality of Service (QoS), individual services must fulfill Service Level Objectives (SLOs); however, SLO fulfillment is dependent on resources (e.g., processing or storage), which are scarcely available within the Edge. Hence, when distributing services over heterogeneous devices, this raises the question of where to deploy each service to best fulfill both its own SLOs as well as those imposed by dependent services. In this paper, we maximize SLO fulfillment of a pipeline-based application by analyzing these dependencies. To achieve this, services and hosting devices alike are extended with a Markov blanket (MB) - a probabilistic view into their internal processes - which are composed into one overarching model. Given a mutable set of services, hosts, and SLOs, the composed MB allows inferring the optimal assignment between services and edge devices. We evaluated our method for a smart city scenario, which assigned pipelined services (e.g., video processing) under constraints from subsequent services (e.g., consumer latency). The results showed how our method can support infrastructure providers by optimizing SLO fulfillment for arbitrary devices currently available.

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• A Privacy Enforcing Framework for Data Streams on the Edge

  2024. Boris Sedlak (et al.). IEEE Transactions on Emerging Topics in Computing, 1-12

  Article

  Recent developments in machine learning (ML) allow for efficient data stream processing and also help in meeting various privacy requirements. Traditionally, predefined privacy policies are enforced in resource-rich and homogeneous environments such as in the cloud to protect sensitive information from being exposed. However, large amounts of data streams generated from heterogeneous IoT devices often result in high computational costs, cause network latency, and increase the chance of data interruption as data travels away from the source. Therefore, this article proposes a novel privacy-enforcing framework for transforming data streams by executing various privacy policies close to the data source. To achieve our proposed framework, we enable domain experts to specify high-level privacy policies in a human-readable form. Then, the edge-based runtime system analyzes data streams (i.e., generated from nearby IoT devices), interprets privacy policies (i.e., deployed on edge devices), and transforms data streams if privacy violations occur. Our proposed runtime mechanism uses a Deep Neural Networks (DNN) technique to detect privacy violations within the streamed data. Furthermore, we discuss the framework, processes of the approach, and the experiments carried out on a real-world testbed to validate its feasibility and applicability.

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Show all publications by Praveen Kumar Donta at Stockholm University