In-network Placement of Reusable Computing Tasks in an SDN-based Network Edge

Amadeo, Marica; Campolo, Claudia; Lia, Gianmarco; Molinaro, Antonella; Ruggeri, Giuseppe

doi:10.1109/tmc.2023.3237765

Cited by 8 publications

(2 citation statements)

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“…In the next few years, the research and industrial developments will most probably allow quantum facilities to be integrated into the current continuous computing paradigm [1]- [4], resulting in a novel and more advanced architecture. In such a composite architecture, resource management algorithms need to be used to determine where the computation should be performed (assignment problem) and when (scheduling problem) [49].…”

Section: A Reference Architecturementioning

confidence: 99%

Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture

Mastroianni,

Plastina,

Settino

et al. 2024

IEEE Trans. Quantum Eng.

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Modern Cloud/Edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed Edge/Fog nodes, centralized data centers and quantum devices. The optimal assignment and scheduling of computation on the different nodes is a very difficult problem, with NP-hard complexity. In this paper, we explore the possibility of solving this problem with Variational Quantum Algorithms, which can become a viable alternative to classical algorithms in the near future. In particular, we compare the performances, in terms of success probability, of two algorithms, i.e., Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). The simulation experiments, performed for a set of simple problems, show that the VQE algorithm ensures better performances when it is equipped with appropriate circuit ansatzes that are able to restrict the search space. Moreover, experiments executed on real quantum hardware show that the execution time, when increasing the size of the problem, grows much more slowly than the trend obtained with classical computation, which is known to be exponential.INDEX TERMS quantum computing, cloud/edge computing, resource assignment

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Section: A Reference Architecturementioning

confidence: 99%

Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture

Mastroianni,

Plastina,

Settino

et al. 2024

IEEE Trans. Quantum Eng.

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show abstract

Section: A Reference Architecturementioning

confidence: 99%

Quantum Computing Management of a Cloud/Edge Architecture

Mastroianni

Scarcello

Vinci

2023

Proceedings of the 20th ACM International Conference on Computing Frontiers

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1 Modern Cloud/Edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed Edge/Fog nodes, centralized data centers and quantum devices. The optimal assignment and scheduling of computation on the different nodes is a very difficult problem, with NP-hard complexity. In this paper, we explore the possibility of solving this problem with variational quantum algorithms, which can become a viable alternative to classical algorithms in the near future. In particular, we compare the performances, in terms of success probability, of two algorithms, i.e., Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). The simulation experiments, performed for a set of simple problems, show that the VQE algorithm ensures better performances when it is equipped with appropriate circuit ansatzes that are able to restrict the search space. Moreover, experiments executed on real quantum hardware show that the execution time, when increasing the size of the problem, grows much more slowly than the trend obtained with classical computation, which is known to be exponential.Index Terms-quantum computing, cloud/edge computing, resource assignment I. INTRODUCTIONCloud/Edge architectures are required to include and integrate heterogeneous types of computing devices, with very different capabilities and characteristics, ranging from pervasive sensors and actuators and mobile devices to personal computers, local servers and Cloud data centers. This complex architecture is sometimes referred to as a "continuous computing" platform [1]-[4], and is the subject of intense research aiming to achieve a judicious allocation of computational, storage, and network resources to meet the demands of modern applications. A skillful resource allocation involves the strategic distribution of resources to the different layers, in order to deliver desired outcomes efficiently. This problem encompasses multiple dimensions, including cost-effectiveness, energy efficiency, security, and scalability, and its solution becomes increasingly intricate [5], [6].Recent research in this area is inspired by machine learning and AI-driven decision-making algorithms, which aim to adapt resource allocation strategies to the evolving needs of diverse workloads. Indeed, efficiency, latency, and privacy issues foster

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