On-Demand Service Deployment Strategies for Fog-as-a-Service Scenarios

Bozorgchenani, Arash; Tarchi, Daniele; Cerroni, Walter

doi:10.1109/lcomm.2021.3055535

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…In fact, this non-cachingbased method is the pure task offloading as illustrated in many existing works, e.g., [5]- [9]. Note that, as stated in [33], the MEC server is also able to download the task software from the library each time the request is made by the user k, while it only uploads the input parameters. However, the task software acquiring process is timeconsuming, especially during peak time.…”

Section: Task Computingmentioning

confidence: 99%

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

Chen

Alam

et al. 2022

IEEE Trans. Commun.

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In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users' activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

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Section: Task Computingmentioning

confidence: 99%

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

Chen

Alam

et al. 2022

IEEE Trans. Commun.

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“…Moving towards XaaS-awareness, in [34] the authors propose an optimization framework for on-demand service deployment in a XaaS Fog Computing environment, where a resource-constrained latency minimization approach is considered. The system has been studied through numerical modeling, although no practical implementation has been considered.…”

Section: Related Workmentioning

confidence: 99%

A Fog Computing Orchestrator Architecture With Service Model Awareness

Davoli

Cerroni

Borsatti

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Fog Computing can facilitate the adoption of the Everything-as-a-Service paradigm in infrastructure segments that are located closer to the end user, or to the data source, compared to typical Cloud solutions. This enables combining the advantages of flexible service deployment models with the need to cope with the strict requirements -especially in terms of latency -of emerging applications in softwarized networks. Along comes the need to consider aspects of service orchestration specific to the Fog environment and its intrinsically dynamic nature. In this paper we propose an architecture for flexible Fog Computing service orchestration, with a particular focus on the awareness of service deployment models. We discuss the design choices and describe the components and operations of the proposed orchestration system. We then present a complete working implementation of such architecture, including insights on its ability to handle critical orchestration functions such as service discovery and resource monitoring. We also report on the experimental validation of the system and the performance evaluation on realworld equipment, proving the feasibility and the effectiveness of the approach on a dynamic Fog infrastructure. We complement the work by presenting the results of a combinatorial analysis, validated by simulation, of the service model-aware resource selection process. As a result of our investigation, we show that Fog services can be effectively deployed in a matter of a few seconds, or even in less than one second when suitable Fog nodes are available, taking advantage of the awareness of the available service models.

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“…The problem can be solved by using standard solvers such as CPLEX with low execution time on modest hardware. CPLEX is widely used in the literature for problem solving [1,3]. In the following section, we present the results of our study.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Joint Security-vs-QoS Framework: Optimizing the Selection of Intrusion Detection Mechanisms in 5G networks

Bozorgchenani

Zarakovitis²,

Chien

et al. 2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

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The advent of 5G technology introduces new -and potentially undiscovered -cybersecurity challenges, with unforeseen impacts on our economy, society, and environment. Interestingly, Intrusion Detection Mechanisms (IDMs) can provide the necessary network monitoring to ensure -to a big extent -the detection of 5G-related cyberattacks. Yet, how to realize the attack surface of 5G networks with respect to the detected risks, and, consequently, how to optimize the cybersecurity levels of the network, remains an open critical challenge. In respect, this work focuses on deploying multiple distributed Security Agents (SAs) that can run different IDMs over various network components and proposes a cybersecurity mechanism for optimizing the network's attack surface with respect to the Quality of Service (QoS). The proposed approach relies on a new closed-form utility function to describe the trade-off between cybersecurity and QoS and uses multi-objective optimization to improve the selection of each SA detection level. We demonstrate via simulations that before optimization, an increase in the detection level of SAs brings a direct decrease in QoS as more computational, bandwidth and monetary resources are utilized for IDM processing. Thereby, after optimization, we demonstrate that our mechanism can strike a balance between cybersecurity and QoS while showcasing the impact of the importance of different objectives of the joint optimization.

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On-Demand Service Deployment Strategies for Fog-as-a-Service Scenarios

Cited by 10 publications

References 11 publications

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

A Fog Computing Orchestrator Architecture With Service Model Awareness

Joint Security-vs-QoS Framework: Optimizing the Selection of Intrusion Detection Mechanisms in 5G networks

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