A reliability-aware resource provisioning scheme for real-time industrial applications in a Fog-integrated smart factory

Dehnavi, Saeid; Faragardi, Hamid Reza; Kargahi, Mehdi; Fahringer, Thomas

doi:10.1016/j.micpro.2019.05.011

Cited by 34 publications

(17 citation statements)

References 25 publications

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“…The SEcube™ (Secure Environment cube) Open Security Platform ( Figure 2 ) is an open-source security-oriented hardware and software platform, designed and constructed with ease of integration and service-orientation in mind. The hardware part of the platform was designed by Blu5 Group [ 16 ], and the software libraries are provided by an international cooperation within European research institutions [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Method to Increase Dependability in a Cloud-Fog-Edge Environment

Stan

Enyedi

Corches

et al. 2021

Sensors

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Robots can be very different, from humanoids to intelligent self-driving cars or just IoT systems that collect and process local sensors’ information. This paper presents a way to increase dependability for information exchange and processing in systems with Cloud-Fog-Edge architectures. In an ideal interconnected world, the recognized and registered robots must be able to communicate with each other if they are close enough, or through the Fog access points without overloading the Cloud. In essence, the presented work addresses the Edge area and how the devices can communicate in a safe and secure environment using cryptographic methods for structured systems. The presented work emphasizes the importance of security in a system’s dependability and offers a communication mechanism for several robots without overburdening the Cloud. This solution is ideal to be used where various monitoring and control aspects demand extra degrees of safety. The extra private keys employed by this procedure further enhance algorithm complexity, limiting the probability that the method may be broken by brute force or systemic attacks.

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Section: Methodsmentioning

confidence: 99%

Method to Increase Dependability in a Cloud-Fog-Edge Environment

Stan

Enyedi

Corches

et al. 2021

Sensors

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“…Dehnavi et al utilize the hybrid cloud infrastructure for scheduling of real-time tasks in industrial systems [33]. They propose resource provisioning policies to partition a given workload among different computing tiers, including local private clouds, edge nodes, fog nodes, and public cloud data centers.…”

Section: B Real-time Task Schedulingmentioning

confidence: 99%

Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory

et al. 2022

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As the size of data grows rapidly in modern IoT (Internet-of-Things) and CPS (Cyber-Physical System) applications, the memory power consumption of real-time embedded systems increases dramatically. Unlike general-purpose systems where memory consumes about 10% of the CPU power consumption, modern real-time systems have the memory power of 20-50% of CPU power. This is because the memory size of a real-time system should be large enough to accommodate the entire task set, and thus DRAM refresh operations become a major source of power consumption. In this article, we present a new swap scheme for real-time systems, which aims at reducing memory power consumption. To support swap with real-time constraints, we adopt high-speed NVM storage and co-optimize power-savings in CPU and memory. Unlike traditional real-time task models that only consider the executions in CPU, we define an extended task model that characterizes memory and storage paths of tasks as well, and tightly evaluate the worst-case execution time by formulating the overlapped latency between CPU and memory. By optimizing the CPU supply voltage and the memory swap ratio of given task set, our scheme reduces the energy consumption of realtime systems by 31.1% on average under various workload conditions.INDEX TERMS Real-time task scheduling; partial swap; genetic algorithm; power saving; voltage scaling; deadline; high-speed storage; NVM.

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“…Dehnavi et al 25 studied multiple computation environments comprised of public/private cloud, fog, and edge and proposed a heuristic resource provisioning mechanism to partition the mentioned environments' workload. They aimed at providing a resource provision scheme to divide the workload among the computing layers to enable choosing the appropriate size of bandwidth for linking the factory to the cloud data center.…”

Section: Related Workmentioning

confidence: 99%

“…Most studies are reactive, where provisioning decisions are taken after a change of workload in IoT applications. Some reactive studies or threshold‐based rules react to workload oscillations of IoT applications through mathematical techniques such as queuing theory, 18,23 integer linear programming, 20,22,33 Markov approximation algorithm,, 24,27 and some others through Heuristic‐based algorithm 19,25 for dynamic resource provisioning. Meanwhile, the proposed approach is proactive, and the decisions related to resource provisioning are made beforehand based on prediction techniques to reduce the latency time from scaling to operationalization.…”

Section: Related Workmentioning

confidence: 99%

A proactive fog service provisioning framework for Internet of Things applications: An autonomic approach

Faraji‐MehmandarMohammad

Jabbehdari

Javadi

2021

Trans Emerging Tel Tech

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In recent years, Internet of Things (IoT) services have expanded to promote the quality of life in different areas. Cloud connectivity services are so popular now that they have prompted the experts to enhance cloud computing for its utilization in IoT, making everything online in the next few decades. For reducing latency, immediate processing, and network congestion, fog computing has emerged in which cloud computing is expanded to the edge of the network. On the other hand, concerning the limitations in fog hardware resources compared with the cloud, and the dynamic and unpredictable fog environment, the provision of dynamic fog services is a challenge. Automatic matching of the resources based on the workload oscillations of IoT applications leads to allocating minimum fog resources to IoT devices, therefore, the satisfaction of service level agreement (SLA) and quality of service (QoS) parameters. The present article introduces a method based on the control monitoring‐analysis‐planning‐execution having shared knowledge‐base loop and presents an approach for dynamic resource provisioning based on autonomic computing and reinforcement learning techniques. The proposed scheme uses learning automata as a decision‐maker in the planning phase and time series prediction model in the analysis phase. The simulation test results indicated a reduced delay in service provisioning, total cost, and SLA violation compared with other approaches, highlighting the potential of fog computing in ensuring the QoS.

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A reliability-aware resource provisioning scheme for real-time industrial applications in a Fog-integrated smart factory

Cited by 34 publications

References 25 publications

Method to Increase Dependability in a Cloud-Fog-Edge Environment

Method to Increase Dependability in a Cloud-Fog-Edge Environment

Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory

A proactive fog service provisioning framework for Internet of Things applications: An autonomic approach

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