Fog Resource Provisioning in Reliability-Aware IoT Networks

Yao, Jingjing; Ansari, Nirwan

doi:10.1109/jiot.2019.2922585

Cited by 83 publications

(45 citation statements)

References 27 publications

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“…Their goal is to minimize the system cost by meeting QoS requirements. Then, in [24], their work has been extended by evaluating the tradeoff between maximizing the reliability and minimizing the overall system cost. A highly computationally complex ILP model has been described followed by a heuristic-based algorithm able to find suboptimal solutions, albeit achieving better time efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…The data transfer time of a single message can be expressed by using the sensor's data rate , , and the total number of bits to be transmitted . Thus, the data transfer time of each sensor is given by (24), in which the data transfer time is affected by . The gateway bandwidth factor is used to make sure that the formulation considers that a higher number of sensors associated to a single gateway will decrease the available bandwidth per sensor and, consequently, increase the data transfer time.…”

Section: Maximization Of User Requests (Max R)mentioning

confidence: 99%

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Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks

Santos

Wauters

Volckaert

et al. 2021

Journal of Network and Computer Applications

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Recently, with the advent of the Internet of Things (IoT), Smart Cities have emerged as a potential business opportunity for most cloud service providers. However, centralized cloud architectures cannot sustain the requirements imposed by many IoT services. High mobility coverage and low latency constraints are among the strictest requirements, making centralized solutions impractical. In response, theoretical foundations of Fog Computing have been introduced to set up a distributed cloud infrastructure by placing computational resources close to end-users. However, the acceptance of its foundational concepts is still in its early stages. A key challenge still to answer is Service Function Chaining (SFC) in Fog Computing, in which services are connected in a specific order forming a service chain to fully leverage on network softwarization. Also, Low Power Wide Area Networks (LPWANs) have been getting significant attention. Opposed to traditional wireless technologies, LP-WANs are focused on low bandwidth communications over long ranges. Despite their tremendous potential, many challenges still arise concerning the deployment and management of these technologies, making their wide adoption difficult for most service providers. In this article, a Mixed Integer Linear Programming (MILP) formulation for the IoT service allocation problem is proposed, which takes SFC concepts, different LPWAN technologies and multiple optimization objectives into account. To the best of our knowledge, our work goes beyond the current state-of-the-art by providing a complete end-to-end (E2E) resource provisioning in Fog-cloud environments while considering cloud and wireless network requirements. Evaluations have been performed to evaluate in detail the proposed MILP formulation for Smart City use cases. Results show clear trade-offs between the different provisioning strategies. Our work can serve as a benchmark for resource provisioning research in Fog-cloud environments since the model approach is generic and can be applied to a wide range of IoT use cases.

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Section: Related Workmentioning

confidence: 99%

Section: Maximization Of User Requests (Max R)mentioning

confidence: 99%

Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks

Santos

Wauters

Volckaert

et al. 2021

Journal of Network and Computer Applications

View full text Add to dashboard Cite

show abstract

“…Reliability is the ability of the components (Software, Hardware, and Network) in the system to perform its essential functions at different conditions and specific intervals of time [142]. Reliability requirements need to be discussed in each layer of the IoT architecture.…”

Section: ) Reliabilitymentioning

confidence: 99%

An Empirical Study on System Level Aspects of Internet of Things (IoT)

2020

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Internet of Things (IoT) is an integration of the Sensor, Embedded, Computing, and Communication technologies. The purpose of the IoT is to provide seamless services to anything, anytime at any place. IoT technologies play a crucial role everywhere, which brings the fourth revolution of disruptive technologies after the internet and Information and Communication Technology (ICT). The Research & Development community has predicted that the impact of IoT will be more than the internet and ICT on society, which improves the wellbeing of society and industries. Addressing the predominant system-level design aspects like energy efficiency, robustness, scalability, interoperability, and security issues result in the use of a potential IoT system. This paper presents the current state of art of the functional pillars of IoT and its emerging applications to motivate academicians and researches to develop real-time, energy-efficient, scalable, reliable, and secure IoT applications. This paper summarizes the architecture of IoT, with the contemporary status of IoT architectures. Highlights of the IoT system-level issues to develop more advanced real-time IoT applications have been discussed. Millions of devices exchange information using different communication standards, and interoperability between them is a significant issue. This paper provides the current status of the communication standards and application layer protocols used in IoT with the detailed analysis. The computing paradigms like Cloud, Cloudlet, Fog, and Edge computing facilitate IoT with various services like data offloading, resource and device management, etc. In this paper, an exhaustive analysis of Edge Computing in IoT with different edge computing architectures and existing status are deliberated. The widespread adoption of IoT in society has resulted in privacy and security issues. This paper emphasizes on analyzing the security challenges, privacy and security threats, conventional mitigation techniques, and further scope for IoT security. The features like fewer memory footprints, scheduling, real-time task execution, fewer interrupt, and thread switching latency of Real-Time Operating Systems (RTOS) enables the development of time critical IoT applications. Also, this review offers the analysis of the RTOS's suitable for IoT with the current status and networking stack. Finally, open research issues in IoT system development are discussed.

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“…In this work, each IoT device utilizes a local agent which finds a suitable compute node considering various application constraints, and sends data directly to that node. Yao and Ansari [15] propose a system whereby the IoT data are sent directly to suitable compute nodes which use a scheduler for assigning the necessary applications on virtual machines based on an optimization problem. Mansouri and Wong [16] design a quality of experience optimization framework in which the IoT devices send data directly to either a nearby or a remote compute node.…”

Section: A Direct Routing Approachesmentioning

confidence: 99%

edgeRouting: Using Compute Nodes in Proximity to Route IoT Data

Karagiannis

Schulte

2021

IEEE Access

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Due to the proliferation of edge computing, cloud providers have started offering compute nodes at the edge of the network in addition to traditional compute nodes in data centers. So far, various systems have been proposed for processing Internet of Things (IoT) data on both edge and cloud compute nodes in order to reduce the communication latency. However, such systems do not typically consider that the network bandwidth between an edge node and a cloud node can be orders of magnitude higher than the bandwidth between an IoT device and a cloud node. As a result, the IoT data are commonly sent selectively to either edge or cloud nodes disregarding alternative network paths through edge nodes, which may have higher network bandwidth, and lower communication latency. To avoid this, in this paper we analyze the latency of sending data to edge and cloud compute nodes of cloud providers. Based on this analysis, we propose edgeRouting which routes the data through the closest edge compute node. By doing that, edgeRouting exploits both the low propagation delay of nodes at the edge, and the high bandwidth among edge and cloud compute nodes of cloud providers. To evaluate our approach, we perform experiments on a real-world setup with nearby and remote compute nodes of a cloud provider, and we show that edgeRouting reduces the communication latency by up to 55% compared to alternative methods.

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Fog Resource Provisioning in Reliability-Aware IoT Networks

Cited by 83 publications

References 27 publications

Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks

Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks

An Empirical Study on System Level Aspects of Internet of Things (IoT)

edgeRouting: Using Compute Nodes in Proximity to Route IoT Data

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