Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud Supported FinTech Applications

Ren, Xiaodong; Aujla, Gagangeet Singh; Jindal, Anish; Batth, Ranbir Singh; Zhang, Peiying

doi:10.1109/jiot.2021.3064468

Cited by 20 publications

(11 citation statements)

References 35 publications

(36 reference statements)

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“…Ren et al [24] proposed a Software Defined Network (SDN) to counter the challenge of generation of Big data across the different geographical locations, an adaptive recovery mechanism using support vector machine is given as solution. In [25], Mobile Edge Computing (MEC) devices are used, and to identify the offloading rate, current battery level of the devices is used.…”

Section: Offloading For Iot Applicationsmentioning

confidence: 99%

“…Designing the middleware which can learn from the sensory data, battery behaviour, and context inferences through machine learning and processing of the data are quite challenging. Middleware devices encounter limitation during providing service due to resource constraints in terms of power, memory, and bandwidth [23,24]. • Many IoT applications require separate entities to compute and process the tasks on behalf of user devices, like smart home, healthcare, intelligent transport management, Ambient Assisted Living (AAL), Virtual Reality (VR), etc.…”

Section: Future Research Challengesmentioning

confidence: 99%

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Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Bajaj

Sharma

Singh

2021

Complex Intell. Syst.

102

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The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

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Section: Offloading For Iot Applicationsmentioning

confidence: 99%

Section: Future Research Challengesmentioning

confidence: 99%

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Bajaj

Sharma

Singh

2021

Complex Intell. Syst.

102

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“…In the era of Industry 4.0, smart technologies are diffusely used in the Internet of Things [1], [2], such as smart wearable devices [3], [4], smart homes, digital healthcare [5], smart transportation, and vehicular networks [6]. The application of these smart technologies in Industry 4.0 has improved the efficiency and fault tolerance of industrial production, enabling the existing industrial system to face the challenges of complex environments.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Edge Device Selection Using Social Attribute Perception in Industry 4.0

Zhang

Gan

Aujla

et al. 2023

IEEE Internet Things J.

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.

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“…Besides, considering that the backup data may also be damaged or disabled, the persistence of the backup method should also be considered. However, most of the existing studies pay no attention to these aspects, but focus on a classic SDN context 9‐11 …”

Section: Introductionmentioning

confidence: 99%

“…However, most of the existing studies pay no attention to these aspects, but focus on a classic SDN context. [9][10][11] To fill this gap, in this article we propose a distributed data backup and recovery method (DDBR) for the SD-WAN architecture based on a secret sharing scheme. The DDBR method utilizes the storage of the switches and performs an online-offline dual backup of the control plane data.…”

Section: Introductionmentioning

confidence: 99%

Distributed data backup and recovery for software‐defined wide area network controllers

Zhang

Zhong

Yang

et al. 2021

Trans Emerging Tel Tech

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Software-defined wide area network (SD-WAN) is a new type of network architecture that has developed rapidly in recent years. SD-WAN inherits the centralized control architecture of software-defined networking (SDN), but supports more diverse access methods and equipment types and covers a wider area. It is also associated with greater uncertainty in the network environment. These characteristics make the fault management of the SD-WAN controller more challenging, so that the existing SDN-based data backup methods cannot adapt to SD-WAN scenarios. This article proposes an SD-WAN-oriented distributed data backup and recovery method (DDBR) based on an improved secret sharing algorithm. To deploy this method, we design an online-offline dual backup framework based on the data freshness requirements of the controller. Under this framework, dynamic data of the controller is divided into different shares, and then stored into the storage of switches. When the controller fails, data recovery can be performed on the backup controller quickly, which greatly improves the network availability. The outstanding feature of the proposed DDBR method is that it ensures the integrity and confidentiality of the backup data in an unreliable network environment, even when some of the storage nodes fail. Theoretical analysis and evaluation results on file backup example show that the proposed solution has significant advantages over existing methods in terms of backup data storage size, communication overhead, backup and recovery success rate.

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Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud Supported FinTech Applications

Cited by 20 publications

References 35 publications

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Reinforcement Learning for Edge Device Selection Using Social Attribute Perception in Industry 4.0

Distributed data backup and recovery for software‐defined wide area network controllers

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