Towards Enabling Autonomic Computing in IoT Ecosystem

Tahir, Mohammad; Ashraf, Qazi Mamoon; Dabbagh, Mohammad

doi:10.1109/dasc/picom/cbdcom/cyberscitech.2019.00122

Cited by 21 publications

(16 citation statements)

References 12 publications

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“…signal strength, limited bandwidth). However, in FC, such an operation is transferred to one of its various fog nodes to perform the update to the sensors quickly [9].…”

Section: Fog Computing As a Solution Of CC For Iot Ecosystemmentioning

confidence: 99%

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

2021

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The Internet of Things (IoT) has become one of the most known terms in present times, reaching new levels and setting a trend in the world. Evidently, it is the future of connectivity which has turned physical objects into intelligent objects. Therefore, there has been a growing curiosity in the IoT field and this leads to the concept of IoT ecosystem. But the contemporary fragmented ecosystem of regulations, technologies, and systems slows IoT deployments. Thus, we consider multi-tiered computational infrastructure which would be feasible to provide services from the nearest possible location of end devices. To mitigate multi-tier infrastructure issues, Software-Defined Networking (SDN) steps in. The journey behind SDN-supported multi-tier computational infrastructure can be understood from this paper's elaborate study. Next, a comparative analysis on dew, roof, fog and cloud is conducted and the impact of internet on these computing paradigms is briefly explained. Then a novel framework termed "SD-DRFC (Software-Defined Dew, Roof, Fog and Cloud computing)" is proposed for today's IoT ecosystem. The role and functionality of each tier of SD-DRFC framework are adequately explained. A use case focused on the SD-DRFC framework is then presented and simulated by utilizing iFogSim simulator. To evaluate the efficiency of the presented SD-DRFC framework, four QoS parameters (Latency, Network Usage, Cost, and Energy Consumption) are considered in this paper. When comparing the simulation results, the presented SD-DRFC framework performs much better than cloud-only implementation. The advantages and suitability of utilizing this proposed framework have been demonstrated by multiple use-cases which range from conceptual visions to existing running systems.

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“…signal strength, limited bandwidth). However, in FC, such an operation is transferred to one of its various fog nodes to perform the update to the sensors quickly [9].…”

Section: Fog Computing As a Solution Of CC For Iot Ecosystemmentioning

confidence: 99%

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

2021

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“…• If this new plan deviates from the previous, trigger T 4 informs devices that should adapt to this new plan, as we will outline later. • In each manager there is a special reasoning process that triggers T 5 resp.…”

Section: Explicit Triggersmentioning

confidence: 99%

“…The nature of the employed mechanisms that allow autonomous behavior is rarely discussed, and alternative approaches are seldom considered. In fact, we have not found any papers that study the specific mechanisms that are used to achieve this goal in a generalized way, a challenge also identified in [4]. This shows that research on autonomous management for constrained IoT devices is still a maturing field, and that there is a need for a standardized, unified view or methodology that can advance the goal of achieving management systems for IoT that require a minimum of human intervention [5].…”

Section: Introductionmentioning

confidence: 98%

Autonomous IoT Device Management Systems: Structured Review and Generalized Cognitive Model

Bråten

Kraemer

Palma

2021

IEEE Internet Things J.

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Research on autonomous management for largescale deployments of constrained devices is still a maturing field in the Internet of Things (IoT). Although much research has been conducted on how to achieve autonomous management in specific cases, there is a need for literature investigating which mechanisms can achieve such behavior in a generalized way. In this review, we present a comprehensive and structured study of the mechanisms for autonomous device management of constrained IoT devices in the light of management tasks, operational environment, network topology, resource constraints, scalability and management categories. Data extracted from 32 relevant cases is first organized and analyzed according to a synthesized taxonomy of observed adaptation mechanisms, and then combined with state-of-the-art models of autonomous operations, identifying common patterns for autonomous management. Based on our findings we substantiate best practices for designing and implementing solutions around adaptation mechanisms. We then present a generalized model for autonomous device management that describes and explains the processes required for autonomous operation, unifying the insights from previous works as one cohesive archetype.

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“…The auto-scaling [17] issue is an old-style programmed control issue that aims to powerfully tune the resources and measure the resources assigned to reach a specific objective. In particular, it is regularly abstracted as a "MAPE control loop" [18], which continuously repeats itself over time. The auto-scaler uses a time-series database that stores the Monitoring is the first phase in the loop, which continuously collects data needed during analysis and planning to regulate appropriate scaling actions.…”

Section: B Auto-scaler Architecturementioning

confidence: 99%

SBPAM: Secure Based Predictive Autoscaling Model For containerized application

Shenawy¹,

Mousa

Amin³

2021

IJCI. International Journal of Computers and Information

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During the past few years, the virtual technology used in the cloud has become unsuitable for service delivery, with the emergence of containers technology and the spread of its use throughout a wide range of cloud service providers because of the ease of use and provision of the resources used. The institutions have become widely seeking to develop this technology to suit the different needs to provide a good service for the end-user. In this paper, we use machine learning to improve the container orchestration process. Our approach focuses on getting containers cluster resources idle as much as can by scanning and clearing malicious and unwanted fake load to enhance the workers load then using machine learning models to predict loads in advance. Hence, the Auto-Scaler module begins to auto-scale the number of resources to meet the cluster's workload, leading to efficient use of resources.

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Towards Enabling Autonomic Computing in IoT Ecosystem

Cited by 21 publications

References 12 publications

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

Autonomous IoT Device Management Systems: Structured Review and Generalized Cognitive Model

SBPAM: Secure Based Predictive Autoscaling Model For containerized application

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