Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading

Taami, Tania; Krug, Silvia; O’Nils, Mattias

doi:10.1109/wfcs.2019.8757960

Cited by 22 publications

(16 citation statements)

References 8 publications

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“…The recent rapid development of artificial intelligence [1,2], machine learning [3], optical networks [4,5], smart grids [6], cloud computing [7,8], 5G connectivity [9], Blockchain, and Internet of Things (IoT) [10,11] is leading to an exponential growth in data usage across a wide range of engineering and commerce disciplines. Over the last decade, the IoT has been recognized for its remarkable potential in computer science.…”

Section: Introductionmentioning

confidence: 99%

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Yang¹

2023

IJACSA

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Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.

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Section: Introductionmentioning

confidence: 99%

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Yang¹

2023

IJACSA

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“…Recent rapid growth in artificial intelligence [1,2], machine learning [3], optical networks [4,5], smart grids [6], cloud computing [7], 5G connectivity [8], Blockchain [9,10], and Internet of Things (IoT) [11,12] have resulted in an explosion of data in almost all fields of engineering and commerce. With cloud computing, large-scale applications can be deployed quickly and efficiently, affordably on a per-use basis [13].…”

Section: Introductionmentioning

confidence: 99%

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Wang¹,

Liu²

2023

IJACSA

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Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and looselycoupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.

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“…Computational resources can be utilized locally, remotely, or even hybrid in the Edge-Fog-Cloud computing approaches. Although, centralized cloud computing has some pros, such as easy management, high computing power, high storage, and high network capacities, it suffers from some cons, such as not being distributed, having high network latency, high power consumption, low security, and cost [1], [2]. To overcome the mentioned limitations and also to increase Quality of Experience (QoE) for end users, other promising computational approaches, such as FC, has been integrated into the IoT-cloud systems.…”

Section: Introductionmentioning

confidence: 99%

D²FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems

Ataie

Taami

Azizi

et al. 2022

2022 IEEE International Performance, Computing, and Communications Conference (IPCCC)

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The Internet of Things (IoT) has grown at a rapid pace in recent years. It requires a large amount of data and massive computational resources, thus the concept of Fog Computing (FC) has emerged. FC attempts to overcome network latency by bringing computational resources closer to IoT devices. One important part of FC is an offloading m echanism t o make proper decisions for better utilizing of FC node(s), especially for real-time (low latency and high throughput) applications. Generally, offloading p olicies a re c ategorized a s c entralized and distributed. However, by growing numbers of IoT devices which leads to expansion of FC layer beyond the initial configurations, centralized scheduling solutions for time-sensitive tasks suffers from two major challenges: first, i ncreasing c omplexity, and second, non-fault tolerating. In order to address these issues, scalable decentralized/distributed approaches have been developed to schedule tasks through an autonomous collaboration between a small number of nodes (neighbors). Without a thorough picture of the network or nodes' state, it is difficult to design algorithms that make optimum decisions. This paper presents a scalable algorithm for offloading t ime-sensitive t asks t hrough a s emi-network aware distributed scheduling mechanism. Based on the evaluation results obtained for acceptance rate, response time, and network resource usage, the proposed method outperforms the state-of-the-art on average.

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Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading

Cited by 22 publications

References 8 publications

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

D²FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems

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Experimental Characterization of Latency in Distributed IoT Systems with Cloud Fog Offloading

Cited by 22 publications

References 8 publications

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

D2FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems

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Product

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D²FO: Distributed Dynamic Offloading Mechanism for Time-Sensitive Tasks in Fog-Cloud IoT-based Systems