IoTPredict: Collaborative QoS Prediction in IoT

White, Gary; Palade, Andrei; Cabrera, Christian; Clarke, Siobhán

doi:10.1109/percom.2018.8444598

Cited by 43 publications

(25 citation statements)

References 29 publications

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“…The additional processing power available from the embedded GPUs at the edge compared to traditional IoT gateways (e.g., Raspberry Pis and Intel Galileos) allows the devices to run more complex prediction models for the QoS of services in the environment [74], [25], [39]. These predictions can be used as part of a middleware architecture to compose more reliable services even in dynamic environments.…”

Section: Deep Edgesmentioning

confidence: 99%

“…The SDE uses the backward-planning algorithm to identify the concrete services, which can be used to satisfy the request and sends this list of services to the SCEE. The QoS monitor is used to monitor these services and can forecast when a service is about to degrade in quality [39] and predict possible candidate services to switch to when this degradation happens [74], [25]. The SCEE will use these services to create a response for the request using a stigmergic service composition algorithm [76].…”

Section: Deep Edgesmentioning

confidence: 99%

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Urban Intelligence With Deep Edges

White

Clarke

2020

IEEE Access

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With the increased accuracy available from state of the art deep learning models and new embedded devices at the edge of the network capable of running and updating these models there is potential for urban intelligence at the edge of the network. The physical proximity of these edge devices will allow for intelligent reasoning one hop away from data generation. This will allow a range of modern urban reasoning applications that require reduced latency and jitter such as remote surgery, vehicle collision detection and augmented reality. The traffic flow from IoT devices to the cloud will also be reduced as with the increased accuracy from deep learning models only a subset of the data will need to be reported after a first pass analysis. However, the training time of deep learning models can be long, taking weeks on multiple desktop GPUs for large datasets. In this paper we show how transfer learning can be used to update the last layers of pre-trained models at the edge of the network, dramatically reducing the training time and allowing the model to perform new tasks without data ever having to be sent to the cloud. This will also improve the users' privacy, which is a key requirement for urban intelligence applications with the introduction of GDPR. We compare our approach to alternative IoT urban intelligence architectures such as cloud-based architectures and deep learning algorithms trained only on local data. INDEX TERMS Edge computing, transfer learning, deep learning, urban intelligence, IoT, QoS. I. INTRODUCTION Urban intelligence can provide insight into a number of the major problems being faced by our cities, such as air pollution, increased mobile communication network demand, traffic congestion and water floods, etc., through the use of data from IoT sensors and intelligent analytics on the data. This can allow for the efficient running of a smart city, making the most of the resources available. Problems in smart cities such as increased network demand and crowd congestion are set to get increasingly difficult with the UN predicting 60% of people globally will live in cities with at least half a million inhabitants by 2030 [1]. This will exacerbate the current problems in our cities, so it will be critical to mange these problems with intelligent urban reasoning algorithms and a suitable deployment architecture. IoT devices will enable access to a range of devices and information that was not available to urban authorities before. This additional information from air pollution sensors, CCTV cameras, vehicles and so on will allow the development of more advanced urban reasoning applications [2]. The associate editor coordinating the review of this manuscript and approving it for publication was Hao Ji.

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Section: Deep Edgesmentioning

confidence: 99%

Section: Deep Edgesmentioning

confidence: 99%

Urban Intelligence With Deep Edges

White

Clarke

2020

IEEE Access

Self Cite

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“…The system under scalability study in this research is the IoT/M2M platform To estimate the QoS of IoT/M2M applications, previous research such as [29] proposed a collaborative approach, which consists of several IoT/M2M devices sharing their QoS usage experience with other devices in order to predict the [25]. Moreover, we assume each type of application is associated with a given reliability requirement.…”

Section: Identifying Iot/m2m Applications and Estimating Its Qosmentioning

confidence: 99%

“…In [45] the authors fed their proposed algorithm with real-time and archived data of IoT applications for QoS monitoring and estimation purposes. In [29], the authors utilized machine learning algorithms fed with pre-provisioned datasets to conduct QoS monitoring of IoT applications. Despite the good results of these two methods, they could not handle newly appeared applications.…”

Section: Monitoring Qos Changes Of Heterogeneous and Bursty Iot/m2m Amentioning

confidence: 99%

Enhancing Cloud-Based IoT/M2M System Scalability by Dynamic Network Slicing

Bastida¹,

Lin²

2020

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With ever-increasing applications of IoT, and due to the heterogeneous and bursty nature of these applications, scalability has become an important research issue in building cloud-based IoT/M2M systems. This research proposes a dynamic SDN-based network slicing mechanism to tackle the scalability problems caused by such heterogeneity and fluctuation of IoT application requirements. The proposed method can automatically create a network slice on-the-fly for each new type of IoT application and adjust the QoS characteristics of the slice dynamically according to the changing requirements of an IoT application. Validated with extensive experiments, the proposed mechanism demonstrates better platform scalability when compared to a static slicing system.

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“…Next generation technologies such as self-driving cars, smart cities or augmented reality services require new approaches to deal with the network traffic generated by the IoT devices deployed to enable such technologies [1]- [5]. In this context, edge computing has emerged as a promising solution to satisfy the Quality of Service (QoS) requirements of such applications by pushing the data processing horizon towards the edge of the network, and by relieving devices from computationally-intensive tasks, to reduce energy consumption [6]- [9]. Serverless computing, a well-known cloud computing paradigm also sometimes referred to as Function-as-a-Service (FaaS) has eliminated the need for always-on infrastructure through ephemeral containers [10].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Open Source Serverless Computing Frameworks Support at the Edge

Palade

Kazmi

Clarke

2019

2019 IEEE World Congress on Services (SERVICES)

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The proliferation of Internet of Things (IoT) and the success of resource-rich cloud services have pushed the data processing horizon towards the edge of the network. This has the potential to address bandwidth costs, and latency, availability and data privacy concerns. Serverless computing, a cloud computing model for stateless and event-driven applications, promises to further improve Quality of Service (QoS) by eliminating the burden of always-on infrastructure through ephemeral containers. Open source serverless frameworks have been introduced to avoid the vendor lock-in and computation restrictions of public cloud platforms and to bring the power of serverless computing to onpremises deployments. In an IoT environment, these frameworks can leverage the computational capabilities of devices in the local network to further improve QoS of applications delivered to the user. However, these frameworks have not been evaluated in a resource-constrained, edge computing environment. In this work we evaluate four open source serverless frameworks, namely, Kubeless, Apache OpenWhisk, OpenFaaS, Knative. Each framework is installed on a bare-metal, single master, Kubernetes cluster. We use the JMeter framework to evaluate the response time, throughput and success rate of functions deployed using these frameworks under different workloads. The evaluation results are presented and open research opportunities are discussed.

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IoTPredict: Collaborative QoS Prediction in IoT

Cited by 43 publications

References 29 publications

Urban Intelligence With Deep Edges

Urban Intelligence With Deep Edges

Enhancing Cloud-Based IoT/M2M System Scalability by Dynamic Network Slicing

An Evaluation of Open Source Serverless Computing Frameworks Support at the Edge

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