Developing, Provisioning and Controlling Time Critical Applications in Cloud

Zhao, Zhiming; Martin, Paul; Jones, Andrew C.; Taylor, Ian; Stankovski, Vlado; Salado, Guadalupe Flores; Suciu, George; Ulisses, Alexandre; Laat, Cees de

doi:10.1007/978-3-319-79090-9_14

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…In particular, since [24] depends on the ARTIK cloud as its cloud backend, the aforementioned problems and limitations of ARTIK cloud interface still exist. On the other hand, Zhao et al [25] and Heikkinen et al Ref. [26] proposed methodologies and architectural frameworks to build and support time-critical applications in a cloud environment.…”

Section: J Sens Actuator Netw 2019 8 12 4 Of 21mentioning

confidence: 99%

VisKit: Web-Based Interactive IoT Management with Deep Visual Object Detection

Hwang

Lee

Jeong

2019

JSAN

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Various technologies and standards for the Internet of Things (IoT) have defined the way devices should interact with each other to provide an intelligent IoT service to users in an efficient manner. Although the usability of system interface between the platform and users is one of the key factors for the success of IoT ecosystems, the manner in which IoT platforms should interact with users has not been well studied. Current IoT platforms provide a simple list-based interface to manage devices, which result in the degradation of their usability as the number of devices increases. In this paper, we propose an interactive web-based IoT management system where deep object detection techniques are adopted to facilitate user’s device selection and management. The proposed system automatically recognizes the device type from video streams and accordingly generates smart controllers. The users can choose a device by touching an object in the video stream and use a smart controller to control the selected device. Finally, we show the feasibility of the proposed system through the implementation of a prototype which demonstrates a set of user scenarios.

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Section: J Sens Actuator Netw 2019 8 12 4 Of 21mentioning

confidence: 99%

VisKit: Web-Based Interactive IoT Management with Deep Visual Object Detection

Hwang

Lee

Jeong

2019

JSAN

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“…When the load request increased rapidly, the scheduling efficiency decreased instantly and the load was easily imbalanced. In cloud computing, Zhao et al [19] used the Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (SWITCH) project to address the urgent industrial need for developing and executing time critical applications. Mobile edge computing (MEC) [20] was to push mobile computing, network control, and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

Tao

Zheng

et al. 2018

Information

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To address the issue of cloud mixed workloads scheduling which might lead to system load imbalance and efficiency degradation in cloud computing, a novel cloud task staggering peak scheduling policy based on the task types and the resource load status is proposed. First, based on different task characteristics, the task sequences submitted by the user are divided into queues of different types by the fuzzy clustering algorithm. Second, the Performance Counters (PMC) mechanism is introduced to dynamically monitor the load status of resource nodes and respectively sort the resources by the metrics of Central Processing Unit (CPU), memory, and input/output (I/O) load size, so as to reduce the candidate resources. Finally, the task sequences of specific type are scheduled for the corresponding light loaded resources, and the resources usage peak is staggered to achieve load balancing. The experimental results show that the proposed policy can balance loads and improve the system efficiency effectively and reduce the resource usage cost when the system is in the presence of mixed workloads.

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“…To cope with the ever-increasing wireless traffic demands induced by IoT, a hierarchical approach to network deployment with a densely populated femtocell base station has been proposed. Applications, such as cloudified mobile networks [1], virtual infrastructure [2], and industrial communication, also exploit heterogeneous architecture to support ubiquitous, flexible, and reliable connectivity [3]. Such proliferation of wireless base stations and data offloading strategies are to be expected in the near future, presumably yielding the two-tier approach to network design [4].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

Tian

Wang

et al. 2018

Sensors

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This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.

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Developing, Provisioning and Controlling Time Critical Applications in Cloud

Cited by 7 publications

References 9 publications

VisKit: Web-Based Interactive IoT Management with Deep Visual Object Detection

VisKit: Web-Based Interactive IoT Management with Deep Visual Object Detection

Task Staggering Peak Scheduling Policy for Cloud Mixed Workloads

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

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