System modelling and performance evaluation of a three-tier Cloud of Things

Wei, Li; Santos, Igor Leão dos; Delicato, Flávia C.; Pires, Paulo F.; Pìrmez, Luci; Wei, Wei; Song, Houbing; Zomaya, Albert Y.; Khan, Samee U.

doi:10.1016/j.future.2016.06.019

Cited by 114 publications

(42 citation statements)

References 30 publications

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“…It introduces the facility of local processing leading to reduction in data size, lower latency, high throughput and power efficiency of the cloudbased systems. Fog computing technology has been successfully implemented in smart cities [31] and healthcare [23,22,42,49,50,51]. Fog devices are embedded computers such as Intel Edison or Raspberry Pi that acts a gateway between cloud and mobile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 devices.…”

Section: Fog Computingmentioning

confidence: 99%

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Barik

Dubey

Mankodiya

et al. 2018

J Ambient Intell Human Comput

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Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing lead to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded devices are employed to increase the throughput and reduce latency at the edge of network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built a prototype using both Intel Edison and Raspberry Pi for performing a comparative study. We performed a case study on Malaria vector borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression. Also, overlay analysis of geospatial data could be performed. In addition, we discussed energy saving, cost analysis and scalability of proposed framework for efficient data processing. We compared the performance of proposed framework with state of the art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks.

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Section: Fog Computingmentioning

confidence: 99%

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Barik

Dubey

Mankodiya

et al. 2018

J Ambient Intell Human Comput

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“…In addition, a mobile device is under a lot of pressure when connecting to a remote server due to its limited energy source. To reduce the communication cost between the user and a remote data center, the authors in [15] propose a three-tier cloud system, in which the resource provision can be extended to the edge of the network. The technology of mobile edge computing can be used to reduce transmission latency [16].…”

Section: Related Workmentioning

confidence: 99%

Crowd-Funding: A New Resource Cooperation Mode for Mobile Cloud Computing

et al. 2016

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Mobile cloud computing, which integrates the cloud computing techniques into the mobile environment, is regarded as one of the enabler technologies for 5G mobile wireless networks. There are many sporadic spare resources distributed within various devices in the networks, which can be used to support mobile cloud applications. However, these devices, with only a few spare resources, cannot support some resource-intensive mobile applications alone. If some of them cooperate with each other and share their resources, then they can support many applications. In this paper, we propose a resource cooperative provision mode referred to as "Crowd-funding", which is designed to aggregate the distributed devices together as the resource provider of mobile applications. Moreover, to facilitate high-efficiency resource management via dynamic resource allocation, different resource providers should be selected to form a stable resource coalition for different requirements. Thus, considering different requirements, we propose two different resource aggregation models for coalition formation. Finally, we may allocate the revenues based on their attributions according to the concept of the "Shapley value" to enable a more impartial revenue share among the cooperators. It is shown that a dynamic and flexible resource-management method can be developed based on the proposed Crowd-funding model, relying on the spare resources in the network.

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“…All these features make containers extremely attractive for usage not only in datacentres, but also in edge nodes, such as cloudlets and IoT gateways . Nevertheless, enhanced orchestration solutions are required to fully exploit the potential of containerized services for IoT devices at the edge of the network …”

Section: Introductionmentioning

confidence: 99%

“…15,16 Nevertheless, enhanced orchestration solutions are required to fully exploit the potential of containerized services for IoT devices at the edge of the network. [17][18][19] In this paper, we investigate a novel approach to better support user mobility for stateless microservices. Indeed, to achieve better scalability and resiliency, several cloud-based applications are being redesigned to be stateless, 20 by saving application state and context data into dedicated storage.…”

Section: Introductionmentioning

confidence: 99%

Providing ultra‐short latency to user‐centric 5G applications at the mobile network edge

Farris

Taleb

Flinck

2017

Trans Emerging Tel Tech

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Mobile Edge Computing (MEC) will play a key role in next-generation mobile networks to extend the range of supported delay-sensitive applications. Furthermore, an increasing attention is paid to provide user-centric services, to better address the strict requirements of novel immersive applications. In this scenario, MEC solutions need to efficiently cope with user mobility, which requires fast relocation of service instances to guarantee the desired Quality of Experience. However, service migration is still an open issue, especially for resource-constrained edge nodes interconnected by high-latency and low-bandwidth links. In this paper, by leveraging the potential of lightweight container-based virtualization techniques, we investigate a novel approach to support service provisioning in dynamic MEC environments. In particular, we present a framework where proactive service replication for stateless applications is exploited to drastically reduce the time of service migration between different cloudlets and to meet the latency requirements. The performance evaluation shows promising results of our approach with respect to classic reactive service migration. INTRODUCTIONSeveral classes of new applications are challenging the current network and cloud infrastructures. As illustrated in a recent report from International Telecommunication Union, 1 applications such as tactile internet, mobile gaming, and augmented reality are pushing new requirements, especially in terms of latency. To guarantee the desired Quality of Experience (QoE) for the end user, not only the network access delay, but also the cloud service processing should be performed within a few milliseconds. To achieve the 1 ms latency dream for the next-generation 5G cloud-enabled services, both network and cloud providers should jointly cooperate and investigate novel approaches. In this regard, academic and industrial research communities have focused on Mobile Edge Computing (MEC) solutions to exploit processing and storage capabilities at the edge of the network, as near as possible to the end user. 2,3 Indeed, the deployment of micro datacentres in the network access points, known as cloudlets, 4,5 can guarantee remarkable benefits of low latency interaction and scalability, by balancing the workload over the distributed edge infrastructure. Furthermore, bringing the cloudlet concept into the Internet of Things (IoT) scenario results in the definition of the so-called Fog Computing paradigm, which envisages a highly virtualized infrastructure composed of distributed edge nodes to monitor and analyse the most time-sensitive data generated by network connected devices. [6][7][8] In a MEC environment, a key challenge is represented by the user mobility, which could cause significant application degradation, even in small scenarios, as reported in a previous study.9,10 To guarantee service continuity and to meet the strict requirements of application latency, the MEC framework needs to properly cope with service migration between edge nodes. I...

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System modelling and performance evaluation of a three-tier Cloud of Things

Cited by 114 publications

References 30 publications

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Crowd-Funding: A New Resource Cooperation Mode for Mobile Cloud Computing

Providing ultra‐short latency to user‐centric 5G applications at the mobile network edge

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