A new Infrastructure as a Service for IoT-Cloud

Hammoudi, Sarra; Aliouat, Zibouda; Harous, Saad

doi:10.1109/iwcmc.2018.8450503

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…TTP splits a TDMA round into time slots (TS). It allocates each server in the communication system to one slot during which it broadcasts its data [8,28]. The TTC/P is very important because it ensures safety-critical, fault-tolerant high-speed networks.…”

Section: System Recovery In Paas-cbirmentioning

confidence: 99%

“…A system works properly even if a server is out of order needs data replication, so it is important that data be organized when stored on distributed servers [8].…”

Section: Introductionmentioning

confidence: 99%

“…It allows the storage and access to data via the Internet rather than an individual PC's resources. Cloud computing provides unlimited storage and processing resources for users [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Platform as a Service (PaaS) Model on Public Cloud for CBIR System

Hadi¹,

Aliouat²,

Hammoudi³

2020

ISI

Self Cite

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Medical image processing requires handling a huge amount of data. Unstructured big data can create some issues related to latency. Distributed architectures based on parallelism can alleviate a latency problem. Also, achieving image availability in case of a server failure in such a huge system increases the latency time. To solve the challenges of latency in image processing in an enormous system, we propose a new platform for information retrieval in databases consisting of digital imaging communication in medicine (DICOM) files. The platform is based on a Decision Tree. The servers in this platform are distributed and work in a parallel way. Also, a fault tolerant system based on time triggered protocol is proposed to ensure image availability and minimize image recovery latency in the case of a server failure. The main goal of this proposal is to select images from DICOM files similar to an image proposed in a query, using the principle of content based image retrieval (CBIR). Also, this platform helps radiologists with the diagnosis of medical images.

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Section: System Recovery In Paas-cbirmentioning

confidence: 99%

“…A system works properly even if a server is out of order needs data replication, so it is important that data be organized when stored on distributed servers [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Platform as a Service (PaaS) Model on Public Cloud for CBIR System

Hadi¹,

Aliouat²,

Hammoudi³

2020

ISI

Self Cite

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“…The IoT-Cloud network uses the Infrastructure as a Service (IAAS) architecture [11]. The IoT-Cloud network remains active 24 × 7 [12].…”

Section: Introductionmentioning

confidence: 99%

Neural-Hill: A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

Achar

2023

IEEE Access

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The age of the Fourth Industrial Revolution (4IR) is the era of smart technologies and services. The Internet of Things (IoT) is at the heart of these smart services. The IoTs are resource-constrain devices. They act as middleware in intelligent systems and maintain communications between cloud servers and smart services. Processing related to intelligent decision-making, including data processing, cleaning, feature extraction, and analysis, is performed on the cloud servers. The IoT devices respond according to the decisions the applications run on the cloud servers make. The massive number of internet-connected devices is increasing by 8% per year. The cloud infrastructure backing these enormous numbers of IoT devices must be scheduled efficiently to maintain Quality of Service (QoS). An optimized scheduling scheme is essential to minimize the cost and enhance scalability. This paper proposes an innovative and novel algorithm, Neural-Hill, which combines the Deep Neural Network (DNN) and Random Restart variant of the Hill Climbing algorithm to schedule IoT-Cloud resources efficiently and ensure scalability. It is a preemptive scheduling algorithm designed to operate in dynamic task scheduling. The performance of the Neural-Hill algorithm has been evaluated in terms of optimal solution-finding time, execution time, routing overhead, and throughput. The experimental results demonstrate the significant quality of service improvement with the assurance of better scalability.

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