Cloud Computing Technologies for Smart Agriculture and Healthcare

Urmila Shrawankar, Latesh Malik, Sandhya Arora, Urmila Shrawankar, Latesh Malik, Sandhya Arora

• Éditeur: Chapman and Hall/CRC
• Année: 2021
• ISBN:
  9781000508918
• Édition: 1
• Sujet: Informatica
• Sous-sujet: Elaborazione di dati su cloud

The Cloud isan advanced and fast-growing technology in the current era. The computing paradigm has changed drastically. It provided a new insight into the computing world with newcharacteristics including on-demand, virtualization, scalability and many more. Utility computing, virtualization and service-oriented architecture (SoA) are the key characteristics of Cloud computing. The Cloud provides distinct IT services over the webon a pay-as-you-go and on-demand basis. Cloud Computing Technologies for Smart Agriculture and Healthcare covers Cloud management and itsframework. It also focuses howthe Cloudcomputing framework can be integrated with applications based on agriculture and healthcare.

Features:

• Contains a systematic overview of the state-of-the-art, basic theories, challenges, implementation, and case studies on Cloudtechnology

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• Discussesof recent research results and future advancement in virtualization technology

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• Focuses on core theories, architectures, and technologies necessary to develop and understand the computing models and its applications

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• Includes a wide range of examples that uses Cloud technology for increasing farm profitability and sustainable production

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• Presents the farming industrywith Cloud technology that allows it toaggregate, analyze, and share data across farms and the world

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• Includes Cloud-based electronic health records with privacy and security features

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• Offers suitable IT solutions to the global issues in the domain of agriculture and health care for society

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This reference book is aimed at undergraduate and post-graduate programs. It will also help research scholars in their research work. This book alsobenefits like scientists, business innovators, entrepreneurs, professionals, and practitioners.

Section III Cloud for Healthcare

10 Cloud Model for Real-Time Healthcare Services

Urmila Shrawankar and Girish Talmale

Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur, India

DOI: 10.1201/9781003203926-10

CONTENTS

1. 10.1 Introduction
2. 10.1.1 Objectives of Research
3. 10.1.2 Organization
4. 10.2 Related Work
5. 10.3 Different Cloud Computing Uses in Real-Time Healthcare Services
6. 10.4 Cloud Computing in Healthcare Applications
7. 10.4.1 Healthcare Data Management, Data Sharing, and Access in the Cloud
8. 10.4.2 Preventive Medical Care Using Cloud Computing
9. 10.5 Issues and Challenges in Using Cloud Computing in Healthcare
10. 10.6 Real-Time Virtual Machine Scheduling Framework of the Cloud Environment
11. 10.6.1 Real-Time Healthcare Sensing and Actuation in the Cloud Environment
12. 10.6.2 Real-Time Patients and Physician Interactions
13. 10.7 Case Study of Different Healthcare Cloud Providers
14. 10.8 Conclusions
15. Acknowledgment
16. References

10.1 Introduction

The real-time applications for healthcare have grown in interest in recent years. The Cloud computing model is used to store and analyze the healthcare data in an efficient and cost-effective way (Sisu et al., 2014). Cloud service providers such as Amazon provide virtual machines on lease for computing. The virtual machine allocation is done using provisioning policies, requirement, time, etc. (Li et al., 2018). The Cloud computing model is extensively used in providing real-time medical services such as a hospital data management system that stores all hospital patients’ data, which is further analyzed to improve healthcare service, as represented in Figure 10.1 (Ning et al., 2019). The emergency healthcare service is provided using a Cloud computing model in case of emergency. The medical advisors database, real-time patient monitoring, and equipment control service are also provided using a Cloud computing model.

Figure 10.1 Real-time Cloud computing service in healthcare.

The healthcare services are heavily dependent upon the data collected through the various healthcare sensor and medical equipment. The storage and computation of these healthcare data is done on Cloud computing due to limited storage and computation power of these devices. These smart healthcare devices range from small devices such as temperature sensors to large medical equipment such as MRI scanners.

The main technology used in implementation of Cloud computing model is virtualization. The Cloud computing model is used to maximize the resource utilization but on other hand it compromise deadline and service quality (Chen et al., 2019). The virtualization creates the various dedicated virtual machines to handle these computing and storage requirement. The hypervisor, which is also called a virtual machine manager, is used to separate this virtual machine from the physical machine (Silva et al., 2020). The healthcare applications are time sensitive and to run this application on the Cloud environment depends upon various parameters like processing nodes, nature of tasks, and deadline of tasks. In this paper we are proposing the cluster-based real-time scheduling techniques for the allocation of virtual machines for time-sensitive healthcare applications such as remote patient monitoring systems, remote surgeries, etc., to ensure the timely execution of various real-time tasks (Mubarakali, 2020). This paper proposed the model of virtual machine allocations to process these smart healthcare tasks within the deadline and achieve high system utilization (Zanjal and Talmale, 2016). The smart healthcare system includes many embedded sensors like ECG sensors, temperature sensors, motion sensors, etc. (Talmale and Urmila, 2020). These smart devices used in healthcare applications generate periodic jobs and the proposed system is used to process these jobs on the Cloud to satisfy heavy computation and resource requirements (Taher et al., 2019). The proposed system allocates virtual machines using cluster-based real-time scheduling to these jobs to ensure the timely executions of these jobs.

10.1.1 Objectives of Research

The main objectives of this research work are as follows:

• To provide the overview of Cloud computing service for real-time healthcare service.
• To discuss the different benefits, challenges, and issues of Cloud computing in healthcare.
• To present the real-time virtual machine scheduling framework for Cloud computing.
• To discuss various case studies for healthcare Cloud providers.

10.1.2 Organization

The chapter organization is as follows: Section 1 gives the details about the background of related work completed. Section 3 presents the system model for real-time scheduling framework on the Cloud environment. Section 4 presents the real-time task allocation and scheduling techniques. Results are described in Section 5.

10.2 Related Work

The real-time computation demand increases due to smart applications such as healthcare. The real-time system used the Cloud environment to address this high computational demand (Ibarz et al., 2020). Running this smart real-time healthcare system on the Cloud environment and the allocation of resources is the main research area in recent years due to the following reasons like service reliability, maximizing utilization, timely response, etc. Apache spark provides real-time computation of large-scale healthcare data. Google Tensor Flow also used real-time scheduling techniques for their GPU architecture (Bhattacharya et al., 2019).

Cloud computing is the best computing platform for efficient computing and storage smart real-time application of healthcare applications (Rizk et al., 2020). The virtual machines must be assigned in an efficient way to real-time healthcare system applications (Mirobi and Arockiam, 2019). The real-time application tasks are executed on the remote Cloud computing platform (Stavrinides and Helen, 2019). The comparison of virtual machine scheduling proposed on Cloud computing platform (Khan et al., 2020). The dynamic distributed virtual machine scheduling techniques are proposed for efficient sharing of resources (Dhule and Shrawankar, 2020). The Ecalyptus is using round-robin scheduling of virtual machines (Zheng et al., 2019). The OpenNebula is used to schedule the virtual machine using rank algorithms for physical machines (Jain et al., 2019).

The real-time scheduling used for the multiprocessing nodes are categories into two main types. In partition-based scheduling, the tasks sets are assigned to dedicate processing nodes and scheduled using existing global scheduling techniques (Han et al., 2018). The advantage of partition-based scheduling approach is the task allocation done in the existing mature uniprocessing scheduling used. The tasks are not...