case study on software as a service (saas) based emergency healthcare in india

European Journal of Scientific Research ISSN 1450-216X Vol.69 No.3 (2012), pp. 461-472 © EuroJournals Publishing, Inc. 2012 http://www.europeanjournalofscientificresearch.com

Case Study on Software as a Service (SaaS) Based Emergency

Healthcare in India

N. Karthikeyan Research Scholar, Anna University of Technology, Tirunelveli

Tamilnadu, India E-mail: [email protected]

Tel: +91-09943546569

R. Sukanesh Professor, Department of ECE, Thiagarajar College of Engineering

Madurai, Tamilnadu, India E-mail: [email protected]

Abstract

A report from a health care accreditation group says that miscommunication between patients and health care providers are the root cause for the lacuna in providing urgent medical attention to the people in need. Illiteracy is the key root in developing countries deaths resulted from uncertain diseases constitute a serious public health problem. If the mentioned above type of patients carry smart card enabled patient records, they need not express their patient history to the medical practitioners. Likewise, doctors also can’t view or edit the modern DICOM images instantly. In this study, we intend to indicate the features of sharing the patient data through smart cards using cloud computing in hospitals and how cloud computing can rule the health care industry. In this internet era, communication is global. Doctors can open, edit and share the DICOM images by using our cloud based software tool. The cost of investing and hardware and software has been a big deterrent towards automation. In Cloud Computing, IT-related capabilities and resources are provided as services, via the distributed computing on-demand. Our cloud computing based healthcare model believes that holds tremendous potential to transform the Indian healthcare industry. Keywords: SaaS, Cloud computing, Distributed Computing, Modern health care

1. Introduction In developed countries, high speed broad band networks, increasing of computing power and growth of internet have changed the path of information services. Inter connected Data resources, Storage devices and super computers can be broken by the users around the world as a mono unified resource. In the view of growing, repetitive or resource-intensive information technology tasks can be outsourced to service providers, which execute the task and often provide the results at a minimum cost. That means, a new model is emerging in which computing is obtainable as a utility by third parties whereby the user is payable only for consumption. This service-oriented approach (SOA) offering a large computing service can be scalable and flexible.

Case Study on Software as a Service (SaaS) Based Emergency Healthcare in India 462

Distributed computing is coming in the new forms as Grid and Cloud computing. These new forms promised to deliver Information Technology (IT) as a service. In this paper, we described how these new forms of distributed computing will help for modern health care services.

According to the report of National Sample Survey Organization (NSSO) in India, nearly 65 per cent of poor indians get into debt and one percent fall below the poverty line each year because of illness. Free public hospitals are not an option for the poor people. Because, two out of five doctors are absent, and there is 50 percent chance of receiving the wrong treatment [1].

In our country, human life can be affected by heart attack, kidney failure, road accidents and etc. Heart disease is the single largest cause of death in the country with heart attacks being responsible for one third of all deaths caused by heart diseases. The actual diagnosis of a heart attack must be made by a doctor after referring to the patient’s symptoms.

The doctor must: Review the Patients complete medical history. Give a Physical examination Use an electrocardiogram (ECG) to discover any abnormalities caused by damaged to the heart. Use a blood test to detect abnormal levels of certain enzymes in the blood stream. Consider the second case; we believe precious lives can be saved in road accidents, if police

and community volunteers provide urgent care to victims while rushing them to hospital. But, in most Indian cities including Delhi, the capital that has 35 Emergency Medical Service (EMS) ambulances and Children’s Acute Transport Services (CATS) for a population of 12 million. Most of the ambulance attendees may not know about the injured patient history.

In the above two cases, If the medical practitioners or the ambulance attendees knew the patient history in advance, they can give treatment immediately and can save the life of the patient. Patients can’t express their medical history at that moment due to the following reasons

Illiteracy Vernacular moment Unconscious Differently abled patients Mentally disordered Not only in the above two cases, reviewing of patient history is an essential one. Memorizing

the patient history and maintaining the medical records becomes very difficult by hospitals. Nowadays a new trend Paperless health care is being introduced and followed by the modern health care industry.

Doctor is a health care provider who is concerned with promoting, maintaining or restoring human health through the study, diagnosis, and treatment of disease, injury and other physical and mental impairments. Today various software tools are used by the doctors for medical diagnosis. Some of the softwares are used for viewing the medical images and some are used for processing the medical images. An important issue in medical imaging is the ability to access the processed data quickly and easily. Web based solutions are given by software developers [2]. Healthcare applications, services, and data once in the realm of a local computer or local server safely secure in your building are now in the domain of the public Internet is called as “Medical Cloud”.

In this paper, we introduce a SaaS[Software as a Service] based healthcare system for patients and doctors. We have structured the rest of the paper is as follows. In Section 2, we describe previous research issues; Section 3 describes our SaaS architecture; Section 4 describes User-interface of the service. Finally, Section 5 describes discussion and conclusion. 2. Previous Research Computer systems that consist of millions of clients or nodes in the whole Internet, change always, and are managed by many different people and organizations. A variety of research groups started concentrating on computer systems that qualify as self-managing, self-organizing, self-healing, and so forth. They create software and tools to enable scientists to address complex and large-scale computing

463 N. Karthikeyan and R. Sukanesh

and data analysis problems. Data intensive computing has become an important characteristic of modern large scale scientific, health care and engineering applications. But, It is very difficult to undertake the management of resources and scheduling the applications in large scale distributed systems.

Rajkumar Buyya and Manzur Murshed (2002) have developed a Java-based discrete-event Grid simulation toolkit called GridSim[15]. This toolkit enables to model and simulate the heterogeneous of Grid resources. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management.

Grid computing Middlewares written for Unix operating system limits the effective utilization of computing for Windows based desktop computers. Akshay Luther, Rajkumar Buyya, Rajiv Ranjan, and Srikumar Venugopal (2005) have introduced Microsoft’s .NET based distributed system called Alchemi [16]. It provides programming environment to construct enterprise/desktop grids and to develop grid applications. It also supports cross platform services.

In Life sciences, Mathematical modeling has been used to analyze the huge volume of medical data. A large number of mathematical modeling becomes complex due to processing time, incompatible formats and etc. A grid computing based mathematical model will solve the complexities.

DICOM Grid [4] is a nationwide healthcare IT leader that delivers a cost-effective Internet-based solution for digital medical imaging that allows medical facilities and physicians to easily and securely archive, search, exchange, share and act on critical medical imaging data.

Xingchen Chu, Andrew Lonie, Peter Harris, S.Randall Thomas, and Rajkumar Buyya (2006) have developed an interactive web based portal for renal physiology. By using this portal, medical practitioners can create, schedule, monitor and visualize the kidney models [17].

N.Karthikeyan and R.Sukanesh (2010) implements a Medical grid [18] using Alchemi. This grid provides a platform to find the edges and segmenting the high volume medical data in lesser time interval. The results indicate how medical images are segmented in minimum time.

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as “services” to the users under a usage-based payment model. Usage can be evaluated by giving SLA (Service Level Agreement) [19] between the Service provider and Customer. Evaluating Cloud computing based application model in heterogeneous environment is very difficult. To overcome this challenge, Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, and Cesar A. F. De Rose introduce a tool called Cloudsim [20]. 3. Hypotheses To create an emergency health care mechanism for the patients and distributing image processing tools for medical practitioners. we test the following hypotheses:

H1: Comparison between Web service model and Cloud service model based on process time. H2: Comparison between Web service model and Cloud service model based on resource

utilization. H3: Comparison between Web service model and Cloud service model based on memory

utilization. 4. Research Method Cloud computing provides the services referred as SaaS(Software as a Service), IaaS(Infrastructure as a Service) and PaaS(Platform as a Service) .

Software as a Service is described as follows: “Rather than buying a Software license for an application such as ERP or Customer Relationship Management and installing this software on individual machines, a business signs up to use the application hosted by the company that develops and sells the software, giving the buyer more flexibility to switch vendors and perhaps fewer headaches in maintaining the software [7].”


Our proposed healthcare model is based on SaaS. Our Healthcare SaaS provides 2 Services. 1. Smart Card Service for illiterate peoples 2. Image processing tool delivery for doctors.

Health care organizations worldwide are implementing smart health cards supporting a wide variety of features and applications. It can improve the security and privacy of patient information, provide the secure carrier for portable medical records, reduce healthcare fraud, support new processes for portable medical records, provide secure access to emergency medical information, enable compliance with government initiatives and mandates, and provide the platform to implement other applications as needed by the healthcare organization. When we use smart cards for single sign on process no problem will occur. But, for updating and transacting the huge volume medical data needs a new infrastructure called Cloud computing.

In India, some major players in the field are looking to build their own networks. Theoretically, the definition of the cloud is to cover availability anywhere within a particular organization’s network. Initially, private cloud is more likely to be adopted compared with public cloud where the hospital has to share the data externally. The concept needs to be developed further in countries outside the US [6]. So, we have decided to take steps to implement private cloud. 4.1. Case Studies

Tamilnadu is one of the 28 states of India. Its capital and largest city is Chennai. It lies in the southernmost part of the Indian Peninsula and is bordered by the union territory of Puducherry, and the states of Kerala, Karnataka, and Andhra Pradesh. It is bound by the Eastern Ghats in the north, the Nilgiri, the Anamalai Hills, and Palakkad on the west, by the Bay of Bengal in the east, the Gulf of Mannar, the Palk Strait in the south east, and by the Indian Ocean in the south.

Ramanathapuram District is an administrative district of Tamil Nadu state in southern India. The city of Ramanathapuram is the district headquarters. Rameshwaram is an island situated in the gulf of manner at the very tip of the Indian peninsula which is a very important pilgrim centre of the Indians. Fisheries play a vital role in this districts economy. Technology involved in various aspects (Television & Mobile etc.,) in the life style of fishermen. It enables the fishermen to access online weather forecast, height of the wave and location of the fish. Technology development also introduced healthcare systems for the beneficial of human. According to Census report taken on 2001, in tamilnadu 4,184,273 persons are literate without educational level [5]. Illiteracy is one of the basic reason behind unaware of modern health care.

In Ramanathapuram, Syed Ammal Hospital was established in the year 1957. It holds more than 100 beds and it serves more than 300 outpatients per day. A.R. Hospital was established in the year 2006. It holds 50 beds and it serves 50 out patients per day. We have started by providing our services to the mentioned above hospitals as a test case.

Our work is divided into 3 layers. 1. Base layer 2. Distribution layer 3. User interface layer. Fig.1 shows the layered architecture of the proposed work. Depending on the service request send by the user and role based authority healthcare services would be provided to the hospitals. Base layer is fully built by hardware. It is nothing but a data centre and nodes. For our proposed work minimal configuration is enough to implement. But, when it is extended high configured servers and advanced data centres are needed. Distribution layer is fully equipped with software. Java and AJAX technologies are used in this layer.


Figure 1: Layered Architecture of proposed system

4.1.1. Smart Card Service for Illiterate peoples Worldwide smart cards are introduced to carry the information anywhere. France government provides two cards for their civilians.1.Vitale Card 2. Health professional Card. Vitale cards are issued to store the patient’s insurance details [6] and Health professional cards are issued for health care providers. In health care, Smart cards play a vital role in insurance only. Our aim of the work is “SAY NO TO FAMILY DOCTORS”. Consider, when a patient is affected by heart attack, assume the distance between the patient’s home and hospital is 7 KM. Patient has to travel at least 10 mins to get medical aid. These 10 mins delay will cause irreparable and irreversible damage to the patient. To avoid these situations we have proposed a novel idea. Based on our idea, A Medical camp has been hosted in Rameswaram island and Ramanathapuram city. Doctors diagnose the patients and patient history data are recorded in smart cards. Patient history includes storing the data related to Personal details, Smoking & drinking habits, Tobacco & alcohol consumption, slumber details and family history. While entering the history of the patient, the patient has to give 3 mobile numbers. Whenever the smart card is shown, an SMS will be sent to the mentioned mobile numbers. SMS consists of Patient Name and Address of the Hospital name. Patient relatives can identify the patient through this SMS. In Critical situations, Doctors can retrieve the history of the patient through smart cards and they can give immediate treatment. 4.1.2. Image processing Tools delivery for Doctors Image processing plays a vital role in medical informatics. Recent days CT, MRI and other medical modalities occupies the world of medical informatics. National Electrical Manufacturer Association [NEMA] introduced DICOM (Digital Imaging and communication for Medical) for viewing and distribution of medical images. DICOM is an image file format used for storing the medical data. In internet, many third party software tools and open source tools are available for viewing the DICOM images. Medical practitioners can install the software in their own computer and they can view the DICOM images. Suppose if they are not available in front of their own computer they can’t view the DICOM images. To give a solution for this problem we are going to distribute DICOM viewer. By using this cloud based DICOM viewer, Doctors can view the images anywhere at any time. 2D to 3D converter is used to convert a 2D image to 3D image. Existing Segmentation algorithms have been introduced to segment a medical image and doctors can mark the special characters for the segmented image. Basic edge detection methods are introduced to find the edges of the medical image. So, medical practitioners can apply these tools to the medical images and they can share the images with other medical practitioners to get opinion about critical cases.


4.1.3. Programs and Methods For cloud computing to take off, there need to be tools available that enable a developer to build and deploy an application without having to download anything to their desktop. This requires an on-demand development tool that sits on top of the cloud and provides a development Platform as a Service (PaaS) [7, 9, 11]. Virtualization [14] is an essential catalyst for cloud computing. To deliver highly available and flexible services (i.e., computation as a service), and owing to the maturity of virtualization technology, Virtual Machines (VMs) are used as a standard for object deployment in the cloud. As the virtualization leader, VMware [8] builds on this solid foundation with platforms and solutions to power the cloud infrastructure, build and run robust cloud applications, and supply end-user computing as a cloud-based service.

In our work, Wampserver has been installed in Vmware for database applications. WampServer is a Windows web development environment. It allows the user to create web applications with Apache, PHP and the MySQL database. It also comes with PHP MyAdmin to easily manage the databases. Now cloud environment is ready to store our distributed applications. In our proposed work, patient details, Smart card reader SDK, image processing tools and reports will be stored in CLOUD. Patient medical records are created and stored in MySQL. Now the patient medical database is ready for CLOUD. Apache Tomcat is an open source software implementation of the Java Servlet and Java Server Pages technologies. It is installed to support Java agents.

Medical records are stored in database. A Key value will be stored in smart cards; this value will be act as criteria to search the records in the database. ACR38 smart card reader [10] is used to read and write the patient medical data in smart cards. ACR38 Series offers a plug-and-play solution that eliminates the need for driver installation and driver-system compatibility assessment. Its USB interface facilitates communication between a computer and a smart card very easily, allowing for a seamless implementation of smart-card based applications in a PC environment. The ACR38 Smart Card Reader is compliant with ISO-7816 and EMV Level 1 specifications. It supports ISO-7816 Class A, B and C (5V, 3V and 1.8V) cards, including all microprocessor cards with T=0, T=1, protocols, as well as most of the popular memory cards in the market. Java Software development kit is a powerful tool which is used to write hardware interface programs. Here we have created a user interface panel for ACR38 Smart card reader. This software panel is stored in VMware. It will not be installed in client machines. The software panel will be distributed to the users by a single click.

Our portal has 4 modules. They are • Admin login • Doctor Login • Doctor Registration • Patient Registration

These modules are providing the rights to the users to access the modules. For example patients can view the history of the patient. But, he or she can’t edit the history of their record. Using Doctor’s registration form module, doctors can update the relevant information include registration number and specialization. If all necessary data are filled, the data will be forwarded to administrator module. Administrator has to give rights to doctors to access the modules. Using their user name and password the doctors can login to the module and they can read or write the patient records.

Patient registration form is open to all the civilians of Ramanathapuram. The patient registration database consists of more than 40 fields. Database has been installed in VMware. So, database will be distributed to the users. A special feature SMS service is introduced in this module to save the patient life.

So far we have discussed about how the patient history is retrieved from cloud. Apart from this service a new service has been introduced. This service helps the users to access the smart card instantly. There is no need of configuring the reader to access the cards. A single click will retrieve a panel from the cloud. The panel consists of 3 buttons viz 1. Initialize 2. Connect 3. Cloud retrieval. The user has to select the reader type from the panel and has to click initialize button. When this button is clicked by the user, reader program will be executed from cloud. That process is shown in figure 2.


Connect button is used to check whether the smart card is place in the reader or not. When Cloud retrieval button is activated by the user the system will connect with cloud. Basically two operations READ and WRITE are performed to read and write the data to the smart cards respectively. Pass button displays the contents of the smart card in the web based form. Implementation steps are shown in the figures 2, 3 and 4.

Figure 2: Smart card reader software retrieval process Step 1

Figure 3: Smart card reader software retrieval process Step 2


Figure 4: Smart card reader software retrieval process Step 3 and retrieved patient data

Image J [13] is a open source tool which is used as image processing tool. It is written in Java, which allows it to run on Linux, Mac OS X and Windows, in both 32-bit and 64-bit modes. Fig.5 shows the implementation of “Image J” tool in cloud. This tool will be very useful when the specialist is not available in front of the patient. Doctors can upload the Medical image to cloud and the specialist can open the Medical image by using Image J tool and the specialist can give the opinion about the disease. Fig.6 shows the implementation of uploading the medical image to cloud.

Figure 5: Implementation of Image J tool in cloud


Figure 6: Medical image uploader

4.2. Results of Hypotheses Testing

4.2.1. Comparison between Web service model and Cloud service model based on process time. (H1) CPU Time taken to consume the execution process is called as process time. Traditional web applications are based on web services. Web Services can convert your application into a Web-application, which can publish its function or message to the rest of the world. Although the simplicity of Web services is an advantage in some respects, it can also be a hindrance. Web services use plain text protocols that use a fairly verbose method to identify data. HTTP and HTTPS (the core Web protocols) are simple, they weren't really meant for long-term sessions. Typically, a browser makes an HTTP connection, requests a Web page and maybe some images, and then disconnects. In a typical CORBA or RMI environment, a client connects to the server and might stay connected for an extended period of time. The server may periodically send data back to the client. This kind of interaction is difficult with Web services, and you need to do a little extra work to make up for what HTTP doesn't do for you. It makes processing time high. Cloud computing environment maximum reduces the processing time and resource utilization for achieve effective computing for high volume medical data.

The first hypothesis is stated based on this processing time for Web and Cloud services model. Figure 7 shows the comparison of processing time for Web and Cloud service model. Table 1: Results summary of H1

Events Process Time No of Events Cloud Model Web service Model

50 0.156 0.365 100 0.272 0.527 150 0.395 0.742 200 0.499 0.917 250 0.606 1.078


Figure 7: Comparison of processing time for Web and Cloud service model

4.2.2. Comparison between Web Service Model and Cloud Service Model Based on Resource Utilization and Memory Utilization (H2) The second hypothesis is stated based on resource utilization and memory utilization. Utilization is the percentage of time that a component is actually engaged, as compared with the total time that the component is available for utilize. Database server tools and operating system tools are used to measure the resource utilization. Whenever a system resource, such as a CPU or a particular disk, is occupied by a transaction or query, it is unavailable for processing other requests. Pending requests must wait for the resources to become available before they can complete. When a component is too busy to keep up with all its requests, the frequently used component becomes a bottleneck in the flow of activity. Various third party utilities are available in the market to monitor and analyze the performance of the resources. Table 2: Results summary of H2

Events Resource Utilized in % Cloud Model Web Service Model

50 17 29 100 17.9 35 150 18.3 43 200 19.1 49 250 19.7 53

No of Cloud Source Utilized Memory in %

1 16 2 12 3 10.5 4 9.7 5 8.3


Figure 8: Comparison between Web service model and Cloud service model based on resource utilization and memory utilization

4.3. Scope of the Research

In this research, we investigate the processing time, memory utilization and resource utilization for web and cloud based health care model. Initially, we have studied Alchemi tool to implement a medical grid for medical practitioners. Recently, we have switched over from grid computing to cloud computing to provide emergency health care for the patients. At the time of implementation we have faced a lot of hurdles. Initial investment cost is very high to implement the cloud computing in health care services [3]. Creating a Virtualization segment was a very big challenge for us. We have studied VM ware and implemented the challenged one. 4.4. Sample

In this research, the data are real. 5. Summary and Concluding Remarks The Results of the study shows that SaaS based solution for sharing the patient history and distribution of Image processing tools for medical practitioners. It has been achieved through using Java, AJAX[11] and Virtualization tools. Here we have analyzed the processing time and percentage of resource utilization taken for events performed by the users.

This work has been implemented in two hospitals. If government hospitals utilize the cloud computing, sure we can save the people from uncertainty attacks and also can achieve effective and efficient computing in health care. Image cache management for multiple queries is a major issue of our system. We propose further study of the above issue in another research with the same methodology applied in this research. If our software is implemented in Mobile phones, it will be useful in emergency situations for the patients and medical practitioners.

Collectively, our results provide Software as a service for human beings in emergency situations and distributed image processing tool for medical practitioners. And also we have compared web services and cloud computing services based on processing time and resource utilization. References [1] http://southasia.oneworld.net/ictsfordevelopment/health-insurance-through-smart-cards-for-

india2019s-poor. [2] Seyyed Ehsan Mahmoudi, Alireza Akhondi-Asl, Roohollah Rahmani, Shahrooz Faghih-Roohi,

Vahid Taimouri, Ahmad Sabouri,Hamid Soltanian-Zadeh “Web-based interactive 2D/3D medical image processing and visualization software” Computer methods and programs in biomedicine 2010.


[3] http://www.expresscomputeronline.com/20100208/expressintelligententerprise17.shtml [4] Michal Vossberg, Thomas Tolxdorff, Associate Member, IEEE, and Dagmar Krefting “DICOM

Image Communication in Globus-Based Medical Grids”, IEEE Transaction on Information Technology in Bio-Medicine, Vol. 12, No. 2, March 2008.

[5] Online cited http://www.censusindia.gov.in/Census_Data_2001/Census_data_finder/C_Series/Literates_and_educational_level.htm

[6] Online cited http://www.smartcardalliance.org/resources/lib/Smart_Card_Healthcare_Applications_FINAL pdf.

[7] Online cited http://ai.kaist.ac.kr/~jkim/cs489-2007/Resources/DeliveringSWasaService.pdf [8] Online cited http://www.vmware.com/solutions/cloud-computing/index.html. [9] Online cited

http://delivery.acm.org/10.1145/1730000/1721672/p50armbrust.pdf?key1=1721672&key2=323 9788921&coll=DL&dl=ACM&ip=124.124.59.60&CFID=10631970&CFTOKEN=80488648

[10] Online cited http://www.acr38u.com/ [11] Online cited http://cloudcomputing.sys-con.com/node/650408 [12] http://spywarefiles.prevx.com/RRFHJD30986535/ACOS3%20SAMPLE%20CODE.EXE.html [13] Online cited http://rsbweb.nih.gov/ij/ [14] Online cited http://www-didc.lbl.gov/papers/HPCN99.pdf [15] Rajkumar Buyya, Manzur Murshed “GridSim: a toolkit for the modeling and simulation of

distributed resource management and scheduling for Grid computing”, Concurrency and Computation: Practice and Experience (CCPE), Volume 14, Issue 13-15, Pages: 1175-1220, ISSN: 1532-0626, Wiley Press, New York, USA, November - December 2002.

[16] Akshay Luther, Rajkumar Buyya, Rajiv Ranjan, and Srikumar Venugopal, “Alchemi: A .NET-Based Enterprise Grid Computing System”, Proceedings of the 6th International Conference on Internet Computing (ICOMP'05), June 27-30, 2005, Las Vegas, USA.

[17] Xingchen Chu, Andrew Lonie, Peter Harris, S.Randall Thomas, and Rajkumar Buyya, “KidneyGrid: A Grid Platform for Integration of Distributed Kidney Models and Resources”, Proceedings of the 4th International Workshop on Middleware for Grid Computing (MGC 2006, ACM Press, New York, USA), Nov. 27, 2006, Melbourne, Australia.

[18] Karthikeyan, N.; Sukanesh, R.: “A Study on Alchemi enabled Medical services”, 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010Issue Date: 9-11 July 2010: 28 – 31

[19] Kyong Hoon Kim, Wan Yeon Lee, Jong Kim, and Rajkumar Buyya, “SLA-Based Scheduling of Bag-of-Tasks Applications on Power-Aware Cluster Systems”, IEICE Transactions on Information and Systems, Volume E93-D, Number 12, Pages: 3194-3201, ISSN: 0916-8532, Institute of Electronics, Information and Communication Engineers (IEICE) Press, Tokyo, Japan, Dec. 2010.

[20] Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms, Software: Practice and Experience, Volume 41, Number 1, Pages: 23-50, ISSN: 0038-0644, Wiley Press, New York, USA, January 2011.

case study on software as a service (saas) based emergency healthcare in india

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