Scientific Applications of Cloud– Health care, Geoscience and Biology, Business and Consumer Applications - CRM and ERP

 Scientific Applications of Cloud – Health care, Geoscience and Biology, Business and Consumer Applications - CRM and ERP

Scientific applications are a sector that is increasingly using cloud computing systems and technologies. Cloud computing systems meet the needs of different types of applications in the scientific domain: high-performance computing (HPC) applications, high-throughput computing (HTC) applications, and data-intensive applications. For instance, the MapReduce programming model provides scientists with a very simple and effective model for building applications that need to process large datasets. Therefore it has been widely used to develop data-intensive scientific applications. Problems that require a higher degree of flexibility in terms of structuring of their computation model can leverage platforms such as Aneka, which supports MapReduce and other programming models. We now discuss some interesting case studies in which Aneka has been used.

Application in Healthcare:

Cloud-Based ECG Data Analysis:

Cloud technologies facilitate the analysis of electrocardiogram (ECG) data remotely. Wearable computing devices equipped with ECG sensors continuously monitor the patient's heartbeat. The collected data is transmitted to the cloud-hosted Web service for efficient and timely analysis.

Benefits of Cloud Computing in Healthcare:

Cloud infrastructure provides elasticity, allowing it to scale according to the demands of ECG data analysis. The ubiquity of cloud computing ensures accessibility from any internet-connected device, enabling remote monitoring and integration with on-premises hospital systems. Cost savings are achieved through a pay-per-use model, eliminating the need for large upfront investments in computing infrastructure.

Three Layers of Cloud Computing Stack:

The cloud-based platform for ECG monitoring leverages the three layers of the cloud computing stack: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).SaaS application stores ECG data in Amazon S3 and issues processing requests. The runtime platform dynamically adjusts the number of instances for workflow engine and Aneka based on processing demands.

Workflow for ECG Processing:

ECG processing jobs involve operations such as waveform extraction and comparison with a reference waveform to detect anomalies. Anomalies trigger notifications to doctors and first-aid personnel for immediate action on specific patients. The cloud-based infrastructure ensures quick and efficient processing of ECG data.

Advantages of Cloud Technology in Healthcare:

Elasticity of the cloud infrastructure eliminates the need for large, upfront investments by hospitals. Ubiquitous access to cloud computing technologies allows for easy integration with other hospital systems and ensures minimal downtime. Cost savings are realized through flexible pricing models based on pay-per-use and volume prices for service requests, aligning costs with effective usage.

Online healthcare monitoring system

Application in Biology:

Gene Expression Profiling for Cancer Diagnosis:
Gene expression profiling measures the expression levels of thousands of genes simultaneously, aiding in understanding cellular responses to medical treatments.
This approach is crucial in drug design, helping scientists identify the effects of specific treatments. In cancer diagnosis, gene expression profiling assists in classifying tumors more accurately by analyzing mutated genes responsible for uncontrolled cell growth.

Challenges in Gene Expression Data Analysis:
The dimensionality of gene expression datasets is high, ranging from thousands to tens of thousands of genes. Learning classifiers, such as the extended Classifier System (XCS), are employed for sample classification, but their effectiveness with high-dimensional datasets is not well-explored. CoXCS, a variant of XCS, addresses high-dimensional datasets effectively by dividing the search space into subdomains and solving classification problems concurrently.

Cloud-Based Implementation:
Cloud-CoXCS is a cloud-based implementation of CoXCS that utilizes Aneka for parallel processing of classification problems. The algorithm is controlled by strategies defining the composition of outcomes and whether the process needs iteration. Cloud-CoXCS leverages the dynamic nature of XCS, allowing scalability with Aneka to efficiently allocate compute resources based on varying demands over time.

Parallelization and Scalability with Aneka:
CoXCS divides the search space into subdomains, enabling parallel processing of classification problems. The computationally intensive process is efficiently parallelized in the cloud, leveraging Aneka's scalable middleware. Aneka's dynamic allocation of resources aligns with the variable compute requirements of the XCS algorithm, offering a distinctive advantage.

Advantages of Cloud-Based Gene Expression Analysis:
Cloud-based solutions offer scalability, allowing the allocation of resources based on computational demands. Accessibility from any internet-connected device ensures widespread use and integration with existing systems. Cost-effectiveness is achieved through a pay-per-use model, aligning costs with actual usage and eliminating the need for substantial upfront investments in computing infrastructure.

Cloud-CoXCS:  An environment for microarray data processing

Application in Geoscience

Role of GIS in Geoscience Applications:

Geographic Information System (GIS) is a vital component of geoscience applications, capturing, managing, and analyzing geospatial data. The increasing deployment of sensors and satellites for monitoring results in a substantial rise in data volume in geoscience applications. GIS applications have diverse uses, ranging from advanced farming to civil security and natural resources management, contributing to various domains.

Challenges in Satellite Image Processing:

Satellite remote sensing produces large volumes of raw images, often in the order of hundreds of gigabytes. The processing of these images involves both input/output and compute-intensive tasks, including data transformations and corrections. Cloud computing offers a solution for executing these demanding tasks efficiently, providing the necessary infrastructure to support geoscience applications.

Cloud-Based Workflow for Satellite Image Processing:

The Department of Space, Government of India, has developed a cloud-based workflow for satellite image processing. The system integrates technologies across the computing stack, including Software as a Service (SaaS) for geocode generation and data visualization, and Platform as a Service (PaaS) using Aneka for importing data and executing image-processing tasks. A Xen private cloud and Aneka dynamically provision resources based on demand, demonstrating the adaptability and scalability of cloud computing in handling geospatial data.

SaaS and PaaS Integration in Cloud Implementation:

The SaaS application within the cloud-based workflow offers services for essential tasks such as geocode generation and data visualization. Aneka at the PaaS level controls the importing of data and execution of image-processing tasks, transforming raw satellite images into usable GIS products. The integrated system streamlines the workflow, showcasing the synergy between SaaS and PaaS components in a cloud-based geoscience application.

Efficient Resource Provisioning and Offloading:

Cloud computing technologies, exemplified by the developed system, effectively offload local computing facilities from excessive workloads in satellite image processing.

The dynamic provisioning of resources by Aneka in a Xen private cloud allows the computing infrastructure to grow or shrink based on demand. This project highlights how cloud computing optimally addresses the challenges of processing massive geospatial data, providing elastic computing infrastructures for enhanced efficiency.

Business and consumer applications

The business and consumer sector is the one that probably benefits the most from cloud computing technologies. Moreover, the elastic nature of cloud technologies does not require huge up-front investments, thus allowing new ideas to be quickly translated into products and services that can comfortably grow with the demand. The combination of all these elements has made cloud computing the preferred technology for a wide range of applications, from CRM and ERP systems to productivity and social-networking applications.

CRM and ERP

Customer relationship management (CRM) and enterprise resource planning (ERP) applications are market segments, CRM applications the more mature.

Cloud CRM applications constitute a great opportunity for small enterprises and start-ups to have fully functional CRM software without large up-front costs and by paying subscriptions. CRM can be easily moved to the cloud.

ERP solutions on the cloud are less mature and have to compete with well-established in-house solutions. ERP systems integrate several aspects of an enterprise: finance and accounting, human resources, manufacturing, supply chain management, project management. The goal is to provide a uniform view and access to all operations that need to be performed to sustain a complex organization. But transition to cloud-based models is more difficult. For this reason cloud ERP solutions are less popular than CRM solutions at this time.

Salesforce.com

Salesforce.com is the most popular and developed CRM solution available today. As of today more than 100,000 customers have chosen Safesforce.com to implement their CRM solutions. The application provides customizable CRM solutions that can be integrated with additional features developed by third parties. Salesforce.com is based on the Force.com cloud development platform.

Salesforce.com  architecture

Microsoft dynamics CRM

Microsoft Dynamics CRM is the solution implemented by Microsoft for customer relationship management. Dynamics CRM is available either for installation on the enterprise’s premises or as an online solution priced as a monthly per-user subscription. The system is completely hosted in Microsoft’s datacenters across the world and offers to customers a 99.9% SLA.

 The application provides users with facilities for marketing, sales, and advanced customer relationship management. Dynamics CRM Online features can be accessed either through a Web browser interface or programmatically by means of SOAP and RESTful Web services. This allows Dynamics CRM to be easily integrated with both other Microsoft products and line-of-business applications.