Efficient data and CPU-intensive job scheduling algorithms for healthcare cloud. (May 2018)

• Record Type: Journal Article
• Authors: Sahoo, Prasan Kumar; Dehury, Chinmaya Kumar
• Is Part Of: Computers & electrical engineering. Volume 68(2018)
• Page Start: 119
• Page End: 139
• Publication Date: 2018-05
• Subjects: Cloud computing -- Scheduling -- Data-intensive -- CPU-intensive; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.04.001
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Classify the incoming healthcare related jobs based on the computation and I/O data payload ratio. Minimize the total execution time for computation intensive jobs. Minimize the delay caused due to insufficient network resources. Utilize the resources while the virtual machine (VM) is busy for I/O operations. Reconfigure the VM to meet the job execution deadline based on the healthcare related jobs types. Abstract: Cloud computing platform is used to improve the operational efficiency of business processes and to provide services to users. The fast growth of healthcare industry is shifting its traditional business model to cloud-enabled business model, which can fulfill the resource demand of different applications in healthcare industries. The job of healthcare system can vary from a simple patient record retrieval to a complex biomedical image analysis. On the other hand, the shared configurable computing, storage and other resources are provided to the users as a service over the Internet on rented basis. Although huge amount of resources are provided by the cloud to the healthcare systems to carry out complex time-consuming and data-intensive operations, scheduling of diverse healthcare applications onto large numbers of physical servers is an evolving issue, which needs to be addressed. In this paper, a scheduling framework is designed for the intelligent distribution of healthcare related jobs based on their types by taking advantage of existing heterogeneous distributed data center management solutions. This loosely coupled architecture works atop the existing solutions whose components can run in parallel on different nodes. The proposed framework for cloud computing can not only handle a variety of jobs but also can recover itself from any accidental crash.