High-Performance Isolation Computing Technology for Smart IoT Healthcare in Cloud Environments

• Published in: IEEE internet of things journal Vol. 8; no. 23; pp. 16872 - 16879
• Main Authors: Zhang, Yin, Sun, Yi, Jin, Renchao, Lin, Kaixiang, Liu, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Delays; Health care; Health services; high-performance isolation technology; intelligent medical diagnosis; Internet of Things; Medical diagnostic imaging; Medical equipment; Message processing; Performance evaluation; Protocols; Resource management; Service introduction; smart IoT healthcare; Tenants
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2021.3051742

The development of the smart medical industry and equipment has made great progress due to the fusion of the IoT, cloud computing, and big data. In smart IoT healthcare, patients can collect vital parameters from various medical sensors attached to them to detect diseases and make initial diagnoses by themselves. With the powerful storage and computing functions of cloud computing, medical sensor devices deployed in a cloud environment can effectively solve the problems that the devices are highly dispersed, heterogeneous, and have limited processing capabilities. As a result, this method can effectively provide customized and scalable smart medical services for patients. However, because these medical resources share computing resources on the cloud platform, changes in equipment workloads will lead to service performance competition among tenants. Therefore, determining how to achieve performance isolation between tenants and guarantee the service-level agreements (SLAs) of the tenants has become the most concerning issue for cloud service providers. In this article, we propose a performance isolation algorithm for multitenant IoT clouds, which can effectively provide performance isolation between tenants. Experiments show that the message processing delay of tenants working within the allocated quota can be reduced by 82%.