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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications' execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956015 | PMC |
| http://dx.doi.org/10.3390/s22062379 | DOI Listing |
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Article Synopsis
- Edge-cloud computing is gaining popularity due to its benefits, but task scheduling remains a major challenge for enhancing service quality and making efficient use of resources.
- This paper introduces a new scheduling algorithm that takes resource diversity into account to boost task completion rates and resource usage while adhering to deadline constraints.
- By utilizing a genetic algorithm with a unique mutation technique linked to resource heterogeneity, the authors demonstrate superior performance compared to thirteen other scheduling methods through extensive testing.
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