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<i title="The World Academy of Research in Science and Engineering">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/l2ghh6vzxbh45awjxwipefhnue" style="color: black;">International Journal of Emerging Trends in Engineering Research</a>
This study introduces Mob-Cloud, a mobility aware adaptiveoffloading system that incorporates a mobile device as a thick client, ad-hoc networking, cloudlet DC, and remote cloud to improve the performance and availability of microservices services. These cloudlet cloud has emerged as a popular model for bringingthe benefits of cloud computing to the proximity of mobile devices. the microservices preliminary goal is to improve service availability as well as performance and mobility features.<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.30534/ijeter/2021/32972021">doi:10.30534/ijeter/2021/32972021</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fqypjlg5lrhv5cdzwvwji7fusq">fatcat:fqypjlg5lrhv5cdzwvwji7fusq</a> </span>
more »... impact of dynamic changes in mobile content (e.g., network status, bandwidth, latency, and location) on the task offloading model is observed by proposing a mobility aware adaptive task offloading algorithm aware microservices, which makes a task offloading decision at runtime on selecting optimal wireless network channels and suitable offloading resources. The decision problem, which is well-known as an NP-hard issue, is the subject of this work. However, for the entire proposed microservices system has the following phases: I adaptive offloading decision based on real-time information, (ii) workflow task scheduling phase, (iii) mobility model phase to motivate end-users to invoke cloud services seamlessly while roaming, and (iv) faulttolerant phase to deal with failure (either network or node). We conduct real-world experiments on the built instruments to assess the online algorithm's overall performance. Compared to baseline task offloading solutions, the evaluation findings show that online algorithm incorporates dynamic adjustments on offloading decision during run-timeand achieves a massive reduction in overall response time with better service availability.
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