چكيده به لاتين
cloud computing is the most up-to-date technology response for people who want to perform their heavy and frequent computing tasks without having to buy expensive hardware and services. With the advancement of human societies and the increase in diseases that have cardiovascular disease as the first mortality factor in the world, and for quick and inexpensive responses, the concept of remote surveillance healthcare has been introduced as a technology-based solution. To monitor heart disease remotely, ECG heart rate sensors are designed to receive and process patient heartbeat signals. By intelligent personalized mobile devices and enhanced communication, you can instantly collect and disseminate patient ECG data through a mobile application.
Considering the many requests that are sent to the sources for computing, and taking into account the specific conditions of heart patients, we need to respond very quickly and allocate the appropriate resources for quick calculation of these types of requests, so that other requests can also be made. Good quality service. In order to reduce the response time, this can be improved with three approaches, the first approach is to select optimal mash, the second approach to resource allocation and the third approach to prioritizing ECG tasks. On the other hand, it is a challenge to us, given the mobility of the user and the storage of patient information in different frames, and the need for all patient information for immediate calculations. Also, when processing requests are sent to a fog, some requests may require immediate calculations or complex requests with a very large volume that can involve all sources of fog. Meanwhile, when an ECG request is sent to a fog, resource allocation to these requests should prioritize the ECG request, and the fastest computing with the lowest response time, while other requests can also provide the necessary resources. With appropriate response times. For this purpose, cloud resources can also be taken into account in certain circumstances.
In this research, for the first time considering the user mobility and a new method for allocating resources with the approach of using the most local resources and the lowest response time, we have investigated optimal choice methods. Finally, a bundle for mobile phones is provided. Using the proposed method in this research, we have reached 75 ms in response time compared to other existing methods, and we achieved 10% improvement in the number of accepted tasks compared to other available methods.
