انتخاب ماشین مجازی کم مصرف بر اساس رتبه بندی منابع و رویکرد ضریب بهره برداری
Abstract
1- Introduction
2- Related work
3- Problem formulation
4- System architecture model
5- Proposed algorithm
6- Experimental result and performance analysis
7- Conclusion
References
Abstract
IoT leads to abrupt variations producing an immense number of data streams for storage, which is a considerable task in the heterogeneous cloud computing environment. Extant techniques consider task deadlines for virtual machine (VM) allocation and migration. This creates a resource famine leading to haphazard and numerous VM migrations, high energy consumption and unbalanced resource utilization. To solve this issue, an energy-efficient resource ranking and utilization factor-based virtual machine selection (ERVS) approach is proposed. ERVS encompasses the resource requirement rate for task classification, comprehensive resource balance ranking, processing element cost and the resource utilization square model for migration. It evaluates overloaded and underloaded hosts and types of VM by predicting CPU utilization rate and energy consumption. Based on this, tasks are sorted and VMs are optimally assigned, which enhances the resource utilization rate, reducing the number of live VM migrations. The experiments evaluate the ability of the proposed approach to diminish energy consumption without violation of service level agreements.
Introduction
IoT is regarded as a new epoch for green IT computational fields. It enables a cloud-based computer system to manipulate things remotely using sensor devices. The deployed sensors gather environmental data, which are analysed to determine suitable actions [1]. Smart agriculture, transportation, cities, grids and healthcare, and novel inventory system applications, all use IoT technology. According to a statement from IBM, each day approximately 2.5 exabytes of data are produced from sensor devices, and in the year 2020, nearly 49 billion devices will be linked [2]. A suitable infrastructure and platforms are required to accumulate and process the sensor data. For instance, in a smart agriculture environment, soil and weather data are measured and stored in the cloud. The computational analysis of stored data is carried out based on an optimal threshold value, which is used to regulate the production of agriculture yield, and the analysed reports are sent to farmers or end users to enable them to take accurate action for preserving the loss [3,4]. A fertilizer recommendation is made based on the computational analysis of weather predictions and soil nutrition parameters.