Abstract
Cloud Internet of Things (IoT) is a novel paradigm, where the limitations of IoT associated devices in terms of storage, data access, scalability, networking and computing, and complex analysis are solved through use of the cloud computing infrastructure. The pervasive adoption of cloud in the IoT framework, makes the underlying data centers exacerbate problems like the environmental carbon footprint and operational costs which arise from the high energy consumption of computing servers. Several works proposed virtual machine placement and task scheduling algorithms to reduce the energy consumption of the underlying cloud infrastructure. However, each algorithm uses a different environment, experimental setup, power consumption model and workload for its evaluation, making it difficult to compare among them. In this paper, we give a classification and evaluation of 13 different algorithms using a unified setup, with the aim of achieving an objective comparison. The workload used for the evaluation is selected to typify IoT applications, such as connected vehicles, wide area measurement systems for the power grid, and smart meters for advanced meter infrastructure. The detailed performance analysis is elaborated in this paper.
Original language | English |
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Article number | 8437124 |
Pages (from-to) | 5166-5176 |
Number of pages | 11 |
Journal | IEEE Internet of Things Journal |
Volume | 5 |
Issue number | 6 |
DOIs | |
Publication status | Published - Dec 2018 |
Keywords
- Cloud Internet of Things (IoT)
- Energy-aware task scheduling algorithms
- Energy-aware virtual machine placement algorithms
- Green cloud computing
- IoT
ASJC Scopus subject areas
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications