An IoT-oriented data placement method with privacy preservation in cloud environment

© 2018 Elsevier Ltd IoT (Internet of Things) devices generate huge amount of data which require rich resources for data storage and processing. Cloud computing is one of the most popular paradigms to accommodate such IoT data. However, the privacy conflicts combined in the IoT data makes the data placement problem more complicated, and the resource manager needs to take into account the resource efficiency, the power consumption of cloud data centers, and the data access time for the IoT applications while allocating the resources for the IoT data. In view of this challenge, an IoT-oriented Data Placement method with privacy preservation, named IDP, is designed in this paper. Technically, the resource utilization, energy consumption and data access time in the cloud data center with the fat-tree topology are analyzed first. Then a corresponding data placement method, based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is designed to achieve high resource usage, energy saving and efficient data access, and meanwhile realize privacy preservation of the IoT data. Finally, extensive experimental evaluations validate the efficiency and effectiveness of our proposed method

Dynamic Resource Allocation for Load Balancing in Fog Environment

Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM