Study of Task Scheduling Strategy based on Trustworthiness

MapReduce is a distributed computing model for cloud computing to process massive data. It simplifies the writing of distributed parallel programs. For the fault-tolerant technology in the MapReduce programming model, tasks may be allocated to nodes with low reliability. It causes the task to be reexecuted, wasting time and resources. This paper proposes a reliability task scheduling strategy with a failure recovery mechanism, evaluates the trustworthiness of resource nodes in the cloud environment and builds a trustworthiness model. By using the simulation platform CloudSim, the stability of the task scheduling algorithm and scheduling model are verified in this paper.

Trust in the Adoption of Internet of Things for Smart Agriculture in Developing Countries

Trust in online environments is based on beliefs in the trustworthiness of a trustee, which is composed of three distinct dimensions - integrity, ability, and benevolence. Zimbabwe has slowly adopted Internet of Things for smart agriculture as a way of improving on food security in the country, though there is hesitancy by most farmers citing trust issues as monitoring of crops, animals and farm equipment’s would be done online through connecting several devices and accessing data. Farmers are facing difficulties in trusting that the said technology has the ability to perform as expected in a specific situation or to complete a required task, i.e. if the technology will work consistently and reliably in monitoring the environment, nutrients, temperatures and equipment status. The integrity of the collected data as it will be used for decision making. There is a growing need to determine how trust in the technology influence the adoption of IoT for smart agriculture in Zimbabwe. The mixed methodology was used to gather data from 50 A2 model farmers randomly sampled in Zimbabwe. The findings revealed that McKnight etal. trust in technology model can be used to influence the adoption of IoT through trusting that the technology will be reliable and will operate as expected.Additional constructs such as security and distrust of technology can be used as reference for future research.

Task-Decomposition based Anomaly Detection of Massive And High-Volatility Session Data on Academic Backbone Network

The Science Information Network (SINET) is a Japanese academic backbone network for more than 800 universities and research institutions. The characteristic of SINET traffic is that it is enormous and highly variable. In this paper, we present a task-decomposition based anomaly detection of massive and highvolatility session data of SINET. Three main features are discussed: Tash scheduling, Traffic discrimination, and Histogramming. We adopt a task-decomposition based dynamic scheduling method to handle the massive session data stream of SINET. In the experiment, we have analysed SINET traffic from 2/27 to 3/8 and detect some anomalies by LSTM based time-series data processing.