A review on orchestration distributed systems for IoT smart services in fog computing

This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda

ANCS: Achieving QoS through Dynamic Allocation of Network Resources in Virtualized Clouds

To meet the various requirements of cloud computing users, research on guaranteeing Quality of Service (QoS) is gaining widespread attention in the field of cloud computing. However, as cloud computing platforms adopt virtualization as an enabling technology, it becomes challenging to distribute system resources to each user according to the diverse requirements. Although ample research has been conducted in order to meet QoS requirements, the proposed solutions lack simultaneous support for multiple policies, degrade the aggregated throughput of network resources, and incur CPU overhead. In this paper, we propose a new mechanism, called ANCS (Advanced Network Credit Scheduler), to guarantee QoS through dynamic allocation of network resources in virtualization. To meet the various network demands of cloud users, ANCS aims to concurrently provide multiple performance policies; these include weight-based proportional sharing, minimum bandwidth reservation, and maximum bandwidth limitation. In addition, ANCS develops an efficient work-conserving scheduling method for maximizing network resource utilization. Finally, ANCS can achieve low CPU overhead via its lightweight design, which is important for practical deployment.

The Research of MapReduce on the Cloud Computing

MapReduce is a kind of model of program that is use in the parallel computing about large scale data muster in the Cloud Computing[1] , it mainly consist of map and reduce . MapReduce is tremendously convenient for the programmer who can’t familiar with the parallel program .These people use the MapReduce to run their program on the distribute system. This paper mainly research the model and process and theory of MapReduce .

Modeling and Simulation on Fluid Dynamic Depth-Setting TUV Based on FLUENT

The prerequisite factors for the simulation and controlling of underwater vehicles are hydrodynamic coefficients, which we must measure accurately. In this paper, we took a complex shape remotely operated vehicle as the test model, and figured out the important coefficients that can be applicable for the open-shelf depth-setting TUV through a series of hydrodynamic tests. In order to boost the reliability of a depth-setting TUV system, we carry out numerical simulation and hydrodynamics method on the body of the TUV through the Dynamic Analysis of a single rudder .Comparing with empirical equation, the method has be predicted a high accuracy in hydrodynamic characteristic prediction of depth-setting towed vehicle . After changing the experiment environment of the TUV as possible as we can, a suitability algorithm can be used on the distribute system. And then, the pre-design and later validation of TUV with the conclusion of the simulation based on FLUENT which contribute to shorter calculating period and lower economy could show better depth-setting consequent. The paper draw the Process of the Dynamic Depth-setting TUV for three parts including simulation of the rubber , algorithm experiment of the body and the experiment on TUV.