A SDN-based On-Demand Path Provisioning Approach across Multi-domain Optical Networks

The interconnection of remote datacentres with optical networks are emerging use cases and such orchestration of multi-domains require the design of new network control, management, and orchestration architectures. Such heterogeneity needs to adopt end-to-end services like on-demand path provisioning. It is acknowledged that such scenarios are more complexed and have fundamental limitations in terms of high performance and delay. To address these issues, and as a means to cope with the complexity growth, research in this area is considering the concept of Software-Defined Network (SDN) orchestration for multi-domain optical networks to coordinated the control of heterogeneous systems. This paper presents a SDN path provisioning approach across Multi-Domain Optical Networks. The aim is to develop an efficient on-demand path provisioning platform in a software defined optical network at the control plane to dynamically manage the network's load, especially in emergency scenarios. The proposed distributed system architecture will help to solve the longstanding problem of inter-domain path provisioning. Our proposed architecture is implemented and validated in a control plane testbed to validate the approach. The paper also evaluated the factors such Quality of Service (QoS) of the network deployment associated with delay or control overhead. Our results show that the method will reduce additional delays in a multi-domain optical network, where high capacity and low latency are requirements for data-intensive applications and cloud services. The proposed method also maintains the total number of flows as low as possible to make the algorithm fast and reduce overheads

Study on Packet Forwarding Mechanism by Narrowing Relay Range in Ad Hoc Networks

早大学位記番号:新7461早稲田大

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training, Journal of Telecommunications and Information Technology, 2017, nr 4

The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works

Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture

Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks

Given the emergence of mobile cloud computing (MCC), its associated energy implications are witnessed at larger scale. With offloading computationally intensive tasks to the cloud datacentres being the basic concept behind MCC, most of the mobile terminal resources participating in the MCC collaborative execution are wasted as they remain idle until the mobile terminals receive responses from the datacentres. This is an additional wastage of resources alongside the cloud resources are already being addressed as massive energy consumers. Though the energy consumed of the idle mobile resources is insignificant in comparison with the cloud counterpart, such consumptions have drastic impacts on the mobile devices causing unnecessary battery drains. To this end, this paper proposes Mobilouds which encompass a multi-tier processing architecture with various levels of process cluster capacities and a software application to manage energy efficient utilization of such process clusters. Our proposed Mobilouds framework encourages the mobile device participation in the MCC collaborative execution, thereby reduces the presence of idle mobile resources and utilizes such idle resources in the actual task execution. Our performance evaluation results demonstrate that the Mobilouds framework offers the most energy-time balancing process clusters for task execution by effectively utilizing the available resources, in comparison with an entire cloud offloading strategy using 5G/4G networks

IoT-B&B: Edge-Based NFV for IoT Devices with CPE Crowdsourcing

For embracing the ubiquitous Internet-of-Things (IoT) devices, edge computing and Network Function Virtualization (NFV) have been enabled in branch offices and homes in the form of virtual Customer-Premises Equipment (vCPE). A Service Provider (SP) deploys vCPE instances as Virtual Network Functions (VNFs) on top of generic physical Customer-Premises Equipment (pCPE) to ease administration. Upon a usage surge of IoT devices at a certain part of the network, vCPU, memory, and other resource limitations of a single pCPE node make it difficult to add new services handling the high demand. In this paper, we present IoT-B&B, a novel architecture featuring resource sharing of pCPE nodes. When a pCPE node has sharable resources available, the SP will utilize its free resources as a "bed-and-breakfast" place to deploy vCPE instances in need. A placement algorithm is also presented to assign vCPE instances to a cost-efficient pCPE node. By keeping vCPE instances at the network edge, their costs of hosting are reduced. Meanwhile, the transmission latencies are maintained at acceptable levels for processing real-time data burst from IoT devices. The traffic load to the remote, centralized cloud can be substantially reduced