Testbed

About: Testbed is a research topic. Over the lifetime, 10858 publications have been published within this topic receiving 147147 citations. The topic is also known as: test bed.

Programmable agents for flexible QoS management in IP networks

Abstract: Network programmability seems to be a promising solution to network management and quality of service (QoS) control. Software mobile-agents technology is boosting the evolution toward application-level control of network functionalities. Code may be deployed in the network dynamically and on demand for the benefit of applications or application classes. Agents support a dynamic distribution of control and management functions across networks, thus increasing flexibility and efficiency. We propose to use mobile-agent technology to overcome some of the problems inherent in current Internet technology. We focus our attention to QoS monitoring, being locally significant in network subdomains, and realize a QoS management strategy in response to variations of user, customer of application requirements, and of the network state. We describe our experience and the results obtained from our testbed, where software agents are instantiated, executed, migrated, and suspended in order to implement flexible QoS management in IP networks.

Design and Implementation of an Open Source Framework and Prototype For Named Data Networking-Based Edge Cloud Computing System

Abstract: Named data networking (NDN) and edge cloud computing (ECC) are emerging technologies that are considered as the most representative technologies for the future Internet. Both technologies are the promising enabler for the future Internet such as fifth generation (5G) and beyond which requires fast information response time. We believe that clear benefits can be achieved from the interplay of NDN and ECC and enables future network technology to be much more flexible, secure and efficient. In this paper, therefore, we integrate NDN with ECC in order to achieve fast information response time. Our framework is based on N-Tier architecture and comprises of three main Tiers. The NDN is located at the Tier1 (Things/end devices) and comprises of all the basic functionalities that connect Internet of Things (IoT) devices with Tier 2 (Edge Computing), where we have deployed our Edge node application. The Tier 2 is then further connected with Tier 3 (Cloud Computing), where our Cloud node application is deployed at multiple hops on the Microsoft Azure Cloud machine located in Virginia, WA, USA. We implement an NDN-based ECC framework and the outcomes are evaluated through testbed and simulations in terms of interest aggregation, round trip time (RTT), service lookup time with single query lookup time and with various traffic loads (load-based lookup time) from the IoT devices. Our measurements show that enabling NDN with edge computing is a promising approach to reduce latency and the backbone network traffic and capable of processing large amounts of data quickly and delivering the results to the users in real time.

Using overlays for efficient data transfer over shared wide-area networks

Abstract: Data-intensive applications frequently transfer large amounts of data over wide-area networks. The performance achieved in such settings can often be improved by routing data via intermediate nodes chosen to increase aggregate bandwidth. We explore the benefits of overlay network approaches by designing and implementing a service-oriented architecture that incorporates two key optimizations -- multi-hop path splitting and multi-pathing - within the GridFTP file transfer protocol. We develop a file transfer scheduling algorithm that incorporates the two optimizations in conjunction with the use of available file replicas. The algorithm makes use of information from past GridFTP transfers to estimate network bandwidths and resource availability. The effectiveness of these optimizations is evaluated using several application file transfer patterns: one-to-all broadcast, all-to-one gather, and data redistribution, on a wide-area testbed. The experimental results show that our architecture and algorithm achieve significant performance improvement.