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.

Detecting anomalous sensor events in smart home data for enhancing the living experience

Abstract: The need to have a secure lifestyle at home is in demand more than ever. Today's home is more than just four walls and a roof. Technology at home is on the rise and the place for smart home solutions is growing. One of the major concerns for smart home systems is the capability of adapting to the user. Personalizing the behavior of the home may provide improved comfort, control, and safety. One of the challenges of this goal is tackling anomalous events or actions. This work proposes using machine learning techniques to address this issue of detecting anomalous events or actions in smart environment datasets. The approaches are validated using real-world sensor data captured from a smart home testbed.

Dynamic bandwidth management and adaptive applications for a variable bandwidth wireless environment

Abstract: This article describes an approach for providing dynamic quality of service (QoS) support in a variable bandwidth network, which may include wireless links and mobile nodes. The dynamic QoS approach centers on the notion of providing QoS support at some point within a range requested by applications. To utilize dynamic QoS, applications must be capable of adapting to the level of QoS provided by the network, which may vary during the course of a connection. To demonstrate and evaluate the dynamic QoS concept, we have implemented a new protocol called dynamic resource reservation protocol (dRSVP) and a new QoS application program interface (API). The paper describes this new protocol and API and also discusses our experience with adaptive streaming video and audio applications that work with the new protocol in a testbed network, including wireless local area network connectivity and wireless link connectivity emulated over the wired Ethernet. Qualitative and quantitative assessments of the dynamic RSVP protocol are provided.

DOME: a diverse outdoor mobile testbed

Abstract: A series of complex dependencies conspire to make it difficult to model mobile networks, including mobility, channel and radio characteristics, and power consumption. To address these challenges, we have designed and built a testbed for large-scale mobile experimentation, called the Diverse Outdoor Mobile Environment. DOME consists of computer-equipped buses, battery-powered nomadic nodes, organic WiFi APs, and a municipal WiFi mesh network. While the construction of a testbed such as DOME presents a significant engineering challenge, this paper describes a concrete set of scientific results derived from this experience. We argue that a broad range of mobility experiments could be performed in a testbed which provides the properties of temporal, technological, and spatial diversity. We demonstrate these properties in our testbed through analysis of data collected from DOME over a period of four years. Finally, we use DOME to provide insight into several open problems in mobile systems research.

5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning

Abstract: The current cellular technology and vehicular networks cannot satisfy the mighty strides of vehicular network demands. Resource management has become a complex and challenging objective to gain expected outcomes in a vehicular environment. The 5G cellular network promises to provide ultra-high-speed, reduced delay, and reliable communications. The development of new technologies such as the network function virtualization (NFV) and software defined networking (SDN) are critical enabling technologies leveraging 5G. The SDN-based 5G network can provide an excellent platform for autonomous vehicles because SDN offers open programmability and flexibility for new services incorporation. This separation of control and data planes enables centralized and efficient management of resources in a very optimized and secure manner by having a global overview of the whole network. The SDN also provides flexibility in communication administration and resource management, which are of critical importance when considering the ad-hoc nature of vehicular network infrastructures, in terms of safety, privacy, and security, in vehicular network environments. In addition, it promises the overall improved performance. In this paper, we propose a flow-based policy framework on the basis of two tiers virtualization for vehicular networks using SDNs. The vehicle to vehicle (V2V) communication is quite possible with wireless virtualization where different radio resources are allocated to V2V communications based on the flow classification, i.e., safety-related flow or non-safety flows, and the controller is responsible for managing the overall vehicular environment and V2X communications. The motivation behind this study is to implement a machine learning-enabled architecture to cater the sophisticated demands of modern vehicular Internet infrastructures. The inclination towards robust communications in 5G-enabled networks has made it somewhat tricky to manage network slicing efficiently. This paper also presents a proof of concept for leveraging machine learning-enabled resource classification and management through experimental evaluation of special-purpose testbed established in custom mininet setup. Furthermore, the results have been evaluated using Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). While concluding the paper, it is shown that the LSTM has outperformed the rest of classification techniques with promising results.

QoS Guarantees and Service Differentiation for Dynamic Cloud Applications

Abstract: Cloud elasticity allows dynamic resource provisioning in concert with actual application demands. Feedback control approaches have been applied with success to resource allocation in physical servers. However, cloud dynamics make the design of an accurate and stable resource controller challenging, especially when application-level performance is considered as the measured output. Application-level performance is highly dependent on the characteristics of workload and sensitive to cloud dynamics. To address these challenges, we extend a self-tuning fuzzy control (STFC) approach, originally developed for response time assurance in web servers to resource allocation in virtualized environments. We introduce mechanisms for adaptive output amplification and flexible rule selection in the STFC approach for better adaptability and stability. Based on the STFC, we further design a two-layer QoS provisioning framework, DynaQoS, that supports adaptive multi-objective resource allocation and service differentiation. We implement a prototype of DynaQoS on a Xen-based cloud testbed. Experimental results on representative server workloads show that STFC outperforms popular controllers such as Kalman filter, ARMA and, Adaptive PI in the control of CPU, memory, and disk bandwidth resources under both static and dynamic workloads. Further results with multiple control objectives and service classes demonstrate the effectiveness of DynaQoS in performance-power control and service differentiation.