Emulation Scenario Research Articles

The global emission mitigation potential of avoiding waste and product lifespan extension by Chinese households

This paper examines the emission mitigation potential of Chinese households’ low-carbon behavior by 2030 through a global carbon footprint scenario analysis. The emission reduction effect is estimated by comparing the projected global emissions in 2030 in a lifestyle emulation scenario and a low-carbon scenario, in which Chinese households adopt low-carbon consumption behaviors. Lifestyle emulation is modeled based on what we call “world Engel curves”, which describe how the expenditure share of a certain consumption good depends on the total per capita expenditures for household consumption (which depends on income). By including a dynamic link between household lifestyle changes and GDP, we then obtain the emission projections under different scenarios in 2030, based on the historical data for 49 countries from 1995 to 2011 from EXIOBASE. Our results show that adopting a mild low-carbon lifestyle by households helps only little in terms of reducing GHG emissions. Reducing avoidable waste and expanding the lifetime of products are not enough to help meeting the 2 °C goal. More drastic changes are required.

Simulation of a Tele-Surgery process through a Live Video Streaming service, using Simu5G and Wowza

Telematic services that require low latency for real-time applications and that make use of wireless mobile networks are increasingly popular. In the case of Tele-Surgery services that employ Live Video Streaming (LVS), latency times of the order of 1ms are needed. Given the difficulty of implementing real 5G test scenarios that enable this type of service to be characterized, this paper presents an emulation scenario that uses Simu5G to simulate the network, Wowza as a real video server, OBS Studio for transmission and VLC media player for content playback. This emulation scenario makes it possible to modify such parameters as bitrate, bandwidth, frequency and numerology index in order to evaluate different network configurations. By varying these parameters in a controlled way, packet losses are obtained for different bitrate values. The best quality video was obtained with a bitrate of 3000 Kbps.

A Distributed Traffic Replay Framework for Network Emulation

Traffic replay can effectively improve the fidelity of network emulation and can support emerging network verification and network security evaluation. A distributed traffic replay framework for network emulation, named DTRF, is designed for large-scale and high-fidelity network traffic emulation on the cloud platform. A fast traffic replay strategy bypassing the kernel is proposed to improve the traffic replay performance in the single node by alleviating the problem of high overhead from the kernel protocol stack. To conveniently expand the emulation scale, an extendable method of workflow orchestration that can realize the flexible control and configuration of the execution process of emulation tasks is proposed. The experimental results show that, compared with that of the traditional method, the throughput of traffic replay is increased by 3.71 times on average, the number of concurrent streams is increased by 4.18 times on average, and the timestamp error of the replayed packets is reduced by 97.8% on average. The DTRF can expand the emulation scenario to a massive heterogeneous network.

Behavioral Study of Software-Defined Network Parameters Using Exploratory Data Analysis and Regression-Based Sensitivity Analysis

To provide a low-cost methodical way for inference-driven insight into the assessment of SDN operations, a behavioral study of key network parameters that predicate the proper functioning and performance of software-defined networks (SDNs) is presented to characterize their alterations or variations, given various emulated SDN scenarios. It is standard practice to use simulation environments to investigate the performance characteristics of SDNs, quantitatively and qualitatively; hence, the use of emulated scenarios to typify the investigated SDN in this paper. The key parameters studied analytically are the jitter, response time and throughput of the SDN. These network parameters provide the most vital metrics in SDN operations according to literature, and they have been behaviorally studied in the following popular SDN states: normal operating condition without any incidents on the SDN, hypertext transfer protocol (HTTP) flooding, transmission control protocol (TCP) flooding, and user datagram protocol (UDP) flooding, when the SDN is subjected to a distributed denial-of-service (DDoS) attack. The behavioral study is implemented primarily via univariate and multivariate exploratory data analysis (EDA) to characterize and visualize the variations of the SDN parameters for each of the emulated scenarios, and linear regression-based analysis to draw inferences on the sensitivity of the SDN parameters to the emulated scenarios. Experimental results indicate that the SDN performance metrics (i.e., jitter, latency and throughput) vary as the SDN scenario changes given a DDoS attack on the SDN, and they are all sensitive to the respective attack scenarios with some level of interactions between them.

High quality hypergraph partitioning for logic emulation

Dividing a large-scale design into parts for hardware prototyping and emulation is very important for integrated circuit design flow. In this paper, we present a suite of algorithms to solve the partitioning problem in general field programmable gate arrays (FPGAs) prototyping and hardware emulation. Our k-way hypergraph partitioner is based on a multi-level framework and its performance is similar to the well-known partitioners hMetis (Karypis et al., 1999) and KaHyPar (Akhremtsev et al., 2017). Based on this framework, we take up some algorithms which solve partitioning problems with varying objectives and constraints extracted from logic emulation scenarios. These objectives and constraints take FPGA resources, interconnection resources, flip-flops, and clock domains into account, which are all important factors in FPGA prototyping and hardware emulation. We test our algorithms using ISPD98 benchmarks and several industry cases. The experimental results show that our algorithms are efficient and effective in solving different partitioning problems.

Using IoTCloudSamples as a software framework for simulations of edge computing scenarios

Realizing the potential of edge computing and networks connecting the edge and the cloud, researchers from academia and industries have increasingly developed techniques and tools for edge infrastructures and applications. This paper focuses on supporting complex edge application interactions, which span different layers and subsystems in edge-cloud environments. This paper addresses (i) diverse types of software components for emulating realistic functionality and configurations, like data transformation and service API interoperability, and (ii) techniques for connecting emulated scenarios to real edge software development and operations, and to IoT and cloud counterparts. We present IoTCloudSamples as a software framework with (i) modeling and implementation of diverse types of IoT, edge and cloud elements for complex edge scenarios, (ii) methods for constructing and steering emulations to study the interoperability across layers among different edge platforms and protocols, and (iii) extensive emulated scenarios and experiments integrated with real-world IoT and cloud services. IoTCloudSamples supports the approach of edge-simulation-as-code to allow the reuse and runtime steering of realistic emulation operations. We will present examples from our real-world projects concentrating on edge analytics applications.

Cloud-Based Experimental Platform for the Space-Ground Integrated Network

The space-ground integrated network (SGIN) is an important direction of future network development and is expected to play an important role in edge computing for the Internet of Things (IoT). Through integration with an SGIN, IoT applications can provide services with long-distance and wide-coverage features. However, SGINs are typical large-scale and time-varying networks for which new network technologies, protocols, and applications must be rigorously evaluated and validated. Therefore, a reliable experimental platform is necessary for SGINs. This paper presents a cloud-based experimental platform for the SGIN context named SGIN-Stack. First, the architecture of SGIN-Stack, which combines the Systems Tool Kit (STK) and OpenStack, is described. Based on this architecture, a seamless linkage between OpenStack and STK is achieved to realize synchronous, dynamic, and real-time network emulation for an SGIN, and the dynamic differential compensation technology and a random number generation algorithm are applied to improve the emulation accuracy for satellite links. Finally, an emulation scenario is constructed that includes six space-based backbone nodes, sixty-six space-based access nodes, and a ground station. Based on this emulation scenario, experiments concerning the satellite link delays, bit error ratio (BER), and throughput are carried out to prove the high fidelity of our SGIN-Stack platform. Emulation experiments involving satellite orbital maneuvers and attitude adjustments show that SGIN-Stack can be used for dynamic and real-time SGIN emulation.

An adaptive end‐to‐end RTO calculation framework for DTNs with scheduled connectivity

SummaryIn this work, we propose an end‐to‐end retransmission framework for dynamically calculating efficient retransmission time‐out intervals in delay‐tolerant networks (DTNs) with scheduled connectivity. The proposed framework combines deterministic and statistical information about the network state to calculate worst‐case estimates about the expected round trip times. Such information includes connectivity schedules, convergence layer protocols specifics, communication link characteristics, and network statistics about the maximum expected packet error rates and storage congestion. We detail the implementation of the proposed framework within the end‐to‐end application data conditioning layer proposed for the DTN architecture, realized by the Delay‐Tolerant Payload Conditioning protocol, as part of the Interplanetary Overlay Network–DTN reference implementation, and evaluate its performance in a complex deep‐space emulation scenario in our DTN testbed. Our results show that our approach achieves great accuracy in round‐trip time estimations and, therefore, faster retransmissions of lost data, in comparison to the statically configured retransmission mechanism of the original Delay‐Tolerant Payload Conditioning protocol. As a result, in‐order data reception rate and storage requirements on the receiver side are significantly improved, at minimum or even zero extra cost in transmission overhead due to duplicate transmissions.

A Traffic Cluster Entropy Based Approach to Distinguish DDoS Attacks from Flash Event Using DETER Testbed

The detection of distributed denial of service (DDoS) attacks is one of the hardest problems confronted by the network security researchers. Flash event (FE), which is caused by a large number of legitimate requests, has similar characteristics to those of DDoS attacks. Moreover DDoS attacks and FEs require altogether different handling procedures. So discriminating DDoS attacks from FEs is very important. But the research involving DDoS detection has not laid enough emphasis on including FEs scenarios in the experiments. In this paper, we are using traffic cluster entropy as detection metric not only to detect DDoS attacks but also to distinguish DDoS attacks from FEs. We have validated our approach on cyber-defense technology experimental research laboratory (DETER) testbed. Different emulation scenarios are created on DETER using mix of legitimate, flash, and different types of attacks at varying strengths. It is found that, when flash event is triggered, source address entropy increases but the corresponding traffic cluster entropy does not increase. However, when DDoS attack is launched, traffic cluster entropy also increases along with source address entropy. An analysis of live traces on DETER testbed clearly manifests supremacy of our approach.

IoT4S: a new architecture to exploit sensing capabilities in smart cities

Smart cities offer a challenging way to design future networks, optimising services, costs and management activities. This requires ICT technologies able to interact with the environment. In particular, sensing activities become essential to support new and advanced services for the public good. The drawback is the explosion of the amount of heterogeneous information that has to be stored and processed. Thus, specific solutions for big data management become crucial. In this paper, we present a new architecture, called IoT4S, for the fine-grained storage of environmental information collected through several heterogeneous sensing devices. It provides a high abstraction layer for the description of both sensing infrastructures and sensed data, which can be exported as 'things' to the internet. Also, we propose a new two-layer system for data storage to support IoT4S in the data dissemination service, which is a hybrid solution based on both SQL-like and NoSQL technologies. The paper provides an in-depth description of the IoT4S design, implementation details and a preliminary analysis of its behaviour in real and emulated scenarios.

A QUANTITATIVE EVALUATION METHOD OF GROUND CONTROL POINTS FOR REMOTE SENSING IMAGE REGISTRATION

Ground control point (GCP) extraction is an essential step in automatic registration of remote sensing images. However the lack of quantitative and objective methods for analyzing the GCP quality becomes the bottleneck that prevents the broad development of automatic image registration. Although several measurements for evaluating the number, accuracy and distribution of GCPs have been proposed in recent years, some of them are redundant and the evaluation of dispersion is not efiective enough. In this paper, a method for an objective and quantitative evaluation of GCP quality is proposed. The proposed method consists of three parts: measurement calculation, cost function calculation and flnal validation. In the flrst part, two new measurements are proposed to evaluate the number, dispersion and isotropy of GCPs, and the root mean square of GCP residuals using leave- one-out method (RMSloo) is used to evaluate the accuracy. In the second part, seed cost functions are utilized to transform the measurements into a limited value range as well as to be desired on the ascending direction. Subsequently, all the seed cost functions are combined by a total cost function to provide an integrated evaluation. In the third part, the GCP scenario is validated by the accepted threshold depending on the value of the total cost function. To evaluate the performance of the proposed method, experiments using four typical emulated scenarios of GCP distribution and two sets of real GCPs in SAR images are considered. The results demonstrate that the proposed GCP evaluation method performs more efiectively than the existing methods, especially in the evaluation of dispersion quality.

Elementary secure-multiparty computation for massive-scale collaborative network monitoring: A quantitative assessment

Recently, Secure-Multiparty Computation (SMC) has been proposed as an approach to enable inter-domain network monitoring while protecting the data of individual ISPs. The SMC family includes many different techniques and variants, featuring different forms of “security”, i.e., against different types of attack (er), and with different levels of computation complexity and communication overhead. In the context of collaborative network monitoring, the rate and volume of network data to be (securely) processed is massive, and the number of participating players is large, therefore scalability is a primary requirement. To preserve scalability one must sacrifice other requirement, like verifiability and computational completeness that, however, are not critical in our context. In this paper we consider two possible schemes: the Shamir’s Secret Sharing (SSS), based on polynomial interpolation on prime fields, and the Globally-Constrained Randomization (GCR) scheme based on simple blinding. We address various system-level aspects and quantify the achievable performance of both schemes. A prototype version of GCR has been implemented as an extension of SEPIA, an open-source SMC library developed at ETH Zurich that supports SSS natively. We have performed a number of controlled experiments in distributed emulated scenarios for comparing SSS and GCR performance. Our results show that additions via GCR are faster than via SSS, that the relative performance gain increases when scaling up the data volume and/or number of participants, and when network conditions get worse. Furthermore, we analyze the performance degradation due to sudden node failures, and show that it can be satisfactorily controlled by containing the fault probability below a reasonable level.

An optical burst reordering model for time-based and random selection assembly strategies

Contention resolution schemes in optical burst switched networks (OBS) as well as contention avoidance schemes delay burst delivery and change the burst arrival sequence. The burst arrival sequence usually changes the packet arrival sequence and degrades the upper layer protocols performance, e.g., the throughput of the transmission control protocol (TCP). In this paper, we present and analyze a detailed burst reordering model for two widely applied burst assembly strategies: time-based and random selection. We apply the IETF reordering metrics and calculate explicitly three reordering metrics: the reordering ratio, the reordering extent metric and the TCP relevant metric. These metrics allow estimating the degree of reordering in a certain network scenario. They estimate the buffer space at the destination to resolve reordering and quantify the number of duplicate acknowledgements relevant for investigations on the transmission control protocol. We show that our model reflects the burst/packet reordering pattern of simulated OBS networks very well. Applying our model in a network emulation scenario, enables investigations on real protocol implementations in network emulation environments. It therefore serves as a substitute for extensive TCP over OBS network simulations with a focus on burst reordering.

Experiences with node virtualization for scalable network emulation

During the development of network protocols and distributed applications, their performance has to be analyzed in appropriate environments. Network emulation testbeds provide a synthetic, configurable network environment for comparative performance measurements of real implementations. Realistic scenarios have to consider hundreds of communicating nodes. Common network emulation approaches limit the number of nodes in a scenario to the number of computers in an emulation testbed. To overcome this limitation, we introduce a virtual node concept for network emulation. The key problem for node virtualization is a transparent, yet efficient separation of node resources. In this paper, we provide a brief survey of candidate node virtualization approaches to facilitate scalable network emulation. Based on the gathered insights, we propose a lightweight virtualization solution to achieve maximum scalability and discuss the main points regarding its implementation. We present extensive evaluations that show the scalability and transparency of our approach in both a traditional wired infrastructure-based, and in two wireless ad hoc network emulation scenarios. The measurements indicate that our solution can push the upper limit of emulation scenario sizes by a factor of 10–28. Given our emulation testbed consisting of 64 computers, this translates to possible scenario sizes of up to 1792 nodes. In addition to the evaluation of our virtualization approach, we discuss key concepts for controlling comprehensive emulation scenarios to support scalability of our system as a whole.

VIRTUAL FACTORY SIMULATOR FRAMEWORK FOR LINE PROTOTYPING

Presented in the paper is a virtual factory simulator (VFS) framework as a 3D solid-based factory simulator to be used as a line prototyping tool for an AMS (automated manufacturing system). The VFS framework supports a 3D resource & factory layout modeling, a physical emulation, and a performance simulation: the physical emulation is used for the kinematics simulation, interference check between resources, and the generation & verification of device program; and the performance simulation is used for the material flow & control logic evaluation and system performance evaluation. The proposed VFS framework supports a fully integrated graphical modeling environment for emulation scenario modeling and material flow & control logic modeling. Therefore, the proposed VFS meets the modeling requirements of high modeling power, ease of model building & validation, and ease of communication with stakeholders compared to other commercial system. Based on the VFS framework, a virtual factory simulator named VM-Factory® has been developed in C++ with OpenGL and a DEVS engine, and its validity was demonstrated by applying it in constructing a virtual factory of an FMS (flexible manufacturing system).