Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments

Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance

Plastic pollution in the ocean

Plastic pollution in the ocean was first reported by scientists in the 1970s, yet in recent years it has drawn tremendous attention from the media, the public, and an increasing number of scientists spanning diverse fields, including polymer science, environmental engineering, ecology, toxicology, marine biology, and oceanography. In the oceans, the threat to marine life comes in various forms, such as overexploitation and harvesting, dumping of waste, pollution, alien species, land reclamation, dredging and global climate change. The extremely visible nature of much of this contamination is easy to convey in shocking images of piles of trash on coastlines, marine mammals entangled in fishing nets, or seabird bellies filled with bottle caps, cigarette lighters, and colourful shards of plastic. Even without these images, anyone who has visited a beach has certainly encountered discarded cigarette butts, broken beach toys left behind, or pieces of fishing gear or buoys that have washed ashore

Using Travel Simulation to Investigate Driver Response to In-Vehicle Route Guidance Systems,

A major application for developed satellite navigation systems is the in-vehicle route guidance market. As systems become cheaper to purchase and easier to install and indeed car manufacturers begin to fit the equipment as standard in new vehicles, the potential market for such systems in the developed world is massive. But what are the consequences of giving navigational assistance to car drivers? How will drivers respond to this information? Such information is liable to have a big impact upon driver route choice behaviour and is also subject to their interpretation of the guidance and action upon receiving it. This response may change under different travel circumstances. The impact of collective response to driver guidance is also of importance to traffic engineers and city planners, since routing through environmentally sensitive areas or heavily congested corridors should be avoided. The overall network effects are therefore of key importance to ensure efficient routing and minimal disruption to the road network. It is quite difficult to observe real-life behaviour on a consistent basis, since there are so many confounding variables in the real-world, traffic is never the same two days running, let alone hour by hour and a rigorous experimental environment is required, since control of experimental conditions is paramount to being able to confidently predict driver behaviour in response to navigational aids. Also the take up of guidance systems is still in its infancy, so far available only to a niche market of specialist professionals and those with disposable income. A need to test the common publics’ response to route guidance systems is therefore required. The development of travel simulation techniques, using portable computers and specialist software, gives robust experimental advantages. Although not totally realistic of the driving task, these techniques are sufficient in their realism of the decision element of route selection, enough to conduct experimental studies into drivers’ route choice behaviour under conditions of receiving simulated guidance advice. In this manner driver response to in-vehicle route guidance systems can be tested under a range of hypothetical journey making travel scenarios. This paper will outline the development of travel simulation techniques as a tool for in-vehicle route guidance research, including different methods and key simulation design requirements. The second half of the paper will report in detail on the findings from a recently conducted experiment investigating drivers’ response to route guidance when in familiar and unfamiliar road networks. The results will indicate the importance of providing meaningful information to drivers under these two real-life circumstances and report on how demands for route guidance information may vary by type of journey. Findings indicate that the guidance acceptance need not only depend on the optimum route choice criteria, it is also affected by network familiarity, quality and credibility of guidance advice and personal attributes of the drivers

A 3D immersive discrete event simulator for enabling prototyping of factory layouts

There is an increasing need to eliminate wasted time and money during factory layout design and subsequent construction. It is presently difficult for engineers to foresee if a certain layout is optimal for work and material flows. By exploiting modelling, simulation and visualisation techniques, this paper presents a tool concept called immersive WITNESS that combines the modelling strengths of Discrete Event Simulation (DES) with the 3D visualisation strengths of recent 3D low cost gaming technology to enable decision makers make informed design choices for future factories layouts. The tool enables engineers to receive immediate feedback on their design choices. Our results show that this tool has the potential to reduce rework as well as the associated costs of making physical prototypes

Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim

Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations, that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate based optimization algorithm that uses a trust region based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling (LHS), and central composite design (CCD)—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required database. As the number of design variables grows, the computational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically reduces the total number of calls to the expensive simulation runs during the optimization process

RepFlow: Minimizing Flow Completion Times with Replicated Flows in Data Centers

Short TCP flows that are critical for many interactive applications in data centers are plagued by large flows and head-of-line blocking in switches. Hash-based load balancing schemes such as ECMP aggravate the matter and result in long-tailed flow completion times (FCT). Previous work on reducing FCT usually requires custom switch hardware and/or protocol changes. We propose RepFlow, a simple yet practically effective approach that replicates each short flow to reduce the completion times, without any change to switches or host kernels. With ECMP the original and replicated flows traverse distinct paths with different congestion levels, thereby reducing the probability of having long queueing delay. We develop a simple analytical model to demonstrate the potential improvement of RepFlow. Extensive NS-3 simulations and Mininet implementation show that RepFlow provides 50%--70% speedup in both mean and 99-th percentile FCT for all loads, and offers near-optimal FCT when used with DCTCP.Comment: To appear in IEEE INFOCOM 201