Fault Management in Distributed Systems

In the past decade, distributed systems have rapidly evolved, from simple client/server applications in local area networks, to Internet-scale peer-to-peer networks and large-scale cloud platforms deployed on tens of thousands of nodes across multiple administrative domains and geographical areas. Despite of the growing popularity and interests, designing and implementing distributed systems remains challenging, due to their ever- increasing scales and the complexity and unpredictability of the system executions. Fault management strengthens the robustness and security of distributed systems, by detecting malfunctions or violations of desired properties, diagnosing the root causes and maintaining verifiable evidences to demonstrate the diagnosis results. While its importance is well recognized, fault management in distributed systems, on the other hand, is notoriously difficult. To address the problem, various mechanisms and systems have been proposed in the past few years. In this report, we present a survey of these mechanisms and systems, and taxonomize them according to the techniques adopted and their application domains. Based on four representative systems (Pip, Friday, PeerReview and TrInc), we discuss various aspects of fault management, including fault detection, fault diagnosis and evidence generation. Their strength, limitation and application domains are evaluated and compared in detail

Secure Time-Aware Provenance for Distributed Systems

Operators of distributed systems often find themselves needing to answer forensic questions, to perform a variety of managerial tasks including fault detection, system debugging, accountability enforcement, and attack analysis. In this dissertation, we present Secure Time-Aware Provenance (STAP), a novel approach that provides the fundamental functionality required to answer such forensic questions – the capability to “explain” the existence (or change) of a certain distributed system state at a given time in a potentially adversarial environment. This dissertation makes the following contributions. First, we propose the STAP model, to explicitly represent time and state changes. The STAP model allows consistent and complete explanations of system state (and changes) in dynamic environments. Second, we show that it is both possible and practical to efficiently and scalably maintain and query provenance in a distributed fashion, where provenance maintenance and querying are modeled as recursive continuous queries over distributed relations. Third, we present security extensions that allow operators to reliably query provenance information in adversarial environments. Our extensions incorporate tamper-evident properties that guarantee eventual detection of compromised nodes that lie or falsely implicate correct nodes. Finally, the proposed research results in a proof-of-concept prototype, which includes a declarative query language for specifying a range of useful provenance queries, an interactive exploration tool, and a distributed provenance engine for operators to conduct analysis of their distributed systems. We discuss the applicability of this tool in several use cases, including Internet routing, overlay routing, and cloud data processing

Resin extrusion printhead for 3D printing

A 3D printer comprising a pump, stepper motor adapted to drive the pump and a printing arm connected to a curing source and nozzle

High-frequency multi-pulse inkjet

A printing system and method for forming an ink droplet through the use of a multi-pulse driving signal to increase the printing frequency without reducing the droplet size by applying a multi-pulse driving signal to a small nozzle and to increase the inkjet printing speed by using a smaller nozzle to produce the same-size droplet using a multi-pulse driving signal, which allow for higher printing frequency due to the smaller nozzle size as dictated by the fundamental droplet formation dynamics

Are Consumers Willing to Pay More for Sustainable Products? A Study of Eco-Labeled Tuna Steak

A high demand for seafood leads to overfishing, harms the long-term health of seafood stocks, and threatens environmental sustainability in oceans. Sustainability certification is one of the major sustainability movements and is known as eco-labeling. For instance, in the tuna industry, leading tuna brands have committed to protecting sea turtles by allowing the tracing of the source of their tuna “from catch to can.” This paper relies on an Internet survey on consumers from Kentucky conducted in July 2010. The survey investigates household-level tuna steak (sashimi grade) consumption and examines consumer preferences for eco-labeling (“Certified Turtle Safe” (CTS) in this study) while mimicking individuals’ seafood procurement processes. A random parameter logit model is utilized, and willingness-to-pay measures are calculated based on model estimation results. It was found that respondents on average preferred turtle-safe-labeled tuna steak and were likely to pay more for it; however, they were less likely to purchase wild-caught species, and insignificant results were found for pre-frozen. Moreover, significant heterogeneities were found across individuals regarding tuna steak purchases. The findings indicate evidence of public support for environmental friendliness, particularly with regard to eco-labeling

A dynamic analysis of industrial energy efficiency and the rebound effect: implications for carbon emissions and sustainability

Energy efficiency improvement (EEI) is generally known to be a cost-effective measure for meeting energy, climate, and sustainable growth targets. Unfortunately, behavioral responses to such improvements (called energy rebound effects) may reduce the expected savings in energy and emissions from EEI. Hence, the size of this effect should be considered to help design efficient energy and climate targets. Currently, there are significant differences in approaches for measuring the rebound effect. Here, we used a two-step procedure to measure both short- and long-term energy rebound effects in the Swedish manufacturing industry. In the first step, we used data envelopment analysis (DEA) to measure energy efficiency. In the second step, we use the efficiency scores and estimated a derived energy demand equation including rebound effects using a dynamic panel regression model. This approach was applied to a firm-level panel dataset covering 14 sectors in Swedish manufacturing over the period 1997-2008. We showed that, in the short run, partial and statistically significant rebound effects exist within all manufacturing sectors, meaning that the rebound effect decreased the energy and emission savings expected from EEI. The long-term rebound effect was in general smaller than the short-term effect, implying that within each sector, energy and emission savings due to EEI are larger in the long run compared to the short run. Using our estimates of energy efficiency and rebound effect, we further performed a post-estimation analysis to provide a guide to policy makers by identifying sectors where EEI have the most potential to promote sustainable economic growth with the lowest environmental impact