The Raincore API for clusters of networking elements

Clustering technology offers a way to increase overall reliability and performance of Internet information flow by strengthening one link in the chain without adding others. We have implemented this technology in a distributed computing architecture for network elements. The architecture, called Raincore, originated in the Reliable Array of Independent Nodes, or RAIN, research collaboration between the California Institute of Technology and the US National Aeronautics and Space Agency's Jet Propulsion Laboratory. The RAIN project focused on developing high-performance, fault-tolerant, portable clustering technology for spaceborne computing . The technology that emerged from this project became the basis for a spinoff company, Rainfinity, which has the exclusive intellectual property rights to the RAIN technology. The authors describe the Raincore conceptual architecture and distributed services, which are designed to make it easy for developers to port their applications to run on top of a cluster of networking elements. We include two applications: a Web server prototype that was part of the original RAIN research project and a commercial firewall cluster product from Rainfinity

Tolerating multiple faults in multistage interconnection networks with minimal extra stages

Adams and Siegel (1982) proposed an extra stage cube interconnection network that tolerates one switch failure with one extra stage. We extend their results and discover a class of extra stage interconnection networks that tolerate multiple switch failures with a minimal number of extra stages. Adopting the same fault model as Adams and Siegel, the faulty switches can be bypassed by a pair of demultiplexer/multiplexer combinations. It is easy to show that, to maintain point to point and broadcast connectivities, there must be at least S extra stages to tolerate I switch failures. We present the first known construction of an extra stage interconnection network that meets this lower-bound. This 12-dimensional multistage interconnection network has n+f stages and tolerates I switch failures. An n-bit label called mask is used for each stage that indicates the bit differences between the two inputs coming into a common switch. We designed the fault-tolerant construction such that it repeatedly uses the singleton basis of the n-dimensional vector space as the stage mask vectors. This construction is further generalized and we prove that an n-dimensional multistage interconnection network is optimally fault-tolerant if and only if the mask vectors of every n consecutive stages span the n-dimensional vector space

The Raincore Distributed Session Service for Networking Elements

Motivated by the explosive growth of the Internet, we study efficient and fault-tolerant distributed session layer protocols for networking elements. These protocols are designed to enable a network cluster to share the state information necessary for balancing network traffic and computation load among a group of networking elements. In addition, in the presence of failures, they allow network traffic to fail-over from failed networking elements to healthy ones. To maximize the overall network throughput of the networking cluster, we assume a unicast communication medium for these protocols. The Raincore Distributed Session Service is based on a fault-tolerant token protocol, and provides group membership, reliable multicast and mutual exclusion services in a networking environment. We show that this service provides atomic reliable multicast with consistent ordering. We also show that Raincore token protocol consumes less overhead than a broadcast-based protocol in this environment in terms of CPU task-switching. The Raincore technology was transferred to Rainfinity, a startup company that is focusing on software for Internet reliability and performance. Rainwall, Rainfinity’s first product, was developed using the Raincore Distributed Session Service. We present initial performance results of the Rainwall product that validates our design assumptions and goals

Explore the murine cardiac 20S proteasomes : molecular composition and regulation.

20S proteasome, essential component of protein degradation mechanism, is important to maintain homeostasis. Its malfunctions have been associated with several pathological conditions. This study presents an extensive study of murine cardiac 20S proteasome. Using biochemical methods, 20S proteasome have been purified to 95%. Proteomic study identified all 20S proteasome subunits. Endogenous phosphorylation was also documented. Furthermore, several associating kinases and phosphatase were identified. They regulated its activities. In PKCå over-expression mice, 20S proteasome expression level was up-regulated, but its peptidase activities did not increase. áB crystallin were recruited to PKCå subproteome in the transgenic mice, which also associated with 20S proteasome. This association was enhanced in the transgenic mice and has been reported to inhibit 20S proteasome activities. It suggested áB crystallin play a role in cardiac 20s proteasome regulation

Computing in the RAIN: a reliable array of independent nodes

The RAIN project is a research collaboration between Caltech and NASA-JPL on distributed computing and data-storage systems for future spaceborne missions. The goal of the project is to identify and develop key building blocks for reliable distributed systems built with inexpensive off-the-shelf components. The RAIN platform consists of a heterogeneous cluster of computing and/or storage nodes connected via multiple interfaces to networks configured in fault-tolerant topologies. The RAIN software components run in conjunction with operating system services and standard network protocols. Through software-implemented fault tolerance, the system tolerates multiple node, link, and switch failures, with no single point of failure. The RAIN-technology has been transferred to Rainfinity, a start-up company focusing on creating clustered solutions for improving the performance and availability of Internet data centers. In this paper, we describe the following contributions: 1) fault-tolerant interconnect topologies and communication protocols providing consistent error reporting of link failures, 2) fault management techniques based on group membership, and 3) data storage schemes based on computationally efficient error-control codes. We present several proof-of-concept applications: a highly-available video server, a highly-available Web server, and a distributed checkpointing system. Also, we describe a commercial product, Rainwall, built with the RAIN technology

Research on the Influence of Music

As for question 1, based on the directed relationship between influencers and followers, we building a network of musicians based on influential relationships. A Music Influence Evaluation Model (MIEM) was also established, and the model formula is shown in the text. We then select the top 200 artists in the “music influence” ranking to build a subnet. The larger the subnet node, the more lines are extended. Indicating that the node represents the musician’s influence is large and extensive. From the graph, we can see that Bob Dylan is influential, but the breadth of influence is not enough; Miles Davis influenced a wide range of music factions.As for question 2, we have developed a Music Similarity Evaluation Model (MSEM) to calculate the contribution parameters of fifteen different music metrics. Using fully connected neural networks combined with triple loss to solve the answer. According to the characteristics of Triple Loss, we can make the similar nodes in the space closer together and the dissimilar nodes further apart. After training, our neural network is able to distinguish artists very well. The results were obtained: artists within genres are far more similar than artists between genres, and a classification image of musicians from different genres was produced.As for question 3, a comparative plot of characteristics revealed that music genres also have their own particular musical characteristics. The comprehensive analysis concludes that the difference between genres is mainly reflected by the six features of valence, tempo, mode, key, acousticness, and instrumentalness, and this result is verified by k-means clustering. By plotting the percentage of influence as well as the change of musical characteristics, it was concluded that the influence of genres changes over time; some musical characteristics in genres also change over time. Finally, the similarity between each faction is calculated and plotted as a heat map, and the genres with high similarity must have interrelated relationships with each other.As for question 4, we have developed a Music Influence T-test Model (MITM). We hypothesized that “influencers” would not influence followers to create music, and a t-test using SPSS rejected the original hypothesis and concluded that “influencers” would influence followers to create music. Additionally, Contagious Evaluation Model(CEM) was also be created. We established the “contagious” index and calculated the Pearson correlation coefficients between “contagious” and 15 musical characteristics, and obtained the results: energy, loudness, and acousticness are more “contagious” than other characteristics. Results: energy, loudness and acousticness are more “contagious” than other features.As for question 5, a time series plot of the variation for each musical characteristic with year was plotted and the analysis yielded the following conclusion: There are characteristics that signify revolutions in musical evolution from these data. For example, the music after 1960s showed changes characterized by higher rhythmicity, faster tempo, and fewer spoken words. Based on these musical evolutionary changes, combined with the “musical influence” we calculated earlier, we select five musical change-makers: The Beatles, Bob Dylan, The Rolling Stones, Miles Davis and Jimi Hendrix.As for question 6, we combined musical influences to identify the most influential musicians in each genre in each era as dynamic influencers to represent the music of the genre in that period. Creating images of their musical characteristics over time and analyzing them in relation to the history of musical development led to the conclusion that an artist’s musical identity changes with technology, social development, and changes in genre representation?As for question 7, a Network Connectivity Evaluation Model(NCEM) was developed to measure which artists in the music network were heavily influenced by external factors during the time period. The first and middle of the 20th century were found to be highly connected online, and this period coincided with a period of social upheaval, with the Cold War, World War II, the Industrial Revolution, and the rapid development of the Internet having a great impact on music, from which many new musical styles were born

Allometry of Bud Dynamic Pattern and Linkage Between Bud Traits and Ecological Stoichiometry of Nitraria tangutorum Under Fertilizer Addition

Affected by the pressure and constraints of available resources, plant growth and development, as well as plant life history strategies, usually vary with environmental conditions. Plant buds play a crucial role in the life history of woody plants. Nitraria tangutorum is a common dominant woody species in desertified areas of northern China and its growth is critical to the desert ecosystem. Revealing the allometry of N. tangutorum aboveground bud fates and the linkage between bud traits and plant nutrient contents and stoichiometric ratios can be useful in understanding plant adaptation strategy. We applied seven nitrogen and phosphorus fertilizer addition treatments to natural N. tangutorum ramets in Ulan Buh Desert in three consecutive years. We surveyed three types of aboveground buds (dormant buds, vegetative buds, and reproductive buds) in each N. tangutorum ramet, then measured the plant carbon (C), nitrogen (N), and phosphorus (P) contents and ratios during three consecutive years. We specified that reserve growth potential (RGP), vegetative growth intensity (VGI) and sexual reproduction effort (SRE) are the three indices of bud dynamic pattern. The results showed that the bud dynamic pattern of N. tangutorum ramets differed significantly among different fertilizer addition treatments and sampling years. The allometry of RGP, VGI, and SRE was obvious, showing size dependence. The allometric growth relationship fluctuated among the sampling years. The linkage between bud traits and plant stoichiometric characteristics of N. tangutorum ramets showed close correlation with plant P content, C:P and N:P ratios, no significant correlation with plant C content, N content and C:N ratio. These results contribute to an improved understanding of the adaptive strategies of woody plants growing in desert ecosystems and provide insights for adoption of effective measures to restore and conserve plant communities in arid and semi-arid regions

Further improvement of fluidized bed models by incorporating zone method with Aspen Plus interface

While providing a fast and accurate tool of simulating fluidized beds, the major limitation of classical zero-dimensional ideal reactor models used in process simulators, such as models built into commercial software (e.g. Aspen Plus®), has been the difficulties of involving thermal reciprocity between each reactor model and incorporating heat absorption by the water wall and super-heaters which is usually specified as model inputs rather than predicted by the models themselves. This aspect is of particular importance to the geometry design and evaluation of operating conditions and flexibility of fluidized beds. This paper proposes a novel modelling approach to resolve this limitation by incorporating an external model that marries the advantages of zone method and Aspen Plus in a robust manner. The improved model has a relatively modest computing demand and hence may be incorporated feasibly into dynamic simulations of a whole power plant

Cyclic performance evaluation of a polyethylenimine/silica adsorbent with steam regeneration using simulated NGCC flue gas and actual flue gas of a gas-fired boiler in a bubbling fluidized bed reactor

To accelerate the deployment of Carbon Capture and Storage (CCS) based on the solid amine adsorbents towards a practical scale application relevant to Natural Gas Combined Cycle (NGCC) power plants, this study has evaluated the cyclic performance of a polyethylenimine/silica adsorbent of kg scale in a laboratory scale bubbling fluidized bed reactor. A high volumetric concentration 80?90 vol% of steam mixed with N2 and CO2 has been used as the stripping gas during a typical temperature swing adsorption (TSA) cycle. Both the simulated NGCC flue gas and the actual flue gas from a domestic gas boiler have been used as the feed gas of the CO2 capture tests with the solid adsorbent. Various characterization has been carried out to elucidate the possible reasons for the initial capacity decline under the steam regeneration conditions. The effect of presence of CO2 in the stripping gas has also been studied by comparing the working capacities using different regeneration strategies. It has been demonstrated that the breakthrough and equilibrium CO2 adsorption capacities can be stabilized at approximately 5.9 wt% and 8.6 wt%, respectively, using steam regeneration for both the simulated and actual natural gas boiler flue gases. However, using a concentration of 15 vol% CO2 in the stripping gas has resulted in a significantly low working capacity at a level of 1.5 wt%, most likely due to the incomplete C