OrderlessChain: Do Permissioned Blockchains Need Total Global Order of Transactions?

Existing permissioned blockchains often rely on coordination-based consensus protocols to ensure the safe execution of applications in a Byzantine environment. Furthermore, these protocols serialize the transactions by ordering them into a total global order. The serializability preserves the correctness of the application's state stored on the blockchain. However, using coordination-based protocols to attain the global order of transactions can limit the throughput and induce high latency. In contrast, application-level correctness requirements exist that are not dependent on the order of transactions, known as invariant-confluence (I-confluence). The I-confluent applications can execute in a coordination-free manner benefiting from the improved performance compared to the coordination-based approaches. The safety and liveness of I-confluent applications are studied in non-Byzantine environments, but the correct execution of such applications remains a challenge in Byzantine coordination-free environments. This work introduces OrderlessChain, a coordination-free permissioned blockchain for the safe and live execution of I-confluent applications in a Byzantine environment. We implemented a prototype of our system, and our evaluation results demonstrate that our coordination-free approach performs better than coordination-based blockchains

FabricCRDT: A Conflict-Free Replicated Datatypes Approach to Permissioned Blockchains

With the increased adaption of blockchain technologies, permissioned blockchains such as Hyperledger Fabric provide a robust ecosystem for developing production-grade decentralized applications. However, the additional latency between executing and committing transactions, due to Fabric's three-phase transaction lifecycle of Execute-Order-Validate (EOV), is a potential scalability bottleneck. The added latency increases the probability of concurrent updates on the same keys by different transactions, leading to transaction failures caused by Fabric's concurrency control mechanism. The transaction failures increase the application development complexity and decrease Fabric's throughput. Conflict-free Replicated Datatypes (CRDTs) provide a solution for merging and resolving conflicts in the presence of concurrent updates. In this work, we introduce FabricCRDT, an approach for integrating CRDTs to Fabric. Our evaluations show that in general, FabricCRDT offers higher throughput of successful transactions than Fabric, while successfully committing and merging all conflicting transactions without any failures.Comment: In Proceedings of the 20th International Middleware Conference (Middleware '19). ACM 201

The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey

Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. With the rapid growing size of real world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. In addition, we establish connections between GNN systems, graph processing systems and DL systems.Comment: Accepted at ACM Computing Survey

Modeling of Carbon Black Fragmentation During High‐Intensity Dry Mixing Using the Population Balance Equation and the Discrete Element Method

A complex interaction between the process design and the properties of carbon black (CB) during dry mixing of cathode material influences the microstructure and thus the performance of the Li-ion battery. The description of these interactions by means of a coupling of the mixing process simulation and the fragmentation of CB is the focus of this work. The discrete element method provides information about the frequency and intensity of the stress. The change of the CB size distribution is done by the population balance equation. The material strength as well as the fracture behavior are represented with simple models. The calibration of the model parameters is performed using the Nelder–Mead algorithm. The calibrated models provide good agreement with the measurements of the size distributions from experimental investigations. Transfer of the calibrated parameters to other process settings is possible and provides good agreement in some cases. Recalibration of the fracture behavior improves the accuracy of the model so that it can be used as a predictive tool

Challenges in Ecofriendly Battery Recycling and Closed Material Cycles: A Perspective on Future Lithium Battery Generations

The global use of lithium-ion batteries of all types has been increasing at a rapid pace for many years. In order to achieve the goal of an economical and sustainable battery industry, the recycling and recirculation of materials is a central element on this path. As the achievement of high 95% recovery rates demanded by the European Union for some metals from today’s lithium ion batteries is already very challenging, the question arises of how the process chains and safety of battery recycling as well as the achievement of closed material cycles are affected by the new lithium battery generations, which are supposed to enter the market in the next 5 to 10 years. Based on a survey of the potential development of battery technology in the next years, where a diversification between high-performance and cost-efficient batteries is expected, and today’s knowledge on recycling, the challenges and chances of the new battery generations regarding the development of recycling processes, hazards in battery dismantling and recycling, as well as establishing a circular economy are discussed. It becomes clear that the diversification and new developments demand a proper separation of battery types before recycling, for example by a transnational network of dismantling and sorting locations, and flexible and high sophisticated recycling processes with case-wise higher safety standards than today. Moreover, for the low-cost batteries, recycling of the batteries becomes economically unattractive, so legal stipulations become important. However, in general, it must be still secured that closing the material cycle for all battery types with suitable processes is achieved to secure the supply of raw materials and also to further advance new developments

FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems

Federated Machine Learning (FL) has received considerable attention in recent years. FL benchmarks are predominantly explored in either simulated systems or data center environments, neglecting the setups of real-world systems, which are often closely linked to edge computing. We close this research gap by introducing FLEdge, a benchmark targeting FL workloads in edge computing systems. We systematically study hardware heterogeneity, energy efficiency during training, and the effect of various differential privacy levels on training in FL systems. To make this benchmark applicable to real-world scenarios, we evaluate the impact of client dropouts on state-of-the-art FL strategies with failure rates as high as 50%. FLEdge provides new insights, such as that training state-of-the-art FL workloads on older GPU-accelerated embedded devices is up to 3x more energy efficient than on modern server-grade GPUs.Comment: Preprint. Under Revie

Characterization of protein-interaction networks in tumors

<p>Abstract</p> <p>Background</p> <p>Analyzing differential-gene-expression data in the context of protein-interaction networks (PINs) yields information on the functional cellular status. PINs can be formally represented as graphs, and approximating PINs as undirected graphs allows the network properties to be characterized using well-established graph measures.</p> <p>This paper outlines features of PINs derived from 29 studies on differential gene expression in cancer. For each study the number of differentially regulated genes was determined and used as a basis for PIN construction utilizing the Online Predicted Human Interaction Database.</p> <p>Results</p> <p>Graph measures calculated for the largest subgraph of a PIN for a given differential-gene-expression data set comprised properties reflecting the size, distribution, biological relevance, density, modularity, and cycles. The values of a distinct set of graph measures, namely <it>Closeness Centrality</it>, <it>Graph Diameter</it>, <it>Index of Aggregation</it>, <it>Assortative Mixing Coefficient</it>, <it>Connectivity</it>, <it>Sum of the Wiener Number</it>, <it>modified Vertex Distance Number</it>, and <it>Eigenvalues </it>differed clearly between PINs derived on the basis of differential gene expression data sets characterizing malignant tissue and PINs derived on the basis of randomly selected protein lists.</p> <p>Conclusion</p> <p>Cancer PINs representing differentially regulated genes are larger than those of randomly selected protein lists, indicating functional dependencies among protein lists that can be identified on the basis of transcriptomics experiments. However, the prevalence of hub proteins was not increased in the presence of cancer. Interpretation of such graphs in the context of robustness may yield novel therapies based on synthetic lethality that are more effective than focusing on single-action drugs for cancer treatment.</p