The proceedings contain 25 papers. The special focus in this conference is on NASA Formal Methods. The topics include: Specification Quality Metrics Based on Mutation and Inductive Incremental Model Checking;formal Ve...
ISBN:
(纸本)9783030557539
The proceedings contain 25 papers. The special focus in this conference is on NASA Formal Methods. The topics include: Specification Quality Metrics Based on Mutation and Inductive Incremental Model Checking;formal Verification of parallel Prefix Sum;from Passive to Active: Learning Timed Automata Efficiently;benchmarking Software Model Checkers on Automotive Code;Strengthening Deterministic Policies for POMDPs;paRoT: A Practical Framework for Robust Deep Neural Network Training;parameter Synthesis and Robustness Analysis of Rule-Based Models;falsification of Cyber-Physical systems with Constrained Signal Spaces;a Transformation of Hybrid Petri Nets with Stochastic Firings into a Subclass of Stochastic Hybrid Automata;run-Time Assurance for Learning-Enabled systems;heterogeneous Verification of an Autonomous Curiosity Rover;generating Correct-by-Construction distributed Implementations from Formal Maude Designs;verifying Band Convergence for Sampled Control systems;model Checking Timed Hyperproperties in Discrete-Time systems;sampling distributed Schedulers for Resilient Space Communication;per-Location Simulation;preface;automated Requirements-Based Testing of Black-Box Reactive systems;neural Simplex Architecture;simplifying Neural Networks Using Formal Verification;constraining Counterexamples in Hybrid System Falsification: Penalty-Based Approaches;hpnmg: A C++ Tool for Model Checking Hybrid Petri Nets with General Transitions;constraint Caching Revisited.
Blockchain technologies are on the rise, and Hyperledger Fabric is one of the most popular permissioned blockchain platforms. In this paper, we re-architect the validation phase of Fabric based on our analysis from fi...
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ISBN:
(纸本)9781728149516;9781728149509
Blockchain technologies are on the rise, and Hyperledger Fabric is one of the most popular permissioned blockchain platforms. In this paper, we re-architect the validation phase of Fabric based on our analysis from fine-grained breakdown of the validation phase's latency. Our optimized validation phase uses a chaincode cache during validation of transactions, initiates state database reads in parallel with validation of transactions, and writes to the ledger and databases in parallel. Our experiments reveal performance improvements of 2x for CouchDB and 1.3x for LevelDB. Notably, our optimizations can be adopted in a future release of Hyperledger Fabric.
Long-term archival storage systems must protect data from powerful attackers that might try to corrupt or censor (part of) the documents. They must also protect the corresponding metadata information, which is essenti...
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ISBN:
(数字)9781728142227
ISBN:
(纸本)9781728142227
Long-term archival storage systems must protect data from powerful attackers that might try to corrupt or censor (part of) the documents. They must also protect the corresponding metadata information, which is essential to maintain and rebuild the stored data. In this practical experience report, we present METABLOCK, a metadata protection system leveraging the Ethereum distributed ledger. We combine METABLOCK with an existing secure long-term data archival system to provide a scalable design that allows external auditing, data validation and efficient data repair. We reflect on our experiences in using a blockchain for metadata protection, with the goal of providing valuable insights and lessons for developers of such secure systems, by highlighting the potential and limitations of the approach. Our prototype is available on Github.
Due to individual unreliable commodity components, failures are common in large-scale distributed storage systems. Erasure codes are widely deployed in practical storage systems to provide fault tolerance with low sto...
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ISBN:
(纸本)9781728112466
Due to individual unreliable commodity components, failures are common in large-scale distributed storage systems. Erasure codes are widely deployed in practical storage systems to provide fault tolerance with low storage overhead. However, the commonly used random data placement in storage systems based on erasure codes induces to heavy cross-rack traffic, load imbalance, and random access, which slow down the recovery process upon failures. In this paper, with orthogonal arrays, we define a Deterministic Data Distribution (D-3) of blocks to nodes and racks, and propose an efficient failure recovery approach based on D-3. D3 not only uniformly distributes data/parity blocks among storage servers, but also balances the repair traffic among racks and storage servers for failure recovery. Furthermore, D-3 also minimizes the cross-rack repair traffic for data layouts against a single rack failure and provides sequential access for failure recovery. We implement D-3 in Hadoop distributed File System (HDFS) with a cluster of 28 machines. Our experiments show that D-3 significantly speeds up the failure recovery process compared with random data distribution, e.g., 2.21 times for (6, 3)-RS code in a system consisting of eight racks and three nodes in each rack.
With the rapid increase in the size and volume of cloud services and data centers, architectures with multiple job dispatchers are quickly becoming the norm. Load balancing is a key element of such systems. Neverthele...
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CRDTs are distributed data types that make eventual consistency of a distributed object possible and non adhoc. Geo-distributedsystems are spread across multiple data centers at different geographic locations to ensu...
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ISBN:
(数字)9781728142227
ISBN:
(纸本)9781728142227
CRDTs are distributed data types that make eventual consistency of a distributed object possible and non adhoc. Geo-distributedsystems are spread across multiple data centers at different geographic locations to ensure availability and performance despite network partitions. These systems must accept updates at any replica and propagate these updates asynchronously to every other replica. Conflict-Free Replicated Data Types (CRDTs) ensures eventual consistency in the replicas despite asynchronous delivery of updates. Extending this idea to fog computing servers where connection reliability is low, eventual consistency amongst the servers is required. We configure Kubernetes, an open-source container orchestration system used for automating deployment, scaling, and management of containerized applications, and use it for cluster deployment of CRDT based low resource intensive AntidoteDB can be used for deployment on fog servers to ensure eventual consistency amongst these servers. We have developed an automated benchmarking tool for benchmarking edge computing applications.
Major research topics on parallel and distributed frameworks focus on reliability, performance and programmability of large scale systems for, e.g., HPC or Big Data. The solutions proposed are often directly impacted ...
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ISBN:
(纸本)9781538655559
Major research topics on parallel and distributed frameworks focus on reliability, performance and programmability of large scale systems for, e.g., HPC or Big Data. The solutions proposed are often directly impacted by the large scale nature of the problems. Differently, high-throughput data stream generation is an important challenge for many scientific and industrial applications which is typically well suited for small to medium scale systems, and which has to respect specific constraints about, e.g., speed, throughput or output location. In this paper we present a framework dedicated to this class of problems. We propose a performance-oriented runtime system architecture able to generate constrained data streams issued from jobs dynamically submitted by the user. Our architecture is designed to scale from a single host to a medium-sized cluster with large topology flexibility to achieve high throughput capabilities while being widely adaptive to a variety of problems. We provide experimental evidence of the ability of our framework to meet high-throughput constraints on an industrial use-case, i.e., professional digital printing, that may require tens of Gbit/s sustained output rates. We show in our measurements that our system scales and reaches data rates close to the maximum throughput of our experimental cluster.
distributed machine learning (ML) has played a key role in today's proliferation of AI services. A typical model of distributed ML is to partition training datasets over multiple worker nodes to update model param...
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parallel I/O performance is crucial to sustaining scientific applications on large-scale High-Performance Computing (HPC) systems. However, I/O load imbalance in the underlying distributed and shared storage systems c...
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ISBN:
(纸本)9781728112466
parallel I/O performance is crucial to sustaining scientific applications on large-scale High-Performance Computing (HPC) systems. However, I/O load imbalance in the underlying distributed and shared storage systems can significantly reduce overall application performance. There are two conflicting challenges to mitigate this load imbalance: (i) optimizing system-wide data placement to maximize the bandwidth advantages of distributed storage servers, i.e., allocating I/O resources efficiently across applications and job runs;and (ii) optimizing client-centric data movement to minimize I/O load request latency between clients and servers, i.e., allocating I/O resources efficiently in service to a single application and job run. Moreover, existing approaches that require application changes limit wide-spread adoption in commercial or proprietary deployments. We propose iez, an "end-to-end control plane" where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load information for distributed storage server global data placement while our design model leverages trace-based optimization techniques to minimize I/O load request latency between clients and servers. We evaluate our proposed system on an experimental cluster for two common use cases: synthetic I/O benchmark IOR for large sequential writes and a scientific application I/O kernel, HACC-I/O. Results show read and write performance improvements of up to 34% and 32%, respectively, compared to the state of the art.
The present paper describes a new parallel time-domain simulation algorithm using a high performance computing environment - Julia - for the analysis of power system dynamics in large networks. The parallel algorithm ...
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The present paper describes a new parallel time-domain simulation algorithm using a high performance computing environment - Julia - for the analysis of power system dynamics in large networks. The parallel algorithm adapts a parallel-in-space decomposition scheme to a previously sequential algorithm in order to develop a new parallelizable numerical solution of the power system equations. The parallel-in-space decomposition is based on the block bordered diagonal form, which reformulates the network admittance matrix into sub-blocks that can be solved in parallel. For the optimal spatial decomposition of the network, a new extended graph partitioning strategy is developed for load balancing and minimizing the communication between subnetworks. The new parallel simulation algorithm is tested using standard test networks of varying complexity. The simulation results are compared to those obtained from a sequential implementation in order to validate the solution accuracy and to determine the performance improvement in terms of computational speedup. Test simulations are conducted using the ForHLR II supercomputing cluster and show a huge potential in computational speedup with increasing network complexity.
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