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20th IEEE International symposium on Mixed and Augmented Reality (ISMAR)

作者： Gattullo, Michele Laviola, Enricoandrea Fiorentino, Michele Uva, Antonio E. Polytech Univ Bari Bari BA Italy

ISBN: (纸本)9781665412988

This research investigates the application of positive computing principles to Virtual Reality (VR) training scenarios where the Virtual Environment (VE) has not a direct influence on operator learning. We propose to place the 3D models of the only objects needed for the task in a VE consisting of 360 degrees panoramas of natural environments. We made a preliminary evaluation of the user experience which showed that the hedonic quality is significantly higher with this VE than a 3D modeled empty room. However, we also observed a reduction of the pragmatic quality, due to potential distractions. Thus, further research is needed to demonstrate the efficacy of our positive computing approach in training against a traditional one based on the faithful 3D reproduction of the real environment.

关键词： Positive computing Virtual Reality Industrial Training computing methodologies Modeling and simulation Simulation support systems Simulation environments

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Confidential Serverless Made Efficient with Plug-In Enclaves 21

Confidential Serverless Made Efficient with Plug-In Enclaves

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acm/IEEE 48th Annual International symposium on Computer Architecture (ISCA)

作者： Li, Mingyu Xia, Yubin Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665433334

Serverless computing has become a fact of life on modern clouds. A serverless function may process sensitive data from clients. Protecting such a function against untrusted clouds using hardware enclave is attractive for user privacy. In this work, we run existing serverless applications in SGX enclave, and observe that the performance degradation can be as high as 5.6 x to even 422.6 x . Our investigation identifies these slowdowns are related to architectural features, mainly from page-wise enclave initialization. Leveraging insights from our overhead analysis, we revisit SGX hardware design and make minimal modification to its enclave model. We extend SGX with a new primitive-region-wise plugin enclaves that can be mapped into existing enclaves to reuse attested common states amongst functions. By remapping plugin enclaves, an enclave allows in-situ processing to avoid expensive data movement in a function chain. Experiments show that our design reduces the enclave function latency by 94.74-99.57%, and boosts the autoscaling throughput by 19-179 x.

关键词： Intel SGX Serverless Confidential computing

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ITER8: Online Experimentation in the Cloud 12

ITER8: Online Experimentation in the Cloud

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12th Annual acm symposium on Cloud computing (SoCC)

作者： Toslali, Mert Parthasarathy, Srinivasan Oliveira, Fabio Huang, Hai Coskun, Ayse K. Boston Univ Boston MA 02215 USA IBM Res Hawthorne NY USA

ISBN: (纸本)9781450386388

Online experimentation is an agile software development practice that plays an essential role in enabling rapid innovation. Existing solutions for online experimentation in Web and mobile applications are unsuitable for cloud applications. There is a need for rethinking online experimentation in the cloud to advance the state-of-the-art by considering the unique challenges posed by cloud environments. In this paper, we introduce Iter8, an open-source system that enables practitioners to deliver code changes to cloud applications in an agile manner while minimizing risk. Iter8 embodies our novel mathematical formulation built on online Bayesian learning and multi-armed bandit algorithms to enable online experimentation tailored for the cloud, considering both SLOs and business concerns, unlike existing solutions. Using Iter8, practitioners can safely and rapidly orchestrate various types of online experiments, gain key insights into the behavior of cloud applications, and roll out the optimal versions in an automated and statistically rigorous manner.

关键词： performance distributed systems online experimentation multi-armed bandit

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Integrating Parallel and distributed computing in Early computing Classes 2024

Integrating Parallel and Distributed Computing in Early Comp...

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proceedings of the 55th acm Technical symposium on Computer Science Education V. 2

作者： Alan Sussman Sushil Prasad Charles Weems Sheikh Ghafoor Ramachandran Vaidyanathan University of Maryland College Park MD USA University of Texas at San Antonio San Antonio TX USA University of Massachusetts Amherst MA USA Tennessee Tech University Cookeville TN USA Louisiana State University Baton Rouge LA USA

ISBN: (纸本)9798400704246

Parallel and distributed computing (PDC) has become pervasive in all aspects of computing, so it is essential that students include parallelism and distribution in the computational thinking that they apply to problem solving, from the very beginning of their computing education. With all computing devices that students use currently having multiple cores as well as a GPU in many cases, many students' favorite applications use multiple cores and/or distributed processors. However, we are still teaching them to solve problems using only sequential thinking. Why?This hands-on workshop will demonstrate how easy it is to open students' eyes to exploiting concurrency in problem solving. You will participate in unplugged activities that will help students to recognize examples of PDC concepts and concurrency in the world around them. You will learn how freely available libraries can be used to exploit parallelism using shared memory parallel programming, using both plugged and unplugged activities that are usable in a classroom *** equipment or prior experience is required, but a laptop that can run C++, Java, and Python is recommended for following along with some code examples. The activities and discussion models have been used successfully to teach PDC concepts in early computing courses and will be available after the workshop. Participants will receive a stipend of 400 to defray their cost of registration and one-night hotel stay. The CDER center will also have a booth in the exhibition hall for additional support.

关键词： computing education early computing class hpc education, shared memory parallel programming pdc education

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Fast distributed approximation for TAP and 2-edge-connectivity

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distributed computing 2020年第2期33卷 145-168页

作者： Censor-Hillel, Keren Dory, Michal Technion Dept Comp Sci Haifa Israel

The tree augmentation problem (TAP) is a fundamental network design problem, in which the input is a graph G and a spanning tree T for it, and the goal is to augment T with a minimum set of edges Aug from G, such that T boolean OR Aug is 2-edge-connected. TAP has been widely studied in the sequential setting. The best known approximation ratio of 2 for the weighted case dates back to the work of Frederickson and JaJa (SIAM J Comput 10(2):270-283, 1981). Recently, a 3/2-approximation was given for unweighted TAP by Kortsarz and Nutov (acm Trans Algorithms 12(2):23, 2016). Recent breakthroughs give an approximation of 1.458 for unweighted TAP (Grandoni et al. in: proceedings of the 50th annual acm SIGACT symposium on theory of computing (STOC 2018), 2018), and approximations better than 2 for bounded weights (Adjiashvili in: proceedings of the twenty-eighth annual acm-SIAM symposium on discrete algorithms (SODA), 2017;Fiorini et al. in: proceedings of the twenty-ninth annual acm-SIAM symposium on discrete algorithms (SODA 2018), New Orleans, LA, USA, 2018. 10.1137/1.9781611975031.53). In this paper, we provide the first fast distributed approximations for TAP. We present a distributed 2-approximation for weighted TAP which completes in O(h) rounds, where h is the height of T. When h is large, we show a much faster 4-approximation algorithm for the unweighted case, completing in O(D+root nlog*n) rounds, where n is the number of vertices and D is the diameter of G. Immediate consequences of our results are an O(D)-round 2-approximation algorithm for the minimum size 2-edge-connected spanning subgraph, which significantly improves upon the running time of previous approximation algorithms, and an O(h(MST)+root nlog*n)-round 3-approximation algorithm for the weighted case, where h(MST) is the height of the MST of the graph. Additional applications are algorithms for verifying 2-edge-connectivity and for augmenting the connectivity of any connected spanning subgraph to 2.

关键词： distributed graph algorithms Approximation algorithms distributed network design Connectivity augmentation

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Simple and Optimal Algorithms for Heavy Hitters and Frequency Moments in distributed Models 25

Simple and Optimal Algorithms for Heavy Hitters and Frequenc...

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proceedings of the 57th Annual acm symposium on Theory of computing

作者： Zengfeng Huang Zhongzheng Xiong Xiaoyi Zhu Zhewei Wei Fudan University Shanghai China Renmin University of China Beijing China

ISBN: (纸本)9798400715105

We consider the problems of distributed heavy hitters and frequency moments in both the coordinator model and the distributed tracking model (also known as the distributed functional monitoring model). We present simple and optimal (up to logarithmic factors) algorithms for ℓp heavy hitters and Fp estimation (p ≥ 2) in these distributed *** ℓp heavy hitters in the coordinator model, our algorithm requires only one round and uses Õ(kp−1/p) bits of communication. For p > 2, this is the first near-optimal result. By combining our algorithm with the standard recursive sketching technique, we obtain a near-optimal two-round algorithm for Fp in the coordinator model, matching a significant result from recent work by Esfandiari et al. (STOC 2024). Our algorithm and analysis are much simpler and have better costs with respect to logarithmic factors. Furthermore, our technique provides a one-round algorithm for Fp, which is a significant improvement over a result of Woodruff and Zhang (STOC 2012).Thanks to the simplicity of our heavy hitter algorithms, we manage to adapt them to the distributed tracking model with only a (n) increase in communication. For ℓp heavy hitters, our algorithm has a communication cost of Õ(kp−1/p), representing the first near-optimal algorithm for all p ≥ 2. By applying the recursive sketching technique, we also provide the first near-optimal algorithm for Fp in the distributed tracking model, with a communication cost of Õ(kp−1/2) for all p ≥ 2. Even for F2, our result improves upon the bounds established by Cormode, Muthukrishnan, and Yi (SODA 2008) and Woodruff and Zhang (STOC 2012), nearly matching the existing lower bound for the first time.

关键词： distributed Algorithms

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Shard Manager: A Generic Shard Management Framework for Geo-distributed Applications 21

Shard Manager: A Generic Shard Management Framework for Geo-...

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28th acm symposium on Operating Systems principles (SOSP)

作者： Lee, Sangmin Guo, Zhenhua Sunercan, Omer Ying, Jun Kooburat, Thawan Biswal, Suryadeep Chen, Jun Huang, Kun Cheung, Yatpang Zhou, Yiding Veeraraghavan, Kaushik Damani, Biren Ruiz, Pol Mauri Mehta, Vikas Tang, Chunqiang Facebook Inc Menlo Pk CA 94025 USA

ISBN: (纸本)9781450387095

Sharding is widely used to scale an application. Despite a decade of effort to build generic sharding frameworks that can be reused across different applications, the extent of their success remains unclear. We attempt to answer a fundamental question: what barriers prevent a sharding framework from getting adopted by the majority of sharded applications? We analyze hundreds of sharded applications at Facebook and identify two major barriers: 1) lack of support for geo-distributed applications, which account for most of Facebook's applications, and 2) inability to maintain application availability during planned events such as software upgrades, which happen similar to 1000 times more frequently than unplanned failures. A sharding framework that does not help applications to address these fundamental challenges is not sufficiently attractive for most applications to adopt it. Other adoption barriers include the burden of supporting many complex applications in a one-size-fit-all sharding framework and the difficulty in supporting sophisticated shard-placement requirements. Theoretically, a constraint solver can handle complex placement requirements, but in practice it is not scalable enough to perform near-realtime shard placement at a global scale. We have overcome these adoption barriers in Facebook's sharding framework called Shard Manager. Currently, Shard Manager is used by hundreds of applications running on over one million machines, which account for about 54% of all sharded applications at Facebook.

关键词： shard management sharding availability

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Fast and Accurate Optimizer for Query Processing over Knowledge Graphs 12

Fast and Accurate Optimizer for Query Processing over Knowle...

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12th Annual acm symposium on Cloud computing (SoCC)

作者： Wu, Jingqi Chen, Rong Xia, Yubin Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9781450386388

This paper presents GPL, a fast and accurate optimizer for query processing over knowledge graphs. GPL is novel in three ways. First, GPL proposes a type-centric approach to enhance the accuracy of cardinality estimation prominently, which naturally embeds the correlation of multiple query conditions into the existing type system of knowledge graphs. Second, to predict execution time accurately, GPL constructs a specialized cost model for graph exploration scheme and tunes the coefficients with target hardware platform and graph data. Third, GPL further uses a budget-aware strategy for plan enumeration with a greedy heuristic to boost the overall performance (i.e., optimization time and execution time) for various workloads. Evaluations with representative knowledge graphs and query benchmarks show that GPL can select optimal plans for 33 of 39 queries and only incurs less than 5% slowdown on average compared to optimal results. In contrast, the state-of-the-art optimizer and manually tuned results will cause 100% and 36% slowdown, respectively.

关键词： Query optimization knowledge graphs

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DistRDF2ML-Scalable distributed In-Memory Machine Learning Pipelines for RDF Knowledge Graphs 21

DistRDF2ML-Scalable Distributed In-Memory Machine Learning P...

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30th acm International Conference on Information and Knowledge Management (CIKM)

作者： Draschner, Carsten Felix Stadler, Claus Bakhshandegan, Farshad Lehmann, Jens Jabeen, Hajira Univ Bonn Bonn Germany Univ Leipzig Leipzig Germany Fraunhofer IAIS Dresden Germany Univ Cologne Cologne Germany

ISBN: (纸本)9781450384469

This paper presents DistRDF2ML, the generic, scalable, and distributed framework for creating in-memory data preprocessing pipelines for Spark-based machine learning on RDF knowledge graphs. This framework introduces software modules that transform large-scale RDF data into ML-ready fixed-length numeric feature vectors. The developed modules are optimized to the multimodal nature of knowledge graphs. DistRDF2ML provides aligned software design and usage principles as common data science stacks that offer an easy-to-use package for creating machine learning pipelines. The modules used in the pipeline, the hyper-parameters and the results are exported as a semantic structure that can be used to enrich the original knowledge graph. The semantic representation of metadata and machine learning results offers the advantage of increasing the machine learning pipelines' reusability, explainability, and reproducibility. The entire framework of DistRDF2ML is open source, integrated into the holistic SANSA stack, documented in scala-docs, and covered by unit tests. DistRDF2ML demonstrates its scalable design across different processing power configurations and (hyper-)parameter setups within various experiments. The framework brings the three worlds of knowledge graph engineers, distributed computation developers, and data scientists closer together and offers all of them the creation of explainable ML pipelines using a few lines of code.

关键词： Machine Learning Knowledge Graphs distributed computing Explainable AI Scalable Semantic Processing RDF Preprocessing

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Kauri: Scalable BFT Consensus with Pipelined Tree-Based Dissemination and Aggregation 21

Kauri: Scalable BFT Consensus with Pipelined Tree-Based Diss...

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28th acm symposium on Operating Systems principles (SOSP)

作者： Neiheiser, Ray Matos, Miguel Rodrigues, Luis Univ Lisbon IST INESC ID Lisbon Portugal Univ Fed Santa Catarina DAS Florianopolis SC Brazil

ISBN: (纸本)9781450387095

With the growing commercial interest in blockchains, permissioned implementations have received increasing attention. Unfortunately, the BFT consensus algorithms that are the backbone of most of these blockchains scale poorly and offer limited throughput. Many state-of-the-art algorithms require a single leader process to receive and validate votes from a quorum of processes and then broadcast the result, which is inherently non-scalable. Recent approaches avoid this bottleneck by using dissemination/aggregation trees to propagate values and collect and validate votes. However, the use of trees increases the round latency, which ultimately limits the throughput for deeper trees. In this paper we propose Kauri, a BFT communication abstraction that can sustain high throughput as the system size grows, leveraging a novel pipelining technique to perform scalable dissemination and aggregation on trees. Our evaluation shows that Kauri outperforms the throughput of state-of-the-art permissioned blockchain protocols, such as HotStuff, by up to 28x. Interestingly, in many scenarios, the parallelization provided by Kauri can also decrease the latency.

关键词： distributed Systems Fault Tolerance Blockchain

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