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distributed parallelizability Analysis and Optimization of Legacy Code in Cloud Migration 17

Distributed Parallelizability Analysis and Optimization of L...

引用

IEEE Int Conf on parallel and distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Zhao, Junfeng Qin, Ziyang Yang, Hongji Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Inner illongolia Engn Lab Cloud Comp & Serv Soft Hohhot Peoples R China Univ Leicester Dept Informat Leicester Leics England

ISBN: (纸本)9781728143286

More and more organizations plan to migrate their legacy systems to cloud so as to improve the efficiency of data processing. In order to take full advantage of the parallel virtue of cloud computing, legacy system need to be refactored according to the cloud computing program model. Before that, the parallelizability analysis is the first thing to do. This paper first proposes distributed parallelizability of legacy code in the process of refactoring from Legacy code to Mapreduce code (DPLM), and then derives four parallel-determinable features according to the rule: data dependency, continuous dependency, non-homology and randomness. Then algorithms are developed to detect parallel-determinable features. Only the legacy code which is not satisfied with all the four-point features can be refactored to parallelizable MapReduce code. however, through practice, the situation is discovered that there are some type of legacy codes can't take advantage of the parallelism of MapReduce. To solve this problem, parallel-determinable features is divided into strong and weak features. Weak features can he resolved by reorganizing source input file. Partial strong features can be resolved by iterative grading. Finally, the experiments show the validity of DPLM and optimization method, parallel-determinable features and optimization methods.

关键词： Cloud Migration distributed parallelizability parallel-determinable Features Iterative Grading

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Data-driven Adaptive Network Management with Deep Reinforcement Learning

Data-driven Adaptive Network Management with Deep Reinforcem...

引用

IEEE international symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Ameer Ivoghlian Kevin I-Kai Wang Zoran Salcic The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665421751

Advanced wireless communication technologies are trending towards large-scale, geographically distributed systems, which consist of thousands of co-existing devices with potentially conflicting application requirements, such as high data delivery ratio and low power consumption. At the same time, devices are challenged by varying environmental conditions, which can also lead to degradation in network performance. In this paper, a network management system for large scale LoRaWAN-based wireless networks is proposed, aiming to balance application awareness and fairness. Application awareness and fairness are incorporated into a novel reward function, and deep reinforcement learning is used to select node parameters to optimise this reward function. Simulated results demonstrate that this approach is an effective way to automatically manage large-scale LoRaWAN networks.

关键词： Performance evaluation Degradation Power demand Wireless networks Computational modeling distributed databases Reinforcement learning

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The Impossibility of Fast Transactions

The Impossibility of Fast Transactions

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international symposium on parallel and distributed Processing (IPDPS)

作者： Karolos Antoniadis Diego Didona Rachid Guerraoui Willy Zwaenepoel EPFL IBM Research - Zurich University of Sydney

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

We prove that transactions cannot be fast in an asynchronous fault-tolerant system. Our result holds in any system where we require transactions to ensure monotonic writes, or any stronger consistency model, such as, causal consistency. Thus, our result unveils an important, and so far unknown, limitation of fast transactions: they are impossible if we want to tolerate the failure of even one server.

关键词： Servers Fault tolerance Fault tolerant systems Data models distributed processing Computational modeling Surges

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SMR-X: Flexible parallel State Machine Replication for Cloud Computing 19

SMR-X: Flexible Parallel State Machine Replication for Cloud...

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19th Annual IEEE/ACM international symposium on Cluster, Cloud, and Grid Computing (CCGRID)

作者： Zhou, Meng Wu, Weigang Chen, Zhiguang Xiao, Nong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou Guangdong Peoples R China Mo Key Lab Machine Intelligence & Adv Comp Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728109121

State Machine Replication (SMR) is a fundamental fault tolerant technique for distributed systems. SMR traditionally requires sequential execution of commands at each replica node, so as to guarantee strong consistency among replicas. To achieve high performance at large scale cloud datacenters, SMR has been parallelized by employing multiple threads at each replica. In this paper, we propose SMR-X, a novel parallel SMR scheme, which realizes flexible mapping of commands for parallel executing at each replica. The mapping between clients' requests and work threads is dynamically adjusted according to the load level of work threads. Therefore, workloads of different threads can he well balanced and high system throughput can be achieved. The major challenge in our work lies in the inconsistency problem caused by dynamic changes in request-thread mapping. To cope with this, we design delicate mechanisms to synchronize mapping function, so that strong consistency among replicas can he guaranteed. The correctness of the proposed scheme is rigorously proved and its performance is evaluated via simulations. Simulation results show that SMR-X can achieve better load balance and lower access latency than existing parallel SMR schemes.

关键词： cloud computing data centers distributed systems parallel processing state machine replication

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Pufferscale: Rescaling HPC Data Services for High Energy Physics Applications

Pufferscale: Rescaling HPC Data Services for High Energy Phy...

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IEEE/ACM international symposium on Cluster Computing and the Grid (CCGRID)

作者： Nathanaël Cheriere Matthieu Dorier Gabriel Antoniu Stefan M. Wild Sven Leyffer Robert Ross Inria CNRS IRISA Univ. Rennes Rennes France Argonne National Laboratory Lemont IL USA

ISBN: (数字)9781728160955

User-space HPC data services are emerging as an appealing alternative to traditional parallel file systems, because of their ability to be tailored to application needs while eliminating unnecessary overheads incurred by POSIX compliance. The High Energy Physics (HEP) community is progressively turning towards such services to enable high-throughput accesses under heavy concurrency to billions of event data produced by instruments and consumed by subsequent analysis workflows. Such services would benefit from the possibility to be rescaled up and down to adapt to changing workloads, as experimental campaigns progress , in order to optimize resource usage. This paper formalizes rescaling a distributed storage system as a multi objective optimization problem considering three criteria: load balance, data balance, and duration of the rescaling operation. We propose a heuristic for rapidly finding a good approximate solution, while allowing users to weight the criteria as needed. The heuristic is evaluated with Pufferscale, a new rescaling manager for microservice-based distributed storage systems. To validate our approach in a real-world ecosystem, we showcase the use of Pufferscale as a means to enable storage malleability in the HEPnOS storage system for HEP applications.

关键词： Servers Virtual machining High energy physics distributed databases Load management Measurement Peer-to-peer computing

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ePipe: Near Real-Time Polyglot Persistence of HopsFS Metadata 19

ePipe: Near Real-Time Polyglot Persistence of HopsFS Metadat...

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19th Annual IEEE/ACM international symposium on Cluster, Cloud, and Grid Computing (CCGRID)

作者： Ismail, Mahmoud Ronstrom, Mikael Haridi, Seif Dowling, Jim KTH Royal Inst Technol Stockholm Sweden Oracle Redwood City CA USA

ISBN: (纸本)9781728109121

distributed OLTP databases are now used to manage metadata for distributed file systems, but they cannot also efficiently support complex queries or aggregations. To solve this problem, we introduce ePipe, a databus that both creates a consistent change stream for a distributed, hierarchical file system (HopsFS) and eventually delivers the correctly ordered stream with low latency to downstream clients. ePipe can be used to provide polyglot storage for tile system metadata, allowing metadata queries to be handled by the most efficient engine for that query. For file system notifications, we show that ePipe achieves up to 56X throughput improvement over HDFS INotify and Trumpet with up to 3 orders of magnitude lower latency. For Spotify's Hadoop workload, we show that ePipe can replicate all file system changes from HopsFS to Elasticsearch with an average replication lag of only 330 ins.

关键词： distributed file systems File system Notifications Polyglot Persistence

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A Lyapunov-Function-Based Control Strategy for distributed Generations in Hybrid AC/DC Microgrids 28

A Lyapunov-Function-Based Control Strategy for Distributed G...

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28th IEEE international symposium on Industrial Electronics (IEEE-ISIE)

作者： Seyedalipour, Sajjad Bayhan, Sertac Komurcugil, Hasan KN Toosi Univ Technol Fac Elect Engn Tehran Iran Hamad Bin Khalifa Univ Qatar Environm & Energy Res Inst Doha Qatar Eastern Mediterranean Univ Dept Comp Engn Famagusta Northern Cyprus Turkey

ISBN: (纸本)9781728136660

Microgrids are being emerged increasingly in distributed generation (DG) systems. Instabilities because of dynamic interactions among the power electronic converters available in such systems can be observed. It has been demonstrated that a hybrid network containing a number of power converters can be instable, even though each separate one is stable inherently. Hence, in this paper, a control strategy for parallel voltage-source converters (VSCs) in a hybrid ac/dc microgrid is presented. The proposed strategy is based on Lyapunov function and can guarantee the global asymptotic stability of the whole system simultaneously. The proposed control strategy can be applied to any other multistage converter system. Computer simulations are provided to confirm the analytical results.

关键词： distributed generation voltage-source converter Lyapunov function stability

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A distributed Content placement strategy based on popularity for ICN 17

A Distributed Content placement strategy based on popularity...

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作者： Li, Yingqi Yu, Meiju Li, Ru Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9781728143286

In most distributed systems such as ICN, proper content placement can improve network performance, reduce response time, and reduce source server load. Most ICN cache deployment strategies focus on selecting the appropriate node for caching on the content return path, regardless of the popularity of the content. This paper proposes a distributed content placement strategy based on popularity for ICN. The scheme not only considers the distance between the node on the content return path and the request node, but also considers the content popularity trend, predicts the future popularity of the content with the Markov Chain, and pushes the content with the local popular trend to the network in advance. This paper implements the cache placement strategy on the ndnSIM simulator and performs simulation experiments on the real network topology. At the same time, the comparison scheme greedy scheme is also implemented, and two indicators of network delay and hit rate are evaluated. The experimental results show that the network performance of this scheme is higher than that of the greedy scheme in the network stationary phase, with an average delay of 3% and an increase of 8% cache hit rate.

关键词： ICN content placement strategy distributed push algorithm latency hit rate

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Considerations for a distributed GraphBLAS API

Considerations for a Distributed GraphBLAS API

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Benjamin Brock Aydin Buluç Timothy G. Mattson Scott McMillan José E. Moreira Roger Pearce Oguz Selvitopi Trevor Steil EECS Department University of California Berkeley CA Computational Research Department Lawrence Berkeley National Laboratory Berkeley CA Parallel Computing Labs Intel Hillsboro OR Software Engineering Institute Carnegie Mellon University Pittsburgh PA IBM Thomas J. Watson Research Center Yorktown Heights NY Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA School of Mathematics University of Minnesota Minneapolis MN

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

The GraphBLAS emerged from an international effort to standardize linear-algebraic building blocks for computing on graphs and graph-structured data. The GraphBLAS is expressed as a C API and has paved the way for multiple implementations. The GraphBLAS C API, however, does not define how distributed-memory parallelism should be handled. This paper reviews various approaches for a GraphBLAS API for distributed computing. This work is guided by our experience with existing distributed memory libraries. Our goal for this paper is to highlight the pros and cons of different approaches rather than to advocate for one particular choice.

关键词： Context parallel processing Libraries Data structures Task analysis Message systems Electronics packaging

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Tangle Ledger for Decentralized Learning

Tangle Ledger for Decentralized Learning

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Robert Schmid Bjarne Pfitzner Jossekin Beilharz Bert Arnrich Andreas Polze Hasso Plattner Institute University of Potsdam Germany

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Federated learning has the potential to make machine learning applicable to highly privacy-sensitive domains and distributed datasets. In some scenarios, however, a central server for aggregating the partial learning results is not available. In fully decentralized learning, a network of peer-to-peer nodes collaborates to form a consensus on a global model without a trusted aggregating party. Often, the network consists of Internet of Things (IoT) and Edge computing *** approaches for decentralized learning map the gradient batching and averaging algorithm from traditional federated learning to blockchain architectures. In an open network of participating nodes, the threat of adversarial nodes introducing poisoned models into the network increases compared to a federated learning scenario which is controlled by a single authority. Hence, the decentralized architecture must additionally include a machine learning-aware fault tolerance mechanism to address the increased attack *** propose a tangle architecture for decentralized learning, where the validity of model updates is checked as part of the basic consensus. We provide an experimental evaluation of the proposed architecture, showing that it performs well in both model convergence and model poisoning protection.

关键词： Training Data models Peer-to-peer computing Computer architecture Machine learning distributed databases

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