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检索条件"任意字段=International Symposium Distributed Computing and Applications Business Engineering and Science"

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LCC-Graph: A high-performance graph-processing framework with low communication costs

LCC-Graph: A high-performance graph-processing framework wit...

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international Workshop on Quality of Service

作者： Yongli Cheng Fang Wang Hong Jiang Yu Hua Dan Feng Xiuneng Wang Wuhan National Lab for Optoelectronics Huazhong University of Science and Technology Wuhan China Department of Computer Science & Engineering University of Texas at Arlington USA

With the rapid growth of data, communication overhead has become an important concern in applications of data centers and cloud computing. However, existing distributed graph-processing frameworks routinely suffer from high communication costs, leading to very long waiting times experienced by users for the graph-computing results. In order to address this problem, we propose a new computation model with low communication costs, called LCC-BSP. We use this model to design and implement a high-performance distributed graph-processing framework called LCC-Graph. This framework eliminates the high communication costs in existing distributed graph-processing frameworks. Moreover, LCC-Graph also minimizes the computation workload of each vertex, significantly reducing the computation time for each superstep. Evaluation of LCC-Graph on a 32-node cluster, driven by real-world graph datasets, shows that it significantly outperforms existing distributed graph-processing frameworks in terms of runtime, particularly when the system is supported by a high-bandwidth network. For example, LCC-Graph achieves an order of magnitude performance improvement over GPS and GraphLab.

关键词： Computational modeling Global Positioning System Bandwidth Message systems Receivers Loading Computers

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Enhanced null message algorithm for hybrid parallel simulation systems with large disparity in time step

Enhanced null message algorithm for hybrid parallel simulati...

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international Workshop/symposium on distributed Simulation and Real-Time applications (DS-RT)

作者： Bin Wang Yanlong Zhai Zishuo Wang Han Zhang Duzheng Qing Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Applications Bijing Institute of Technology Beijing China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center China

ISBN: (纸本)9781509035069

Modeling and simulation has received significant attention over the years for its ability to understand and transform real world systems. With the increasing of complexity and scale of the simulated systems, more computational power is required. Parallel discrete event simulation(PDES) is an important technology to integrate parallel computing into modeling and simulation. Time synchronization algorithm is the core of PDES, which determines the correctness and efficiency of the simulation. Traditional conservative synchronization algorithms, such as Chandy-Misra-Bryant(CMB) null message algorithm, use null message to synchronize the state and avoid deadlock among logical processes(LPs). But hybrid simulation systems contains both continuous models and discrete models, continuously changing states is typically modeled using time steps in hybrid simulation system. Therefore, hybrid systems with large disparity in time step are very common in real simulation systems. However, current conservative synchronization algorithm has not been fully investigated for this type of simulation systems and will generate massive synchronization messages. This paper proposed an enhanced null message algorithm for hybrid simulation systems with large disparity in time step. It extended the CMB algorithm by adding simulation time in null message request messages. This could significantly reduce the number of synchronization messages. Experimental results showed that our synchronization algorithm outperforms the CMB algorithm by reducing a great number of synchronization messages.

关键词： Synchronization Computational modeling Atmospheric modeling Adaptation models System recovery Ports (Computers) Complex systems

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Research of O2O-oriented service discovery method based on user context 14

Research of O2O-oriented service discovery method based on u...

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international symposium distributed computing and applications business engineering and science

作者： She Qiping Qing, Li Juan, Deng Zhong, Wu Wuhan Univ Sci & Technol City Coll Wuhan 430083 Peoples R China Wuhan Univ Technol HuaXia Coll Wuhan 430223 Peoples R China Wuhan Business Univ Wuhan 430056 Peoples R China

ISBN: (纸本)9781467365932

In the last few years, O2O (Online To Offline) mode become more and more popular, which brings offline service and online web service together, it is important to consider the quality attributes of offline service when recommending service for users online. But in the field of service discovery system, most of the studies focused on function and quality requirements of online service, not on offline service. Furthermore, the existing service discovery methods are generally based on users' function and quality requirements, ignoring the fact that users may have different needs in different contexts. In this paper, we proposed a context-based O2O-ortiented service discovery method, selecting service with function and quality requirements first, and then filtering service with context information, to get the contextual personalized services for users.

关键词： user context online service offline service service discovery

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Secure mobile agent in M-Commerce over internet

Secure mobile agent in M-Commerce over internet

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international Conference on Emerging Trends in engineering, Technology and science (ICETETS)

作者： M. Malathy S. Jasmine Smilee J. Niranjan Samuel Kings College of Engineering Punalkulam Pudukkottai Tamilnadu India

ISBN: (纸本)9781467367264

Mobile agent technology offers a new computing paradigm in which a program, in the form of a software agent, can suspend its execution on a host computer, transfer itself to another agent-enabled host on the network, and resume execution on the new host. Recent mobile agents are able to categorize in a various ways of distributed equipped embedded intelligence agents systems. This information provides an indication of the variety of threats affecting the mobile agent systems. The developers of mobile agent facing security problems based on their applications. In M-Commerce of business information agent system is built based on mobile agent technology. It sends out agents to different hosts in an electronic marketplace. The agents collect and report information such as prices and availabilities about products specified by users. Security is a main issue of mobile agent systems, particularly when soft cash dealings are concerned.

关键词： Mobile agents Mobile communication Computer crime business Sensitivity Software agents

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MR-Advisor: A Comprehensive Tuning Tool for Advising HPC Users to Accelerate MapReduce applications on Supercomputers

MR-Advisor: A Comprehensive Tuning Tool for Advising HPC Use...

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international symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： Md. Wasi-Ur-Rahman Nusrat Sharmin Islam Xiaoyi Lu Dipti Shankar Dhabaleswar K. Panda Department of Computer Science and Engineering The Ohio State University

ISBN: (纸本)9781509061099

MapReduce is the most popular parallel computing framework for big data processing which allows massive scalability across distributed computing environment. Advanced RDMA-based design of Hadoop MapReduce has been proposed that alleviates the performance bottlenecks in default Hadoop MapReduce by leveraging the benefits from RDMA. On the other hand, data processing engine, Spark, provides fast execution of MapReduce applications through in-memory processing. Performance optimization for these contemporary big data processing frameworks on modern High-Performance computing (HPC) systems is a formidable task because of the numerous configuration possibilities in each of them. In this paper, we propose MR-Advisor, a comprehensive tuning tool for MapReduce. MR-Advisor is generalized to provide performance optimizations for Hadoop, Spark, and RDMA-enhanced Hadoop MapReduce designs over different file systems such as HDFS, Lustre, and Tachyon. Performance evaluations reveal that, with MR-Advisor's suggested values, the job execution performance can be enhanced by a maximum of 58% over the current best-practice values for user-level configuration parameters. To the best of our knowledge, this is the first tool that supports tuning for both Apache Hadoop and Spark, as well as the RDMA and Lustre-based advanced designs.

关键词： Tuning Sparks Benchmark testing Engines Big data Middleware Computer architecture

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Hadoop multi node cluster resource analysis

Hadoop multi node cluster resource analysis

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Colossal Data Analysis and Networking (CDAN)

作者： Kapil Pandey Anand Gadwal Prashant Lakkadwala Acropolis Technical Campus Truba College of Engineering and Technology Indore (MP)

ISBN: (纸本)9781509006700

In the Computer System we have basically three types of Resources; they are Software, Hardware and Data. Data is the most important resource of computer system, because whatever computing we are doing is just because of data. Data science deals with large amount of data to infer knowledge from the data sets to rationalize the information to achieve business values. Traditionally information was in structured form, means that was generally generated by business transactions. This information was easily process with our traditional data management system. In past few years the amount of digital data generated was grown exponentially. This large amount of data was unstructured, which cannot be processed and extracted efficiently from our traditional system. It includes Text files, sensor data, log data, web data, social networking data etc. The major reason behind the generation of this unstructured data is various applications used via internet e.g. Smart devices, web, mobile, social media and sensor Devices. For achieving the business goals this data is essential to mine. This large amount of unstructured data is called BIG DATA [1]. It's in large volume, its varying. There are various tools are available for processing of this large amount of data. Hadoop is one of the popular and efficient tool for the processing of Big Data. Hadoop provide a framework that allows us distributed computing and run tasks in parallel such that such type of complex data can be processed efficiently with respect to time, performance and resources. This paper covers the major resources used by Hadoop cluster.

关键词： distributed databases Big data business Data mining Social network services Servers Google

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D^2PS: A Dependable Data Provisioning Service in Multi-tenant Cloud Environment

D^2PS: A Dependable Data Provisioning Service in Multi-tenan...

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IEEE international Symposim on High Assurance Systems engineering

作者： Renyu Yang Tianyu Wo Chunming Hu Jie Xu Mingming Zhang School of Computer Science and Engineering Beihang University Beijing China School of Computing University of Leeds Leeds UK

Software as a Service (SaaS) is a software delivery and business model widely used by Cloud computing. Instead of purchasing and maintaining a software suite permanently, customers only need to lease the software on-demand. The domain of high assurance distributed systems has focused greatly on the areas of fault tolerance and dependability. In a multi-tenant context, it is particularly important to store, manage and provision data services to customers in a highly efficient and dependable manner due to a large number of file operations involved in running such services. It is also desirable to allow a user group to share and cooperate (e.g., co-edit) on some specific data. In this paper we present a dependable data provisioning service in a multi-tenant Cloud environment. We describe a metadata management approach and leverage multiple replicated metadata caching to shorten the file access time, with the improved efficiency of data sharing. In order to reduce frequent data transmission and data access latency, we introduce a distributed cooperative disk cache mechanism that supports effective cache placement and pull-push cache synchronization. In addition, we use efficient component failover to enhance the service dependability whilst avoiding negative impact from system failures. Our experimental results show that our system can significantly reduce both unused data transmission and response latency. Specifically, over 50% network transmission and operational latency can be saved for random reads while 28.24% network traffic and 25% response latency can be reduced for random write operations. We believe that these findings are demonstrating positive results along the right direction of resolving storage-related challenges in a multi-tenant Cloud environment.

关键词： Metadata Cloud computing Software as a service Data communication Synchronization Servers

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Performance Characterization of Hypervisor-and Container-Based Virtualization for HPC on SR-IOV Enabled InfiniBand Clusters

Performance Characterization of Hypervisor-and Container-Bas...

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

作者： Jie Zhang Xiaoyi Lu Dhabaleswar K. Panda Department of Computer Science and Engineering The Ohio State University Columbus OH USA

ISBN: (纸本)9781509036837

Hypervisor (e.g. KVM) based virtualization has been used as a fundamental technology in cloud computing. However, it has the inherent performance overhead in the virtualized environments, more specifically, the virtualized I/O devices. To alleviate such overhead, PCI passthrough can be utilized to have exclusive access to I/O device. However, this way prevents the I/O device from sharing with multiple VMs. Single Root I/O Virtualization (SR-IOV) technology has been introduced for high-performance interconnects such as InfiniBand to address such sharing issue while having ideal performance. On the other hand, with the advances in container-based virtualization (e.g. Docker), it is also possible to reduce the virtualization overhead by deploying containers instead of VMs so that the near-native performance can be obtained. In order to build high-performance HPC cloud, it is important to fully understand the performance characteristics of different virtualization solutions and virtualized I/O technologies on InfiniBand clusters. In this paper, we conduct a comprehensive evaluation using IB verbs, MPI benchmarks and applications. We characterize the performance of hypervisor-and container-based virtualization with PCI passthrough and SR-IOV for HPC on InfiniBand clusters. Our evaluation results indicate that VM with PCI passthrough (VM-PT) outperforms VM with SR-IOV (VM-SR-IOV), while SR-IOV enables efficient resource sharing. Overall, the container-based solution can deliver better performance than the hypervisor-based solution. Compared with the native performance, container with PCI passthrough (Container-PT) only incurs up to 9% overhead on HPC applications.

关键词： Virtualization Performance evaluation Virtual machine monitors Containers Cloud computing Hardware Benchmark testing

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A Scheduling Algorithm for Hadoop MapReduce Workflows with Budget Constraints in the Heterogeneous Cloud

A Scheduling Algorithm for Hadoop MapReduce Workflows with B...

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

作者： Andrew Wylie Wei Shi Jean-Pierre Corriveau Yang Wang School of Computer Science Carleton University Ottawa Ontario Faculty of Business and IT University of Ontario Institute of Technology Oshawa Ontario Chinese Academy of Science Shenzhen Institute of Advanced Technology Shenzhen P. R. China

ISBN: (纸本)9781509036837

In recent years cloud services have gained much attention as a result of their availability, scalability, and low cost. One use of these services has been for the execution of scientific workflows as part of Big Data Analytics, which are employed in a diverse range of fields including astronomy, physics, seismology, and bioinformatics. There has been much research on heuristic scheduling algorithms for these workflows due to the problem's inherent complexity, however existing work has mainly considered execution in a utility grid environment using a generic distributed framework. For our research, we consider the ever-increasingly popular Apache Hadoop framework for scheduling workflow onto resources rented from cloud service providers. Contrary to other distributed frameworks, the Hadoop MapReduce model imposes a functional style onto application definition, and as such presents an interesting and unapproached challenge for workflow scheduling. Investigated in our work is budget-constrained workflow scheduling on the Hadoop MapReduce platform, wherein we devise both an optimal and a heuristic approach to minimize workflow makespan while satisfying a given budget constraint. We have implemented modifications to the Apache Hadoop framework to allow fully integrated workflow scheduling. These modifications are novel and have led to the completion of the first generic workflow scheduler fully integrated with the Apache Hadoop framework. Both the framework modifications and the proposed scheduler implementation have been extensively tested via execution on multiple workflow applications, which demonstrates the ability of our implementation to handle all possible workflow substructures. Results from our empirical studies further establish these facts.

关键词： Optimal scheduling Cloud computing Scheduling Scheduling algorithms Electronic mail Engines

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Heterogeneous Streaming

Heterogeneous Streaming

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

作者： Chris J. Newburn Gaurav Bansal Michael Wood Luis Crivelli Judit Planas Alejandro Duran Paulo Souza Leonardo Borges Piotr Luszczek Stanimire Tomov Jack Dongarra Hartwig Anzt Mark Gates Azzam Haidar Yulu Jia Khairul Kabir Ichitaro Yamazaki Jesus Labarta Department of Electrical Engineering Visvesvaraya National Institute of Technology Nagpur India Department of Computer Science Kasetsart University Bangkok Thailand

ISBN: (纸本)9781509036837

This paper introduces a new heterogeneous streaming library called hetero Streams (hStreams). We show how a simple FIFO streaming model can be applied to heterogeneous systems that include manycore coprocessors and multicore CPUs. This model supports concurrency across nodes, among tasks within a node, and between data transfers and computation. We give examples for different approaches, show how the implementation can be layered, analyze overheads among layers, and apply those models to parallelize applications using simple, intuitive interfaces. We compare the features and versatility of hStreams, OpenMP, CUDA Streams and OmpSs. We show how the use of hStreams makes it easier for scientists to identify tasks and easily expose concurrency among them, and how it enables tuning experts and runtime systems to tailor execution for different heterogeneous targets. Practical application examples are taken from the field of numerical linear algebra, commercial structural simulation software, and a seismic processing application.

关键词： Libraries Graphics processing units Concurrent computing Data transfer Semantics Tuners Parallel processing

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