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Reconciling dynamic system sizing and content locality through hierarchical workload forecasting

Reconciling dynamic system sizing and content locality throu...

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IEEE 18th international conference on parallel and distributed systems (ICPADS)

作者： Tirado, Juan M. Higuero, Daniel Isaila, Florin Carretero, Jesus Univ Carlos III Madrid Dept Comp Sci & Engn Leganes Madrid Spain

ISBN: (纸本)9781467345651;9780769549033

the cloud has recently surged as a promising paradigm for hosting scalable Web systems serving a large number of users with large workload variations. It makes possible to dynamically add and remove resources to horizontally scalable architectures in order to save costs, while maintaining the quality of service. However, in order to achieve these goals the resource management of a platform must include policies and mechanisms for dynamically resizing the system, redistributing content and redirecting user requests. In this work, we address the problem of reconciling dynamic system sizing and content locality. there are three main contributions of our study. First, we address the problem of determining the system size by employing a hierarchical prediction framework that proactively provisions resources based on statistical models of the incoming workload. Second, we show how to employ the hierarchical prediction framework for designing a dispatching mechanism which can be used with any request distribution policy. third, we propose two novel prediction-based locality-aware request distribution policies: Oblivious Locality-Aware Request Distribution (OLARD) and Affinity-Based Locality-Aware Request Distribution (ABLARD). We demonstrate the advantages of using our hierarchical prediction framework and how our approach achieves a high content locality, while adapting to unexpected workload changes.

关键词： cloud computing forecasting content locality resource provisioning

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parallel Processing of Massive EEG Data with MapReduce

Parallel Processing of Massive EEG Data with MapReduce

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IEEE 18th international conference on parallel and distributed systems (ICPADS)

作者： Wang, Lizhe Chen, Dan Ranjan, Rajiv Khan, Samee U. Kolodziej, Joanna Wang, Jun Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China China Univ Geosciences Sch Comp Beijing Peoples R China CSIRO ICT Ctr Informat Engn lab Marshfield 2122 WI USA N Dakota State Univ Dept Elect & Comp Engn Grand Forks ND 58201 USA Univ Technol Inst Comp Sci Krakow Poland Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781467345651;9780769549033

Analysis of neural signals like electroencephalogram (EEG) is one of the key technologies in detecting and diagnosing various brain disorders. As neural signals are non-stationary and non-linear in nature, it is almost impossible to understand their true physical dynamics until the recent advent of the Ensemble Empirical Mode Decomposition (EEMD) algorithm. the neural signal processing with EEMD is highly compute-intensive due to the high complexity of the EEMD algorithm. It is also data-intensive because 1) EEG signals contain massive data sets 2) EEMD has to introduce a large number of trials in processing to ensure precision. the MapReduce programming mode is a promising parallel computing paradigm for data intensive computing. To increase the efficiency and performance of the neural signal analysis, this research develops parallel EEMD neural signal processing with MapReduce. In this paper, we implement the parallel EEMD with Hadoop in a modern cyberinfrastructure. Test results and performance evaluation show that parallel EEMD can significantly improve the performance of neural signal processing.

关键词： Data-intensive computing MapReduce parallel processing parallel PROCESSING (COMPUTERS) signal processing Electroencephalography Brain Diseases Key technology MAPPING(MAthEMATICAL) Signal analysis data parallel computing data flow computing

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Topic 8: distributed systems and Algorithms

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18th international conference on Euro-Par parallel Processing

作者： Goscinski, Andrzej Mavronicolas, Marios Shi, Weisong Meng, Teo Yong Topic Committee United States

ISBN: (纸本)9783642328206

the increasing significance of distributed computing becomes more and more crucial with the prevail of technological advances that make Global computing a reality in modern world. Indeed, it is hard to imagine some application or computational activity and process that falls outside distributed computing. With the large advent of distributed systems, we are faced with the real challenges of distributed computation: How do we cope with asynchrony and failures? How (and how well) do we achieve load balancing? How do we model and analyze malicious and selfish behavior? How do we address mobility, heterogeneity and the dynamic nature of participating processes? What can we achieve in the presence of disconnecting operations that cause network partitioning?

关键词： distributed computer systems

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WS-TaaS: A Testing as a Service Platform for Web Service Load Testing

WS-TaaS: A Testing as a Service Platform for Web Service Loa...

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IEEE 18th international conference on parallel and distributed systems (ICPADS)

作者： Yan, Minzhi Sun, Hailong Wang, Xu Liu, Xudong Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781467345651;9780769549033

Web services are widely known as the building blocks of typical service oriented applications. the performance of such an application system is mainly dependent on that of component web services. thus the effective load testing of web services is of great importance to understand and improve the performance of a service oriented system. However, existing Web Service load testing tools ignore the real characteristics of the practical running environment of a web service, which leads to inaccurate test results. In this work, we present WS-TaaS, a load testing platform for web services, which enables load testing process to be as close as possible to the real running scenarios. In this way, we aim at providing testers with more accurate performance testing results than existing tools. WS-TaaS is developed on the basis of our existing Cloud PaaS platform: Service4All. First, we briefly introduce the functionalities and main components of Service4All. Second, we provide detailed analysis of the requirements of Web Service load testing and present the conceptual architecture and design of key components. third, we present the implementation details of WS-TaaS on the basis of Service4All. Finally, we perform a set of experiments based on the testing of real web services, and the experiments illustrate that WS-TaaS can efficiently facilitate the whole process of Web Service load testing. Especially, comparing with existing testing tools, WS-TaaS can obtain more effective and accurate test results.

关键词： web services cloud computing load testing testing as a service

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Proceedings of the 2012 IEEE 18th international conference on parallel and distributed systems, ICPADS 2012

Proceedings of the 2012 IEEE 18th International Conference o...

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18th IEEE international conference on parallel and distributed systems, ICPADS 2012

ISBN: (纸本)9780769549033

the proceedings contain 146 papers. the topics discussed include: a remote view system to allow real-time walkthrough;power control algorithm based on SNR cost function in cognitive radio system;visualization for anomaly detection and data management by leveraging network, sensor and GIS techniques;geographic routing with minimal local geometry;accelerating Volkov's hybrid implementation of Cholesky factorization on a Fermi GPU;GPU parallel computing of spherical panorama video stitching;GPU-based calculation for scattering characteristics of complex targets from background radiance in infrared spectrum;a GPU-based implementation of WRF MYNN PBL/surface layer scheme;parallel and distributed processing of remote sensing data on large displays;GPU-accelerated computation of 3D laser radar range imaging of arbitrary coarse targets;and design and development of an adaptive workflow-enabled spatial-temporal analytics framework.

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Optimizing Dynamic Programming on Graphics Processing Units via Data Reuse and Data Prefetch with Inter-Block Barrier Synchronization

Optimizing Dynamic Programming on Graphics Processing Units ...

引用

IEEE 18th international conference on parallel and distributed systems (ICPADS)

作者： Wu, Chao-Chin Wei, Kai-Cheng Lin, Ting-Hong Natl Changhua Univ Educ Dept Comp Sci & Informat Engn Changhua 500 Taiwan

ISBN: (纸本)9781467345651;9780769549033

Our previous study focused on accelerating an important category of DP problems, called nonserial polyadic dynamic programming (NPDP), on a graphics processing unit (GPU). In NPDP applications, the degree of parallelism varies significantly in different stages of computation, making it difficult to fully utilize the compute power of hundreds of processing cores in a GPU. To address this challenge, we proposed a methodology that can adaptively adjust the thread-level parallelism in mapping a NPDP problem onto the GPU, thus providing sufficient and steady degrees of parallelism across different compute stages. this work aims at further improving the performance of NPDP problems. Subproblems and data are tiled to make it possible to fit small data regions into shared memory and reuse the buffered data for each tile of subproblems, thus reducing the amount of global memory access. However, we found invoking the same kernel many times, due to data consistency enforcement across different stages, makes it impossible to reuse the tiled data in shared memory after the kernel is invoked again. Fortunately, the inter-block synchronization technique allows us to invoke the kernel exactly one time with the restriction that the maximum number of blocks is equal to the total number of streaming multiprocessors. In addition to data reuse, invoking the kernel only one time also enables us to prefetch data to shared memory across inter-block synchronization point, which improves the performance more than data reuse. We realize our approach in a real-world NPDP application - the optimal matrix parenthesization problem. Experimental results demonstrate invoking a kernel only one time cannot guarantee performance improvement unless we also reuse and prefetch data across barrier synchronization points.

关键词： dynamic programming GPU parallel computing tiling optimization

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Efficient Multi-Keyword Ranked Query on Encrypted Data in the Cloud

Efficient Multi-Keyword Ranked Query on Encrypted Data in th...

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IEEE 18th international conference on parallel and distributed systems (ICPADS)

作者： Xu, Zhiyong Kang, Wansheng Li, Ruixuan Yow, KinChoong Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100864 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Suffolk Univ Math & Comp Sci Dept Boston MA 02119 USA Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9781467345651;9780769549033

Cloud computing is becoming increasingly prevalent in recent years. It introduces an efficient way to achieve management flexibility and economic savings for distributed applications. To take advantage of computing and storage resources offered by cloud service providers, data owners must outsource their data onto public cloud servers which are not within their trusted domains. therefore, the data security and privacy become a big concern. To prevent information disclosure, sensitive data has to be encrypted before uploading onto the cloud servers. this makes plain text keyword queries impossible. As the total amount of data stored in public clouds accumulates exponentially, it is very challenging to support efficient keyword based queries and rank the matching results on encrypted data. Most current works only consider single keyword queries without appropriate ranking schemes. the multi-keyword query problem was being considered only recently. MRSE [1] is one of the first research works to define and address the problem of effective yet secure ranked multi-keyword search over encrypted cloud data. However, the keyword dictionary used in MRSE is static and must be rebuilt when the number of keywords in the dictionary increases. It also has severe out-of-order problems in the matching results and does not take the keyword access frequencies into account, which greatly affects its usability. In this paper, we propose a novel approach, called MKQE, to address these issues. Only minor changes in the dictionary structure have to be done when extra keywords are introduced. We also introduce new trapdoor generation and scoring algorithms to make in-order query results. Furthermore, the keyword access frequency is considered so as to select an adequate matching file set. We conduct extensive simulations and the results prove that our approach performs much better than previous solutions.

关键词： multi-keyword query ranked query encrypted data cloud computing heavy tail

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Topic 5: parallel and distributed Data Management

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18th international conference on Euro-Par parallel Processing

作者： Talia, Domenico Delis, Alex Dutta, Haimonti Zaslavsky, Arkady Topic Committee United States

ISBN: (纸本)9783642328206

the ever-increasing data volumes used to empower contemporary data-intensive applications as well as aggregations of computing systems call for novel approaches and efficient techniques in the management of geographically dispersed data. Despite recent advances, Internet-scale requirements for both applications and underlying systems require effective provisioning, staging,manipulation, continuous maintenance and monitoring of data hosted in multiple, pre-existing autonomous, distributed and often heterogeneous systems. Evidently, the notions of parallelism and concurrent execution at all levels remain key elements in attaining scalability and effective management for nearly-all modern data-intensive applications. Moreover, as underlying computing environments get transformed through the introduction of novel infrastructures, enhanced capacities and extended functionalities, new solutions are sought to cope with these changes.

关键词： Information management

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iROW: An Efficient Live Snapshot System for Virtual Machine Disk

iROW: An Efficient Live Snapshot System for Virtual Machine ...

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IEEE 18th international conference on parallel and distributed systems (ICPADS)

作者： Li, Jianxin Liu, Hanqing Cui, Lei Li, Bo Wo, Tianyu Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China

ISBN: (纸本)9781467345651;9780769549033

the high-availiablity of mission-critical data and services hosted in a virtual machine (VM) is one of the top concerns in a cloud computing environment. the live disk snapshot is an emerging technology to save the whole state and the data of a VM at a specific point of time, and be used for quick disaster recovery. However, the existing VM disk snapshot systems suffer from long operation time and I/O performance degradation problems during snapshots creating and managing, and thereby affecting the performance of the VM and its services. To address such issues, we designed an efficient VM disk snapshot system, named iROW (improved Redirect-on-Write). In iROW, a bitmap based light-weight index scheme is adopted to replace the existing multi-level index tree structure to reduce query cost. Additionally, through a combination of Redirect-on-Write (ROW) and Copy-on-Demand (COD) schema to avoid extra copy operation on the first write after snapshot with Copy-on-Write (COW) schema, and the file fragmentation problem caused by ROW snapshot after long-term using. Finally, iROW gives a unified disk space allocation function by the host machine's file system. We have implemented iROW in qemu-kvm 0.12.5 and conducted some experiments. the implementation of iROW completely obey the interfaces of the block device driver in QEMU, so it is transparent to the upper system or applications and original disk image formats can be also supported. the experimental results show that iROW has obvious performance advantages in snapshot creating and management operations. Compared with the existing qcow2 disk image in KVM, when the VM disk size is 50GB, and the cluster size is 64KB (the default cluster size of qcow2), the snapshot creation and rollback time is only about 6% and 3% of original qcow2's. With the increasing of the VM disk size, iROW has more performance advantages on snapshot creation and rollback operations. In addition, the I/O performance of iROW is better than qcow2. When t

关键词： Cloud computing Reliability Virtual Machine Snapshot Virtual Disk Image

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Estimation of Maximum Allowable Time-Delay Bound for System Stability of Network Controlled parallel DC/DC Buck Converters

Estimation of Maximum Allowable Time-Delay Bound for System ...

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18th international conference on Automation and computing (ICAC) - Integration of Design and Engineering

作者： Khalil, Ashraf Wang, Jihong Univ Birmingham Sch Elect Elect & Comp Engn Birmingham W Midlands England Univ Warwick Sch Engn Coventry W Midlands England

ISBN: (纸本)9781908549006

the current trend in distributed energy system control is moving toward implementing quasi-decentralized control strategy where the control signals are exchanged over shared dedicated control or communication networks which reduces the complexity in the wiring and enhances the reliability. Transmitting control signals through shared networks induces time delays and data losses which may destabilize the system. So it is important to identify what the maximum allowable delay bound (MADB) can be tolerant with respect to system stability. the paper proposes a new method for estimating the allowable time delay under which the system stability can be maintained. the proposed method is originated from analyzing the network controlled parallel DC/DC Buck converters. the influences of the system parameters and controllers on the boundary of the MADB are studied. Simulation using Matlab SimPowerSystem Toolbox is performed to verify the stability bound determined by the MADB. the results are compared with the methods previously reported in the literature and the new method is proved to be simpler in estimation procedures and easier in applications to practical systems.

关键词： networked control system time delay maximum allowable delay bound parallel DC/DC Buck converters stability

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