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检索条件"任意字段=19th International Conference on Parallel and Distributed Computing Systems, PDCS 2006"

共 205 条记录，以下是51-60 订阅

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Dynamic Virtual Resource Renting Method for Maximizing the Profits of a Cloud Service Provider in a Dynamic Pricing Model

Dynamic Virtual Resource Renting Method for Maximizing the P...

引用

19th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Zhou, Ao Wang, Shangguang Sun, Qibo Zou, Hua Yang, Fangchun Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China

ISBN: (纸本)9781479920815

With an increasing number of cloud service providers (CSP) delivering services to customers from the cloud, maximizing the profits of CSPs becomes a critical problem. Existing methods are difficult to solve the problem because they do not make full use of temporal price differences. this paper introduces a dynamic virtual resource renting method that attempts to dynamically adjust the virtual resource rental strategy according to price distribution and task urgency. We first pretreat the historical price series and adopt the outlier detection technique to filter the extreme price. then, considering task urgency and price distribution, we design a weak equilibrium operator to calculate the acceptable price for each type of virtual resource. All types of virtual resources that are at an acceptable price are inserted into a set. Finally, we design a novel rental decision-making algorithm to select the most profitable resource from the set. We provide an extensive evaluation of our method using Amazon EC2 spot price dataset and normally distributed price dataset. the results demonstrate the effectiveness of our method.

关键词： cloud computing profit maximization virtual resource renting non-uniform mutation operator

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Toward more parallel frequent itemset mining algorithms

Toward more parallel frequent itemset mining algorithms

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19th IASTED international conference on parallel and distributed computing and systems

作者： Kessl, Robert Tvrdík, Pavel Department of Computer Science and Engineering Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

ISBN: (纸本)9780889867048

this paper presents improvements of the parallel-FIMI method for statical load balancing of mining of all frequent itemsets on a distributed-memory (DM) parallel machine. this method probabilistically partitions the space of all frequent itemsets into partitions of approximately the same size. the improvements consist in paralelization of the approximate partitioning of the search space and of dynamic reordering of items during construction of prefix-based equivalence classes. the new versions of the method achieve nearly linear speedups up to 10 processors.

关键词： Data mining

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Scalable synchronization of clocks in distributed environments

Scalable synchronization of clocks in distributed environmen...

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19th IASTED international conference on parallel and distributed computing and systems

作者： Liu, Jun Computer Science Department University of North Dakota United States

ISBN: (纸本)9780889867048

In many distributed applications, identifying the sequence of event occurrence is useful. Doing so requires local clocks to be properly synchronized with each other. this paper presents a method of pseudo-synchronization of clocks by making use of the occurrence time of events included in direct "happened-before" relations. the basic idea is to approximate the actual clock shift between two clocks using the minimum transmission delay of messages between a pair of hosts. Evaluation results suggest that non-base clocks are closely synchronized with a base clock without violating the direct "happened-before" relations.

关键词： distributed computer systems

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Auto-Resource Provisioning for MapReduce-Based Multiple Object Tracking in Video 18

Auto-Resource Provisioning for MapReduce-Based Multiple Obje...

引用

19th international conference on distributed computing and Networking (ICDCN)

作者： Singh, Gurinderbeer Majumdar, Shikharesh Rajan, Sreeraman Carleton Univ Syst & Comp Engn Dept Ottawa ON Canada

ISBN: (纸本)9781450363723

Use of complex image analysis and globally optimal techniques make the current Multiple Object Tracking (MOT) methods for video analysis computationally slow. An important issue in this context is meeting the specific latency requirement for a given application while processing large scale video data. this is especially important in emergency situations such as accidents, natural calamities, and terrorist attacks. this paper introduces a latency reducing MapReduce/Hadoop-based parallel solution for MOT. the system includes an Auto-Resource Provisioning technique for determining the number of Hadoop nodes required to process the MOT job within a user specified deadline. the estimated number of nodes are then provisioned by the system and the MOT application is executed on the Hadoop cluster comprising the desired number of nodes. A prototype is built using the AWS EC2 cloud. A performance analysis is performed using measurements made on the prototype and insights gained into system behavior and performance are presented.

关键词： Video Processing Multiple Object Tracking Cloud computing distributed systems MapReduce Apache Hadoop

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Lightweight Management of Authorization Update on Cloud Data

Lightweight Management of Authorization Update on Cloud Data

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19th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Cui, Zongmin Zhu, Hong Shi, Jie Chi, Lianhua Yan, Ke Huazhong Univ Sci & Technol Sch Comp Sci & technol Wuhan 430074 Peoples R China Jinan Univ Guangzhou Guangdong Peoples R China Singapore Management Univ Singapore 178902 Singapore Univ Technol Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479920815

While outsourcing data to cloud, security and efficiency issues should be taken into account. However, it is very challenging to design a secure and efficient mechanism supporting authorization updates. In this paper, we aim to provide a mechanism supporting authorization updates which only incurs a lightweight cost of authorization updates and meanwhile supports a high level of security. this mechanism is consisted of two encryption schemes performed in different layers. the inner-layer encryption scheme is performed on the original plaintext and the generated ciphertext is called innerlayer ciphertext;while a part of the inner-layer ciphertext is encrypted by the outer-layer encryption scheme to generate ciphertext, called out-layer ciphertext. these two encryption schemes are both performed by data owner. the innerlayer encryption realizes the initial authorization policy, while the outer-layer encryption reflects the updated authorization policy. We implement the proposed mechanism and conduct extensive experiments. the experimental results demonstrate that the proposed mechanism outperforms previous existing approaches, e.g. single-layer encryption and double-layer encryption.

关键词： authorization updates access control cloud computing lightweight management

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A Load-sharing Architecture for High Performance Optimistic Simulations on Multi-core Machines

A Load-sharing Architecture for High Performance Optimistic ...

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19th international conference on High Performance computing (HiPC)

作者： Vitali, Roberto Pellegrini, Alessandro Quaglia, Francesco Univ Rome DIIAG Rome Italy

ISBN: (纸本)9781467323703;9781467323727

In parallel Discrete Event Simulation (PDES), the simulation model is partitioned into a set of distinct Logical Processes (LPs) which are allowed to concurrently execute simulation events. In this work we present an innovative approach to load-sharing on multi-core/multiprocessor machines, targeted at the optimistic PDES paradigm, where LPs are speculatively allowed to process simulation events with no preventive verification of causal consistency, and actual consistency violations (if any) are recovered via rollback techniques. In our approach, each simulation kernel instance, in charge of hosting and executing a specific set of LPs, runs a set of worker threads, which can be dynamically activated/deactivated on the basis of a distributed algorithm. the latter relies in turn on an analytical model that provides indications on how to reassign processor/core usage across the kernels in order to handle the simulation workload as efficiently as possible. We also present a real implementation of our load-sharing architecture within the ROme OpTimistic Simulator (ROOT-Sim), namely an open-source C-based simulation platform implemented according to the PDES paradigm and the optimistic synchronization approach. Experimental results for an assessment of the validity of our proposal are presented as well.

关键词： discrete event simulation distributed algorithms multiprocessing systems parallel architectures performance evaluation

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A mobile agent based workflow rescheduling approach for grids

A mobile agent based workflow rescheduling approach for grid...

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19th IASTED international conference on parallel and distributed computing and systems

作者： Dong, Fangpeng Akl, Selim G. School of Computing Queen's University Kingston ON K7L3N6 Canada

ISBN: (纸本)9780889867048

this paper addresses the workflow rescheduling problem in the Grid where resource performance is fluctuating and hard to predict. Unlike existing rescheduling approaches targeting the same problem, the proposed a mobile agent based distributed approach. Once a workflow is submitted to a central workflow scheduler, an initial schedule for tasks in that workflow will be created first. then, at the workflow run-time, mobile agents which carry those tasks will reschedule themselves by a rescheduling algorithm when resource performance changes.

关键词： Mobile agents

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19th international conference on Computer Applications in Industry and Engineering, CAINE 2006

19th International Conference on Computer Applications in In...

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19th international conference on Computer Applications in Industry and Engineering, CAINE 2006

ISBN: (纸本)9781604236750

the proceedings contain 61 papers. the topics discussed include: evaluation methodology for virtual reality software tools;image compression of facial photographs based on BTC/TSVQ local processing;a novel method for person identification by comparing retinal patterns;achieving near-optimal distortion reduction with invisible watermarking RSSI algorithms;ModVis: an information visualization tool for gene module discovery;adaptive coverage in heterogeneous sensor network;a COTS framework for sensor fusion using dynamic Bayesian networks in livestock production;computer-aided item selection for the cycle counting procedure;performance modeling of hierarchical memories;modeling the dynamic load in distributed control systems (DCS);task-migration support for parallel computing on shared virtual memory clusters;and a novel parallel hardware and software solution for a large-scale biologically realistic cortical simulation.

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A Dynamic Hybrid Resource Provisioning Approach for Running Large-scale Computational Applications on Cloud Spot and On-demand Instances

A Dynamic Hybrid Resource Provisioning Approach for Running ...

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19th IEEE international conference on parallel and distributed systems (ICPADS)

作者： Lu, Sifei Li, Xiaorong Wang, Long Kasim, Henry Palit, Henry Hung, Terence Legara, Erika Fille Tupas Lee, Gary Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9781479920815

Testing and executing large-scale computational applications in public clouds is becoming prevalent due to cost saving, elasticity, and scalability. However, how to increase the reliability and reduce the cost to run large-scale applications in public clouds is still a big challenge. In this paper, we analyzed the pricing schemes of Amazon Elastic Compute Cloud (EC2) and found the disturbance effect that the price of the spot instances can be heavily affected due to the large number of spot instances required. We proposed a dynamic approach which schedules and runs large-scale computational applications on a dynamic pool of cloud computational instances. We use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks so as to further reduce the cost without significantly increasing the completion time. Our proposed method takes the dynamic pricing of cloud instances into consideration, and it reduces the cost and tolerates the failures for running large-scale applications in public clouds. We conducted experimental tests and an agent based Scalable complex System modeling for Sustainable city (S-3) application is used to evaluate the scalability, reliability and cost saving. the results show that our proposed method is robust and highly flexible for researchers and users to further reduce cost in real practice.

关键词： component Cloud computing resource provisioning cost analysis fault tolerance spot instance spot price reliability workflow scheduling

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Fast distributed Stochastic Gradient Descent for Big Data Classification 23

Fast Distributed Stochastic Gradient Descent for Big Data Cl...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xiao, Mingfei Wang, Huihui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Large-scale classification is an important task of machine learning, especially in the smart city field, which is a big data environment. In recent years, single-threaded optimization algorithms can no longer meet the needs of big data applications. When a single machine is trained, it will face the problem of insufficient memory and limited computing capacity. therefore, distributed algorithms have been proposed to solve big data classification, and have received much attention. Particularly, distributed gradient descent (GD) is a widely-used algorithm for solving big data problem. However, distributed algorithms based on GD always converge slowly and suffer from expensive time in the training process. To alleviate this problem, we propose a novel fast distributed stochastic gradient descent algorithm for big data classification in this paper. By formulating the original optimization problem as a series of subproblems for classification in each machine. In particular, we exploit a fast stochastic method combined with the Nesterov acceleration method for sub-problem optimization in parallel to improve the convergence speed, and thus reduce the training time. the experimental results on some public and benchmark data sets show that our proposed distributed algorithm can converge faster with the competitive accuracy performance compared with distributed GD-based methods, which is suitable to big data classification. © 2021 IEEE.

关键词： Training Machine learning algorithms Smart cities Big Data Benchmark testing Classification algorithms distributed algorithms

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