this paper presents a compile-time placement method of mobile relational operators MROs in a large scale environment. MROs are self adaptive to changing run-time conditions by deciding their execution place if they di...
ISBN:
(纸本)9780769530499
this paper presents a compile-time placement method of mobile relational operators MROs in a large scale environment. MROs are self adaptive to changing run-time conditions by deciding their execution place if they discover compile-time estimation errors. Proposed placement methods tend to have a main drawback with MROs running over a large scale environment: their focus is on finding optimal performance depending on single-point estimation at compile-time, instead of optimal performance over an estimation interval. We propose: (i) to determine the migration space of a MRO including the sites on which the MRO is allowed to migrate during its execution, and (ii) to find the robust site which will allow acceptable response time in an estimation interval. Performance study shows that, with a risk of loosing around 6% in response time, it is possible to gain tip to 300% withthe proposed robust placement.
A web application of scientific computing over the Internet is presented, for distributedcomputing, programming in MPI, using the resolution of a typical statistical problem in packaging. this problem is found in man...
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ISBN:
(纸本)9789899843400
A web application of scientific computing over the Internet is presented, for distributedcomputing, programming in MPI, using the resolution of a typical statistical problem in packaging. this problem is found in many industries, and is solved empirically. As no analytical solution seems viable, a Monte Carlo approach was adopted. After briefly describing a previous series programming solution of the problem, its parallel programming version is detailed. From the authors' experience, the web environment is clearly recommended for scientific computing, for academic and industrial purposes.
Withthe advent of IoT and the associated variety of pervasive and context-aware applications, there is an increasing requirement to support the execution of these applications on devices with limited processing power...
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Withthe advent of IoT and the associated variety of pervasive and context-aware applications, there is an increasing requirement to support the execution of these applications on devices with limited processing power. this is a cost-intensive process as this usually requires the deployment of centralized computing infrastructure accessible via a cloud interface. We envision a distributed execution environment comprised of diverse computing resources as an alternative solution to this problem. this execution environment can be defined as a mobile cloud. However, the inherent unreliability of the mobile cloud requires developers to add reliability within the applications separately making the development process tedious. In this position paper, we present Aegis - a framework that provides a reliable and unified computing platform with built-in failure detection and repair mechanisms. Aegis draws upon the actor based execution model as well as stream processing applications to provide a reliable overlay over unreliable environments. Aegis also relives developers of the task of adding reliability mechanisms to applications separately. 1877-0509 (C) 2017 the Authors. Published by Elsevier B.V.
the paper is dedicated to an open T-system (OpenTS) - a programming system that supports automatic parallelization of computations for high-performance and distributedapplications. In this paper, we describe the syst...
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ISBN:
(纸本)3540281266
the paper is dedicated to an open T-system (OpenTS) - a programming system that supports automatic parallelization of computations for high-performance and distributedapplications. In this paper, we describe the system architecture and input programming language as well as system's distinctive features. the paper focuses on the achievements of the last two years of development, including support of distributed, meta-cluster computations.
When datasets are distributed on different sources, finding out their intersection while preserving the privacy of the datasets is a widely required task. In this paper we address the Privacy Preserving Set Intersecti...
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ISBN:
(纸本)9780769530499
When datasets are distributed on different sources, finding out their intersection while preserving the privacy of the datasets is a widely required task. In this paper we address the Privacy Preserving Set Intersection (PPSI) problem, in which each of the N parties learns no elements other than the intersection of their N private datasets. We propose an efficient protocol in the malicious model, where the adversary may control arbitrary number of parties and execute the protocol for its own benefit. A related work in [12] has a correctness probability of (N-1/N)(N) (N is the size of the encryption scheme's plaintext space), a computation complexity of O(N(2)S(2)lgN) (S is the size of each party's data set). Our PPSI protocol in the malicious model has a correctness probability of (N-1/N)(N-1), and achieves a computation cost of O(c(2)S(2)lgN) (c is the number of malicious parties and c <= N-1).
Finding a vast array of applications, the problem of computingthe convex hull of a set of sorted points in the plane is one of the fundamental tasks in pattern recognition, morphology and image processing. the main c...
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ISBN:
(纸本)9781424452910
Finding a vast array of applications, the problem of computingthe convex hull of a set of sorted points in the plane is one of the fundamental tasks in pattern recognition, morphology and image processing. the main contribution of this paper is to show a simple parallel algorithm for computingthe convex hull of a set of n sorted points in the plane and evaluate the performance on the dual quad-core processors. the experimental results show that, our implementation achieves a speed-up factor of approximately 7 using 8 processors. Since the speed-up factor of more than 8 is not possible, our parallel implementation for computingthe convex hull is close to optimal. Also, for 2 or 4 processors, we achieved a super linear speed up.
Finite difference time domain (FDTD) method is a robust and accurate algorithm which is widely used in computational electromagnetic field and the simulation of optical phenomenon. In this paper, parallel FDTD based o...
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ISBN:
(纸本)9780769548791
Finite difference time domain (FDTD) method is a robust and accurate algorithm which is widely used in computational electromagnetic field and the simulation of optical phenomenon. In this paper, parallel FDTD based on overlapped domain decomposition is used to simulate the band gap of photonic crystals and the quantum efficiency of thin-film solar cells. the light-trapping effect is also analyzed by parallel FDTD, it's very important to improve light absorption. Numerical result demonstrates that the accuracy and the speedup of parallel FDTD are very high for large scale problem.
Using parallel Geographic Image Processing System, the flooding disaster will be monitoring and evaluating in time. Using ParGIP to establish background database and process RS images, we can get the losses of the dis...
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ISBN:
(纸本)0780378407
Using parallel Geographic Image Processing System, the flooding disaster will be monitoring and evaluating in time. Using ParGIP to establish background database and process RS images, we can get the losses of the disaster by overlaying operation in 24 hours. According to the experiment in the Poyang Lake region, this method can promote the speed and the efficiency of the monitoring and evaluating of flooding disaster to several times.
Given a set of tasks with certain characteristics, e.g., data size, estimated execution time and a set Of processing nodes withtheir own parameters, the goal of task scheduling is to allocate tasks at nodes so that t...
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ISBN:
(纸本)9780769530499
Given a set of tasks with certain characteristics, e.g., data size, estimated execution time and a set Of processing nodes withtheir own parameters, the goal of task scheduling is to allocate tasks at nodes so that the total makespan is minimized. the problem has been studied under various assumptions concerning task and node parameters withthe resulting problem statements usually being NP-complete. List scheduling (LS) heuristics such as MaxMin and MinMin together with genetic algorithms (GAs) were applied in the past to find solutions. In this paper we investigate new heuristics for boththe LS and the GA paradigm withthe specific aim of improving the performance of the standard algorithms when task computations involve large data transfers. Experimental results under various environment assumptions illustrate the merits of the new algorithms.
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