Service-oriented Grid frameworks offer resources and facilities to support the design and execution of distributedapplications in different domains, ranging from scientific applications and public computing projects ...
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ISBN:
(纸本)9781424437511
Service-oriented Grid frameworks offer resources and facilities to support the design and execution of distributedapplications in different domains, ranging from scientific applications and public computing projects to commercial and industrial applications. A critical issue in such a context is the management Of the heterogeneity of resources and services offered by a Grid, including computers, data, and software tools provided by different organizations. This paper presents a general architecture of a service-oriented information system, which exploits the characteristics of a multi-domain and semantically enriched metadata model. The main objective of the information system is to uniformly manage service-oriented applications and basic resources by assuring metadata persistence through an XML distributed database, without merely relying on the functionalities of persistent Grid services. The information system has been implemented on the basic services of the WSRF-based Globus Toolkit 4 and its performance has been evaluated in a testbed.
Nowadays, common systems in the area of high performance computing exhibit highly hierarchical architectures. As a result, achieving satisfactory;application performance demands an adaptation of the respective paralle...
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ISBN:
(纸本)9781424437511
Nowadays, common systems in the area of high performance computing exhibit highly hierarchical architectures. As a result, achieving satisfactory;application performance demands an adaptation of the respective parallel algorithm to such systems. This, in turn, requires knowledge about the actual hardware structure even at the application level. However, the prevalent Message Passing Interface (MPI) standard (at least in its current version 2.1) intentionally hides heterogeneity from the application programmer in order to assure portability In this paper, we introduce the MPIXternal library which tries to Circumvent this obvious semantic gap within the current MPI standard. For this pur pose, the library offers the programmer additional features that should help to adapt applications to today's hierarchical systems in a convenient and portable way.
Tightly-coupled parallelapplications in cloud systems may suffer from significant performance degradation because of the resource over-commitment issue. In this paper, we propose a dynamic approach based on the adapt...
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ISBN:
(纸本)9781509021406
Tightly-coupled parallelapplications in cloud systems may suffer from significant performance degradation because of the resource over-commitment issue. In this paper, we propose a dynamic approach based on the adaptive control over time-slice for virtual clusters, in order to mitigate the performance degradation for parallelapplications in cloud and avoid the negative impact effectively on other non-parallelapplications meanwhile. The key idea is to reduce the synchronization overhead inside and across virtual machines (VMs) in cloud systems, by dynamically adjusting the time-slices of VMs in terms of the spinlock latency at runtime. Such a design is motivated by our experimental finding that VM's time slice is a key factor determining the synchronization overhead as well as the parallel execution performance. We perform the evaluation on a real cluster environment deployed with XEN, using five well-known benchmarks with 10+ applications. Experiments show that our approach obtains 1.5-10x performance gain for running parallelapplications, than other state-of-the-art solutions (including Credit Scheduling of Xen and the well-known methods like Co-Scheduling and Balance Scheduling), with nearly unaffected impact on the performance of non-parallelapplications.
Many real-world applications such as positioning, navigation, and target tracking for autonomous vehicles require the estimation of some time-varying states based on noisy measurements made on the system. Particle fil...
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ISBN:
(纸本)9780769546766
Many real-world applications such as positioning, navigation, and target tracking for autonomous vehicles require the estimation of some time-varying states based on noisy measurements made on the system. Particle filters can be used when the system model and the measurement model are not Gaussian or linear. However, the computational complexity of particle filters prevents them from being widely adopted. parallel implementation will make particle filters more feasible for real-time applications. Effective resampling algorithms like the systematic resampling algorithm are serial. In this paper, we propose the shared-memory systematic resampling (SMSR) algorithm that is easily parallelizable on existing architectures. We verify the performance of SMSR on graphics processing units. Experimental results show that the proposed SMSR algorithm can achieve a significant speedup over the serial particle filter.
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mi...
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ISBN:
(纸本)9781479942930
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies. Among the generalized itemsets, the most peculiar high-level patterns are those with many contrasting correlations among items at different abstraction levels. They represent misleading situations that are worth analyzing separately by experts during manual inspection. This paper proposes a novel cloud-based service, named MGI-CLOUD, to efficiently mine misleading multiple-level patterns, i.e., theMisleading Generalized Itemsets, on a distributed computing environment. MGI-CLOUD consists of a set of distributed MapReduce jobs running in the cloud. As a case study, the system has been contextualized in a real-life scenario, i.e., the analysis of traffic law infractions committed in a smart city environment. The experiments, performed on real datasets, demonstrate the efficiency and effectiveness of MGI-CLOUD.
Acceleration for the training process of Deep Neural Networks (DNNs) has been the focus of deep learning field. There were many researches of accelerating deep learning on different platforms. Among them, Intel Xeon P...
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ISBN:
(纸本)9781538637906
Acceleration for the training process of Deep Neural Networks (DNNs) has been the focus of deep learning field. There were many researches of accelerating deep learning on different platforms. Among them, Intel Xeon Phi Co-processor is a many-core platform which provides both strong programmability and high performance. But previous work about Intel Many Integrated Core (MIC) focused on parallel computing only in MIC. In this paper, we speed up the training process of DNNs applied for automatic speech recognition with CPU+MIC architecture. In this architecture, the training process of DNNs is executed both on MIC and CPU. We apply several optimization methods for I/O and calculation and set up experiments to approve these methods. Putting all methods together, results show that our optimized algorithm acquires about 20x speedup compared with the original sequential algorithm on CPU which uses one core.
applications in which multiple users share the states in real-time over a network have been rapidly spreading, but network latency degrades their quality of service (QoS) and quality of experience (QoE). Although Fog ...
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ISBN:
(纸本)9781665435772
applications in which multiple users share the states in real-time over a network have been rapidly spreading, but network latency degrades their quality of service (QoS) and quality of experience (QoE). Although Fog computing effectively mitigates this problem, user allocation methods suitable for these applications with strict latency requirements have not yet been studied. Therefore, this paper proposes both offline and online methods that assume state sharing for user allocation in Fog environments. These methods not only reduce the mean of delays within each group composed of users who share the same states but also guarantee fairness among the users. The simulations demonstrate that our methods complete the allocation in a realistic time and outperform the baseline methods and architectures.
Many scientific workflow applications are data intensive. Large quantities of simulation generated data or data collected from sensors or instruments drive the process flow of the applications, and the processing of t...
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ISBN:
(纸本)9780769546766
Many scientific workflow applications are data intensive. Large quantities of simulation generated data or data collected from sensors or instruments drive the process flow of the applications, and the processing of the data is commonly done at a different location from where the data is stored. Advanced networks such as hybrid networks make it feasible for high level applications to request network paths and service provisioning. However, current applications tune the execution quality rarely considering network resources, and by selecting only optimal software services and computing resources. Including network services in the resource scheduling adds an extra dimension workflow applications to optimize the runtime performance but also introduces new challenges, in particular in heterogeneous infrastructures. In this paper we continue our previous work on a system called NEtWork QoS Planner (NEWQoSPlanner) which is able to select network resources in the context of workflow composition and scheduling. We will discuss how NEWQoSPlanner invokes network services to achieve dynamic resource optimization for workflows, and how to apply such planner in heterogeneous infrastructures.
Smart devices, mobile robots, ubiquitous sensors, and other connected devices in the Internet of Things (IoT) increasingly require real-time computations beyond their hardware limits to process the events they capture...
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ISBN:
(纸本)9781509036820
Smart devices, mobile robots, ubiquitous sensors, and other connected devices in the Internet of Things (IoT) increasingly require real-time computations beyond their hardware limits to process the events they capture. Leveraging cloud infrastructures for these computational demands is a pattern adopted in the IoT community as one solution, which has led to a class of Dynamic Data Driven applications (DDDA). These applications offload computations to the cloud through distributed Stream processing Frameworks (DSPF) such as Apache Storm. While DSPFs are efficient in computations, current implementations barely meet the strict low latency requirements of large scale DDDAs due to inefficient inter-process communication. This research implements efficient highly scalable communication algorithms and presents a comprehensive study of performance, taking into account the nature of these applications and characteristics of the cloud runtime environments. It further reduces communication costs within a node using an efficient shared memory approach. These algorithms are applicable in general to existing DSPFs and the results show significant improvements in latency over the default implementation in Apache Storm.
Clouds are more and more becoming a credible alternative to parallel dedicated resources. The pay-per-use pricing policy however highlights the real cost of computing applications. This new criterion, the cost, must t...
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ISBN:
(纸本)9781509036820
Clouds are more and more becoming a credible alternative to parallel dedicated resources. The pay-per-use pricing policy however highlights the real cost of computing applications. This new criterion, the cost, must then be assessed when scheduling an application in addition to more traditional ones as the completion time or the execution flow. In this paper, we tackle the problem of optimizing the cost of renting computing instances to execute an application on the cloud while maintaining a desired performance (throughput). The target application is a stream application based on a DAG pattern, i.e., composed of several tasks with dependencies, and instances of the same execution task graph are continuously executed on the instances. We provide some theoretical results on the problem of optimizing the renting cost for a given throughput then propose some heuristics to solve the more complex parts of the problem, and we compare them to optimal solutions found by linear programming.
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