the challenge of deploying mission critical services upon virtualised shared network models is the allocation of both radio and cloud resources to the critical actors who require prioritized and high-quality services....
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the challenge of deploying mission critical services upon virtualised shared network models is the allocation of both radio and cloud resources to the critical actors who require prioritized and high-quality services. this paper describes the design and deployment of an intelligent orchestration cycle to manage end-to-end slices on a NFV architecture. this novel tool includes the monitoring of the network elements at different levels and the processing of the gathered data to produce the corresponding alert mitigation actions.
Cloud resources can be dynamically provisioned according to application-specific requirements and are payed on a per-use basis. this gives rise to a new concept for parallelprocessing: Elastic parallel computations. ...
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ISBN:
(数字)9783030494322
ISBN:
(纸本)9783030494315;9783030494322
Cloud resources can be dynamically provisioned according to application-specific requirements and are payed on a per-use basis. this gives rise to a new concept for parallelprocessing: Elastic parallel computations. However, it is still an open research question to which extent parallel applications can benefit from elastic scaling, which requires resource adaptation at runtime and corresponding coordination mechanisms. In this work, we analyze how to address these system-level challenges in the context of developing and operating elastic parallel tree search applications. Based on our findings, we discuss the design and implementation of TASKWORK, a cloud-aware runtime system specifically designed for elastic parallel tree search, which enables the implementation of elastic applications by means of higher-level development frameworks. We show how to implement an elastic parallel branch-and-bound application based on an exemplary development framework and report on our experimental evaluation that also considers several benchmarks for parallel tree search.
Work-efficient task-parallelalgorithms enforce ordering between tasks using queuing primitives. Such algorithms offer limited parallelism due to queuing constraints that result in data movement and synchronization bo...
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ISBN:
(纸本)9781728136134
Work-efficient task-parallelalgorithms enforce ordering between tasks using queuing primitives. Such algorithms offer limited parallelism due to queuing constraints that result in data movement and synchronization bottlenecks. Speculatively relaxing order of tasks across cores using the Galois framework shows promise as false dependencies generated by strict queuing constraints are mitigated to unlock task parallelism. However, relaxed ordering results in redundant work, for which Galois relies on static measures to improve work-efficiency. this paper proposes a dynamic multi-level parent-child task dependency checking mechanism in Galois to prune redundant work by exploiting monotonic properties of shared data values. Evaluation on a 40-core Intel Xeon multicore shows an average of 2x performance improvements over state-of-the-art ordered and relax ordered graph algorithms.
the Internet of things (IoT) presents an extensive area for research, based on its growing importance in a multitude of different domains of everyday life, business and industry. In this context, different aspects of ...
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ISBN:
(纸本)9783030503154;9783030503161
the Internet of things (IoT) presents an extensive area for research, based on its growing importance in a multitude of different domains of everyday life, business and industry. In this context, different aspects of data analytics, e.g. algorithms or system architectures, as well as their scientific investigation play a pivotal role in the advancement of the IoT. therefore, past research has presented a multitude of architectural approaches to enable data processing and analytics in various IoT domains, addressing different architectural challenges. In this paper, we identify and present an overview of these challenges as well as existing architectural proposals. Furthermore, we categorize found architectural proposals along various dimensions in order to highlight the evolution of research in this field and pinpoint architectural shortcomings. the results of this paper show that several challenges have been addressed by a large number of IoT system architectures for data analytics while others are either not relevant for certain domains or need further investigation. Finally, we offer points of reference for future research based on the findings of this paper.
Optimization of searching the best possible action depending on various states like state of environment, system goal etc. has been a major area of study in computer systems. In any search algorithm, searching best po...
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ISBN:
(纸本)9781728143927
Optimization of searching the best possible action depending on various states like state of environment, system goal etc. has been a major area of study in computer systems. In any search algorithm, searching best possible solution from the pool of every possibility known can lead to the construction of the whole state search space popularly called as minimax algorithm. this may lead to a impractical time complexities which may not be suitable for real time searching operations. One of the practical solution for the reduction in computational time is Alpha Beta pruning Instead of searching for the whole state space, we prune the unnecessary branches, which helps reduce the time by significant amount. this paper focuses on the various possible implementations of the Alpha Beta pruning algorithms and gives an insight of what algorithm can be used for parallelism. Various studies have been conducted on how to make Alpha Beta pruning faster. parallelizing Alpha Beta pruning for the GPUs specific architectures like mesh(CUDA) etc. or shared memory model(OpenMP) helps in the reduction of the computational time. this paper studies the comparison between sequential and different parallel forms of Alpha Beta pruning and their respective efficiency for the chess game as an application.
We propose a new memetic algorithm to minimize the makespan for the flowshop scheduling problem in two variants: the classic permutation setting and the no-wait statement. Our algorithm hybridizes the local search tec...
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By the Fourth Industrial Revolution and the 10 strategic technology of the Gartner Group, Artificial Intelligence(AI) technology was important and affected many areas. One of the ways to accelerate AI services is the ...
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ISBN:
(纸本)9781450389259
By the Fourth Industrial Revolution and the 10 strategic technology of the Gartner Group, Artificial Intelligence(AI) technology was important and affected many areas. One of the ways to accelerate AI services is the Python-based parallelprocessing library. High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. this migration towards orchestration rather than implementation, coupled withthe growing need for parallel computing (e.g., due to big data and the end of Moore's law), necessitates rethinking how parallelism is expressed in programs.[1] In this paper, take a look at a Python-based distributed parallelprocessing library, one of the ways to accelerate AI services, and use it to compare serial and parallelprocessing times.
Flipping sentiment while preserving sentence meaning is challenging because parallel sentences withthe same content but different sentiment polarities are not always available for model learning. We introduce a metho...
ISBN:
(纸本)9781950737901
Flipping sentiment while preserving sentence meaning is challenging because parallel sentences withthe same content but different sentiment polarities are not always available for model learning. We introduce a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose a model that learns to minimize the sentiment and content losses in a fully end-to-end manner. Our model is simple and offers well-balanced results across two domains: Yelp restaurant and Amazon product reviews.(1)
To cope withthe rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional arra...
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ISBN:
(纸本)9781728162515
To cope withthe rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce IleAT, an array-based numerical programming framework for large-scale parallelprocessing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly I owering the bartier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.
Population-based search algorithms, such as the Differential Evolution approach, evolve a pool of candidate solutions during the optimization process and are suitable for massively parallelarchitectures promoted by t...
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