Data Stream Processing is a pervasive computing paradigm with a wide spectrum of applications. Traditional streaming systems exploit the processing capabilities provided by homogeneous Clusters and Clouds. Due to the ...
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At a decade of distributed cloud, hyper-automation, digitalization, Internet of things (IoT) world, use of large volume IT resources in business is inevitable. Organizations use vast amount of IT systems to automate p...
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As a non-depleting renewable energy source that is not harmful to the environment is called solar energy created by sunlight. A year's worth of global energy needs is met by the amount of sunlight that enters the ...
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Dogri is a language with low computational resources. there are hardly any digital resources available for this language. the major hindrance to developing these resources is the lack of corpora. this paper describes ...
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Function-as-a-Service (FaaS) has emerged as a revolutionary service platform, abstracting the complexities of hardware, operating systems, and web hosting services. this allows developers to focus solely on implementi...
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ISBN:
(数字)9798350385625
ISBN:
(纸本)9798350385632
Function-as-a-Service (FaaS) has emerged as a revolutionary service platform, abstracting the complexities of hardware, operating systems, and web hosting services. this allows developers to focus solely on implementing their service applications, making FaaS an ideal platform for the scalable manipulation of large data sets. Traditionally deployed on the cloud, FaaS now faces a new frontier: the network edge. Leveraging the edge offers several potential benefits, including reduced latency and improved resource utilization, making it a promising approach for efficient FaaS deployment. As the daily volume and complexity of data we handle continues to grow, adopting a parallelcomputing paradigm has become increasingly important to ensure fast and efficient execution of computational tasks. Addressing this need, ComFaaS distributed embarks on a comprehensive comparison of the capabilities of parallelized edge and cloud environments for FaaS deployment. Utilizing benchmark programs meticulously crafted to simulate event-triggered scenarios, ComFaaS distributed aims to provide valuable insights into the performance and potential of FaaS at the edge, paving the way for a future where parallelcomputing empowers the efficient and scalable processing of ever-growing data volumes.
Cloud-applications are the new industry standard way of designing Web-applications. With Cloud computing, applications are usually designed as microservices, and developers can take advantage of thousands of such exis...
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ISBN:
(纸本)9781665435383
Cloud-applications are the new industry standard way of designing Web-applications. With Cloud computing, applications are usually designed as microservices, and developers can take advantage of thousands of such existing microservices, involving several hundred of cross-component communications on different physical resources. Microservices orchestration (as Kubernetes) is an automatic process, which manages each component lifecycle, and notably their allocation on the different resources of the cloud infrastructure. Whereas such automatic cloud technologies ease development and deployment, they nevertheless obscure debugging and performance analysis. In order to gain insight on the composition of services, distributed tracing recently emerged as a way to get the decomposition of the activity of each component within a cloud infrastructure. this paper aims at providing methodologies and tools (leveraging state-of-the-art tracing) for getting a wider view of application behaviours, especially focusing on application performance assessment. In this paper, we focus on using distributed traces and allocation information from microservices to model their dependencies as a hierarchical property graph. By applying graph rewriting operations, we managed to project and filter communications observed between microservices at higher abstraction layers like the machine nodes, the zones or regions. Finally, in this paper we propose an implementation of the model running on a microservices shopping application deployed on a Zonal Kubernetes cluster monitored by Open Telemetry traces. We propose using the flow hierarchy metric on the graph model to pinpoint cycles that reveal inefficient resource composition inducing possible performance issues and economic waste.
Hyper-relational knowledge graphs (HKGs) significantly enhance traditional triple-based knowledge graphs (KGs) by introducing role-value pairs. Recently, Transformer-based hyper-relational knowledge graph completion (HKGC) methods have gained widespread adoption and achieved substantial advancements. However, these methods primarily focus on entity prediction during training, leading to suboptimal performance on evaluationmetrics like Hit@k. In HKGs, entities and relations are interdependent, and the exclusive emphasis on entity prediction during Transformer training limits the self-attention mechanism's ability to capture relational properties information adequately. this limitation hinders the model's ability in learning entity prediction tasks efficiently. therefore, in this paper, we propose MTL-HKGC, a multi-task learning framework aimed at augmenting the model's capacity to grasp relational properties information by incorporating a relation prediction task. this augmentation, in turn, enhances the effectiveness of entity prediction. Additionally, we introduce an effective dynamic loss balancing method that adjusts loss weights dynamically based on task difficulty changes during training. this approach enables the model to prioritize the entity prediction task after mastering simpler relation prediction task, thus enhancing HKGC performance. Our experimental findings on two prominent HKGC datasets validate the effectiveness of our proposed MTL-HKGC.
distributed deep learning is becoming increasingly important due to the size of deep neural networks. the sheer volume of the input datasets used in the process can have a significant negative effect on the training t...
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Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dat...
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It is proposed an algorithm finding the optimal array placement in the distributed memory of a parallelcomputing system. this work is a step towards the development of a new generation of parallelizing compilers for ...
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It is proposed an algorithm finding the optimal array placement in the distributed memory of a parallelcomputing system. this work is a step towards the development of a new generation of parallelizing compilers for computing systems with a distributed memory. Such compilers may be useful for manycore systems on chip with addressable local memory (not cache) for each core. this is especially important for systems on chip with a many processor cores, where data exchange with RAM is a performance bottleneck. the construction of special auxiliary program graph for estimation of minimum number of data transfers is obtained. the arrays placement withthe optimal number of data transfers is constructing in this paper. (C) 2021 the Authors. Published by Elsevier B.V.
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