Online storage and use of digital assets and applications are both aspects of cloud computing. through a computer network, distributed information systems store and transmit data. Data and effort have both grown for s...
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Service discovery is a vital process that enables low latency provisioning of Internet of things (IoT) applications across the computing continuum. Unfortunately, it becomes increasingly difficult to identify a proper...
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ISBN:
(纸本)9783031396977;9783031396984
Service discovery is a vital process that enables low latency provisioning of Internet of things (IoT) applications across the computing continuum. Unfortunately, it becomes increasingly difficult to identify a proper service within strict time constraints due to the high heterogeneity of the computing continuum. Moreover, the plethora of network technologies and protocols commonly used by IoT applications further hinders service discovery. To address these issues, we introduce a novel Mobile Edge Service Discovery using the DNS (MESDD) algorithm, which uses a so-called Intermediate Discovery Code to identify suitable service instances. MESDD uses geofences for fine-grained service segmentation based on a naming scheme that identifies users' locations across the computing continuum. We deployed a real-life distributedcomputing continuum testbed and compared MESDD withthree related methods, outperformed by 60% after eight update iterations.
Geo-location, also known as measurement report (MR) location, is a technique to determine the geographic location of user equipment (UE) and the behaviour attribute of telephone traffic based on wireless signals measu...
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Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and ...
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ISBN:
(纸本)9781665460873
Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and distributed, but supports the mobility of data and processes (actors), and (2) algorithms to distribute computations between nodes in a manner that improves overall performance while considering energy use in the system. With appropriate programming tools, we can distribute a given computation in a way that makes effective use of edge devices to improve performance while lowering energy consumption. the paper describes our work building on ideas based on the Actor model of computation. these include characterizing the relation of performance and energy consumption in parallel computation, and methods to support scalable placement mechanisms under dynamically changing network conditions and computational loads on edge devices. the paper will conclude with a presentation with a summary of open research problems.
Recently, AI and deep neural networks have found extensive applications in mobile devices, drones, carts, and more. To meet the demands of processing large-scale data and providing DNN inference services with minimal ...
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ISBN:
(纸本)9798400716713
Recently, AI and deep neural networks have found extensive applications in mobile devices, drones, carts, and more. To meet the demands of processing large-scale data and providing DNN inference services with minimal latency, there is a need. However, IoT devices, withtheir limited computing capabilities, are not well-suited for AI inference. Moreover, considering the diverse requirements of different services, it is necessary to provide inference services that address these variations. To address these challenges, many previous studies have explored collaborative approaches between edge servers and cloud servers by partitioning DNN models. However, these methods face difficulties in finding optimal partitioning points for splitting DNN models and are heavily influenced by network bandwidth since intermediate computation results need to be transmitted to other devices. In this paper, we propose the Adaptive block-based DNN network inference framework. this involves breaking down a large DNN model into block-level networks, training them using knowledge distillation techniques to enable inference only through each block network. Subsequently, dynamic block-level inference calculations are offloaded based on the computing capabilities of edge clusters, providing inference results. Even when using multiple devices, our method is not affected by network bandwidth since only input images need to be transmitted. Experimental results demonstrate that our approach consistently reduces inference latency as the number of devices increases. Additionally, by controlling the trade-off between latency and accuracy, we can provide inference services tailored to various latency requirements.
the proceedings contain 28 papers. the topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of c...
ISBN:
(纸本)9789897587474
the proceedings contain 28 papers. the topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of cloud-native application execution over the edge cloud continuum enabled by DVFS;energy-aware node selection for cloud-based parallel workloads with machine learning and infrastructure as code;security-aware allocation of replicated data in distributed storage systems;performance analysis of mdx ii: a next-generation cloud platform for cross-disciplinary data science research;data orchestration platform for AI workflows execution across computing continuum;framework for decentralized data strategies in virtual banking: navigating scalability, innovation, and regulatory challenges in thailand;and anomaly detection for partially observable container systems based on architecture profiling.
Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large ...
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Matrix inversion is a fundamental operator in MIMO (multiple-input multiple-output) technology, which is a key technology in wireless communication. MIMO technology is characterized by a strong correlation between its...
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ISBN:
(纸本)9798350350227;9798350350210
Matrix inversion is a fundamental operator in MIMO (multiple-input multiple-output) technology, which is a key technology in wireless communication. MIMO technology is characterized by a strong correlation between its underlying operations and matrix inversion. Traditional Von Leuwen architecture processors are limited by the performance bottleneck of sequential execution, which hinders the performance of matrix inversion and limits the development of MIMO technology. the paper proposes a self-timed dynamically reconfigurable architecture to address the performance bottleneck caused by sequential execution in matrix arithmetic. this bottleneck has limited the development of MIMO technology. the architecture consists of several coarse-grained operators interconnected by an on-chip network to form an array for massively parallel computation. the experiments were conducted using an FPGA platform. the results demonstrate that the proposed architecture in this paper has excellent solving performance while significantly reducing the power consumption of the entire system. Specifically, when solving the inverse matrix of a fourth-order matrix, the dynamic power consumption is only 1.976W.
the Information and Communication Technology (ICT) sector sets its net-zero emission goal to be achieved by 2050 to fight the climate change because it will constitute as highly as 20% of the global energy consumption...
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the graph coloring problem, a fundamental NP-hard challenge, has numerous applications in scheduling, register allocation, and network optimization. Traditional sequential algorithms for graph coloring are computation...
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