In the realm of distributedcomputing networks, managing service fluctuations and optimizing energy consumption are paramount challenges. This paper introduces the Service Fluctuation Mitigated Energy and Effective Co...
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ISBN:
(纸本)9798350377675;9798350377682
In the realm of distributedcomputing networks, managing service fluctuations and optimizing energy consumption are paramount challenges. This paper introduces the Service Fluctuation Mitigated Energy and Effective computing Contract (SrvfmEECC), which is a novel framework designed to address these challenges. The SrvfmEECC provides two contracting models, including Full Time Contract (FTC) and Part Time Contract (PTC), for computing nodes (CNs) upon their integration into the network. The framework strategically distributes computing services (CSs) across CNs to mitigate service fluctuations and optimize energy efficiency. Innovative aspects of SrvfmEECC include an initial performance threshold evaluation, specialized monitoring for FTC nodes, and a dynamic task allocation algorithm that prioritizes task reuse, PTC CN preferences, and load balancing. Numerical analysis demonstrates that SrvfmEECC can reach superior total gain and energy savings, particularly in larger network scales. The proposed algorithm enhances operational efficiency and contributes to environmental sustainability, marking a significant advancement in distributedcomputing resource management.
Software Defined systems (SDS) abstract the actual hardware at different layers with software components, layers such as Networking, Storage, Security, Servers, Data Centers, Clouds etc. This abstraction facilitates c...
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The growth of the Internet of Things (IoT) has led to an increased demand for contactless payment via IoT devices. However, machine-to-machine (M2M) payment in the IoT has been limited by poor centralized transaction ...
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ISBN:
(纸本)9798350381993;9798350382006
The growth of the Internet of Things (IoT) has led to an increased demand for contactless payment via IoT devices. However, machine-to-machine (M2M) payment in the IoT has been limited by poor centralized transaction management, given the distributed nature of the IoT, which results in massive communication overhead. Blockchain technology provides a promising solution for M2M payments in the IoT, but the current blockchain-based solutions for contactless payment lack security and scalability. To address this, a new customised Committee Member Auction mechanism has been proposed that is both safe and scalable for resource-constrained IoT devices. This mechanism also reduces the block mining time by integrating the BLAKE hashing algorithm, as opposed to SHA-256. The proposed framework was implemented utilizing an ESP32 microcontroller and RC522 RFID reader to record over 100 transactions each for SHA-256 and Blake. The results obtained validate that Blake hashing algorithm along with Bidirectional blockchain outperforms SHA-256 by reducing 30% of mining time. Besides improved mining efficiency, this scheme is also found to be resistant against Long Range Attack, Double Spend Attack and Eclipse attack. The proposed scheme thus offers a more secure and scalable option for contactless payments in IoT systems.
Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. However, existing pruning approaches heavil...
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ISBN:
(纸本)9798350339864
Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. However, existing pruning approaches heavily rely on training data for guiding the pruning strategies, making them ineffective for federated learning over distributed and confidential datasets. Additionally, the memory- and computation-intensive pruning process becomes infeasible for recourse-constrained devices in federated learning. To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices. We introduce two key modules in FedTiny to adaptively search coarse- and finer-pruned specialized models to fit deployment scenarios with sparse and cheap local computation. First, an adaptive batch normalization selection module is designed to mitigate biases in pruning caused by the heterogeneity of local data. Second, a lightweight progressive pruning module aims to finer prune the models under strict memory and computational budgets, allowing the pruning policy for each layer to be gradually determined rather than evaluating the overall model structure. The experimental results demonstrate the effectiveness of FedTiny, which outperforms state-of-the-art approaches, particularly when compressing deep models to extremely sparse tiny models. FedTiny achieves an accuracy improvement of 2.61% while significantly reducing the computational cost by 95.91% and the memory footprint by 94.01% compared to state-of-the-art methods.
PDC at UM, is a series of "codeless" modules consisting of visualizations, simulations, and demonstrations which introduce Parallel and distributedcomputing (PDC) concepts in early computing courses. These ...
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ISBN:
(纸本)9798350364613;9798350364606
PDC at UM, is a series of "codeless" modules consisting of visualizations, simulations, and demonstrations which introduce Parallel and distributedcomputing (PDC) concepts in early computing courses. These materials are codeless because they do not require students to write or understand code. Instead, students read a short introduction to a PDC concept and Then engage with a web-based visualization and/or (code-based) demonstration reinforcing the concept. The codeless nature of these modules makes them suitable for computing and non computing majors. To test the effectiveness of our modules we introduced them into two CSI courses and designed and administered a pre/posttest. Our results show statistically significant results: those who engaged with our modules substantially improved their knowledge and understanding of PDC concepts. Our modules also improved student attitudes, confidence and self-efficacy with respect to PDC topics. We also provide some qualitative observations of our study and identify common misconceptions students have about PDC.
The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributedcomputingsystems, particularly when dealing with heterogeneous devices and applications ...
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In demanding environments like space, avionics or robotics, computingsystems face challenges affecting their lifetime and reliability. Traditional approaches like static hardware redundancy prove ineffective for mode...
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ISBN:
(纸本)9798350364613;9798350364606
In demanding environments like space, avionics or robotics, computingsystems face challenges affecting their lifetime and reliability. Traditional approaches like static hardware redundancy prove ineffective for modern, complex systems. This work presents an overview of self-aware, reliable, and reconfigurable computingsystems leveraging Field Programmable Gate Arrays (FPGAs). Despite increasing application demands, FPGA resources remain limited, posing challenges in implementing static redundancy alongside high performance and energy efficiency. Addressing these challenges requires innovative resilience concepts, emphasizing adaptability, reliability, and energy optimization. This paper surveys existing literature and identifies open challenges in achieving resilient computingsystems for demanding applications.
In today's healthcare landscape, emerging technologies serve as crucial foundations, and the integration of blockchain technology into this rapidly evolving digital framework is invaluable. However, the healthcare...
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ISBN:
(纸本)9798350381993;9798350382006
In today's healthcare landscape, emerging technologies serve as crucial foundations, and the integration of blockchain technology into this rapidly evolving digital framework is invaluable. However, the healthcare industry has long struggled with managing the immense and ever-expanding amount of big data electronic health records (EHRs) collected from various sources, including the Internet of Things (IoT), wearable devices, and mobile applications. The storage of all these big data EHRs on the chain creates blockchain bloat, leading to slow transaction speed and high storage costs. More importantly, it incurs security and privacy leakage problems while sharing patients' sensitive data transparently with a wide range of users in a distributed network. To address these challenges, we propose a secure and privacy-preserving data management system for distributed off-chain networks called PrivOff based on blockchain technology, which stores big EHRs separately in the decentralized file system with efficient access control enforcement and flexible revocation to prevent data breaches. In addition, patients can securely share data without revealing their unique identities to ensure privacy. The evaluation results and security analysis reveal that PrivOff can notably reduce the blockchain storage burden while offering data security, patient privacy, and high data availability.
Polar science is an umbrella term for several research disciplines practiced in Earth's polar regions and other planets. Polar science research includes studies performed on land (such as geology and archeology on...
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ISBN:
(纸本)9798350377521;9798350377514
Polar science is an umbrella term for several research disciplines practiced in Earth's polar regions and other planets. Polar science research includes studies performed on land (such as geology and archeology on the circumpolar tundra), air (atmospheric research), sky (planetary observations due to dark, clear, and clean skies during the polar nights), and ocean (marine studies in the Arctic and the Southern Oceans). Many polar science research works look into overlapping complementary studies. distributedcomputing is a discipline of computer science that involves computations across multiple distinct physically separated computing resources, such as computer clusters, clouds, and the edge. distributedcomputing has enabled efficient, scalable, and elastic execution of complex computational problems on utility hardware without expecting access to expensive infrastructure or supercomputers. In this paper, we screen 770 for the interdisciplinary research of distributedcomputing used in or developed for polar science. After systematically removing the irrelevant studies, we assess the full text of 72 papers to understand the distributedsystems research landscape for polar science. We then specifically study 22 polar science research works that develop or heavily utilize distributedcomputing frameworks or principles in detail. Our study finds distributed execution frameworks instrumental for polar science due to the complex and real-time computational needs, coupled with the remote location requiring efficient network bandwidth availability and usage to access remote resources.
With the growth of multi-cloud computing across a heterogeneous substrate of public cloud, edge, and on-premise sites, observability has been gaining importance in comprehending the state of availability and performan...
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ISBN:
(纸本)9798350368543;9798350368536
With the growth of multi-cloud computing across a heterogeneous substrate of public cloud, edge, and on-premise sites, observability has been gaining importance in comprehending the state of availability and performance of large-scale geo-distributedsystems. Collecting, processing and analyzing observability data from multiple geo-distributed clouds can be naturally modelled as dataflows comprising chained functions. These observability flows pose a unique set of challenges including (a) keeping cost budgets, resource overheads, network bandwidth consumed and latency low, (b) scaling to a large number of clusters, (c) adapting the volume of observability data to satisfy resource constraints and service level objectives (d) supporting diverse engines per dataflow depending on each processing function of the flow and (e) automating and optimizing placement of observability processing functions including closed-loop orchestration. Towards this end, we propose Octopus, a multi-cloud multi-engine observability processing framework. In Octopus, declarative observability dataflows (DODs) serve as an intent-driven abstraction for site reliability engineers (SREs) to specify self-driven observability dataflows. A dataflow engine in Octopus, then orchestrates these DODs to automatically deploy and self-manage observability dataflows over large fabrics spanning multiple clouds and clusters. Octopus supports a mix of streaming and batch functions and supports pluggable run-time engines, thereby enabling flexible composition of multi-engine observability flows. Our early deployment experience with Octopus is promising. We have successfully deployed production-grade metrics analysis and log processing data flows in an objective-optimized fashion across 1 cloud and 10 edge clusters spanning continents. Our results indicate data volume and WAN bandwidth savings of 2.3x and 56%, respectively, the ability to support auto-scaling of DODs as input load varies, and the ability to flexi
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