Edge computing has become a promising computing paradigm for building IoT (Internet of things) applications, particularly applications with latency and privacy constraints. However, these applications typically tend t...
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ISBN:
(纸本)9798400702341
Edge computing has become a promising computing paradigm for building IoT (Internet of things) applications, particularly applications with latency and privacy constraints. However, these applications typically tend to be compute-intensive and compute resources are limited at the edge when compared to the cloud, so it is important to efficiently utilize all computing resources available at the edge. A key challenge in utilizing these resources is the scheduling of different computing tasks in a dynamically varying, highly hybrid computing environment. We describe the design, implementation, and evaluation of a dynamic distributed scheduler for the edge that constantly monitors the current state of the computing infrastructure and dynamically schedules various computing tasks to ensure that all application constraints are met in another paper. Based on that, this paper mainly proposes a profile evaluation method and results when applying an augmented reality application on distributed systems at the edge. Withthat work done, we propose and implement a good solution to efficiently distribute edge AI applications at the edge.
the use of the Federated Learning paradigm could be disruptive in robotics, where data are naturally distributed among teams of agents and centralizing them would increase latency and break privacy. Unfortunately ther...
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ISBN:
(纸本)9798400704734
the use of the Federated Learning paradigm could be disruptive in robotics, where data are naturally distributed among teams of agents and centralizing them would increase latency and break privacy. Unfortunately there are a lack of robot oriented framework for federated learning that use state of the art machine learning libraries. ROS2 (Robot Operating Systems) is a standard de-facto in robotics for building up teams of robots in a multi-node fully distributed manner. In this paper we presents the integration of ROS2 with PyTorch allowing an easy training of a global machine learning model starting from a set of local datasets. We present the architecture, the used methodology and finally we discuss the experimentation results over a well-known public dataset.
Traditional droop control method cannot achieve effective reactive power distribution when distributed generators are paralleled. therefore, this paper proposes a droop control scheme based on adaptive virtual impedan...
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ISBN:
(纸本)9798350365818;9798350365801
Traditional droop control method cannot achieve effective reactive power distribution when distributed generators are paralleled. therefore, this paper proposes a droop control scheme based on adaptive virtual impedance to solve the problem of uneven distribution of reactive power. the proposed method calculates the impedance difference between the two distributed power sources based on the feedback of line current and voltage, and introduces the adaptive virtual impedance compensation scheme accordingly. the proposed scheme reduces the amount of data to be transmitted during communication, and the system design is simple. the simulation in this paper demonstrate that, the proposed method reduces the percentage error in reactive power distribution from 10.5% to 0.4% under load variations and from 21% to 0.1%, when there is a sudden change in the impedance of one of the lines.
Many time-critical and data-intensive distributedapplications for the computing continuum depend on low-latency, scalable, and highly available distributed key value storages. In this paper, we introduce SDKV, a scal...
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ISBN:
(纸本)9798400702341
Many time-critical and data-intensive distributedapplications for the computing continuum depend on low-latency, scalable, and highly available distributed key value storages. In this paper, we introduce SDKV, a scalable -Smart and distributed Key-Value- store for the Edge-Cloud continuum to automatically place data in close proximity to clients resulting in low response times. the clients of SDKV can influence data availability and access latency by specifying the number of replicas and the desired level of data consistency (strong or eventual) on a per key-value pair basis, which favors the support of a wide range of applications. Results reveal that for different workloads and client access behaviors, SDKV outperforms existing distributed data storages and their data placement algorithms by 12-69% for both consistency models. Moreover, the proposed placement algorithm of SDKV provides fast decision times and scales linearly withthe number of keys.
Unmanned Aerial Vehicles (UAVs) have experienced considerable expansion in civilian applicationsthanks to their operational simplicity and versatility. this document introduces a distributed navigation strategy tailo...
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ISBN:
(纸本)9798350373981;9798350373974
Unmanned Aerial Vehicles (UAVs) have experienced considerable expansion in civilian applicationsthanks to their operational simplicity and versatility. this document introduces a distributed navigation strategy tailored for UAV formations in conducting post-avalanche search-and-rescue operations. the deployment of UAV formations is identified as a interesting approach with respect to individual UAV operations in environments characterized by dynamism and complexity. this configuration simplifies the allocation of several and different sensors across the formation, reducing the payload on single vehicles, enhancing robustness, and improving overall operational efficiency. the proposed navigation algorithm involves a consensus-based Kalman filter for distributed state estimation. the efficacy of this method was validated through its application in a realistic scenario, demonstrating the capability to identify several victims and preserve situational awareness while circumventing areas yet to be searched. this approach is presented as a viable substitute for search-and-rescue missions that traditionally require significant human involvement.
Tokenized Intelligence (TI) aims to enhance network performance by combining machine learning and blockchain-based tokenomics in software-defined networks (5G/6G). this paper examines the potential for incentivizing d...
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ISBN:
(纸本)9798350369588;9798350369595
Tokenized Intelligence (TI) aims to enhance network performance by combining machine learning and blockchain-based tokenomics in software-defined networks (5G/6G). this paper examines the potential for incentivizing decentralized edge computingthrough economic rewards, which can facilitate the efficient training of AI models. TI enables the coordination of a decentralized network of nodes, which allows for collaborative sharing of resources and decreases the expenses linked to centralized cloud-based solutions. TI is utilized to design customized logical networks that effectively optimize the use of computing, storage, and networking resources within a common physical infrastructure. this framework, which is decentralized and distributed, speeds up data analysis and reduces delays. It also provides economic rewards for edge nodes that participate in model training activities. the core of TI lies in its ability to improve network slicing strategies, such as best effort, high availability, and low latency, while also incentivizing edge nodes based on their contributions to the training process of the machine learning model. this economic incentive mechanism guarantees the efficient allocation of resources according to the specific needs of machine learning applications, thereby enhancing overall network performance.
the demand for distributedapplications has significantly increased over the past decade, with improvements in machine learning techniques fueling this growth. these applications predominantly utilize Cloud data cente...
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ISBN:
(纸本)9798400702341
the demand for distributedapplications has significantly increased over the past decade, with improvements in machine learning techniques fueling this growth. these applications predominantly utilize Cloud data centers for high-performance computing and Fog and Edge devices for low-latency communication for small-size machine learning model training and inference. the challenge of executing applications with different requirements on heterogeneous devices requires effective methods for solving NP-hard resource allocation and application scheduling problems. the state-of-the-art techniques primarily investigate conflicting objectives, such as the completion time, energy consumption, and economic cost of application execution on the Cloud, Fog, and Edge computing infrastructure. therefore, in this work, we review these research works considering their objectives, methods, and evaluation tools. Based on the review, we provide a discussion on the scheduling methods in the computing Continuum.
the manual deployment of applicationsdistributed across the cloud, fog, and edge is error-prone and complex. TOSCA is a standard for modeling the deployment of cloud applications in a vendor-neutral and technology-in...
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ISBN:
(纸本)9798400702341
the manual deployment of applicationsdistributed across the cloud, fog, and edge is error-prone and complex. TOSCA is a standard for modeling the deployment of cloud applications in a vendor-neutral and technology-independent manner that is also suitable for the fog and edge continuum. However, there exist various TOSCA orchestrators with different functionalities. thus, selecting an appropriate TOSCA orchestrator requires technical expertise since all the available orchestrators must be analyzed regarding technical, functional, legal, and organizational requirements. In this paper, we tackle this issue and present a systematic technology review of TOSCA orchestrators. Our goal is to support project managers, developers, and researchers in selecting a suitable TOSCA orchestrator. For this, we select actively maintained general-purpose open-source TOSCA orchestrators. Moreover, we introduce the TOSCA Orchestrator Classification Framework and present a selection support system.
Metadata exchange is crucial for efficient geo-distributed fog computing. Existing solutions for metadata exchange overlook geo-awareness or lack adequate failure tolerance. We propose HFCS, a novel hybrid communicati...
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ISBN:
(纸本)9798400702341
Metadata exchange is crucial for efficient geo-distributed fog computing. Existing solutions for metadata exchange overlook geo-awareness or lack adequate failure tolerance. We propose HFCS, a novel hybrid communication system that combines hierarchical and peer-to-peer elements, along with edge pools. HFCS utilizes a gossip protocol for dynamic metadata exchange. In simulation, we investigate the impact of node density and edge pool size on HFCS performance. We observe a performance improvement for clustered node distributions, aligning well with real-world scenarios. HFCS outperforms a hierarchical and a P2P approach in task fulfillment at a slight cost to failure detection.
the objective of this study is to address a scheduling issue arising in production workshops, specifically dealing with unrelated parallel machine scheduling integrating flexible and periodic maintenance interventions...
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ISBN:
(纸本)9798350373981;9798350373974
the objective of this study is to address a scheduling issue arising in production workshops, specifically dealing with unrelated parallel machine scheduling integrating flexible and periodic maintenance interventions. Machines, while operational, consume varying amounts of energy based on their state: idle states consume less energy than active ones. Additionally, energy consumption is influenced by boththe production job and the specific machine. Our goal is to minimize two functions: the first pertains to reducing production and maintenance earliness/tardiness, while the second focuses on minimizing energy consumption. To address this problem, a Mixed Integer Linear Program (MILP) is proposed. the epsilon-constrained method is employed for computingthe Pareto front, and further adaptation involves applying the Multi-Objective Simulated Annealing algorithm (MOSA) to handle instances of substantial size. the proposed MILP model demonstrates the ability to accurately determine the Pareto front for up to 30 production jobs and 2 machines on literature benchmarks within a timeframe of less than 1 hour. Moreover, the computed metrics proves the effectiveness of the proposed MOSA.
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