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Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Ren et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Blockchain is a Byzantine fault tolerant (BFT) system wherein decentralized nodes execute consensus protocols to drive the agreement process on new blocks added to a distributed ledger. Generally, two-round communications among [Formula: see text] nodes are required to tolerate up to [Formula: see text] faults in BFT-based consensus networks. This communication pattern corresponds to the worse-case scenario of consensus achievement, even under asynchronous network conditions. Nevertheless, it is not uncommon for a network to operate under better conditions, where a consensus can be reached with a lower communication cost. Hence, with the addition of a faster optimistic path toward an agreement, the idea of dual-mode consensus has been proposed as a promising approach to enhance the performance of asynchronous BFT protocols. However, this opportunity is not completely exploited by existing dual-mode protocols as the fast path can be followed only in a nonfaulty and synchronous network. This article presents a novel dual-mode protocol consisting of fast and backup subprotocols. To create different consensus committees for fast and backup-mode operations, the network contains both active and passive nodes. A consensus can be expedited through a fast-mode operation when majority of the active nodes can communicate synchronously. Under non-ideal conditions, the backup protocol takes over the agreement process from its fast-mode counterpart without starting over the suspended round. The safety and liveness of the proposed protocol are guaranteed with lower communication costs, which balance the trade-off between protocol efficiency and availability., Competing Interests: The authors have declared that no competing interests exist.

The orchestration of applications and their components over heterogeneous clouds is recognized as being critical in solving the problem of vendor lock-in with regards to distributed and cloud computing. There have been recent strides made in the area of cloud application orchestration with emergence of the TOSCA standard being a definitive one. Although orchestration by itself provides a considerable amount of benefit to consumers of cloud computing services, it remains impractical without a compelling reason to ensure its utilization by cloud computing consumers. If there is no measurable benefit in using orchestration, then it is likely that clients may opt out of using it altogether. In this paper, we present an approach to cloud orchestration that aims to combine an orchestration model with a cost and policy model in order to allow for cost-aware application orchestration across heterogeneous clouds. Our approach takes into consideration the operating cost of the application on each provider, while performing a forward projection of the operating cost over a period of time to ensure that cost constraints remain unviolated. This allows us to leverage the existing state of the art with regards to orchestration and model-driven approaches as well as tie it to the operations of cloud clients in order to improve utility. Through this study, we were able to show that our approach was capable of providing not only scaling features but also orchestration features of application components distributed across heterogeneous cloud platforms., Competing Interests: The authors have declared that no competing interests exist.

Recently, cloud computing has drawn significant attention from both industry and academia, bringing unprecedented changes to computing and information technology. The infrastructure-as-a-Service (IaaS) model offers new abilities such as the elastic provisioning and relinquishing of computing resources in response to workload fluctuations. However, because the demand for resources dynamically changes over time, the provisioning of resources in a way that a given budget is efficiently utilized while maintaining a sufficing performance remains a key challenge. This paper addresses the problem of task scheduling and resource provisioning for a set of tasks running on IaaS clouds; it presents novel provisioning and scheduling algorithms capable of executing tasks within a given budget, while minimizing the slowdown due to the budget constraint. Our simulation study demonstrates a substantial reduction up to 70% in the overall task slowdown rate by the proposed algorithms.

With advent of various mobile devices with powerful networking and computing capabilities, the users' demand to enjoy live video streaming services such as IPTV with mobile devices has been increasing rapidly. However, it is challenging to get over the degradation of service quality due to data loss caused by the handover. Although many handover schemes were proposed at protocol layers below the application layer, they inherently suffer from data loss while the network is being disconnected during the handover. We therefore propose an efficient application-layer handover scheme to support seamless mobility for P2P live streaming. By simulation experiments, we show that the P2P live streaming system with our proposed handover scheme can improve the playback continuity significantly compared to that without our scheme.