In this age of digital computing, security is very essential. When building and implementing systems, security in distributedsystems presents special issues that must be taken into account. There are various security...
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We present a decentralized hole punching mechanism built into the peer-to-peer networking library libp2p [1]. Hole punching is crucial for peer-to-peer networks, enabling each participant to directly communicate to an...
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ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
We present a decentralized hole punching mechanism built into the peer-to-peer networking library libp2p [1]. Hole punching is crucial for peer-to-peer networks, enabling each participant to directly communicate to any other participant, despite being separated by firewalls and NATs. The decentralized libp2p hole punching protocol leverages protocols similar to STUN (RFC 8489 [2]), TURN (RFC 8566 [3]) and ICE (RFC 8445 [4]), without the need for any centralized infrastructure. Specifically, it doesn't require any previous knowledge about network participants other than at least one (any arbitrary) node to bootstrap peer discovery. The key insight is that the protocols used for hole punching, namely address discovery and relaying protocols, can be built such that their resource requirements are negligible. This makes it feasible for any participant in the network to run these, thereby enabling the coordination of hole punch attempts, assuming that at least a small fraction of nodes is not located behind a firewall or a NAT.
Researchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to the limitations of current quantum devices. Soft...
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ISBN:
(纸本)9798350377217;9798350377200
Researchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to the limitations of current quantum devices. Software simulators are time and memory-consuming, making hardware emulators an attractive alternative. This article introduces AMARETTO (quAntuM ARchitecture EmulaTion TechnOlogy), designed for quantum computing emulation on low-tier Field-Programmable gate arrays (FPGAs), supporting Clifford+T and rotational gate sets. It simplifies and accelerates the verification of quantum algorithms using a Reduced-Instruction-Set-Computer (RISC)-like structure and efficient handling of sparse quantum gates. A dedicated compiler translates OpenQASM 2.0 into RISC-like instructions. AMARETTO is validated against the Qiskit simulators. Our results show successful emulation of sixteen qubits on a AMD Kria KV260 SoM. This approach rivals other works in emulated qubit capacity on a smaller, more affordable FPGA.
This paper proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile Cloud-Edge computing. The basic idea is that the Cloud Service Center (CSC) recr...
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ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
This paper proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile Cloud-Edge computing. The basic idea is that the Cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint Mobile Devices (MDs). In RACORAM. the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. Specifically, a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC's cost under different scenarios.
This paper provides a comprehensive analysis of the financial impact of Behind-the-Meter (BTM) distributed Energy Resources (DERs) on utility economics. It specifically examines how these technologies are reshaping ut...
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ISBN:
(纸本)9798350372793;9798350372786
This paper provides a comprehensive analysis of the financial impact of Behind-the-Meter (BTM) distributed Energy Resources (DERs) on utility economics. It specifically examines how these technologies are reshaping utility revenues, focusing on fixed and volumetric charges in residential sectors. A key contribution of this work is the development of a Stochastic Average Revenue Impact (SARI) metric, designed to measure the expected average revenue impact on utilities due to DER adoption. This research offers insights into the evolving landscape of utility economics in the context of decentralized energy resources, highlighting both individual and network-wide effects.
In this paper we present a fully distributed, asynchronous, and general purpose optimization algorithm for Consensus Simultaneous Localization and Mapping (CSLAM). Multi-robot teams require that agents have timely and...
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ISBN:
(纸本)9798350384581;9798350384574
In this paper we present a fully distributed, asynchronous, and general purpose optimization algorithm for Consensus Simultaneous Localization and Mapping (CSLAM). Multi-robot teams require that agents have timely and accurate solutions to their state as well as the states of the other robots in the team. To optimize this solution we develop a CSLAM back-end based on Consensus ADMM called MESA (Manifold, Edge-based, Separable ADMM). MESA is fully distributed to tolerate failures of individual robots, asynchronous to tolerate communication delays and outages, and general purpose to handle any CSLAM problem formulation. We demonstrate that MESA exhibits superior convergence rates and accuracy compare to existing state-of-the art CSLAM back-end optimizers.
Due to privacy and cost reasons, distributed machine learning in Wide-Area Networks(DML-WAN) is becoming an emerging and popular collaborative learning paradigm. However, heterogeneity in computing power and data dist...
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Challenges like network latency, bandwidth limitations, and varied node resources are encountered by distributed databases in edge computing environments. This paper examines a distributed database synchronization mec...
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The construction of a distributed heterogeneous data platform for power grid dispatching faces challenges of diversity, large scale, and high performance. However, existing data platform design methods in both the pow...
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ISBN:
(纸本)9798350375145;9798350375138
The construction of a distributed heterogeneous data platform for power grid dispatching faces challenges of diversity, large scale, and high performance. However, existing data platform design methods in both the power and computer science fields struggle to meet practical production requirements effectively. This paper constructs a distributed data storage architecture model for power grid dispatching, defining the elements and their relationships within the architecture. Additionally, it proposes methods for managing massive source data and distributed heterogeneous database clusters. Based on these findings, a power grid dispatching business data platform is designed. Test results indicate that the proposed architecture effectively supports the efficient execution of power grid dispatching business, providing a specialized data platform design paradigm for the power industry.
In this paper we propose a distributed optimization framework for Italian energy communities, characterized by the possibility of receiving a monetary state incentive based on the amount of "shared energy" c...
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ISBN:
(纸本)9798350358513;9798350358520
In this paper we propose a distributed optimization framework for Italian energy communities, characterized by the possibility of receiving a monetary state incentive based on the amount of "shared energy" consumed. Renewable energy produced by members of the community can be "shared", i.e., other members of the community not physically connected to renewable energy sources can consume energy at a reduced cost due to state incentives according to specific rules. In our model we assume that users may have access to either, neither or both, battery energy storage systems (BESS) and renewable energy sources. We propose a cooperative distributed optimization framework which is the basis for distributed predictive control of a network of BESSs that changes the hourly amount of shared energy consumed in the community. The proposed approach can be used to preserve privacy of user consumption data. We provide numerical results indicating that the total cost of energy for a community is significantly reduced by the proposed approach and that cooperation among the BESS owned by the members of the energy community is beneficial.
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