Search for: All records

We propose a rapid restoration strategy against PNE-node failure during postdisaster cooperation among DC providers and optical-network carriers. Our strategy reduces disruption and improves DC-service restoration by 35% in 20% less time compared to baseline. 

Cooperation among telecom carriers and datacenter (DC) providers (DCPs) is essential to ensure resiliency of network-cloud ecosystems. To enable efficient cooperative recovery in case of resource crunch, e.g., due to traffic congestion or network failures, we previously studied several frameworks for cooperative recovery among different stakeholders (e.g., telecom carriers and DCPs). Now, we introduce a novel Multi-entity Cooperation Platform (MCP) for implementing cooperative recovery planning, to achieve efficient use of carriers’ valuable optical-network resources during recovery. We adopt a Distributed Ledger Technology (DLT) that ensures decentralized and tamper-proof information exchange among stakeholders to achieve open and fair cooperation. To support diverse types of cooperation, we develop a state machine representing the MCP operation and define state transitions associated to stakeholders’ cooperation within the state machine. Moreover, we propose a signaling system in MCP to ensure simple and reliable state transitions for stakeholders during the cooperative recovery planning in large ecosystems. We experimentally demonstrate a proof-of-concept DLT-based MCP on a testbed. We showcase a DCP-carrier cooperative planning process, showing the flexibility of the proposed MCP to support diverse types of cooperation. 

Large-scale network-cloud ecosystems are fundamental infrastructures to support future 5G/6G services, and their resilience is a primary societal concern for the years to come. Differently from a single-entity ecosystem (in which one entity owns the whole infrastructure), in multi-entity ecosystems (in which the networks and datacenters are owned by different entities) cooperation among such different entities is crucial to achieve resilience against large-scale failures. Such cooperation is challenging since diffident entities may not disclose confidential information, e.g., detailed resource availability. To enhance the resilience of multi-entity ecosystems, carriers are important as all the entities rely on carriers’ communication services. Thus, in this study we investigate how to perform carrier cooperative recovery in case of large-scale failures/disasters. We propose a two-stage cooperative recovery planning by incorporating a coordinated scheduling for swift recovery. Through preliminary numerical evaluation, we confirm the potential benefit of carrier cooperation in terms of both recovery time and recovery cost/burden reduction. 

We investigate the problem of future disaster-resilient optical network-cloud ecosystems. We introduce our solutions considering openness/disaggregation and cooperation for single- and multi-entity network-cloud ecosystems, respectively. 

Abstract In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time‐varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generatein situthat are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time‐varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem — learning a function‐space neural network to reproduce flow map samples under a fixed integration scheme — leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map. 

This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of $7.63\mathrm{g}\text{−}\mathrm{days}$is used to set upper limits on the dark-matter-electron scattering cross section for dark matter masses between 0.5 and $1000\mathrm{MeV}/c^2$, as well as upper limits on dark photon kinetic mixing and axionlike particle axioelectric coupling for masses between 1.2 and $23.3\mathrm{eV}/c^2$. Compared to an earlier HVeV search, sensitivity was improved as a result of an increased overburden of 225 meters of water equivalent, an anticoincidence event selection, and better pile-up rejection. In the case of dark-matter-electron scattering via a heavy mediator, an improvement by up to a factor of 25 in cross section sensitivity was achieved. Published by the American Physical Society2025