Energy efficiency is now a significant concern for network infrastructure and next-generation network devices are expected to embed advanced power management capabilities. However, the effective exploitation of advanced power management capabilities in network devices which adaptively meet network load and operational constraints is still a considerable challenge due to the stochastic properties of the actual traffic load. In this paper, a statistical optimal local control policy for dynamic control of power state configurations according to the actual traffic load is proposed to minimize the power consumption while meeting the performance constraints. The packet-level statistical features of network traffic load is modeled as a first-order Markov chain and the dynamic power state selection problem is formulated as a Markov decision process, which can be solved using dynamic programming. In addition, we discuss the possibility of implementing the proposed scheme in real network devices, and design a case study in an NetFPGA frequency scaled router. Simulation results are presented to show the effectiveness of the proposed scheme.
In this paper two contemporary technological novelties are combined to introduce the concept of a blockchain-based MaaS, with the aim of pinpointing where and how business value can be created through data-based services of such a system. Towards this purpose, an integrated version of the Business Model Canvas is deployed, combining the advantages of the Lean Canvas and the Ethics Canvas. The overview of data flows among the versatile system stakeholders are outlined to highlight the potential benefits for diverse industries through sharing and collaboration.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.