WebDec 9, 2024 · Based on the above observation, the mobility-aware edge service placement problem is investigated aiming at optimizing two objectives involving service latency and deployment cost. The problem is formulated as a multi-objective context multi-armed bandit learning with a dominant objective (CMABDO) [23]. WebA cost-aware edge server optimization deployment method that can find better solution to place the edge micro datacenter (MDC) compared with the state-of-art server deployment strategies in terms of latency for applications and utilization of resources. In edge computing systems, it is crucial issue to select suitable placement sites and quantity of servers so …
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WebTo enable these services, a set of edge servers needs to be deployed to the roadsides. Such deployment should offer low-latency services to end users, while keeping a low deployment or maintenance cost, which is a nontrivial task. In this article, we study the edge server placement problem in a metropolitan area. WebCost-aware edge server placement International Journal of Web and Grid Services 2024 Journal article DOI: 10.1504/IJWGS.2024.119275 EID: 2-s2.0-85121000155 Part of ISSN: 17411114 17411106 Contributors : Zhang, Q.; Wang, S.; Zhou, A.; Ma, X. Show more detail Source : Shangguang Wang via Scopus - Elsevier create a new chime account
Cost-aware automatic scaling and workload-aware replica …
Web[23] Santoyo-González A., Cervelló-Pastor C., Latency-aware cost optimization of the service infrastructure placement in 5G networks, ... An energy-aware edge server placement algorithm in mobile edge computing, in: 2024 IEEE International Conference on Edge Computing, ... WebNov 18, 2024 · The core idea of CFS is to minimize the number of ENs being deployed, as the construction cost of edge nodes (e.g., EN setup cost) is usually much greater than the cost of a standard server (e.g., server cost) [ 6 ]. As shown in Algorithm 1, we will first model the connections between BSs according to the delay threshold \ (\theta \) (line 3). Webedge server, and, finally, identify the optimal solution by mixing integer programming. Concerning energy, Li and Wang [34] studied the energy-aware edge server placement problem and transformed the edge server placement problem into a multi-objective optimisation problem. They designed an energy-aware edge server placement algorithm create a new column and value pandas