TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization

Technology providers heavily exploit the usage of edge‐cloud data centers (ECDCs) to meet user demand while ECDCs are large energy consumers. Concerning decrease expenditure ECDCs, task placement is one most prominent solutions for effective allocation and consolidation such tasks onto physical machine (PM). Such must also consider additional optimizations beyond power include other objectives, including network‐traffic effectiveness. In this study, we present a multi‐objective virtual (VM) scheme (considering VMs as fog tasks) called TRACTOR, which utilizes an artificial bee colony optimization algorithm network‐aware assignment PMs. The proposed aims minimize network traffic interacting dissipation center's switches To evaluate VM solution, Virtual Layer 2 (VL2) three‐tier topologies modeled integrated into CloudSim toolkit justify effectiveness solution in mitigating consumption ECDC. Results indicate that our method able reduce by 3.5% decreasing 15% 30%, respectively, without affecting QoS parameters.