LECC: Location, energy, carbon and cost-aware VM placement model in geo-distributed DCs

Rawas, Soha; Zekri, Ahmed; El-Zaart, Ali

doi:10.1016/j.suscom.2021.100649

Cited by 3 publications

(2 citation statements)

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“…• LECC. This method is derived from [11], which heuristically explores the VM migration scheme for minimizing the carbon emission cost. Based on the findings of that study, the proposed baseline method is designed to identify overloaded and underloaded PMs based on MAD and minimum CPU utilization, respectively.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…Chen et al [10] provided a proactive adjustment for the upper CPU utilization, employing a statistical measure of dispersion that assigns higher weights to values with larger deviations from the median. Rawas et al [11] proposed a location-aware VM consolidation approach (LECC) for geo-distributed cloud DCs, which evaluates several overloaded detection methods in advance and then selected the minimum carbon and cost data center for migrating VMs. Nevertheless, the aforementioned methods may struggle to accurately predict requests with large variations that exhibit significant noises in the data, which leads to undesired VM migrations and SLA violations.…”

Section: Adaptive Overloaded Detectionmentioning

confidence: 99%

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CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

Zhao,

Zhou,

2024

IEEE Trans. Sustain. Comput.

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The rapid growth and widespread adoption of cloud computing have led to significant electricity costs and environmental impacts. Traditional approaches that rely on static utilization thresholds are ineffective in dynamic cloud environments, and simply consolidating virtual machines (VMs) to minimize energy costs does not necessarily result in the lowest carbon footprints. In this paper, a deep reinforcement learning (DRL) based framework called CFWS is proposed to enhance the energy efficiency of renewable energy sources (RES) supplied data centers (DCs). CFWS incorporates an adaptive thresholds adjustment method TCN-MAD by evaluating the predicted probability of a physical machine (PM) being overloaded to prevent unnecessary VM migrations and mitigate service level agreement (SLA) violations due to imbalanced workload distribution. Additionally, CFWS introduces a novel action space in the DRL algorithm by representing VM migrations among geo-distributed cloud data centers as flattened indices to accelerate its execution efficiency. Simulation results demonstrate that CFWS can achieve a superior optimization of energy costs and carbon footprints, saving 5.67% to 13.22% brown energy with maximized RES utilization. Furthermore, CFWS reduces VM migrations by up to 86.53% and maintains the lowest SLA violations within suboptimal execution time in comparison to the state-of-art algorithms.

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Section: Baseline Methodsmentioning

confidence: 99%

Section: Adaptive Overloaded Detectionmentioning

confidence: 99%