REE-TM：面向多样化云工作负载的可靠且高能效流量管理模型
REE-TM: Reliable and Energy-Efficient Traffic Management Model for Diverse Cloud Workloads
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中文总结 AI 辅助
该研究针对云工作负载多样性带来的资源管理问题，提出REE-TM流量管理模型，采用TG-QNN结合QBHO算法估计工作负载以检测资源拥塞，相比无REE-TM方案可靠性最高提升30.25%、能效最高提升23%。
中文摘要 AI 辅助
工作负载需求的多样性对云服务的高效资源分配与管理有着关键影响。现有文献要么未充分考虑，要么忽视了来自广泛互联网服务用户的作业请求的异构特征。为应对这一情况，本文提出名为可靠且高能效流量管理（REE-TM）的方法，该方法从资源需求变化和预期复杂度角度利用互联网流量的多样性。具体而言，REE-TM对异构作业请求进行分类，并通过选择云基础设施内最可接受的虚拟节点（如虚拟机或容器这类软件定义实例）和物理节点（实际硬件服务器或计算主机）来执行这些请求。为应对基于资源竞争的资源故障与性能下降，本文提出一种新型工作负载估计器“基于托弗利门的量子神经网络”（TG-QNN），其中学习过程或互连权重优化采用量子版黑洞（QBHO）算法实现。主动估计的工作负载用于计算即将到来的互联网流量的熵，并通过分析各种流量状态检测潜在的资源拥塞。本文使用基准数据集通过仿真对REE-TM进行了广泛评估，并与最优版本和无REE-TM的版本进行了比较。对REE-TM的性能评估及与相关指标的比较表明，与无REE-TM的情况相比，REE-TM在确保更高可靠性方面提升了最高达30.25%，在能效方面提升了最高达23%，验证了其有效性。
英文摘要
Diversity of workload demands lays a critical impact on efficient resource allocation and management of cloud services. The existing literature has either weakly considered or overlooked the heterogeneous feature of job requests received from wide range of internet services users. To address this context, the proposed approach named Reliable and Energy Efficient Traffic Management (REE-TM) has exploited the diversity of internet traffic in terms of variation in resource demands and expected complexity. Specifically, REE-TM incorporates categorization of heterogeneous job requests and executes them by selecting the most admissible virtual node (a software-defined instance such as a virtual machine or container) and physical node (an actual hardware server or compute host) within the cloud infrastructure. To deal with resource-contention-based resource failures and performance degradation, a novel workload estimator 'Toffoli Gate-based Quantum Neural Network' (TG-QNN) is proposed, wherein learning process or interconnection weights optimization is achieved using Quantum version of BlackHole (QBHO) algorithm. The proactively estimated workload is used to compute entropy of the upcoming internet traffic with various traffic states analysis for detection of probable resource-congestion. REE-TM is extensively evaluated through simulations using a benchmark dataset and compared with optimal and without REE-TM versions. The performance evaluation and comparison of REE-TM with measured significant metrics reveal its effectiveness in assuring higher reliability by up to 30.25% and energy-efficiency by up to 23% as compared without REE-TM.
