Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on autoscaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., startup time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report indepth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers. 1
A B S T R A C T PurposeThe regimens of weekly irinotecan with platinum have been used for treatment of metastatic small-cell lung cancer (SCLC). We conducted a multi-institution phase II trial to evaluate a novel 21-day schedule of irinotecan and carboplatin in patients with relapsed or extensive SCLC. Patients and MethodsEighty patients were enrolled with the following characteristics: 39 male patients, 41 female patients; median age, 65 years; and Zubrod performance status, 0 to 1 in 85% and 2 in 15% of patients. Dosing schemas were based on the maximum-tolerated dose derived in a previous phase I study. Chemotherapy-naive patients with extensive SCLC were treated with irinotecan 200 mg/m 2 and carboplatin area under the curve (AUC) of 5 (arm A). Patients, who had previously been treated with chemotherapy and had relapsed disease received irinotecan 150 mg/m 2 and carboplatin AUC of 5 (arm B). In each study arm, the treatment was given every 21 days for up to six cycles. ResultsThe most common grade 3 to 4 toxicities included neutropenia (54%), thrombocytopenia (22%), anemia (13%), diarrhea (22%), and nausea/emesis (11%) in both study arms. There were three treatment-related deaths owing to neutropenic sepsis. Among 72 assessable patients, response rates of 65% and 50% were observed, respectively, for arm A and arm B. The median survival for both study arms was identical at 10 months (95% CI, 6 to 14 months). A response rate of 65% was observed in the intracranial disease of 14 patients with known brain metastases. ConclusionThis 21-day regimen of irinotecan and carboplatin seems promising for the treatment of relapsed SCLC.
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