# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 4 Experimental Setup and Scenario-Based Simulation
### Dataset Generation  
There are a few publicly available traces on measuring QoS of cloud services due to protecting business confidentiality and consumers' privacy in commercial clouds. Therefore, to simulate a real cloud computing environment, there is a need to have different workloads to achieve more realistic behaviors reflecting real-world cloud services. A synthetic dataset is generated by deploying an open-source e-commerce SaaS application Magneto on a virtualized private cloud environment. The \"Windows Server 2012R2\" and \"System Center 2012 Virtual Machine Manager\" serve as IaaS, as synthetic workload real-time transactions are emulated on the e-commerce client website. QoS metric response time and throughput are monitored and recorded using Apache Jmeter, an open-source monitoring tool. The PlanetLab and Google cluster data consist of the traces of CPU, memory, disk, and network utilization, which are low-level metrics and do not address user load and performance metrics. The WS-DREAM dataset, which is available to the public, contains traces of performance measure response times and throughput, which are also analyzed. Sub-datasets are derived from the primary dataset to generate workloads corresponding to response time and throughput under different decision rules, as discussed in our previous work Reference [33] for simulation. Table 2 shows the gradually increasing workload parameters that include workload length (Columns 2), generated response time, and Throughput (Columns 3-4) for the corresponding workload distribution percentile (Column 5). Table 2 lists the workload percentage distribution of workload defined by the decision rules (Column 6) and the percentage distribution of each decision rule (Column 7).  
The workload length is specified as the number of million instructions (MI) to be executed by the CPUs. The size of the workload to be processed is selected as the input size. For example, items 9-12 represent the different lengths of workload (278,800-312,400 MI) with a workload size of 0.8 kb to produce the corresponding ranges of response time (0.407-0.663 s) and throughput (10.843-6.235 kbps) for the Probably Violation decision rule. Likewise, items 13-16 reflect the specific workload duration (313,200-320,000 MI) with a workload size of 0.8 kb to produce the corresponding response time ranges (0.782-1.115 s) and throughput (5.808-4.958 kbps) for the Definitely Violate (DV) decision rule.  
A sub-dataset is used for analysis to further explain the percentage distribution of the workload according to each decision rule. For example, item five of Table 2 (columnsix) accounted for 20% of the workload frequency distribution under the Normal (N) decision rule, and (item thirteen, column six) accounted for 15% of the workload frequency distribution under the Definitely Violation (DV) decision rule. Overall, the Definitely No Violation decision rule (DNV) accounted for 5%, Normal (N) 80%, 14% Probably Violation and Definitely violation includes 1%. These clusters closely approximate real-world Cloud services QoS values.