# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 3 Proposed Approach  
### Scalable Resource Allocation  
Cloud resources are expensive, and identifying QoS violations is essential to assist the cloud administrator in monitoring the virtualized environment cloud application's potential violations. QoS violation detection can be performed using the \"Adaptive Neuro-Fuzzy Inference System\" (ANFIS) using fuzzy-if-then rules Ref. [29]. If the decision for QoS violation detection is \"Normal\" (N) or \"Definitely No Violation\" (DNV), then resource scaling is not essential. The \"Probably Violation\" (PV) decision is a warning to signal the system that it would need system check and resource modification to return the system to normal condition. The \"Definitely Violation\" (DV) decision triggers the allocation of resources to VM. The amount of resource allocation depends on the framework configuration, and the metrics being tracked are decided at SLA. Further resource allocation is based on resource monitored logs, VM and Software status, and monitoring parameters. For example, a continuous decision stream that indicates 'normal' response time but 'low' throughput will indicate the need for increased cloud resources to maintain QoS in the normal state. ReSQoV emphasizes VM vertical scalability for cloud application QoS violation using the \"Universal Scalability Law (USL),\" which offers a formal definition of scalability as well as a conceptual framework for comprehending, assessing, comparing, and improving it. It accomplishes this by simulating the effects of crosstalk-induced linear speedup, contention delay, and coherency delay reference [30]. Equation (1) presents the USL with variables. \[C(csv)=\frac{U}{1+\sigma(U-1)+\lambda U(U-1)} \tag{1}\]  
where _C(csv)_ denotes cloud computing scalability, \(U\) denotes system resource load, and \(\sigma\) is the contention-delay parameter characterizing the waiting and queuing of shared resources. The coherence-delay parameter \(\lambda\) represents the penalty for keeping shared writable data consistent. As a result, the scalability efficiency is affected by the values of \(\sigma\) and \(\lambda\). The parameter values are \(0<\sigma\), \(\lambda<1\). Yet _if \(\lambda=0\)_, then (1) is transformed as follows  
\[C(csv)=\frac{U}{1+\sigma(U-1)} \tag{2}\]Coherence is equal to or near zero, as indicated in (2), which excites enhanced scalability, the primary purpose of a scalable architecture design. When the QoS decision is PV or DV, our proposed approach seeks to scale-up resources to VM to maintain the QoS metrics for all process nodes. The system or application has a greater demand for process nodes in these phases, necessitating the provision of extra resources to the virtual machine. It is in line with Equation (1), where machine resources are proportionate, and with the number of processes. Furthermore, Equation (2) indicates that when contention and coherence are null in an ideal scenario, the process node is optimized to its best QoS, resulting in linear scalability. As consistency is defined as the exchange of data across non-local resources, it is most likely to decrease coherence (\(\lambda\)) to zero, where each process node only works with a single resource. There is nearly always some efficiency loss in the real world, and if the reason for this loss can be figured out, remediation is possible. The real-world systems lag behind linear scalability a little and display retrograde scalability at some point. When we calculate this overhead, we discover a significant efficiency loss. Linearity is best thought of as a ratio of the system's performance at a size of one. [PERSON] [31] refers to this as efficiency. If a single node produces 1800 transactions per second, then four nodes should yield 7200 transactions per second. That would be completely efficient. If the system loses a little efficiency with each node and four nodes producing 6500 transactions per second, then the system is around 90% efficient.