# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 1 Introduction  
In cloud computing, the issue of Quality of Service (QoS) is significant. Cloud service providers supply infrastructure for cloud applications to be hosted and accessed online via a subscription rather than being purchased and installed on individual computers. These hosted applications on the infrastructure are called Software as a Service (SaaS). In practice, it is challenging to ascertain the offered QoS as specified in Service Level Agreements (SLA). Cloud infrastructure contains resources that include storage, memory, processor, and bandwidth allocated to virtual machines (VM). In resource allocations to VM, there are many factors that influence optimum resource allocation. Service providers emphasize energy utility, profit generation, SLA, load balancing, and resource utilization. Cloud consumers focus on QoS, availability, service cost, and many other factors. Examination of the dynamics that influence the appropriate allocation of cloud resources from both the service provider and user viewpoints is challenging [1; 2]. It is observed that the quantities of different resources in VMs are predefined, and the varieties of VMs are limited. However, consumer tasks and workloads vary, which necessitate various resource allocations. VMs cannot fulfill all types of customer demand, and consumers cannot wholly utilize all resources in a balanced manner [1; 3]. A study by [4] presented an autonomous resource management approach that works to configure cloud-based applications and required cloud resources themselves. The presented professional approach to QoS management has the potential to self-heal through unexpected setbacks and performance issues. It focuses on QoS parameters in SLA, and resource management isconducted based on workload management. The QoS sensor detects a violation of QoS as a performance control, and the autonomous service manager works for scheduling tasks and managing resources. Typically, cloud auto-scaling solutions are built on reactive policy-based rules that scale-up or scale-down the resources when a particular metric changes. A fundamental challenge for cloud service providers is efficiently provisioning a diverse range of services on their physical infrastructure. They often provide quality of service assurances by enabling the number of VM instances to scale up or down to ensure that QoS requirements are satisfied. Numerous predictive and reactive techniques for dynamically distributing VMs to various services have been proposed [5; 6; 7; 8]. Many approaches have been proposed for scalability and resource allocation using replication without considering the system's performance overheads that include serialization (contention) and resource sharing delay (coherence). Such overheads cause a non-linear relationship between the resources and cloud QoS. It is an important aspect to consider these overheads while adapting the scalability mechanism to maintain QoS in the cloud environment. In this work, we have considered non-linearity, scalability, and performance overheads in our approach. An adaptive framework is proposed to monitor QoS parameters response time, throughput, and availability to verify compliance with the associated SLA and QoS agreed values. This paper presents a model that includes remedial action against possible violations of the QoS metrics. The proposed Resource Quantified Scaling for QoS Violation (ReSQov) model is based on the Universal Scalability Law (USL), which considers scalability overheads and provides cloud service capacity for specific synthetic workloads and generates a capacity model as output. Contention and coherence are measured, and resource allocation in auto-scaling is triggered accordingly. ReSQov has been validated using response time and availability scenarios that demonstrate remedial action in QoS violation scenarios. As the QoS violation detection decision is Probably Violation and Definitely Violation, the remedial action is initiated, and required resources are added to the virtual machine as vertical scaling.