# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 2 Related Work  
Platform scalability is characterized as a cloud platform's capability to provision as many additional resources as an application desires or specific requests [9]. Application scalability implies that the platform maintains its performance as stipulated in the SLAs, even when its workload exceeds. The universal scalability law may be exploited to organize and measure the output of a system for a concurrent workload. The association concerning workload and cloud resources is non-linear due to resource requests and data access delays queuing. To maximize scalability, these variables should be measured and addressed. Therefore, scalability is the ability of the cloud service provider systems to handle requests, operations, or transactions from more users within a specified recurrent period. The scalability should be aligned with the cloud'resources' provision time and performance. A significant aspect of cloud computing is able to forecast potential consumer requests and associated cloud resource provision requirements. Furthermore, it is necessary to have a dynamic and fluctuating user load mechanism that ensures the accepted QoS and efficiency. Researchers showed how vital resource prediction is in maintaining QoS [9]. An adaptive structure for the workload prediction is provided in another study [10]. The resource usage prediction work combines machine learning clustering with stochastic theory. The research in [11] provides a workload prediction model for automated resource provision centered on the'server's prior use history and the'study's central goal is to reduce power usage in virtualized environments and allied networks. A study by Ref. [12] examines an online auction-based system for a cloud-based virtual machine, CPU, and storage resource allocation. The proposed prediction technique emphasizes service availability, placement, and resource updates according to cloud consumer requirements. The system calculates costs for users and resource usage based on the services needed. In [13], the researchers devised an algorithm for reserving cloud resources that takes the SLA into account. Firstly, it tests the availability of required resources to assign to the user locally. It examines the allocation of the projected reserved resource if the workload increases or if the resource is unavailable. Dynamic resource allocation is an essential feature of cloud computing that matches cloud resource distribution based on cloud 'customers' desire for QoS maintenance, including service availability, fault tolerance, reaction time, service dependability, and throughput [14]. MQLB-RAM [15], which stands for Multi-QoS Load Balance Resource Allocation Method, is presented to allocate resources and load balancing. The MQLB-RAM operates as follows: step (1) it selects and finds outs the multiple dimension QoS, in which the customer utilized the pay-per-use services and QoS parameters containing the memory size, processing speed of CPU, and storage size, and; (2) it deals with the resource scheduling problems as well as reduce the cost in terms of both CPU and operations cost. The use of resources is focused on historical data from VMs on resource allocation and utilization, power use, and workload effects in [16]. Algorithms that account for operational expenses to decrease SLA breaches are also mentioned. The presented algorithms also demonstrate a trade-off between service performance and energy usage. The proposed resource allocation approach is tested on the first-come first-serve basis on the CloudSim [17] simulator, and the results of the experiments satisfied the utilized resource and cost ratio [16]. Related Work comparison on cloud scalability and resource provisioning techniques is shown in Table 1. A MAPE-k control loop-based autonomous resource provisioning solution for fog-based IoT applications was presented. It uses a mix of time series prediction models and Bayesian learning-based techniques to analyze and design the MAPE-k control loop.