# A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics
## 2 Literature Review
Decentralized multi-cloud resource allocation faces several challenges, including interoperability issues, increased system complexity, and inconsistent QoS across providers. Security risks, latency due to distributed coordination, and lack of a global resource view further complicate management. Additionally, dynamic scaling can lead to unpredictable costs, while resource contention may affect fairness and performance. Key obstacles to service provisioning include heterogeneous devices, varying QoS needs, and the expanding availability of cloud and fog resources. Using fog infrastructure is essential for overcoming cloud providers' latency restrictions. The growing number of fog infrastructure providers (FIPs) necessitates assessing pricing, location, quality of service, and resources in order to choose the best one. In order to solve this, the authors [24] suggested a scalable, adaptable platform for service placement in multi-cloud and multi-fog scenarios. Each resource manager (RM) of the available providers is given the opportunity to implement their own placement policy after the proposal broadcasts service requirements to them. An optimization task is used to represent the placement problem in order to minimize the overall weighted delay and cost. However, the proposal may face inconsistent decisions due to decentralized RMs and increased network overhead from broadcasting. Its heuristic (MCD1) may not always find optimal solutions. Scalability could be challenged as the number of providers grows. [PERSON] et al. [25] suggested a workflow scheduling technique for Multi-Cloud Environments (MCEs) based on the Asynchronous Advantage Actor-Critic (A3C) method. An agent that adjusts local parameters and adjusts to changes in the environment is used to model each cloud provider. An initialization policy determines which tasks are allocated to virtual machines. To accommodate data from actual scientific workflows, a customized critic network is created. This improves the agents' ability to adapt and make decisions. According to simulation data, the suggested MCWS-A3C performs better than three benchmark approaches in terms of cost, makespan, and resource usage. Nevertheless, in extremely dynamic cloud environments, the A3C algorithm may experience problems with convergence. The difficulty and computational overhead of training numerous agents rise. Deployment in the real world could need a lot of tweaking to accommodate different workflow patterns. [PERSON] et al. [26] compare and contrast Kubernetes and Terraform as orchestration technologies for cloud cost minimization. The study simulates real-time workloads across several clouds to assess their cost-reduction capabilities. Jenkins-integrated Kubernetes is evaluated for its autoscaling capabilities and resource allocation effectiveness. In order to reduce cloud expenses, the study also looks at how machine learning and predictive analytics may be incorporated into the orchestration process. This gives enterprises a way to manage many clouds at a lower cost. New users may find the learning curve for Terraform and Kubernetes to be quite severe. Cost optimization is constrained by the intricacy of integration and multi-cloud settings. Performance and dependability can change according to workload, necessitating adjustment for best outcomes. By combining services from several cloud providers, a multi-cloud environment enables enterprises to create a single, diverse platform. Depending on business requirements, it provides the freedom to execute tasks on either private or public clouds. In multi-cloud systems, efficient load balancing enhances throughput, minimizes makespan, improves resource efficiency, and provides fault tolerance by rerouting traffic in the event of cloud outages. This study [27] compares the performance of several optimization techniques and presents an optimized fault-tolerant load balancing technique utilizing the multi-objective cat swarm optimization algorithm (MCSOFLB).