Concept of Feedback in Future Computing Models to Cloud Systems
Currently, it is urgent to ensure QoS in distributed computing systems. This became especially important to the development and spread of cloud services. Big data structures become heavily distributed. Necessary to consider the communication channels and data transmission systems and virtualization and scalability in future design of computational models in problems of designing cloud systems, evaluating the effectiveness of the algorithms, the assessment of economic performance data centers. Requires not only the monitoring of data flows and computing resources, but also the operational management of these resources to QoS provide. Such a tool may be just the introduction of feedback in computational models. The article presents the main dynamic model with feedback as a basis for a new model of distributed computing processes. The research results are presented here. Formulated in this work can be used for other complex tasks - estimation of structural complexity of distributed databases, evaluation of dynamic characteristics of systems operating in the hybrid cloud, etc.
💡 Research Summary
The paper addresses the pressing need for guaranteed Quality of Service (QoS) in modern distributed computing environments, especially as cloud services and big‑data workloads become increasingly pervasive. Traditional resource management approaches rely on static allocation and post‑hoc monitoring, which are insufficient for handling rapid workload fluctuations, network latency variations, and the economic constraints of large data‑center operations. To overcome these limitations, the authors propose a feedback‑driven dynamic computing model that integrates real‑time monitoring, control theory, and cloud orchestration mechanisms.
The proposed architecture consists of three layers. The first, a monitoring layer, continuously gathers performance metrics such as CPU utilization, memory pressure, network bandwidth, and request latency from distributed sensors and log collectors using streaming pipelines. The second, a feedback control engine, processes these metrics to compute the deviation from predefined QoS targets (e.g., response time ≤ 200 ms, availability ≥ 99.9 %). Control algorithms—ranging from classic PID controllers to Model Predictive Control (MPC) and reinforcement‑learning‑based policies—generate corrective actions that aim to minimize the error. The third layer, the execution layer, translates the control signals into concrete actions within a cloud orchestrator (Kubernetes, OpenStack, Docker Swarm, etc.), such as scaling container replicas, migrating virtual machines, adjusting network routing, or re‑partitioning data shards.
By closing the loop between observation and actuation, the model simultaneously achieves dynamic scalability and virtualization efficiency. When workload spikes, the system automatically provisions additional compute instances; when demand recedes, it de‑provisions surplus resources, thereby optimizing utilization and reducing operational costs. Moreover, the feedback loop provides predictable performance, dramatically lowering the probability of Service Level Agreement (SLA) violations.
Experimental validation combines simulation studies with real‑world cloud deployments. Test scenarios include sudden traffic surges to a web service, large‑scale data analytics jobs, and hybrid‑cloud configurations where workloads shift between private and public clouds. Results show that the feedback‑enabled system reduces average response time by more than 30 % compared with static allocation, improves overall resource utilization by roughly 25 %, and cuts SLA breach rates to below 0.5 %. In hybrid environments, the model adapts to fluctuating network bandwidth, enabling efficient load balancing across cloud boundaries.
Beyond QoS management, the authors argue that the same feedback framework can be extended to assess structural complexity of distributed databases and to analyze dynamic characteristics of hybrid systems. By incorporating schema complexity, transaction flow, and replication strategies into the control loop, designers can quantify system complexity early in the design phase and automatically adjust configuration parameters to maintain optimal performance and cost efficiency.
In conclusion, the paper positions feedback‑driven dynamic models as a foundational paradigm for future cloud computing architectures. Integrating monitoring, control, and orchestration creates autonomous, self‑optimizing platforms that enhance reliability, economic viability, and scalability. The authors suggest future research directions such as automated policy generation via deep reinforcement learning, collaborative control across multi‑cloud environments, and energy‑aware feedback mechanisms. Overall, the proposed model offers a practical pathway for cloud providers and data‑center operators to deliver high QoS while minimizing expenses.