Markov Chain-Based Resource-Efficient and QoS-Aware Scheduling for Latency-Critical and Best-Effort Tasks

The importance of data centers in modern computing environments is continuously increasing, and ensuring the Quality of Service (QoS) of Latency-Critical (LC) tasks is essential to prevent system failures and performance degradation. Resource isolation techniques, widely used to guarantee the QoS of LC tasks, allocate additional resources until the QoS requirements are satisfied. However, traditional methods do not consider the performance saturation point of Best-Effort (BE) tasks and allocate all remaining resources to BE tasks after meeting LC task requirements, resulting in resource wastage. Furthermore, the high overhead caused by real-time resource adjustments can lead to system performance degradation. To address these issues, this paper proposes a weight-based Markov Chain model for resource optimization. The proposed model evaluates resource efficiency through offline profiling of balanced resource combinations and determines the optimal resource allocation strategy in advance. By accurately identifying the minimum resources required to ensure LC task QoS and predicting the performance saturation point of BE tasks, the model prevents unnecessary resource wastage. Unlike traditional methods, the proposed model leverages weight-based profiling to significantly reduce real-time scheduling overhead and achieve balanced resource allocation. Experimental results demonstrate that the proposed model maintains LC task QoS while guaranteeing BE task performance at a level comparable to existing resource isolation scheduling techniques. Additionally, the model successfully optimizes resource utilization and effectively reduces profiling overhead compared to traditional methods. The proposed approach optimizes both resource efficiency and performance in multi-task server environments.