Abstract
Next-generation networks must address challenges such as exponential user growth, escalating traffic demands, and the proliferation of diverse services requiring both high data rates and ultra-low latency. Network slicing has emerged as a critical solution, enabling resource isolation to improve service efficiency. While traditional hard slicing ensures strict resource partitioning, it often results in significant underutilization. To mitigate this limitation, soft slicing allows dynamic resource sharing across slices, improving overall utilization. However, this approach introduces challenges, including potential violations of Quality of Service (QoS) guarantees and reductions in allocated resources compared to initial provisions. This paper presents a comprehensive soft slicing framework that addresses these key challenges by (1) ensuring user-level QoS guarantees, (2) incorporating slice dissatisfaction into the optimization model, (3) implementing a holistic resource management strategy, and (4) supporting hybrid 6G use cases. The problem is formulated as a Mixed Integer Linear Programming (MILP) model, aiming to maximize network utilization while minimizing slice dissatisfaction. Given the NP-hard nature of the problem, we propose the Heuristic Resource Allocation for Soft Slicing (HRASS) algorithm, which achieves near-optimal performance with significantly reduced computational complexity. Experimental results demonstrate that HRASS effectively improves resource utilization while mitigating the limitations of hard slicing.