Summary of "EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference"

Summary (Overview)

• Core Contribution: EasyBalance is a novel cross-layer load-balancing strategy for distributed Mixture-of-Experts (MoE) inference that requires no modification to expert-device mappings, unlike existing approaches based on expert replication or migration.
• Key Insight: Experts from other layers can serve as naturally "redundant" resources for balancing workloads, and multiple micro-batches can simultaneously reside at different MoE layers, enabling cross-layer workload combination.
• Method: EasyBalance greedily schedules subsets of micro-batches at each MoE computation step, deferring others to create balancing opportunities across layers.
• Results: Consistent acceleration of distributed MoE inference across models (Qwen3-30B, Moonlight-16B, Qwen3-235B) and tasks, reducing GPU under-utilization by mostly over 40%.
• Advantages: Instant flexibility across tasks (no task-specific statistics needed) and superior scalability (negligible additional overhead).

Introduction and Theoretical Foundation

Background: MoE Architecture

Mixture-of-Experts (MoE) replaces dense MLP layers with sparsely activated expert networks. For each token, the router selects top-k experts via a gating function:

$$W_{l}, I_{l} = \operatorname{topk}(G(h_{l}^{\prime}))$$

The MoE output is computed as:

$$MLP(h_{l}^{\prime}) = \sum_{i \in I_{l}} W_{l,i} \cdot \operatorname{expert}_{i}(h_{l}^{\prime}) \tag{1}$$

The Load Imbalance Problem

In expert-parallel (EP) distributed inference:

• Tokens are dispatched to remote devices via all-to-all communication
• The computation is bottlenecked by the slowest device (synchronous operations)
• Effective workload at layer $l$: \hat{w}^{(l)} = \max(\boldsymbol{w}^{(l)}) \tag{2}
• GPU utilization: u = \frac{\sum_{l=1}^{l_{max}} \sum_{i=1}^{D} w_{i}^{(l)}}{\sum_{l=1}^{l_{max}} \hat{w}^{(l)} \times D} \tag{3}

Limitations of Existing Approaches

Existing load-balancing methods (expert replication/migration) suffer from:

1. Flexibility: Routing patterns are highly task-dependent; optimal mappings for one task may be suboptimal for others
2. Scalability: Expert replication incurs memory overhead; migration adds communication/synchronization costs

Methodology

Key Observations

1. Cross-Layer Expert Redundancy: Experts from other layers already reside in GPU memory and can serve as "redundant" resources without additional overhead.

2. Cross-Layer Workload Combination: While each micro-batch must follow its own layer-wise sequential order, different micro-batches can reside at different layers simultaneously. Combining workloads is:

• Worst-case safe: $\max(\sum_{j}\boldsymbol{w}_{j}) \leq \sum_{j}\max(\boldsymbol{w}_{j})$
• Mostly effective: The worst case only occurs when workloads peak on the same device; probability decreases with more devices

Algorithm: EasyBalance

Algorithm 1 EasyBalance
Input: Micro-batches T₀,...,T_{N-1}, minimum combination size m
Initialize: l_j ← 0 for all j; S₀ ← all batches; RunSet₀ ← all batches

while |S_i| ≠ 0 do
    i ← i + 1
    Attn_Route_Dispatch(RunSet_{i-1})
    RunSet_i ← Schedule(S_{i-1}, m)
    MoECompute_Combine(RunSet_i)
    for T_j ∈ RunSet_i: l_j ← l_j + 1
    S_i ← {T_j | l_j < l_max}
end while

Scheduling Strategies

Three heuristic strategies explored:

1. MaxUtil: Selects subset maximizing GPU utilization (best performance, but $O(2^n)$ complexity)
2. CumUtil: Sequentially adds batches if they improve utilization (linear complexity)
3. DiffPeak: Adds batches only if their peak device differs from already-selected batches (linear complexity)

Empirical Validation / Results

Main Results

EasyBalance consistently:

• Reduces end-to-end latency across all tested models (Qwen3-30B, Moonlight-16B, Qwen3-235B)
• Decreases GPU under-utilization from approximately 0.35 to 0.20 (mostly >40% improvement)

Scheduling Overhead

The scheduling overhead is negligible (<0.3ms) compared to end-to-end latency.

Orthogonality to EPLB

EasyBalance provides additive performance gains when combined with EPLB (expert placement load balancing), confirming it is orthogonal to expert-map modification methods. Notably, EPLB's gains vary substantially across tasks, while EasyBalance achieves consistent improvements.

Ablation Studies

Micro-batching configurations:

• Optimal performance at 4 micro-batches
• Recommended minimum threshold $m = 0.5 \sim 0.75 \times$ number of micro-batches

Expert-parallel configurations (Table 4):

EasyBalance consistently reduces under-utilization across all EP configurations.

Theoretical and Practical Implications

Theoretical Significance

1. New perspective on redundancy: Demonstrates that cross-layer expert redundancy can be exploited without explicit replication, challenging the assumption that load balancing requires expert-map modification.

2. Probabilistic guarantee: The worst-case scenario (no improvement) has probability decreasing with the number of devices, making cross-layer combination increasingly beneficial at scale.

3. Orthogonality: Establishes a new dimension of load balancing (scheduling-based) that is complementary to existing mapping-based approaches.

Practical Implications

1. Deployment flexibility: EasyBalance works immediately on any MoE model without retraining or expert reconfiguration
2. Cost efficiency: Eliminates memory overhead from expert replication and communication costs from expert migration
3. Scalability: Particularly beneficial for large-scale deployments where routing skew is more pronounced

Conclusion

EasyBalance addresses the load imbalance problem in distributed MoE inference through a fundamentally different approach: instead of modifying expert-device mappings, it leverages cross-layer scheduling of micro-batches. The method exploits two key insights—cross-layer expert redundancy and workload combination—to achieve consistent performance gains with negligible overhead. The approach demonstrates robust effectiveness across diverse models, tasks, and configurations, reducing GPU under-utilization by mostly over 40% while maintaining instant flexibility and superior scalability. Future work could explore more sophisticated scheduling algorithms and extensions to other distributed inference paradigms.