---
title: 'SHARP-QoS: Unified Multi-QoS Prediction'
url: https://www.emergentmind.com/topics/sharp-qos
type: topic
---

# SHARP-QoS: Unified Multi-QoS Prediction

SHARP-QoS is a unified neural architecture for joint prediction of multiple Quality of Service (QoS) parameters in service-oriented computing. It is designed to address the challenges of extreme QoS data sparsity, noise, inherent hierarchical dependencies, and negative transfer in multi-task settings. Unlike conventional methods that individually predict each QoS parameter—resulting in redundant models and poor generalization—SHARP-QoS leverages shared hyperbolic graph representations, adaptive feature-sharing through sparsely-gated routing, and a novel exponential moving average (EMA)-based loss balancing strategy. These innovations enable efficient, robust, and scalable joint prediction across all QoS parameters in a single inference pass [2512.17262].

## 1. Overall Model Structure and Prediction Pipeline

SHARP-QoS predicts $P$ QoS parameters (e.g., response time RT, throughput TP, reliability RE, cost CT) for any user–service pair $(u_i, s_j)$, using a four-stage pipeline:

1. **Preprocessing:**
   - Constructs $P$ bipartite QoS invocation graphs $\mathcal{G}^p$ (one per parameter), and two context graphs: region ($\mathcal{G}^r$) and autonomous system ($\mathcal{G}^a$).
   - Generates initial node features through nonnegative matrix factorization (NMF) on $\mathcal{Q}^p$, and context representations via autoencoders.

2. **Hierarchical Feature Extraction Block (HFEB):**
   - Extracts per-task structural features using a Hyperbolic Hypergraph Graph Convolution Network (HHGCN) on $\mathcal{G}^p$.
   - Extracts contextual features using a Hyperbolic GCN (HyGCN) on $\mathcal{G}^r$ and $\mathcal{G}^a$.
   - Both operate in the Poincaré ball $\mathbb{B}^{d, c}$ (hyperbolic space).

3. **Feature Sharing and Fusion Block (FSFB):**
   - Enables adaptive cross-task feature sharing through sparsely-gated Sub-Network Routing (SNR) for context and Cross-SNR for structural representations.
   - Uses a learned gate to fuse each task’s structural and shared features.

4. **Joint QoS Prediction Module (JQPM):**
   - For each $p$, a small feed-forward network produces user/service embeddings; their inner product gives the predicted QoS score $\hat{\mathcal{Q}}^p$.
   - All task-specific loss terms are combined via the EMA-based strategy.

Joint prediction is achieved by parameter sharing across all $P$ tasks for the HFEB and FSFB, unified loss balancing, and simultaneous output of all predicted QoS matrices in a single model evaluation.

## 2. Hierarchical Hyperbolic Feature Extraction

The model operates on graphs embedded in a constant-curvature hyperbolic space $\mathbb{B}^{d, c} = \{x \in \mathbb{R}^d \mid c\|x\|^2 < 1\}$ with learnable curvature $c$. Key hyperbolic algebraic operations include:

- **Exponential and logarithmic maps:**
  $$
  \exp_{<0>}^{c}(v) = \tanh\bigl(\sqrt{c}\|v\|\bigr)\,\frac{v}{\sqrt{c}\|v\|}, \\
  \log_{<0>}^{c}(x) = \frac{1}{\sqrt{c}} \artanh(\sqrt{c}\|x\|) \frac{x}{\|x\|}
  $$

- **Möbius addition:**
  $$
  x \oplus_c y = \frac{(1+2c\langle x,y\rangle + c\|y\|^2) x + (1-c\|x\|^2) y}{1+2c\langle x,y\rangle + c^2\|x\|^2\|y\|^2}
  $$

- **Möbius matrix–vector multiplication:**
  $$
  W \otimes_c x = \exp_{<0>}^{c}\left(W \log_{<0>}^{c}(x)\right)
  $$

- **Wrapped activation:**
  $$
  \sigma^{\otimes_c}(x) = \exp_{<0>}^{c}(\sigma(\log_{<0>}^{c}(x)))
  $$

Each HyGCN layer performs message passing in the tangent space, Möbius-linear transformation, activation, and returns to Euclidean space with
$$
\begin{aligned}
X_{l+1} &= \sigma^{\otimes_c}\left(\exp_{<0>}^c(\hat A\,\log_{<0>}^c(X_l) W_{l1})\,\oplus_c\,\exp_{<0>}^c(b_{l1})\right) \\
X_{l+2} &= \sigma^{\otimes_c}\left(\exp_{<0>}^c(\log_{<0>}^c(X_{l+1}) W_{l2})\,\oplus_c\,\exp_{<0>}^c(b_{l2})\right)
\end{aligned}
$$

For each QoS graph $\mathcal{G}^p$, two associated hypergraphs (user-based and service-based, incorporating second-hop hyperedges) feed into parallel HHGCNs, after which features are concatenated and passed through a final linear layer to yield the structural feature $\mathcal{Y}^p \in \mathbb{R}^{N \times d}$. Contextual features $\mathcal{Y}^r, \mathcal{Y}^a$ are similarly extracted for region and AS context graphs.

## 3. Adaptive Feature Sharing and Gated Fusion

In the FSFB, SHARP-QoS employs a dual mechanism:

- **Sparsely-Gated Sub-Network Routing (SNR):**
  - Fused context features $\mathcal{Y}^{ra} = \mathcal{Y}^r + \mathcal{Y}^a$ are processed by $K_1$ Dense blocks $\phi_k$, routed per-task via hard-concrete gates $c_k^p \in \{0,1\}$.
  - Each task's sparsely-shared feature representation is:
    $$
    \mathcal{Y}_s^p = \frac{1}{\sum_k c_k^p}\sum_{k=1}^{K_1} c_k^p\,\tilde\phi_k^p(\mathcal{Y}^{ra})
    $$
  - Gates are sampled with a temperature-controlled relaxation during training, thresholded during inference.

- **Cross-Task SNR:**
  - Applies a similar routing scheme (with $K_2$ blocks) to other tasks' structural features, enabling $\mathcal{Y}^{p}_{cs} = \sum_{j \neq p} \Theta(\mathcal{Y}^j)$ and thus $\mathcal{Y}^{p}_{scs} = \mathcal{Y}^p_s + \mathcal{Y}^{p}_{cs}$.

- **Fusion by Learned Gate:**
  - Each task's structural feature $\mathcal{Y}^p$ and aggregated context/structural shared feature $\mathcal{Y}^p_{scs}$ are fused by a sigmoid gate:
    $$
    g^p = \sigma\left([\mathcal{Y}^p || \mathcal{Y}^p_{scs}] W^p_g\right)
    $$
    $$
    \mathcal{Z}^p = g^p \odot \mathcal{Y}^p + (1 - g^p) \odot \mathcal{Y}^p_{scs}
    $$
  - $\mathcal{Z}^p$ serves as the final embedding for each task.

## 4. Dynamic Loss Balancing with EMA

To mitigate negative transfer due to loss scale mismatches across tasks, SHARP-QoS incorporates an EMA-based loss balancing:

- **Per-task Loss:**
  $$
  \mathcal{L}^p = \frac{1}{|\mathrm{TD}|}\sum_{(i,j) \in \mathrm{TD}} |\mathcal{Q}^p_{ij} - \hat{\mathcal{Q}}^p_{ij}|
  $$
- **EMA Calculation:**
  $$
  \tilde{\mathcal{L}}^p_i = \beta\,\tilde{\mathcal{L}}^p_{i-1} + (1-\beta)\,\mathcal{L}^p_i
  $$
- **Dynamic Weights:**
  $$
  w^p = \frac{(\tilde{\mathcal{L}}^p + \epsilon)^{-1}}{\sum_{q=1}^P (\tilde{\mathcal{L}}^q + \epsilon)^{-1}}
  $$
- **Joint Objective:**
  $$
  \mathcal{L}_{\mathrm{EMA}} = \sum_{p=1}^P w^p\,\mathcal{L}^p
  $$
- **Final Loss with Sparsity Regularization:**
  $$
  \mathcal{L}_{\mathrm{final}} = \mathcal{L}_{\mathrm{EMA}} + \lambda \bigl(\mathcal{L}_0^{\mathrm{snr}} + \mathcal{L}_0^{\mathrm{cross\text{-}snr}}\bigr)
  $$

The $\ell_0$ penalty encourages sparse routing, further promoting effective specialization. *A plausible implication is that this mechanism stabilizes training under widely varying QoS parameter scales.*

## 5. Experimental Protocol and Baselines

Experiments were conducted on three datasets, each incorporating region and AS context:

| Dataset       | Users × Services   | QoS Parameters             |
|---------------|-------------------|----------------------------|
| WSDREAM-2T    | 339 × 5825        | RT, TP                     |
| Small-3T      | 112 × 36          | RT, TP, RE                 |
| gRPC-4T       | 57 × 150          | RE, CT, LT, PW             |

- **Feature preprocessing:** Each $\mathcal{Q}^p$ processed with NMF (output dim $d_1 = 128$); region/AS one-hot encoding followed by autoencoder (dim $d_2 = 128$). Features are concatenated ($\mathcal{F}^i \in \mathbb{R}^{(n + m) \times 256}$).
- **Hyperparameters:** 2 HyGCN/HHGCN layers ($L=2$), hidden dim $d=128$, 4 SNR/Cross-SNR blocks ($K_1=K_2=4$), block output $d_o=64$, gate threshold $\delta=0.5$, EMA $\beta=0.99$, $\lambda=10^{-5}$; training with AdamW (lr$=10^{-3}$, decay $10^{-4}$), 10k epochs, patience=400.
- **Evaluation metrics:** Mean Absolute Error (MAE), Root Mean Square Error (RMSE), relative improvement $I(\%)$.
- **Baselines:** Multi-task (DNM, JQSP, MGEN, WAMTL); Single-task (PMF, CMF, DCALF, QoSGNN, llmQoS).

## 6. Quantitative and Qualitative Performance

### Multi-Task and Single-Task Results

- **Multi-task:** SHARP-QoS achieves $\sim$19.5% lower average MAE and $\sim$19.3% lower RMSE versus the best alternative multi-task baseline across all datasets and observed density splits. Relative improvements increase with the number of predicted QoS parameters.
- **Single-task:** On WSDREAM-2T, MAE matches or improves upon all state-of-the-art single-task baselines, despite SHARP-QoS jointly predicting multiple tasks.

*This suggests that joint modeling with adaptive feature sharing does not compromise single-task accuracy.*

### Computational Efficiency and Robustness

- **Model size and runtime:** 1.62M parameters, 133.4G FLOPs per pass, fastest inference (4 × $10^{-8}$ s).
- **Ablation studies:** Removing HHGCN increases error by 1.6–7.9%; removing SNR or Cross-SNR each substantially degrades metrics; omitting any two key modules degrades performance by up to 25% on TP.
- **Loss balancing:** EMA strategy reduces MAE by up to 6.8% and RMSE by up to 3.9% over previous weighting methods; module ablations confirm each novel contribution is critical.
- **Robustness to outliers:** Removing the top 2–10% of outliers yields up to 81.8% MAE and over 70% RMSE improvement on RT.
- **Cold-start:** When 20% of users/services are fully absent from training, MAE increases moderately (e.g., RT MAE from 0.293 to 0.458), indicating graceful degradation due to context-graph sharing.
- **Context graph impact:** Isolated inclusion of region or AS context improves RT/TP MAE by 1.3–3.5%; using disconnected HHGCNs for QoS, RG, and AS outperforms graph merging by $\sim$1%.

*These results demonstrate that SHARP-QoS delivers improved accuracy, computational efficiency, and robustness for joint multi-QoS prediction under challenging real-world conditions* [2512.17262].

## 7. Methodological Significance

SHARP-QoS introduces three methodological innovations established by ablation and comparative study:

- **Hyperbolic hierarchical convolution** enables expressive, scalable representation of graph-structured and hierarchical relationships in sparse heterogenous QoS data.
- **Sparsely-gated adaptive multi-context routing** facilitates efficient feature sharing and specialization across tasks without negative transfer.
- **EMA-based loss balancing** directly addresses scale mismatches between diverse QoS objectives, stabilizing multi-task training.

These innovations collectively allow SHARP-QoS to outperform established single-task and multi-task models, specifically under data sparsity, outlier, and cold-start regimes, while maintaining practical computational overhead. The architecture establishes a new direction for graph-based multi-task models in service-oriented computing by integrating hyperbolic geometry, adaptive neural routing, and dynamic objective weighting [2512.17262].

Source: https://www.emergentmind.com/topics/sharp-qos