Algorithm for service optimization under multi-QoS constraints for data services in a food traceability system
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摘要: 数据服务的理念是大数据时代一种重要的信息服务方式.在面向服务构架（Service-Oriented Architecture，SOA）框架下研究了用于食品安全溯源系统中面向数据服务的多QoS（Quality of Service）约束下服务组合选择优化算法.溯源服务系统是一种数据密集型服务系统，系统的实现需要组合多源的Web服务.通过提取系统的重要Web服务和数据服务QoS指标，建立了食品安全溯源系统的以数据服务为场景的多服务属性约束下优化因子模型—MQBR（Multi-QoS based Benefit Ratio）模型，采用Skyline算法预处理并结合人工智能的启发式方法求解了满足约束条件的服务组合，提升了系统服务选择的效率和质量.通过实验分析证明了算法的有效性，且实现了多QoS约束条件下食品安全溯源系统中服务组合选择算法，提升了服务选择的质量和性能.
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关键词:
- 食品安全溯源 /
- Skyline算法 /
- 多QoS约束 /
- 面向数据服务的服务组合 /
- 启发式算法
Abstract: The concept of data services plays an important role in the era of big data. In this paper, an optimization algorithm for web services in food safety traceability is investigated based on the SOA (Service-Oriented Architecture) framework. Traceability services are commonly data-intensive systems, which need to combine multi-source web services. In this paper, by extracting important QoS (Quality of Service) indexes from the web and data services of the system, a multi-QoS based benefit ratio (MQBR) is established, which is then used on the traceability platform for food security management. Based on the MQBR model, the skyline and heuristic method of artificial intelligence is proposed for optimizing the efficiency and quality of service selection. Experiments are conducted to prove the validity of the algorithm. The methods of our study are designed and applied to a food security management application, with multiple QoS constraints in the traceability systems, to improve the overall performance and service quality. -
表 1 可加性与可乘性计算
Tab. 1 Evaluation of additivity and multiplicativity properties
顺序 分支 循环 并行 可加性 $w_j ^{S}\!=\!w_j ^{S_1}\!+\!w_j ^{S_2 }$ $w_j ^{S}\!=\!w_j ^{S_1 }$或$w_j ^{S}\!=\!w_j ^{S_2 }$ $w_j ^{S}\!=\!k\times w_j ^{S_1 }, $服务$S_1 $循环$k$次 $w_j ^S\!=\!\max (w_j ^{S_1}, w_j ^{S_2 })$ 可乘性 $w_j ^{S}\!=\!w_j ^{S_1 }\times w_j ^{S_2 }$ $w_j ^{S}\!=\!w_j ^{S_1 }$或$w_j ^{S}\!=\!w_j ^{S_2 }$ $w_j ^{S}\!=\!(w_j ^{S_1 })^k, $服务$S_1 $循环$k$次 $w_j ^S\!=\!\max (w_j ^{S_1 }, w_j ^{S_2 })$ 
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表 2 候选服务集QoS参数
Tab. 2 QoS parameters of the candidate web service
功能节点 候选服务 执行时间/ms 可用性(正相关） 可靠性(负相关） 代价(Cost) N1 S11 70 0.6 0.7 1 S12 80 0.7 0.7 1 S13 60 1 0.9 1.2 N2 S21 50 1 0.9 0.8 S22 60 0.4 0.8 0.9 N3 S31 120 0.9 0.9 0.2 S32 100 1 0.7 0.5 N4 S41 201 0.8 0.8 1 S42 243 0.9 0.7 0.7 S43 234 1 0.6 0.4 N5 S51 30 1 1 1.5 S52 40 0.7 0.9 0.6 S53 60 0.6 0.7 1.2
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算法1 FST系统服务组合算法 输入:约束条件$C_{\rm QoS}$, 服务集合${\mathbb{S}}$, 节点集合${\mathbb{N}}$
输出:服务组合列表$A(N)$1. Compute service Pair-wise skyline
2. Select the first node $N\in {\mathbb{N}}$, sort service of $N$ by $H(N)$;
3. $N'={\mathbb{N}}-N$, $j=1$, $A(N)=\varnothing; $
4. $h_j (N)= \max H(N), T=t(N_0, j).$
5. if $T>{\it \Gamma} $, select the service secondly by $H(N)$, go to 4;
6. $T=t(N_0, j)+T; $
7. if $N'\ne \varnothing $, select the next $N_i \in {\mathbb{N}}$
8. $j=1$
9. $h_j (N_i)= \max H(N_i), \;\;T=t(N_i, j); $
10. if $T < {\it\Gamma} $, store the $N_i^j $ to $A(i)$//$N_i^j $为节点$N_i $的第$j$个服务
11. else
select the service secondly by $H(N_i)$, $T=t(N_i, j+1)+T$, go to 6;
if service of $N_i $ search complete, search back $N_{i-1} $.
12. if $\forall a_i \in A(N)$, $a_i \ne\varnothing $, return $A(N)$.
13. else return null.
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