Unbalanced Job Approximation - UJA is a family of low-cost formulas to obtain the throughput of Queueing Networks - QNs with fixed rate servers using Taylor series expansion of job loadings with respect to the mean loading. UJA with one term yields the same throughput as optimistic Balanced Job Bound - BJB, which at some point exceeds the maximum asymptotic throughput. The accuracy of the estimated throughput increases with more terms in the Taylor series. UJA can be used in parametric studies by reducing the cost of solving large QNs by aggregating stations into a single Flow-Equivalent Service Center - FESCs defined by its throughput characteristic. While UJA has been extended to two classes it may be applied to more classes by job class aggregation. BJB has been extended to QNs with delay servers and multiple jobs classes by Eager and Sevcik, throughput bounds by Eager and Sevcik, Kriz, Proportional Bound - PB and Prop. Approximation Bound - PAM by Hsieh and Lam and Geometric Bound - GB by Casale et al. are reviewed.

翻译：不平衡作业近似（Unbalanced Job Approximation, UJA）是一类低成本公式，通过将作业负载相对于平均负载进行泰勒级数展开，来获取具有固定速率服务器的排队网络（Queueing Networks, QNs）的吞吐量。使用一项的UJA得到的吞吐量与乐观平衡作业界（Balanced Job Bound, BJB）相同，而后者在某些情况下会超过最大渐近吞吐量。随着泰勒级数项数的增加，估计吞吐量的精度会提升。通过将多个服务站聚合为单个由吞吐量特性定义的等效流服务中心（Flow-Equivalent Service Center, FESC），UJA可降低求解大型QNs的成本，从而用于参数化研究。尽管UJA已扩展到两类作业，但通过作业类别聚合可将其应用于更多类别。BJB已被Eager和Sevcik扩展至包含延迟服务器和多作业类别的QNs；本文还综述了由Eager和Sevcik、Kriz提出的吞吐量界，Hsieh和Lam提出的比例界（Proportional Bound, PB）与比例近似界（Prop. Approximation Bound, PAM），以及Casale等人提出的几何界（Geometric Bound, GB）。