Optimal benchmarking of quality of service and quality of experience metrics for telecom service providers using a slack based measure in data envelopment analysis

Abstract

With new devices and new network technologies coming up, it has become an inevitable task to provide services of a minimum quality. Setting feasible Service Level Agreements (SLAs) is the need of the hour. This, being a part of network provisioning and providing the best possible Quality of Service (QoS) is very vital and helps improve user perceived quality or the Quality of Experience (QoE). QoE evaluation helps Internet Service Providers (ISPs) understand their user satisfaction better and this goes hand in hand with providing adequate network QoS. Moreover, in this era of competition, the ISPs themselves will have to be evaluated based on their QoE and QoS metrics to know their true position in the market in terms of performance against their peers/competitors. This evaluation is usually done on a per-metric basis. However, we see from current performance data that all the ISPs fare well on some metrics and need improvement in the others. It is a fact that no ISP fares bad on all given metrics and leads to an understanding that per-metric based evaluation may be a biased form of newlineevaluating performance. Hence, this research has attempted to use an intelligent, robust newlinemathematical technique called the Data Envelopment Analysis (DEA) with its Slack newlineBased Measure (SBM) approach. DEA is a proven, tested and tried technique that is in newlineuse in major industries even today. Being a multiple criterion evaluation methodology newlinebased on linear programming, it works well on multiple outputs and multiple inputs. DEA gives the overall, relative efficiency of the ISPs which gives us the true position of the provider against its peers. The Slack Based Measure provides the Output Slacks that show the potential improvement that the lagging ISPs can make to be in par with their peers/competitors. The Output targets that are provided by the technique can be used as benchmarks for SLAs.