In this chapter, a novel proactive HAS strategy has been proposed and evalu- ated, demonstrating significant benefits as compared to the current approaches in terms of QoE and meaningful KPIs such as stalling probability. The proposed
strategy can successfully help prevent stallings at the client’s HAS application during network coverage problems. The “cost to pay” is the collection and sig- nalling of context information, which could however be realistically implemented;
therefore its adoption in a real network should not be difficult.
Even though this work focused on outage conditions of zero bandwidth, we could easily extend this solution to a more general problem where bandwidth may be insufficient (but not zero). Similarly, the same problem could be adjusted for cases of an imminent service disruption such as a handover, where the afore- mentioned HAS strategy can help prevent stallings during the disruption period (i.e. the handover period). This may become possible by exploiting handover- hinting information, a priori. In this way, the user will be better prepared for a potential interruption in his viewing experience.
It would be also interesting as future work to study a scenario of more than one mobile video streaming users using HAS, and investigate how the decisions of one user potentially affect the others. Stability and fairness issues, together with QoE analysis would be of great interest in this case.
Finally, as a general comment, we would like to point out that this work could be revisited once a standard QoE model for HAS becomes available. In that case, we could have the opportunity not only to produce a more accurate optimization problem, but also to enhance the proposed HAS strategy, focusing on the key factors that mostly influence the end-users’ QoE.
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for Predicting the Quality of Service of Overall Telecommunication Systems
Stoyan Poryazov1(✉) , Emiliya Saranova1,2 , and Ivan Ganchev1,3,4
1 Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
stoyan@math.bas.bg, emiliya@cc.bas.bg
2 University of Telecommunications and Post, Sofia, Bulgaria
3 University of Limerick, Limerick, Ireland ivan.ganchev@ul.ie
4 University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria
Abstract. This chapter presents scalable conceptual and analytical performance models of overall telecommunication systems, allowing the prediction of multiple Quality of Service (QoS) indicators as functions of the user- and network behavior. Two structures of the conceptual presentation are considered and an analytical method for converting the presentations, along with corresponding additive and multiplicative metrics, is proposed. A corresponding analytical model is elaborated, which allows the prediction of flow-, time-, and traffic char‐
acteristics of terminals and users, as well as the overall network performance. In accordance with recommendations of the International Telecommunications Union’s Telecommunication Standardization Sector (ITU-T), analytical expres‐
sions are proposed for predicting four QoS indicators. Differentiated QoS indi‐
cators for each subservice, as well as analytical expressions for their prediction, are proposed. Overall pie characteristics and their causal aggregations are proposed as causal-oriented QoS indicators. The results demonstrate the ability of the model to facilitate a more precise dynamic QoS management as well as to serve as a source for predicting some Quality of Experience (QoE) indicators.
Keywords: Overall telecommunication system · Performance model Overall causal QoS indicator · Dynamic QoS management
Telecommunication subservices · Differentiated QoS subservice indicator QoS prediction · Human factors of QoS
1 Introduction
The telecommunication service is the basis for the Information Service Networks. From the very beginning the Internet began its existence as a packet-based communication system without guarantees for the quality of the services, which are provided on a best- effort basis. At the same time, with the evolution of hardware technologies, and services and applications becoming more and more complex, the quality of service (QoS) has
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I. Ganchev et al. (Eds.): Autonomous Control for a Reliable Internet of Services, LNCS 10768, pp. 151–181, 2018.
https://doi.org/10.1007/978-3-319-90415-3_7
become a hot topic and the term “Internet QoS” has widely spread. The question of providing QoS guarantees in the Internet is still open (c.f., for instance, the history of the Internet Engineering Task Force (IETF) standards for Integrated Services (IntServ) and Differentiated Services (DiffServ) as well as the Third Generation Partnership Project (3GPP)/European Telecommunications Standards Institute (ETSI) Internet Protocol (IP) Multimedia Subsystem (IMS) initiative).
The QoS has many aspects – QoS offered by the provider, QoS delivered (QoSD), QoS achieved by the provider, QoS experienced by the user/customer (QoSE or QoSP – QoS perceived) and others. “The understanding of QoSE is of basic importance for the opti‐
mization of the income and the resources of the service provider” [1]. A new attitude towards the QoS has become dominant – QoS and Quality of Experience (QoE) are considered as goods. The agreement is made according to the perceived quality – Expe‐
rience Level Agreement (ELA) [2]. This approach considerably increased interest in the perceived quality among researchers, providers, and users of telecommunication services.
As a result of the intensive research, the definition of QoE evolved and at the moment the QoE is perceived as a degree of satisfaction or irritation of the users of some appli‐
cation or service which is a result of the fulfillment of their expectations about the utility or/and the satisfaction from the application or service in the context of the user’s person‐
ality and the current state [3, 4]. The QoS perceived by users depends not only on the quality offered by the provider but also on the context of the services, including the techno-socio-economic environment, user’s context, and other factors. The importance of the teletraffic models, particularly of the overall QoS indicators, for QoE assessment is emphasized by Fiedler [5].
From among the many services provided by a telecommunication system, this chapter deals with flow-, time-, and traffic characteristics of the connection and commu‐
nication services. The other QoS characteristics of information transmission service are reflected partially and indirectly as a probability of the call attempt abandoning by users.
The main objective of the authors of this chapter is the development of scalable performance models of overall telecommunication systems, as a part of Information Service Networks, including many of the observable system-dependent factors deter‐
mining the values of QoS indicators.
These models may be used for multiple purposes but the aim of this chapter is to develop prediction models for some key QoS indicators’ values, as functions of the user behavior and technical characteristics of the overall telecommunication system. Such values may be useful for the network design, for the management of telecommunication systems’ QoS, and as a source for predicting some QoE indicators.
The work presented in this chapter continues the development of the approach for the conceptual and analytical modeling of overall telecommunication systems (with QoS guarantees), presented in [6].
Firstly, in Sect. 2, a scalable conceptual model of an overall telecommunication system with QoS guarantees is presented. Two structures of the conceptual presentation are compared – the normalized structure and the pie structure. An analytical method for converting the presentations, along with corresponding additive and multiplicative metrics, is proposed. A qualitative extension of the conceptual model, in comparison with [7], is proposed. This includes two new service branches corresponding to the cases
of ‘called party being busy with another call’ and ‘mailing a message’. This allows analyzing telecommunication systems’ QoS indicators as a composition of QoS indi‐
cators of consecutive and parallel subservices.
The developed model is based on: a Bernoulli–Poisson–Pascal (BPP) input flow;
repeated calls; limited number of homogeneous terminals; 11 cases of losses of call attempts (due to abandoning, interrupting, blocking, and unavailable service); and three successful cases (normal interactive communication, communication after call holding, and mailing). The calling (A) and called (B) terminals (and users) are considered sepa‐
rately, but in interaction to each other. This allows formulation of QoS indicators sepa‐
rately for A-, B-, and AB-terminals.
In Sect. 3, on the basis of the developed conceptual model, a corresponding analytical model is elaborated. User behavior parameters and technical characteristics of the tele‐
communication network serve as an input for the model. The model itself is intended for systems remaining in a stationary state. It is insensitive to the distributions of random variables and provides results in the form of mean values of the output parameters. The model is verified for the entire theoretical interval of network load. It allows the predic‐
tion of flow-, time-, and traffic characteristics of A-, B-, and AB-terminals (and users), as well as of the overall network performance.
In accordance with recommendations of the International Telecommunications Union’s Telecommunication Standardization Sector (ITU-T), analytical expressions for the prediction of three QoS indicators are proposed:
• Carried Switching Efficiency, for finding B-terminal (Subservice 1);
• B-Terminal Connection Efficiency, for connection to the B-terminal, which aggre‐
gates the Carried Switching Efficiency;
• Overall Call Attempt Efficiency, for call attempts finishing with fully successful communication, which aggregates the Carried Switching Efficiency, B-Terminal Connection Efficiency, Finding B-User Subservice, and Communication Subservice.
Four differentiated QoS indicators for each subservice are proposed along with analytical expressions for their prediction:
• Carried Switching Efficiency (Ecs), for finding B-terminal (Subservice 1) as per the ITU-T recommendations;
• QoS specific indicator (Qb) of Connection to the B-terminal (Subservice 2);
• QoS specific indicator (Qu) of Finding B-user (Subservice 3);
• QoS specific indicator (Qc) of Communication (Subservice 4).
The four QoS specific indicators are independent. They are components (in multipli‐
cative metrics) of the ITU-T concordant Overall Call Attempt Efficiency indicator (Ec): Ec=Ecs Qb Qu Qc.
The usage of the proposed QoS indicators of telecommunication subservices allows conducting a more specific QoS analysis and more adequate QoS management.
In Sect. 4, in accordance with the ITU-T recommendations, analytical expressions for the prediction of the Overall Traffic Efficiency Indicator and other overall pie
parameters and their causal aggregations are proposed and illustrated numerically. The overall pie characteristics and their causal aggregations could be considered as causal- oriented QoS indicators. The results allow a more precise estimation of the dynamic importance of each reason of call attempts finishing and thus a more precise dynamic effort targeting of the QoS management.
In the Conclusion, possible directions for future research are discussed.