Self-adaptation of service compositions through product line reconfiguration

Bashari, Mahdi; Bagheri, Ebrahim; Du, Weichang

doi:10.1016/j.jss.2018.05.069

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…While the primary goal of the PLD studies is determining the service or product compositions, the market characteristics play an essential role in achieving a practical solution. As a result, customized PLD models are extended to satisfy objectives in many disciplines like information goods (Chellappa and Mehra, 2018), software products (Bashari et al , 2018), automotive industry (Wägemann et al , 2018), sustainable products (Aydin and Badurdeen, 2019) and chemical formulations and products (Torres et al , 2020). So far, however, no model has been proposed to optimize the price and composition of prepaid mobile Internet plans based on the product line design paradigms.…”

Section: Introductionmentioning

confidence: 99%

A multi-attribute model to optimize the price and composition of prepaid mobile Internet plans

Sohrabi

Pishvaee

Hafezalkotob

et al. 2020

JEIM

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PurposePrepaid mobile Internet is one of the most profitable services that are composed of multiple attributes. The overall utility of Internet service can be broken down into the sum of the utility of individual attribute levels. Based on the multi-attribute theory, rational consumers choose the service that yields the highest utility from a number of possible alternatives. Determining the optimal attribute levels that satisfy consumers' preferences and maximize the total revenue of the firm is a challenging multi-attribute decision problem for any mobile operator. When designing mobile Internet services, adopting a robust composition of services against different realizations of competitors' strategies can bring advantages for network operators. The purpose of this study is to determine the optimal attribute levels of prepaid mobile Internet packages with the aim of maximizing the total revenue of the firm by considering the paradigms of multi-attribute utility theory about consumer choices and the issue of uncertainty in counterpart services offered by the competitors.Design/methodology/approachThis paper formulates the problem of multi-attribute pricing and design of mobile Internet plans in a competitive environment by developing deterministic and robust scenario-based mathematical models and considering the paradigms of multi-attribute utility theory about consumer choices. The proposed robust scenario-based models are based on three different paradigms, including maximizing expected revenue, minimizing the negative deviation from expected revenue and minimizing the maximum regret. A comprehensive numerical analysis is conducted to evaluate and compare the efficiency of the proposed models.FindingsThe evaluations reveal that deploying recourse policy can result in higher revenue for the firm when facing uncertainty. By doing sensitivity analysis, this paper shows that consumer preferences for brand attribute and consumers' purchase frequency can influence the revenue of network operators.Originality/valueThis paper develops a novel deterministic multi-attribute product line design (PLD) model to address the problem of determining the price and composition of prepaid mobile Internet plans. Furthermore, the issue of uncertainty in counterpart services offered by the competitors is studied for the first time in the PLD literature.

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Section: Introductionmentioning

confidence: 99%

A multi-attribute model to optimize the price and composition of prepaid mobile Internet plans

Sohrabi

Pishvaee

Hafezalkotob

et al. 2020

JEIM

View full text Add to dashboard Cite

show abstract

“…However, for some specific domains, and depending on the number of the number of FM configurations, this kind of decision strategy can be adopted. The work in [3] presents an adaptation strategy for the self-healing of service compositions. This work models the self-healing as a pseudoboolean optimization problem that is resolved using a SAT solver.…”

Section: Decision Making Process In Dsplsmentioning

confidence: 99%

“…Runtime adaptation mechanisms sometimes are problematic since they tend to be unstable, inefficient and unreliable [2]. Therefore, in order to overcome some of these problems several approaches has been defined [3,4,5], being the Dynamic Software Product Lines (DSPL) one of the most widely used. Dynamic Software Product Lines (DSPL) are a systematic engineering approach that uses SPLs (Software Product Lines) artifacts to model the dynamic variability of a system.…”

Section: Introductionmentioning

confidence: 99%

ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

Ayala

Papadopoulos

Amor

et al. 2021

Journal of Systems and Software

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Dynamic Software Product Lines (DSPLs) are a well-accepted approach for self-adaptation at runtime. In the context of DSPLs, there are plenty of reactive approaches that apply countermeasures as soon as a context change happens, but they often imply making many reconfigurations, which makes the system more unstable. In this paper we propose a proactive approach, ProDSPL, that exploits an automatically learnt model of the system, which anticipates future variations of the system, and generates the best DSPL configuration that can soften the negative impact of future events on the quality requirements of the system. ProDSPL formulates the problem of the generation of dynamic reconfigurationas as a proactive controller over a prediction horizon, which includes a mapping of the valid configurations of the DSPL into linear constraints. Our approach is evaluated and compared against a reactive approach, DAGAME, based also on a DSPL, which uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. The evaluation with a mobile game and randomly generated feature models shows that ProDSPL gives good results with regard to the quality of the configurations generated that tries to anticipate future events and it always enforces the system to make the least possible reconfigurations.

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“…On a more technical level, these authors usually take the discounted sum of rewards as the optimisation objective in MDPs, which is a tradition in the reinforcement learning literature, but which arguably is not suitable for service composition purposes. Bashari et al [20] proposed an automated approach to reconfigure the service composition strategy from the changing environment, the reconfiguration policies were derived from software product line techniques. Zhang et al [8] recognised the necessity of an explicit environment model during service selection and proposed a QoS‐aware monitoring approach.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic analysis of QoS‐aware service composition with explicit environment models

et al. 2020

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In service composition, quality-of-service (QoS) represents a crucial indicator for the policy adoption. Existing composition strategies rarely address the influence of the environment, which may influence QoS and thus lead to sub-optimal composition policies in a dynamic environment. In this study, a model-based service composition approach is proposed. Given the user request, it is possible to first find a set of matching abstract web services (AWSs), and then pull relevant concrete web services (CWSs) based on the AWSs. The set of CWSs can be modelled as a Markov decision process (MDP). In addition, the authors model the environment as a fully probabilistic system, capturing changes of environment probabilistically. The environment model can be further composed of the MDP from the service models, obtaining a monolithic MDP. They demonstrate how the probabilistic verification techniques can be used to find the optimal service selection strategy against their QoS and the environment change. A distinguishing feature of their approach is that the QoS, as well as the dynamic of environment change, is made parametric so that the formal analysis is adaptive to the environment which is of paramount importance for autonomous and self-adaptive systems. Examples and experiments confirm the feasibility of their approach.

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Self-adaptation of service compositions through product line reconfiguration

Cited by 17 publications

References 31 publications

A multi-attribute model to optimize the price and composition of prepaid mobile Internet plans

A multi-attribute model to optimize the price and composition of prepaid mobile Internet plans

ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

Probabilistic analysis of QoS‐aware service composition with explicit environment models

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