Within the paradigm of Mobile Cloud Computing (MCC) mobile devices such as mobile phones and tablets can unload computation to a local Cloud consisting of both static and mobile devices. Due to the proximity, such Cloud can better fulfill service and latency requirements for QoS sensitive applications. However, in order to function properly, such mechanics requires a collaborative approach that not all the devices might follow: some can contribute much less than others and deviate from the Service Level Agreement. This creates a free-riding problem in MCC and a corresponding QoS issue. An approach towards this problem consists of providing incentives to nodes so that they act as inspectors and occasionally audit the recent behavior of the nodes with which they interact. This double role of potential inspectee and inspectors can be modeled within Game Theory (GT) to predict the behavior of the agents. In this work, we show that an appropriate model for this game is a symmetric four-strategies social dilemma. The solution (a.k.a. equilibrium) of the game provides a unique way to set the incentives so as to drive the system toward the desired behavior. However, even the hybrid inspector-inspectee agent approach is in principle open to a potential flaw: the possibility of inspector-inspectee collusion. The main contribution of this work is that the collusion behavior is not an equilibrium of the game, thus hybrid agent rational players should never collude.

Gianini, G., Viola, F., Lena-Cota, G., Lin, J., Hybrid Inspector-Inspectee-Agent Games in Mobile Cloud Computing, in Q2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, (esp, 16-20 November 2020), Association for Computing Machinery, Inc, New York 2020: 95-100. [10.1145/3416013.3426449] [http://hdl.handle.net/10807/177931]

Hybrid Inspector-Inspectee-Agent Games in Mobile Cloud Computing

Lin, Jianyi
2020

Abstract

Within the paradigm of Mobile Cloud Computing (MCC) mobile devices such as mobile phones and tablets can unload computation to a local Cloud consisting of both static and mobile devices. Due to the proximity, such Cloud can better fulfill service and latency requirements for QoS sensitive applications. However, in order to function properly, such mechanics requires a collaborative approach that not all the devices might follow: some can contribute much less than others and deviate from the Service Level Agreement. This creates a free-riding problem in MCC and a corresponding QoS issue. An approach towards this problem consists of providing incentives to nodes so that they act as inspectors and occasionally audit the recent behavior of the nodes with which they interact. This double role of potential inspectee and inspectors can be modeled within Game Theory (GT) to predict the behavior of the agents. In this work, we show that an appropriate model for this game is a symmetric four-strategies social dilemma. The solution (a.k.a. equilibrium) of the game provides a unique way to set the incentives so as to drive the system toward the desired behavior. However, even the hybrid inspector-inspectee agent approach is in principle open to a potential flaw: the possibility of inspector-inspectee collusion. The main contribution of this work is that the collusion behavior is not an equilibrium of the game, thus hybrid agent rational players should never collude.
2020
Inglese
Q2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks
19th ACM symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2020
esp
16-nov-2020
20-nov-2020
9781450381208
Association for Computing Machinery, Inc
Gianini, G., Viola, F., Lena-Cota, G., Lin, J., Hybrid Inspector-Inspectee-Agent Games in Mobile Cloud Computing, in Q2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, (esp, 16-20 November 2020), Association for Computing Machinery, Inc, New York 2020: 95-100. [10.1145/3416013.3426449] [http://hdl.handle.net/10807/177931]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/177931
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