A passivity approach to game-theoretic CDMA power control

This paper follows a game-theoretical formulation of the CDMA power control problem and develops new decentralized control algorithms that globally stabilize the desired Nash equilibrium. The novel approach is to exploit the passivity properties of the feedback loop comprising the mobiles and the base station. We first reveal an inherent passivity property in an existing gradient-type algorithm, and prove stability from the Passivity Theorem. We then exploit this passivity property to develop two new designs. In the first design, we extend the base station algorithm with Zames-Falb multipliers which preserve its passivity properties. In the second design, we broaden the mobile power update laws with more general, dynamic, passive controllers. These new designs may be exploited to enhance robustness and performance, as illustrated with a realistic simulation study. We then proceed to show robustness of these algorithms against time-varying channel gains.     

A game-theoretic approach for the web services scheduling problem

We address the design of an Internet-based business process composed of several web services by using multiobjective optimization and game-theoretic methods. Adopting a suitable representation for the business process, we present a mathematical optimization problem which considers several quality-of-service objectives: cost, execution time, reliability, availability and reputation. The web service scheduling problem is formulated as a multiobjective mixed-integer linear optimization problem and solved through a goal optimization method. The optimal solution of the scheduling problem assigns suppliers to all the tasks that comprise the business process, thus establishing the revenues – utilities – of all the suppliers. We then model the interaction between the suppliers as an incomplete information (Bayesian) game: the structure of the game is common knowledge of all the suppliers, but each supplier knows only his/her own utility function. A characterization of the Bayes–Nash equilibria of the game is provided. The paper includes numerical examples.     

Power-efficient video encoding on resource-limited systems: A game-theoretic approach

Most mobile video applications are often deployed on battery-operated devices. With limited power supply, it is a challenging issue to support video applications with high resolution due to the complex functionality and high resource requirements. Thus, power-efficient design is important in computation intensive applications especially for mobile video terminals. Previous works on power-efficient control in video encoding systems focus on the low complexity design while typically ignoring the impact of scalable design by considering various power consumption involved in the encoding process. This paper is dedicated to developing a power-scalable video encoding (PSVE) strategy for energy-limited mobile terminals. PSVE can help the video encoding terminal to adjust its power consumption budget efficiently so as to enhance the power-scalable capability in mobile video terminals. This paper first establishes game theoretical analysis to model the power consumption problem as a bargaining problem. Then, the tradeoff between encoding effect and power consumption achieved by the use of game theory. The scalable and low power video encoding system based on Nash equilibrium solution is derived through the analysis on the power consumption and encoding effect. Experimental results demonstrate the efficiency of the proposed approach.     

An evolutionary game-theoretic approach to congestion control

This paper investigates a system where a set of users sharing a bottleneck link must choose the transmission rate at which multimedia traffic is received. Users are assumed to be self-regarding and make their decisions with the sole goal of maximizing their perceived quality. We are interested in the dynamic process by which users adapt their data rates and the convergence of this process to equilibria. We propose a novel two-layer model to represent this system: the upper layer is an evolutionary game-theoretic model that captures how users adapt their rates; the lower layer model captures the network performance and the quality perceived by the users. Using the model proposed, we demonstrate analytically and numerically several interesting properties of the system equilibria. In particular, we establish the relationship between system states that have non-negligible steady state probabilities and Nash equilibria of the induced game.     

GMonE: A complete approach to cloud monitoring

The inherent complexity of modern cloud infrastructures has created the need for innovative monitoring approaches, as state-of-the-art solutions used for other large-scale environments do not address specific cloud features. Although cloud monitoring is nowadays an active research field, a comprehensive study covering all its aspects has not been presented yet. This paper provides a deep insight into cloud monitoring. It proposes a unified cloud monitoring taxonomy, based on which it defines a layered cloud monitoring architecture. To illustrate it, we have implemented GMonE, a general-purpose cloud monitoring tool which covers all aspects of cloud monitoring by specifically addressing the needs of modern cloud infrastructures. Furthermore, we have evaluated the performance, scalability and overhead of GMonE with Yahoo Cloud Serving Benchmark (YCSB), by using the OpenNebula cloud middleware on the Grid’5000 experimental testbed. The results of this evaluation demonstrate the benefits of our approach, surpassing the monitoring performance and capabilities of cloud monitoring alternatives such as those present in state-of-the-art systems such as Amazon EC2 and OpenNebula.     

A game theory-based dynamic resource allocation strategy in Geo-distributed Datacenter Clouds

Geo-distributed Datacenter Cloud is an effective solution to store, process and transfer the big data produced by Internet-of-Things (IoT). A key challenge in this distributed system is how to allocate the bandwidth resources among these geo-distributed datacenters of this cloud efficiently. This paper aims to address this challenge by optimizing the transfer bandwidth resources among different geo-distributed datacenters. To this end, we firstly analyze the interaction between the traffic of physical networks and the data flow of Geo-distributed Datacenter Clouds, and then establish a game theory-based model for cloud resource allocation. Based on this model, a dynamic resource allocation strategy and its corresponding algorithm that are adaptable to the Internet conditions are proposed. Since the background traffic, capacity limit of physical networks as well as the flows and resource demands of geo-distributed datacenters are taken into account, this new strategy can achieve the load balance of the physical networks and content transferring among different geo-distributed datacenters effectively. The real-world trace data is adopted to validate the effectiveness and efficiency of the proposed resource allocation strategy. Compared with existing strategies, the evaluation results demonstrate that our proposed strategy can balance the workloads of physical networks, reduce the response delay of cloud applications, and possess an excellent adaptability.     

An investigation of the word-processing software market war in South Korea: A game-theoretic approach

We adopted a game-theoretic approach to studying competition in the software market that is dependent on the network effect, i.e., where the success of the software product is mainly affected by the size of its installed base. The primary goals of our research were: to provide insights into competition between a new entrant with a significant presence in foreign countries and a home-grown incumbent, and to offer evidence in support to the theory of competition in the software market. Our analysis of word-processing software competition in South Korea between 1997 and 2003 suggested that there are several factors that caused the new entrant [Microsoft in this case], to overtake the native incumbent as the market leader. The findings are integrated into a discussion of the managerial implications.     

A sequential cooperative game theoretic approach to scheduling multiple large-scale applications in grids

Scheduling large-scale applications in heterogeneous distributed computing systems is a fundamental NP-complete problem that is critical to obtaining good performance and execution cost. In this paper, we address the scheduling problem of an important class of large-scale Grid applications inspired by the real world, characterized by a huge number of homogeneous, concurrent, and computationally intensive tasks that are the main sources of performance, cost, and storage bottlenecks. We propose a new formulation of this problem based on a cooperative distributed game-theory-based method applied using three algorithms with low time complexity for optimizing three important metrics in scientific computing: execution time, economic cost, and storage requirements. We present comprehensive experiments using simulation and real-world applications that demonstrate the effectiveness of our approach in terms of time and fairness compared to other related algorithms.     

BECloud: A new approach to analyse elasticity enablers of cloud services

Elasticity is a key property of cloud computing but there is a lack of standard elasticity metrics or analysis procedures to easily quantify this performance figure of cloud services. This absence of a unique general elasticity metric makes difficult to consider elasticity as a service level objective in Service Level Agreements, to benchmark cloud services or to explicitly improve the elasticity of scaling and provisioning mechanisms, to mention only some examples. This paper defines a new elasticity metric capable of considering its four main components, scalability, accuracy, time and cost, independently of the service level (infrastructure, platform or software). Furthermore, an analysis procedure to evaluate the behaviour of service elasticity and a benchmarking tool to automate this analysis are presented. The main elasticity enablers of cloud services are identified and analysed using this metric, procedure and tool via real use cases on private and public clouds, drawing interesting conclusions about this important performance aspect of cloud services.     

A novel approach to manage cloud security SLA incidents

Cloud computing is increasingly playing an important role in the service provisioning domain given the economic and technological benefits it offers. The popularity of cloud services is increasing but so are their customers’ concerns about security assurance and transparency of the Cloud Service Providers (CSPs). This is especially relevant in the case of critical services that are progressively moving to the cloud. Examples include the integrated European air traffic control system or public administrations through the governmental clouds. Recent efforts aim to specify security in cloud by using security service level agreements (secSLAs). However, the paucity of approaches to actually control the fulfillment of secSLAs and to react in case of security breaches, often results in distrust in cloud services. In this paper, we present a solution to monitor and enforce the fulfillment of secSLAs. Our framework is able to (a) detect occurrences that lead to unfulfillment of commitments, and (b) also provide mitigation to the harmful events that may or do compromise the validity of secSLAs.     

A cloud based health insurance plan recommendation system: A user centered approach

The recent concept of “Health Insurance Marketplace” introduced to facilitate the purchase of health insurance by comparing different insurance plans in terms of price, coverage benefits, and quality designates a key role to the health insurance providers. Currently, the web based tools available to search for health insurance plans are deficient in offering personalized recommendations based on the coverage benefits and cost. Therefore, anticipating the users’ needs we propose a cloud based framework that offers personalized recommendations about the health insurance plans. We use the Multi-attribute Utility Theory (MAUT) to help users compare different health insurance plans based on coverage and cost criteria, such as: (a) premium, (b) co-pay, (c) deductibles, (d) co-insurance, and (e) maximum benefit offered by a plan. To overcome the issues arising possibly due to the heterogeneous data formats and different plan representations across the providers, we present a standardized representation for the health insurance plans. The plan information of each of the providers is retrieved using the Data as a Service (DaaS). The framework is implemented as Software as a Service (SaaS) to offer customized recommendations by applying a ranking technique for the identified plans according to the user specified criteria.     

DISSECT-CF: A simulator to foster energy-aware scheduling in infrastructure clouds

Infrastructure as a Service (IaaS) systems offer on demand virtual infrastructures so reliably and flexibly that users expect a high service level. Therefore, even with regards to internal IaaS behaviour, production clouds only adopt novel ideas that are proven not to hinder established service levels. To analyse their expected behaviour, new ideas are often evaluated with simulators in production IaaS system-like scenarios. For instance, new research could enable collaboration amongst several layers of schedulers or could consider new optimisation objectives such as energy consumption. Unfortunately, current cloud simulators are hard to employ and they often have performance issues when several layers of schedulers interact in them. To target these issues, a new IaaS simulation framework (called DISSECT-CF) was designed. The new simulator’s foundation has the following goals: easy extensibility, support energy evaluation of IaaSs and to enable fast evaluation of many scheduling and IaaS internal behaviour related scenarios. In response to the requirements of such scenarios, the new simulator introduces concepts such as: a unified model for resource sharing and a new energy metering framework with hierarchical and indirect metering options. Then, the comparison of several simulated situations to real-life IaaS behaviour is used to validate the simulator’s functionality. Finally, a performance comparison is presented between DISSECT-CF and some currently available simulators.     

A game-theoretic and stochastic survivability mechanism against induced attacks in Cognitive Radio Networks

Cognitive Radio Networks (CRNs) are envisioned to provide a solution to the scarcity of the available frequency spectrum. It allows unlicensed secondary users (SUs) to use spectrum bands that are not occupied by licensed primary users (PUs) in an opportunistic manner. This dynamic manner of spectrum access gives rise to vulnerabilities that are unique to CRNs. In the battle over the available spectrum, SUs do not have any means of identifying whether disruption sensed on a band is intentional or unintentional. This problem is further intensified in the case of heterogeneous spectrum, where different bands provide different utilities. A smart malicious agent can use this vulnerability to temporarily disrupt transmissions on certain bands and induce their unavailability on SUs. The motivation for such disruption-induced attacks can be either monopolism, i.e. to capture as much spectrum as possible and make other SUs starve, or denial of service by intentional disruption of other SUs’ communications. This paper proposes an adaptive strategy for robust dynamic spectrum access in the event of induced attacks. Assuming rational players, and considering the notion of channel utility, the optimal strategy is established by modeling such scenarios as zero-sum games that lead to Nash equilibrium. Thereafter, the case of non-stationary channel utilities is investigated, where utilities are subject to abrupt changes due to fluctuations in channel characteristics, as well as arrival and departure of PUs. Through concurrent estimation, learning, and optimal play, it is shown that the proposed mechanism performs robustly even in such dynamic environments. Comparison of the proposed mechanism to other reasonable benchmark strategies in simulation confirms that this mechanism significantly enhances the performance of CRNs.     

A game-theoretic model and analysis of data exchange protocols for Internet of Things in clouds

Big data, Internet of things (IoT), and cloud computing have been recognized a family of technologies for a connected world. Besides hailed hope for the future, there are also challenges to security due to complexity and unpredictability of the Internet, clouds, and data. One of the challenges is information and data exchange, for example, identifying untrustworthy cloud users and analyzing abnormal user behavior during information exchange. This paper addresses exchange mechanism, which is a useful theoretic basis to make secure electronic commerce and electronic business transactions possible. To ensure and verify the property of fairness, a crucial property of exchange mechanism, this paper proposes a specific model for behavior analysis based on the extensive game with imperfect information. Rationality and fairness properties are built in the corresponding game and the game tree. To verify the properties, a tree analysis method is proposed, and a linear time algorithm is given. As a case study, some flaws of the ASW protocol are found.     

Dynamic pricing of call rates: Bayesian approach

In this paper, we present different cases and their possible solutions in the telecommunications market by incorporating dynamically changing call rates over the channel depending upon the network congestion. Since dynamic pricing of call rates is beneficial from both the perspectives of subscribers and service providers, our solution can significantly help to adapt this pricing mechanism in real market scenario. In order to deploy this scheme, we have incorporated the competing network provider's strategy into the mechanism of deciding dynamic price. Establishment of Nash equilibrium with the competing network provider has stabilized our pricing mechanism.     

An extension of duality to a game-theoretic framework

This paper extends some duality results from a standard optimization setup to a noncooperative (Nash) game framework. A Nash game (NG) with coupled constraints is considered. Solving directly such a coupled NG requires coordination among possibly all players. An alternative approach is proposed based on its relation to a special constrained optimization problem for the NG-game cost function, with respect to the second argument that admits a fixed-point solution. Specific separability properties of the NG-game cost are exploited and duality results are developed. This duality extension leads naturally to a hierarchical decomposition into a lower-level NG with no coupled constraints, and a higher-level system optimization problem. In the second part of the paper these theoretical results are applied to a coupled NG with coupled constraints as encountered in optical networks.     

A game-theoretic approach of mixing different qualities of coins

Perpetrators leverage the untraceable feature to conduct illegal behaviors leading security issues with respect to mixing coins. Generally, bad coins are blocked based on a common blacklist. However, the blacklist may not be updated in time, which results in that bad coins escape the blocking. Consequently, perpetrators can still conduct illicit behaviors such as money laundering. In this paper, we apply game theory under imperfect information to study how coins' quality restrain these illicit behaviors under the incomplete scenario. More specifically, we propose a strategy for participants to submit deposits if they hope to mix coins with others even if they are not in blacklist at this time. The deposits will not be refunded when participants are included in the blacklist after mixing. Therefore, no participants have incentives to mix with bad coins. At the last part of this paper, we also simulate the incomes for participants, which indicates that deposits strategy is effective to prevent illicit behaviors.     

多云环境下基于博弈论的用户行为分析模型

针对多云环境下用户的分布式拒绝服务攻击缺乏有效处理机制的现状,从云服务提供商收益角度出发,提出多云环境下基于博弈论的用户行为分析模型。模型首先基于博弈论构造收益矩阵,之后利用模糊隶属度函数判定用户的行为,并进一步评估非协作和协作场景下云服务提供商的资源消耗和收益。经仿真验证,协作模型能够在减少资源消耗的基础上,有效地降低云服务提供商遭受分布式拒绝服务攻击的风险,相对于非协作场景,可以将单位资源的收益提高3倍以上,具有很强的现实意义。     

A review on the computation offloading approaches in mobile edge computing: A game-theoretic perspective

In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.     

A game-theoretic approach to integration of modules

This paper offers a new approach to integration of modules in an intelligent sensor system. Such a system requires that a set of modules-each doing a smaller portion of the overall task-be integrated within a unifying framework. From the perspective of computational systems, this problem holds a considerable interest because it is characterized by a set of coexisting mathematical objectives that need to be optimized simultaneously. In this sense, the design considerations necessitate the introduction of problem solving with multiple objectives. This paper explores these issues in the instance when each module is associated with a mathematical objective that is a function of the outputs of other modules. The integration problem is formulated and what is required of a good solution is presented. This examination interprets the decentralized mediation of conflicting subgoals as promoting a N-player game amongst the modules to be integrated and proposes a game-theoretic integration framework. We model the interaction among the modules as a noncooperative game and argue that this strategy leads to a framework in which the solutions correspond to a compromise decision. The application of this framework in image analysis motivates the hope that a framework such as game-theoretic integration will facilitate the development of general design principles for “modular” systems