In heterogeneous networks (HetNets), low power smallcells, i.e., Wifi, can be offered an economic incentive in order to offload traffic from high-power macrocell, which is usually overloaded. This becomes important in order to maintain efficient operation of the network and generate benefit of tradeoff between macrocell and smallcells. The benefit to smallcells comes from the economic incentive offered by macrocell and the benefit to macrocell is achieved by reducing the load and saving spectrum. However, two important challenges are faced in this cooperation: 1) How much economic incentive can be offered by macrocell, and 2) How much offloading traffic volumes can be admitted by the smallcells. In this paper, we propose a novel game based approach for data offloading scheme to determine the amount of economic incentive a macrocell should offer to smallcells and to determine how much traffic each smallcell should admit from the macrocell. In our proposal, a two-stage non-cooperative Stackelberg game theory is applied to optimize the strategies of both macrocell and smallcells in order to maximize their utilities.

In the area of cloud infrastructure environment, the management tool to monitor and control the cloud resources is the important factor that can drive the cost benefit of the cloud vendors. But most these tools are bundled within the high cost commercial platforms and are optimized to run on desktop computers. With the vision that Mobile Cloud Computing will be the future technology paradigm that dominates the IT industry, we want to create a cloud management tool that is open source, fast, lightweight and mobile friendly. We take the initial steps by implementing our framework using several popular technologies such as RESTful, Java Message Service, JSON, and we call it “High performance and Lightweight Mobile Cloud Infrastructure Monitor and Benchmark Service” or HiLiCloud. The initial testings show competitive evaluation results.

We take the first step to study the price competition in a heterogeneous market cloud computing formed by public provider and cloud broker, all of which are also known as cloud service providers. We formulate a price competition between cloud broker and public provider as a two-stage non-cooperative game. In stage one, where cloud service providers set their service prices to maximize their revenue, we use the Nash equilibrium concept to study the equilibria for the price setting game. Cloud users can select the services (from the cloud broker or public provider) that provide them the best payoff in terms of performance (i.e., delay) and price. To that end, cloud users can adapt their service selection behavior by observing the variations in price and quality of service offered by the different cloud service providers. For the service selection game of cloud users in stage two, we use the evolutionary game model to study the evolution and the dynamic behavior of cloud users. Furthermore, the Wardrop equilibrium and replicator dynamics is applied to determine the equilibrium and its convergence properties of the service selection game. Numerical results illustrate that our game model captures the main factors behind the heterogeneous market cloud pricing and service selection, thus represents a promising framework for the design and understanding of the heterogeneous market cloud computing.

Demand response programs have been considered critical for power grid reliability and efficiency. Especially, the demand response of datacenters has recently received encouraging efforts due to huge demands and flexible power control knobs of datacenters. However, most current efforts focus on owner-operated datacenters, omitting another critical segment of datacenter business: multitenant colocation. In colocation datacenters, while there exist multiple tenants who manage their own servers, the colocation operator only provides facilities such as cooling, reliable power, and network connectivity. Therefore, colocation has a unique feature that challenges any attempts to design a demand response program: uncoordinated power management among tenants. To tackle this challenge, two incentive mechanisms are proposed to coordinate tenant power consumption for demand response under two different scenarios. First, in the case of economic demand response where the operator can adjust an elastic energy reduction target, we show that there is an interaction between the operator and tenant strategies, where each side maximizes its own benefit. Hence, we apply a two-stage Stackelberg game to analyze this scenario and derive this game's equilibria. However, computing these equilibria can be intractable with exhaustive search; therefore, we propose an algorithm to find the Stackelberg equilibria with linear complexity. Second, in the case of emergency demand response where a fixed energy reduction target must be fulfilled, we devise two incentive schemes with the distributed algorithms that can achieve the same optimal social cost. While the first algorithm is based on the dual-decomposition method that is suitable for nonstrategic tenants, the second one is designed for strategic tenants to achieve a unique Nash equilibrium of a bidding game. Finally, trace-based simulations are also provided to illustrate the efficacy of our proposed incentive schemes.

In this paper, we optimize resource allocation for multimedia cloud based on queuing model. Specifically, we optimize the resource allocation in both single multimedia service provider (MSP) scenario and multiple MSPs scenario. In each scenario, we formulate and solve the MSPs' revenue maximization problem under eviction probability constraint of users. Numerical results demonstrate that the proposed optimal allocation scheme can optimally utilize the cloud resources to achieve a maximum revenue.

Pricing is an effective approach for spectrum access control in cognitive radio (CR) networks. In this paper, we study the pricing effect on the equilibrium behaviors of selfish secondary users' (SUs') data packets which are served by a CR base station (BS). From the SUs' point of view, a spectrum access decision on whether to join the queue of the BS or not is characterized through an individual optimal strategy that is joining the queue with a joining probability. This strategy also requires each SU to know the average queueing delay, which is a non-trivial problem. Toward this end, we provide queueing delay analysis by using the M/G/1 queue with breakdown. From the BS's point of view, we consider a duopoly market based on the two paradigms: the opportunistic dynamic spectrum access (O-DSA) and the mixed O-DSA & dedicated dynamic spectrum access (D-DSA). In the first paradigm, two co-located opportunistic-spectrum BSs utilize freely spectrum-holes to serve SUs. Then, we show the advantages of the cooperative scenario due to the unique solution that can be obtained in a distributed manner by using the dual decomposition algorithms. For the second paradigm, there are one opportunistic-spectrum BS and one dedicated-spectrum BS. We study a price competition between two BSs as a Stackelberg game. The cooperative behavior between two BSs is modeled as a bargaining game. In both paradigms, bargain revenues of the cooperation are always higher than those due to competition in both cases. Extensive numerical analysis is used to validate our derivation.

We study the pricing mechanisms and their effects on equilibrium behaviors of self-optimizing secondary users (SUs) sharing a single channel of primary users (PUs) operated by a service provider (SP) in cognitive radio networks. From SUs' point of view, a spectrum access decision on whether to join a queue or not is characterized through an individual optimal strategy. With this strategy, we show that there exists a unique equilibrium in terms of SUs' joining probability. This strategy also requires each SU to know its average queueing delay, which is a non-trivial problem because of multiple SUs service's interruptions from the returns of PUs; we, however, can analyze this queueing delay based on the general distribution of SUs's service time and PUs' traffic model by using renewal theory. We also provide a sufficient condition and iterative algorithms for the convergence of equilibrium points. From the SP's point of view, two pricing mechanisms are proposed with different goals: revenue maximization and social welfare maximization. And the optimal price can be solved efficiently using numerical methods.

The IEEE 1609.4 [1] is a MAC extension of IEEE 802.11p [2] to support multi-channel operations. However, the IEEE 1609.4 does not allow nodes to exchange non-safety messages during the CCH interval. This paper proposes a Vehicular Enhanced Multi-channel MAC protocol (VEMMAC) for Vehicular Ad hoc Networks (VANETs). The VEMMAC adopts the IEEE 1609.4 with alternating sequences of the Control Channel (CCH) interval and the Service Channel (SCH) interval. Different from the IEEE 1609.4, the VEMMAC allows nodes to transmit non-safety messages during CCH interval and broadcast safety messages twice with each in the CCH and SCH interval. Our proposal can utilize the channel resources more efficiently than the IEEE 1609.4. The simulation results show that the proposed VEMMAC protocol achieves higher throughput for service data and is more reliable for safety messages broadcast than other protocols.

Recently overlay/underlay framework in Cognitive Radio (CR) have been studied and demonstrated the benefits such as spectrum efficiency and channel capacity maximization. We assume Secondary Users (SUs) can choose to either acquire a dedicated spectrum with constant payment or to use a Primary User (PU) band for free. However, using PU band yields delayed transmission cost. In this paper, we apply M/M/1 queueing model with heterogeneous service rate to derive the explicit expressions for the expected delays of an arbitrary SU data packets. Based on this, the SUs need to decide whether to use the Primary User (PU) band or to acquire the dedicated band. The interaction between selfish SUs is modeled as a noncooperative game. We prove the existence and uniqueness of a symmetric Nash equilibrium, and characterize the equilibrium behavior explicitly. Then an appropriate price of the dedicated band can be defined. Numerical analysis are used to prove a high degree of accuracy for the derived expressions.

Recently overlay/underlay framework in Cognitive Radio have been studied and it demonstrated the benefits such as spectrum efficiency and channel capacity maximization. Arising from these work, we suppose the secondary users can operate under overlay mode when the primary user is absent and operate under underlay mode when the primary user is present. In this paper, a hybrid overlay/underlay cognitive radio system is modeled as a M/M/1 queue where the rate of arrival and the service capacity are subject to Poisson alternations. Each packet (as a customer) arriving at the queue makes decision to join or balk the queue. Upon arrival, the individual decision of each packet is optimized based on his observation about the queue length and the state of system. We will present the individual strategy of each customer in detail in this paper.