Cuong T. Do

A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing

Large-scale Internet applications, such as content distribution networks, are deployed in a geographically distributed manner and emit massive amounts of carbon footprint at the data center. To provide uniform low access latencies, Cisco has introduced Fog computing as a new paradigm which can transform the network edge into a distributed computing infrastructure for applications. Fog nodes are geographically distributed and the deployment size at each location reflects the regional demand for the application. Thus, we need to control the fraction of user traffic to data center to maximize the social welfare. In this paper, we consider the emerging problem of joint resource allocation and minimizing carbon footprint problem for video streaming service in Fog computing. To solve the largescale optimization, we develop a distributed algorithm based on the proximal algorithm and alternating direction method of multipliers (ADMM). The numerical results show that our algorithm converges to near optimum within fifteen iterations, and is insensitive to step sizes.

Power Control for Interference Management and QoS Guarantee in Heterogeneous Networks

We consider the sum-rate optimization problem with power control for uplink transmission in a heterogeneous network (HetNet) consisting of a macrocell and multiple femtocells. The considered problem includes the HetNets crucial constraints of both cross-tier interference protection and user QoS in terms of outage probability and average delay. We transform the original nonconvex problem into a convex problem and develop a distributed algorithm that can attain the global optimal transmit power values. This algorithm, however, has heavy network overheads, which may lead to increased energy consumption for femtocell user equipment. We propose a new practical near-optimal distributed algorithm that eliminates these network overheads. Numerical results show that the schemes have nearly identical performance.

Spectrum handoff model based on Hidden Markov model in Cognitive Radio Networks

Cognitive Radio Network (CRN) is one of technologies to enhance the spectrum utilization by allowing unlicensed users to exploit the spectrum in an opportunistic manner. In CRN, the spectrum handoff function is a necessary component to provide a resilient service for the unlicensed users. This function is used to discover spectrum holes in a licensed network and avoid interference between unlicensed users and licensed users. Due to the randomness of the appearance of Primary users, disruptions to communications of Secondary users are often difficult to prevent and lead to low throughput of CRN. In our paper, we analyze the status of channels and propose the spectrum handoff model based on Hidden Markov model (HMM) to optimize the spectrum handoff scheme for CRN. Moreover, we compare our method with the random channel selection in the simulation.

Incentive Mechanisms for Economic and Emergency Demand Responses of Colocation Datacenters

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 games 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.

A Lightweight Algorithm for Probability-Based Spectrum Decision Scheme in Multiple Channels Cognitive Radio Networks

Compare with the sensing-based spectrum decision scheme, the probability-based spectrum decision scheme has been shown to yield a shorter queuing delay time in Cognitive Radio (CR) system that consists of many Primary Users (PUs) and Secondary Users (SUs). However, the former scheme had cumbersome algorithms and slowly converging speed. In this paper, by introducing Lagrange function, we propose a lightweight algorithm with the computational effort O(N) to define the optimal distribution probability vector. Numerical results demonstrate a high degree of accuracy for the derived expressions.

Dynamics of service selection and provider pricing game in heterogeneous cloud market

This paper studies price competition in a heterogeneous cloud market formed by public providers and a cloud broker, all of which are also known as cloud service providers (CSPs). We formulate the price competition between CSPs as a two-stage noncooperative game. In stage I, in which CSPs set their service prices to maximize their revenues, we model the pricing game using the noncooperative static game. We provide sufficient conditions for the existence and uniqueness of Nash equilibrium prices, which can be obtained using an iterative algorithm. The convergence properties of the iterative algorithm are characterized using the contract mapping theorem. In stage II, given the prices set by CSPs, cloud users can select the services that provide them the best payoff in terms of performance (i.e., delay) and price. We apply an evolutionary game to study the evolution and dynamic behavior of cloud users. Furthermore, we use the Wardrop equilibrium and replicator dynamics to determine the equilibrium and its convergence properties of the service selection game. To attract users to the equilibrium, we implement the service selection algorithms using population evolution and reinforcement learning approaches. Numerical results illustrate that our game models can provide comprehensive understanding of the heterogeneous CSPs market and service selection in cloud computing. HighlightsA game theoretical model in a Cloud Service Provider market in which there are two stages of competition.In stage I: formulate the competition among CSPs for selling service opportunities as a noncooperative game.In stage II, a Wardrop equilibrium is achievable by cloud users in the service selection game.

Throughput maximization for the secondary user over multi-channel cognitive radio networks

This paper studies average waiting time analysis of opportunistic access in multi-channel cognitive radio networks with a single secondary user and multiple primary users by applying the M/G/1 preemptive priority queueing scheme. By employing convex optimization tool, the secondary user finds the optimal way to distribute the packets to all channels in the system. For sensitive delay network, we proposed an algorithm to distribute the secondary users packets to the only group of channels which satisfy the delay constraint. Simulation are used to validate the results, and simulation results demonstrate a high degree of accuracy for the derived expressions. Results indicate that the performance of the secondary user depends on the data traffic characteristics of the primary users and also the delay constraint of the secondary user.

Power control under QoS and interference constraint in Femtocell cognitive networks

Power control is critical for femtocell networks that allow spectrum sharing among Macrocell and Femtocell. In this paper, we derive an optimal power control strategy toward reducing the CO2 emissions and maximize total throughput under both the probability of dropping a packet due to buffer overflow constraints at the Femtocell user equipment (FUE) and the interference constraints to the Macrocell base station (MBS) for uplink transmission. We use linear programming to solve the CO2 emissions minimization problem. For maximizing the total throughput of FUEs, we propose a distributed power control algorithms by employing geometry convex tool. Numerical results are used to validate the analysis and demonstrate a high degree of accuracy for the derived expressions. Results indicate that the performance of the FUEs depends on not only the interference constraint of the MBS but also the delay constraint of the FUEs.

Toward service selection game in a heterogeneous market cloud computing

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.

Game Theory for Cyber Security and Privacy

In this survey, we review the existing game-theoretic approaches for cyber security and privacy issues, categorizing their application into two classes, security and privacy. To show how game theory is utilized in cyberspace security and privacy, we select research regarding three main applications: cyber-physical security, communication security, and privacy. We present game models, features, and solutions of the selected works and describe their advantages and limitations from design to implementation of the defense mechanisms. We also identify some emerging trends and topics for future research. This survey not only demonstrates how to employ game-theoretic approaches to security and privacy but also encourages researchers to employ game theory to establish a comprehensive understanding of emerging security and privacy problems in cyberspace and potential solutions.