Chuan Pham

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.

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.

Efficient forwarding and popularity based caching for Content Centric Network

In Content Centric Network, caching and forwarding schemes can affect the performance of the whole network. The original caching and forwarding schemes are simple, but these schemes have some drawbacks. Firstly, the original caching scheme stores the same content on several neighboring routers along the request path. This redundant caching does not use the limited storage space available to the routers efficiently. Secondly, the original forwarding scheme in which the data requests are flooded to neighboring routers, also degrades the performance of the network. In this paper, we aim to solve the issues of using cache space of each router efficiently and forwarding the data requests effectively. In this proposal, we divided an Autonomous System (AS) in several groups of routers to cache the popular contents. Routers in a group cooperatively store the data and forward the Interest in order to increase the network performance. To improve the cache hit, the group of routers only store the popular contents and reduce the duplicate content without effecting the redundancy. We used Consistent Hashing to reduce overlapping contents and forward the request efficiently. The content popularity prediction algorithm assists the routers to store the popular contents that pass through them. Finally, we evaluated the performance of our proposed scheme by using a chunk level simulator.

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.

Joint Consolidation and Service-Aware Load Balancing for Datacenters

Workload consolidation is an efficient approach to reduce the power consumption of datacenters, meanwhile load balancing can reduce the datacenters user delay. Despite complexities of 1) the coupling between consolidation and load balancing methods for server allocation, and 2) the heterogeneity of server configurations, we address the joint consolidation and service-aware load balancing problem to minimize the operation cost of datacenters. We first formulate the joint optimization problem, which is NP-hard. We then solve this problem using the Gibbs sampling method. Furthermore, to improve the computation of our approach, we propose the JCL algorithm that combines Gibbs sampling and the ADMM method for parallel and distributed calculations. Simulation results also validate that our method not only reduces the power consumption and delay cost, but also balances the workload in heterogeneous servers.

Hosting virtual machines on a cloud datacenter: A matching theoretic approach

In this paper, the problem of resource allocation in cloud datacenters, that own highly complex and heterogeneous tasks and servers, is considered. To address this problem, a novel framework, dubbed joint operation cost and network traffic cost (JOT) framework, is proposed. This framework combines notions from Gibbs sampling and matching theory to find an efficient solution addressing the NP-hard problem JOT. The proposed model is shown to be capable of controlling the active server set, in a coordinated manner while allocating VMs in order to reduce both operation cost and network traffic cost of the cloud datacenter. We also conduct a case-study to validate our proposed algorithm and the results show that JOT can reduce the total incurred cost by up to 19% compared to the existing non-coordinated approach.

Hosting Virtual Machines on Distributed Datacenters

Almost of cloud services nowadays are built at top geographically distributed infrastructure for better reliability and performance. These cloud providers need an efficient method to control and direct user workload to suitable datacenter, depending on many factors such as: network traffic cost, operation cost budget, energy consumption, etc. In the virtual machine placement problem, current works mainly focus on the efficiency of packing virtual machines into servers and ignore the distributed scenario of datacenters. In this paper, we consider the problem of placing virtual machines to host applications on a shared resource pool based on distributed cloud platforms. We formulate the problem of hosting virtual machines on distributed datacenters as an optimization problem, and propose a distributed framework DHC that can dynamically direct workload between datacenters to maximize total utility of entire datacenters. We also conduct many case studies to validate our method, and evaluate its effectiveness and practicality, using real-workload traffic traces. The simulation results show that our algorithm can dynamically optimize the total utility, depending on various workload of users.

A general and practical consolidation framework in CloudNFV

In cloud computing environment, power consumption is a critical factor for data centers. Effective power consumption control can improve significantly benefit for providers. In this paper, we derive from CloudNFV architecture, an open platform for implementing Network Functions Virtualization based on cloud computing and Software Defined Networking, to obtain an optimal power control strategy toward reducing CO2 emissions and maximize total utility of active resources. We propose a consolidation function for determining the On/Off strategy of each server and use live migration method to rearrange virtual machines in active resource components. The efficiency of our proposed model is demonstrated on the simulation environment.

A novel neuro-fuzzy approach for phishing identification

Together with the growth of Internet, e-commerce transactions play an important role in the modern society. As a result, phishing is a deliberate act by an individual or a group of people to steal personal information such as password, banking account, credit card information, etc. Most of these phishing web pages look similar to the real web pages in terms of website interface and uniform resource locator (URL) address. Many techniques have been proposed to identify phishing websites, such as Blacklist-based technique, Heuristic-based technique, etc. However, the number of victims has been increasing due to inefficient protection technique. Neural networks and fuzzy systems can be combined to join its advantages and to cure its individual illness. This paper proposed a new neuro-fuzzy model without using rule sets for phishing identification. Specifically, the proposed technique calculates the value of heuristics from membership functions. Then, the weights are trained by neural network. The proposed technique is evaluated with the datasets of 11,660 phishing sites and 10,000 legitimate sites. The results show that the proposed technique can identify over 99% phishing sites.

Outliers detection and correction for cooperative distributed online learning in Wireless sensor network

The recent distributed online convex optimization framework has developed in Wireless sensor networks (WSN) provide the promising approach for solving approximately stochastic optimization problem over network of sensors follows distributed manner. In practice, most of real environmental sensing activities are highly dynamic where noisy sensory information often appears and affects to the learning performance. However, the original distributed saddle point (DSPA) algorithm is lack of considering about the consequence of falsification in online learning. Based on the simulation observations conducted in this paper, we figure out the fluctuation and the slow convergence rate leads to overall prediction performance reduction of distributed online least square problem. Therefore, we propose an integrated outliers detection, correction mechanism in order to stabilize prediction and improve convergence rate.