Zichuan Xu

Approximation Algorithms for Capacitated Minimum Forest Problems in Wireless Sensor Networks with a Mobile Sink

To deploy a wireless sensor network for the purpose of large-scale monitoring, in this paper, we propose a heterogeneous and hierarchical wireless sensor network architecture. The architecture consists of sensor nodes, gateway nodes, and mobile sinks. The sensors transmit their sensing data to the gateway nodes for temporary storage through multihop relays, while the mobile sinks travel along predetermined trajectories to collect data from nearby gateway nodes. Under this paradigm of data gathering, we formulate a novel constrained optimization problem, namely, the capacitated minimum forest (CMF) problem, for the decision version of which we first show NP-completeness. We then devise approximation algorithms and provide upper bounds for their approximation ratios. We finally evaluate the performance of the proposed algorithms through experimental simulation. In our experiments, the approximation ratio delivered by the proposed algorithms is always less than 2. In the case of arbitrary gateway capacities, this contrasts our theoretical results which show that the approximation ratio is at most linear in the number of gateways. Our experiments thus indicate that for realistic inputs, our worst case analysis of the approximation ratio is very conservative. The proposed algorithms are the first approximation algorithms for the CMF problem, and our techniques may be applicable to other constrained optimization problems beyond wireless sensor networks.

Network Lifetime Maximization in Delay-Tolerant Sensor Networks with a Mobile Sink

In this paper we investigate the network lifetime maximization problem in a delay-tolerant wireless sensor network with a mobile sink by exploiting a nontrivial tradeoff between the network lifetime and the data delivery delay. We formulate the problem as a joint optimization problem that consists of finding a trajectory for the mobile sink and designing an energy-efficient routing protocol to route sensing data to the sink, subject to the bounded delay on data delivery and the given potential sink location space. Due to NP-hardness of the problem, we then propose a novel optimization framework, which not only prolongs the network lifetime but also improves the other performance metrics including the network scalability, robustness, and the average delivery delay. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithm against other heuristics. The experimental results demonstrate that the proposed algorithm outperforms the others significantly in terms of network lifetime prolongation.

Cloudlet load balancing in wireless metropolitan area networks

With advances in wireless communication technology, more and more people depend heavily on portable mobile devices for businesses, entertainments and social interactions. Although such portable mobile devices can offer various promising applications, their computing resources remain limited due to their portable size. This however can be overcome by remotely executing computation-intensive tasks on clusters of near by computers known as cloudlets. As increasing numbers of people access the Internet via mobile devices, it is reasonable to envision in the near future that cloudlet services will be available for the public through easily accessible public wireless metropolitan area networks (WMANs). However, the outdated notion of treating cloudlets as isolated data-centers-in-a-box must be discarded as there are clear benefits to connecting multiple cloudlets together to form a network. In this paper we investigate how to balance the workload between multiple cloudlets in a network to optimize mobile application performance. We first introduce a system model to capture the response times of offloaded tasks, and formulate a novel optimization problem, that is to find an optimal redirection of tasks between cloudlets such that the maximum of the average response times of tasks at cloudlets is minimized. We then propose a fast, scalable algorithm for the problem. We finally evaluate the performance of the proposed algorithm through experimental simulations. The experimental results demonstrate the significant potential of the proposed algorithm in reducing the response times of tasks.

Capacitated cloudlet placements in Wireless Metropolitan Area Networks

In this paper we study the cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) that consists of many wireless Access Points (APs). Although most existing studies in mobile cloud computing mainly focus on energy savings of mobile devices by offloading computing-intensive jobs from them to remote clouds, the access delay between mobile users and the clouds usually is large and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and has been demonstrated to enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper we consider placing multiple cloudlets with different computing capacities at some strategic local locations in a WMAN to reduce the average cloudlet access delay of mobile users at different APs. We first formulate this problem as a novel capacitated cloudlet placement problem that places K cloudlets to some locations in the WMAN with the objective to minimize the average cloudlet access delay between the mobile users and the cloudlets serving their requests. We then propose a fast yet efficient heuristic. For a special case of the problem where all cloudlets have the identical computing capacity, we devise a novel approximation algorithm with a guaranteed approximation ratio. In addition, We also consider allocating user requests to cloudlets by devising an efficient online algorithm for such an assignment. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are promising and scalable.

Online Algorithms for Location-Aware Task Offloading in Two-Tiered Mobile Cloud Environments

Mobile Cloud Computing (MCC) is emerging as a main ubiquitous computing platform which enables to leverage the resource limitations of mobile devices and wireless networks by offloading data-intensive computation tasks from resource-poor mobile devices to resource-rich clouds. In this paper, we consider an online location-aware offloading problem in a two-tiered mobile cloud computing environment consisting of a local cloudlet and remote clouds, with an objective to fair share the use of the cloudlet by consuming the same proportional of their mobile device energy, while keeping their individual SLA, for which we devise an efficient online algorithm. We also conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising and outperforms other heuristics.

Minimizing the Operational Cost of Data Centers via Geographical Electricity Price Diversity

Data centers, serving as infrastructures for cloud services, are growing in both number and scale. However, they usually consume enormous amounts of electric power, which lead to high operational costs of cloud service providers. Reducing the operational cost of data centers thus has been recognized as a main challenge in cloud computing. In this paper we study the minimum operational cost problem of fair request rate allocations in a distributed cloud environment by incorporating the diversity of time-varying electricity prices in different regions, with an objective to fairly allocate requests to different data centers for processing while keeping the negotiated Service Level Agreements (SLAs) between request users and the cloud service provider to be met, where the data centers and web portals of a cloud service provider are geographically located in different regions. To this end, we first propose an optimization framework for the problem. We then devise a fast approximation algorithm with a provable approximation ratio by exploiting combinatorial properties of the problem. We finally evaluate the performance of the proposed algorithm through experimental simulation on real-life electricity price data sets. Experimental results demonstrate that the proposed algorithm is very promising, which not only outperforms other existing heuristics but also is highly scalable.

Operational cost minimization of distributed data centers through the provision of fair request rate allocations while meeting different user SLAs

Data centers as computing infrastructures for cloud services have been growing in both number and scale. However, they usually consume enormous amounts of electricity that incur high operational costs of cloud service providers. Minimizing these operational costs thus becomes one main challenge in cloud computing. In this paper, we study the operational cost minimization problem in a distributed cloud computing environment that not only considers fair request rate allocations among web portals but also meets various Service Level Agreements (SLAs) between users and the cloud service provider, with an objective to maximize the number of user requests admitted while keeping the operational cost minimized, by exploiting the electricity diversity. To this end, we first propose an adaptive operational cost optimization framework that incorporates time-varying electricity prices and dynamic user request rates. We then devise a fast approximation algorithm with a provable approximation ratio for the problem, by utilizing network flow techniques. Finally, we evaluate the performance of the proposed algorithm through experimental simulations, using real-life electricity price data sets. Experimental results demonstrate that the proposed algorithm is very promising, and the solution obtained is nearly optimal.

Dynamic routing for network throughput maximization in software-defined networks

Software-Defined Networking (SDN) has emerged as the paradigm of the next-generation networking through separating the data control plane from the data plane. The forwarding routing table at each of its switch nodes is usually implemented by expensive and power-hungry Ternary Content Addressable Memory (TCAM) that only has limited number of entries, and the bandwidth at each of its links is bounded too. Under this new network architecture, providing a quality service to users by admitting user requests to meet their resource demands is challenging, and very little attention has ever been paid in this regard. In this paper, we will study online unicast and multicast request admissions in SDNs with the aim to maximize the network throughput under both critical network resources and user bandwidth demand constraints, for which we first propose a novel model to characterize the usage costs of node and link resources. We then devise efficient online algorithms for unicast and multicast requests. We also analyze the competitive ratios of the proposed online algorithms, which are O(log n) and O(Kϵ log n) for unicasting and multicasting, respectively, where n is the network size, K is the maximum number of members in a multicast request, and ϵ is a constant with 0 <; e ≤ 1. We finally evaluate the proposed algorithms empirically through simulations. The simulation results demonstrate that the proposed algorithms are very promising.

Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.

Efficient virtual network embedding via exploring periodic resource demands

Cloud computing built on virtualization technologies promises provisioning elastic computing and communication resources to enterprise users. To share cloud resources efficiently, embedding virtual networks of different users to a distributed cloud consisting of multiple data centers (a substrate network) poses great challenges. Motivated by the fact that most enterprise virtual networks usually operate on long-term basics and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem by embedding as many virtual networks as possible to a substrate network such that the revenue of the service provider of the substrate network is maximized, while meeting various Service Level Agreements (SLAs) between enterprise users and the cloud service provider. For this problem, we propose an efficient embedding algorithm by exploring periodic resource demands of virtual networks, and employing a novel embedding metric that models the workloads on both substrate nodes and communication links if the periodic resource demands of virtual networks are given; otherwise, we propose a prediction model to predict the periodic resource demands of these virtual networks based on their historic resource demands. We also evaluate the performance of the proposed algorithms by experimental simulation. Experimental results demonstrate that the proposed algorithms outperform existing algorithms, improving the revenue from 10% to 31%.