Meitian Huang

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 However, the outdated notion of treating cloudlets as isolated data-centers-in-a-box must be discarded as there are clear benefits to connecting multiple cloudlets together to form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.

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.

Throughput Maximization in Software-Defined Networks with Consolidated Middleboxes

Todays computer networks rely on a wide spectrum of specialized middleboxes to improve their security and performance. Traditional middleboxes that are implemented by dedicated hardware are expensive and hard to manage. A promising technique of consolidated middleboxes - implementing traditional middleboxes in Virtual Machines (VMs) - offers economical yet simplified management of middleboxes in Software-Defined Networks (SDNs). However there are still challenges to realizing user routing requests with network function enforcement (a sequence of middleboxes) while maximizing the network throughput, due to various resource constraints on SDNs, such as forwarding table capacity at each switch, bandwidth resource capacity at each link, and computing resource capacity at each server (Physical Machine). In this paper, we study the problem of maximizing the network throughput of an SDN by admitting as many user requests as possible, where each user request has both bandwidth and computing resource demands to implement its network functions (consolidated middleboxes). We first formulate the problem as a novel network throughput maximization problem. We then provide an Integer Linear Program (ILP) solution for it if the problem size is small, otherwise, we devise two heuristics that strive for the fine tradeoff between the accuracy of solutions and the running times of achieving the solutions. We finally evaluate the performance of the proposed algorithms by simulations, based on real and synthetic network topologies. Experimental results demonstrate that the proposed algorithms are very promising.

QoS-Aware 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 business, entertainments and social interactions. This poses a great challenge of building a seamless application experience across different computing platforms. A key issue is the resource limitations of mobile devices due to their portable size, however this can be overcome by offloading computation-intensive tasks from the mobile devices to clusters of nearby computers called cloudlets through wireless access points. 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-boxes 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 among cloudlets in an WMAN to optimize mobile application performance. We first introduce a novel system model to capture the response time delays of offloaded tasks and formulate an optimization problem with the aim to minimize the maximum response time of all offloaded tasks. We then propose two algorithms for the problem: one is a fast heuristic, and another is a distributed genetic algorithm that is capable of delivering a more accurate solution compared with the first algorithm, but at the expense of a much longer running time. We finally evaluate the performance of the proposed algorithms in realistic simulation environments. The experimental results demonstrate the significant potential of the proposed algorithms in reducing the user task response time, maximizing user experience.

Approximation and Online Algorithms for NFV-Enabled Multicasting in SDNs

Multicasting is a fundamental functionality of networks for many applications including online conferencing, event monitoring, video streaming, and system monitoring in data centers. To ensure multicasting reliable, secure and scalable, a service chain consisting of network functions (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) usually is associated with each multicast request. Such a multicast request is referred to as an NFV-enabled multicast request. In this paper we study NFV-enabled multicasting in a Software-Defined Network (SDN) with the aims to minimize the implementation cost of each NFV-enabled multicast request or maximize the network throughput for a sequence of NFV-enabled requests, subject to network resource capacity constraints. We first formulate novel NFV-enabled multicasting and online NFV-enabled multicasting problems. We then devise the very first approximation algorithm with an approximation ratio of 2K for the NFV-enabled multicasting problem if the number of servers for implementing the network functions of each request is no more than a constant K (1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an online algorithm with a competitive ratio of O(log n) when K = 1, where n is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms outperform other existing heuristics.

Incremental SDN-Enabled Switch Deployment for Hybrid Software-Defined Networks

Software-defined networking (SDN) is a promising technique that has reshaped the landscape of network management. By providing simplified, cost-effective management, SDN has been envisioned as the next-generation network paradigm. However, due to economic, organizational, and technical challenges, replacing all conventional switches in current operational networks by SDN-enabled switches is impractical in the short term. It thus is desirable to deploy SDN-enabled switches into existing networks incrementally, and such a network consisting of SDN-enabled switches and conventional switches is referred to as a hybrid SDN network. The incremental deployment of SDN-enabled switches is challenging because the number of conventional switches that can be replaced is typically limited, due to budget constraints or operational network stability concerns, yet the impact of the deployment should be maximized. In this paper, we deal with the SDN-enabled switch placement problem with the aim to maximize system performance, given

Efficient Algorithms for Throughput Maximization in Software-Defined Networks With Consolidated Middleboxes

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Online unicasting and multicasting in software-defined networks

switches to be replaced, for which we first propose heuristics by replacing conventional switches one by one iteratively. We then devise scalable algorithms that replace multiple switches, instead of a single switch, in each iteration. We finally evaluate the performance of the proposed algorithms based on real and synthetic network topologies. Experimental results demonstrate that the proposed algorithms are promising and exhibiting high scalability.

Throughput Maximization of Delay-Sensitive Request Admissions via Virtualized Network Function Placements and Migrations

Today’s computer networks rely on a wide spectrum of specialized middleboxes to improve network security and performance. A promising emerging technique to implementing traditional middleboxes is the consolidated middlebox technique, which implements the middleboxes as software in virtual machines in software-defined networks (SDNs), offering economical, and simplified management for middleboxes. This however poses a great challenge, that is, how to find a cost-optimal routing path for each user request such that the data traffic of the request will pass through the middleboxes in their orders in the service chain of the request, with the objective to maximize the network throughput, subject to various resource capacity constraints in SDNs. In this paper, we study the network throughput maximization problem in an SDN under two different scenarios: one is the snapshot scenario where a set of requests at one time slot is given, we aim to admit as many requests in the set as possible to maximize the network throughput; another is the online scenario in which requests arrive one by one without the knowledge of future arrivals. Given a finite time horizon consisting of

Delay-Energy Joint Optimization for Task Offloading in Mobile Edge Computing.

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