Fuliang Li

Solving 0-1 knapsack problem by greedy degree and expectation efficiency

Graphical abstractDisplay Omitted HighlightsThe idea based on region partition of items for solving 0-1 knapsack problem.Greedy degree algorithm for putting some items into knapsack early.Dynamic expectation efficiency model for obtaining the candidate objective function value.Static expectation efficiency model for updating the objective function value.The proposed algorithm in this paper has correctness, feasibility, effectiveness, and stability. It is well known that 0-1 knapsack problem (KP01) plays an important role in both computing theory and real life application. Due to its NP-hardness, lots of impressive research work has been performed on many variants of the problem. Inspired by region partition of items, an effective hybrid algorithm based on greedy degree and expectation efficiency (GDEE) is presented in this paper. In the proposed algorithm, initially determinate items region, candidate items region and unknown items region are generated to direct the selection of items. A greedy degree model inspired by greedy strategy is devised to select some items as initially determinate region. Dynamic expectation efficiency strategy is designed and used to select some other items as candidate region, and the remaining items are regarded as unknown region. To obtain the final items to which the best profit corresponds, static expectation efficiency strategy is proposed whilst the parallel computing method is adopted to update the objective function value. Extensive numerical investigations based on a large number of instances are conducted. The proposed GDEE algorithm is evaluated against chemical reaction optimization algorithm and modified discrete shuffled frog leaping algorithm. The comparative results show that GDEE is much more effective in solving KP01 than other algorithms and that it is a promising tool for solving combinatorial optimization problems such as resource allocation and production scheduling.

Accomplishing Information Consistency under OSPF in General Networks

In this paper, we design an LAP based routing algorithm in General Networks (GN) to solve the problem of information consistency of the full network under OSPF with the following operations: (i) decomposing GN into one or more Single-link Networks (SNs) with the approach of depth-first walk, (ii) re-composting the SNs to a network with regular topology structure by adding links, (iii) searching the undirected complete graph of three nodes round by round until it converges to a simple network topology based on region binding, and (iv) processing different converged network topologies with different LAP based routing algorithms. The proposed algorithm is compared with Dijkstra algorithm over some random network topologies. Simulation results show that the proposed algorithm can solve the problem of information consistency of the full network under OSPF and has better performance than Dijkstra algorithm.

Enabling Software Defined Networking with QoS Guarantee for Cloud Applications

Due to the centralized control, network-wide monitoring and flow-level scheduling of Software-Defined-Networking (SDN), it can be utilized to achieve Quality of Service (QoS) for cloud applications and services, such as voice over IP, video conference and online games, etc. However, most existing approaches stay at the QoS framework design and test level, while few works focus on studying the basic QoS techniques supported by SDN. In this paper, we enable SDN with QoS guaranteed abilities, which could provide end-to-end QoS routing for each cloud user service. First of all, we implement an application identification technique on SDN controller to determine required QoS levels for each application type. Then, we implement a queue scheduling technique on SDN switch. It queues the application flows into different queues and schedules the flows out of the queues with different priorities. At last, we evaluate the effectiveness of the proposed SDN-based QoS technique through an experimental analysis. Results show that when the output interface has sufficiently available bandwidth, the delay can be reduced by 28% on average. In addition, for the application flow with the highest priority, our methods can reduce 99.99% delay and increase 90.17% throughput on average when the output interface utilization approaches to the maximum bandwidth limitation.

An IEEE 802.21 Based Heterogeneous Access Network Selection Mechanism

With the rapid development of mobile communication technology, many heterogeneous wireless access network technologies appear, resulting in that mobile terminals often can access different networks. Therefore, an appropriate access network needs to be selected for the mobile terminal according to the context information. In this paper, we propose an IEEE 802.21 based heterogeneous access network selection mechanism. Taking the fairness among terminals and profits of network providers into account, the mechanism can meet the basic requirements of users’ applications in the process of access network selection. We conduct a simulation and performance evaluation on the algorithm according to a typical network topology. Result shows that our algorithm can not only meet the performance demand but also consider network providers’ profits and the fairness during network resource allocation.

NNIRSS: neural network-based intelligent routing scheme for SDN

With the increasing diversification of network applications, SDN tends to be inefficient to satisfy the diversified application demands. Meanwhile, the continuous update of OpenFlow and flow table expansion causes the efficiency of routing and forwarding ability decreased as well as the storage space of ternary content addressable memory (TCAM) occupied by flow tables increased. In this paper, we present NNIRSS, a novel neural network (NN)-based intelligent routing scheme for SDN, which leverages the centralized controller to achieve transmission patterns of data flow by utilizing NN and replaces flow table with well-trained NN in the form of NN packet. The route of data flow can be predicted based on its application type to meet the quality of service requirements of network applications. Furthermore, we devise a radial basis function neural network-based intelligent routing mechanism. With combining APC-III and K-means algorithm, we propose APC-K-means algorithm to determine radial basis function centers. Finally, the simulation results demonstrate that our proposed NNIRSS is feasible and effective. It can reduce storage space of TCAM and routing time overhead as well as improve routing efficiency.

How DHCP Leases Meet Smart Terminals: Emulation and Modeling

Dynamic Host Configuration Protocol (DHCP) provides dynamic use of IP addresses, but it presents challenges to meet smart terminals with great mobility and transient network access patterns. Existing studies have tried to solve this problem through adjusting DHCP lease, which controls how long a host owns an address. However, few studies clearly express the relations among the lease, address utilization and DHCP overhead. In this paper, we uncover how the leases affect address utilization and DHCP overhead with two methods, based on which, we can set the leases for the smart terminals flexibly and judiciously. First of all, we present an emulation technique to evaluate address utilization and DHCP overhead under different leases. It provides an experimental basis for setting the lease for the whole WLAN. Evaluation results show that if the lease is set to 120 min instead of 60 min by default, it can reduce 41.78% DHCP overhead on average and still reserve at least 9.2% address space for the possibly emerging terminals. Then, we model the relationship between the lease and address utilization, as well as the relationship between the lease and DHCP overhead. According to these models, we propose a load-aware DHCP lease time optimization algorithm, which helps to set different leases for each area of the WLAN based on theoretical analysis. Evaluation results show that compared with the default lease for the whole WLAN, a lease combination of {15, 120, 120} for different areas can reduce 36.85% DHCP overhead on average and guarantee there is always 10% available address space.

Adopting SDN Switch Buffer: Benefits Analysis and Mechanism Design

One critical issue in SDN is to reduce the communication overhead between the switches and the controller. Such overhead is mainly caused by handling miss-match packets, because for each miss-match packet, a switch will send a request to the controller asking for forwarding rule. Existing approaches to address this problem generally need to deploy intermediate proxy or authority switches to hold rule copies, so as to reduce the number of requests sent to the controller. In this paper, we argue that using the intrinsic buffer in a SDN switch can also greatly reduce the communication overhead without using additional devices. If a switch buffers each miss-match packet, only a few header fields instead of the entire packet are required to be sent to the controller. Experiment results show that this can reduce 78.7% control traffic and 37% controller overhead at the cost of increasing only 5.6% switch overhead on average. If the proposed flow-granularity buffer mechanism is adopted, only one request message needs to be sent to the controller for a new flow with many arrival packets. Thus the control traffic and controller overhead can be further reduced by 64% and 35.7% respectively on average without increasing the switch overhead.

A State Transition-Aware Energy-Saving Mechanism for Dense WLANs in Buildings

With the explosive growth of smart terminals, access points (APs) are densely deployed in the buildings of enterprise, campus, hotel, and so on, to provide sufficient coverage and capacity for peak user demands. However, existing studies show that during the off-peak periods, not all the capacity is needed and a large fraction of low-utilization or idle APs cause a great deal of energy waste in these buildings. Although many solutions have been proposed to switch on/off the APs according to the user needs, few works consider the energy cost by state transition. In this paper, we propose a state transition-aware energy-saving mechanism for dense wireless local area networks, which can dynamically switch the APs’ states to meet the user needs while controlling the switching frequency and balancing the number of associated users of each AP. First of all, we analyze the most recent user behaviors, which are used to design the energy-saving mechanism. Then, we model the proposed mechanism in order to set relevant parameters reasonably. Finally, evaluation results show that comparing with a typical static strategy, the energy consumption is reduced by 24.3%, and the average available bandwidth is increased by 27.8%. Meanwhile, the switching frequency is reduced by 14.3% as well.

A Utility Function Based Resource Allocation Method for LEO Satellite Constellation System

Low Earth Orbit (LEO) satellite constellation system has been regarded as a very promising satellite mobile communication system for its low propagation delay, global coverage for communication and mature technologies. However, considering its crucial but limited resources on satellites, efficient resource allocation methods are needed to guarantee the network carrying capability under specific requirements. In this paper, we put forward a utility function based resource allocation method for LEO satellite constellation system. We first utilize utility function to represent the resource acquisition satisfaction of adjacent satellites. The bigger the utility is the higher satisfaction the adjacent satellites can get. In addition, we adopt the improved Multiple Population Cloud Differential Evolution Algorithm (MPCDEA) to solve the resource allocation problem, which belongs to the nonlinear mixed integer programming problem. Finally, we evaluate the proposed utility function based resource allocation method according to the topologies of Iridium and Globalstar systems and quantify it with performance indexes like throughput capacity and network capacity. Evaluation results show that our method is feasible and effective.

A Quantum-Inspired Immune Clonal Algorithm Based Handover Decision Mechanism with ABC Supported

In this paper, we propose a QIICA (Quantum-Inspired Immune Clonal Algorithm) based handover decision mechanism with ABC (Always Best Connected) supported. We first utilize fuzzy mathematics and microeconomics to describe application types, QoS (Quality of Service) requirements, access networks and terminals. Then, we present the optimal handover solution of assigning N terminals to M access networks based on the QIICA. At last, we evaluate the handover decision mechanism and make a comparison with the existing mechanisms. Evaluation results show that our mechanism is feasible and effective.