Nodes placement in wireless mesh networks using optimization approaches: a survey

Flow-based switching is increasingly important in accordance with the growing demand for in-network processing for cloud applications. Flow switching performance tends to be degraded in proportion to the number of flow entries. To reduce the number of flow entries, they can be aggregated by applying wildcard fields. Meanwhile, the existence of the wildcard entry adversely affects the use of a hash-based lookup on a flow table, and thus a linear search is inherent in flow switching. However, the linear search is currently the primary cause of performance limitation. To date, two flow tables, one for hash-based lookup and the other for a wildcard-enabled linear search, have been used for flow switching. While hash-based table lookup is much faster than linear search, it needs to be manually updated for every exact match entry. Maintaining a hash-based table of all the flow switches is not feasible from a network operator viewpoint. In this paper, LightFlow, a mechanism to accelerate software flow switching processing and relieve the burden of maintaining the flow table is proposed. In LightFlow, two-dimensional parallelization of a linear search is introduced to accelerate lookup of the wildcard-enabled flow entries. It also introduces a mechanism that allows updating of the hash table to be performed automatically based on the result of wildcardaware table lookup. LightFlow satisfies both the need for fast table lookup and feasibility of flow table management which needs to allow a large number of wildcard entries. Experimental results show that LightFlow can increase the speed of lookup of a wildcard-aware flow table three-fold or more compared to the current GPU-based wildcard search mechanisms.

https://cial.csie.ncku.edu.tw/presentation/group_pdf/LightFlow%20Speeding%20Up%20GPU-based%20Flow%20Switching.pdf

LightFlow: Speeding up GPU-based flow switching and facilitating maintenance of flow table

Technologies for offloading an application for processing a network packet to a graphics processing unit (GPU) of a network device. The network device is configured to determine resource criteria of the application and available resources of the GPU. The network device is further configured to determine whether the available GPU resources are sufficient to process the application based on the resource criteria of the application and the available GPU resources. Additionally, the network device is configured to determine one or more estimated GPU performance metrics based on the resource criteria of the application and the available GPU resources to determine whether to offload the application to the GPU. Other embodiments are described and claimed.

Technologies for offloading network packet processing to a gpu

We present a novel parallel IP address lookup architecture based on graphics processing unit (GPU) via compute unified device architecture (CUDA). Our architecture consists of two functions: 1) host function and 2) device function. The host function is executed by a CPU to construct and update the data structure of IP address lookup executed by the device function in a GPU. Both host and device functions are executed simultaneously to fully utilize computational resources. To shorten the lookup time, a trie-based data structure optimized for CUDA is developed. The trie-based data structure uses multi-bit stride to shorten the trie depth and also improves the efficiency of texture cache in GPUs. The experimental results show that a low-end G92 GPU can achieve a throughput of more than 1.3 billion packets per second for IPv4 routing tables with more than 350K prefixes while a high-end GT200 GPU can further double the performance. By employing dual data structures, the implementation can support several hundred thousand updates per second.

IP Address Lookup by Using GPU

Wireless Mesh Networks (WMNs) can build a communications infrastructure using only routers (called mesh routers), making it possible to form networks over a wide area at low cost. The mesh routers cover clients (called mesh clients), allowing mesh clients to communicate with different nodes. Since the communication performance of WMNs is affected by the position of mesh routers, the communication performance can be improved by optimizing the mesh router placement. In this paper, we present a Coverage Construction Method (CCM) that optimizes mesh router placement. In addition, we propose an integrated optimization approach that combine Simulated Annealing (SA) and Delaunay Edges (DE) in CCM to improve the performance of mesh router placement optimization. The proposed approach can build and provide a communication infrastructure by WMNs in disaster environments. We consider a real scenario for the placement of mesh clients in an evacuation area of Kurashiki City, Japan. From the simulation results, we found that the proposed approach can optimize the placement of mesh routers in order to cover all mesh clients in the evacuation area. Additionally, the DECCM-based SA approach covers more mesh clients than the CCM-based SA approach on average and can improve network connectivity of WMNs.

/pdf/a-delaunay-edges-and-simulated-annealing-based-integrated-2jxz49j0.pdf

A Delaunay Edges and Simulated Annealing-Based Integrated Approach for Mesh Router Placement Optimization in Wireless Mesh Networks

The performance (such as throughput) of wireless mesh networks (WMNs) are affected by many factors such as routing protocols and interferences. To improve the throughput, several studies have proposed efficient routing protocols to find better paths and new link state metrics to accurately represent the wireless link qualities. Most of these works assume that the locations of the mesh routers are pre-defined. In this paper, we propose a mesh router placement scheme based on genetic algorithm accounting for dynamic location for all the mesh routers, and indoor obstacles that can minimize the interferences among the mesh routers. Through extensive simulations, we find that the existence of obstacles such as walls can be exploited to decrease the interferences to improve the performance of the indoor wireless mesh networks. We show that the proposed scheme improves the performance by 30-40% compared to the random selection scheme. Furthermore, the existence of walls improves the performance by as much as 50% compared to the cases without walls.

Mesh Router Placement Exploiting Obstacles for Mitigating Interference

As the traffic of the Internet increases and diversifies, the needs for a fast flexible router have made researchers to work on software routers. The existing software router systems may utilize the cluster structure of multiple machines or GPU systems. Especially, Packet Shader, which uses GPU to exploit GPU's extensive parallelism, shows higher performance compared to other existing software routers. However, Packet Shader does not utilize the memory architecture in the GPU system. Basically, GPU has different types of memories such as constant, texture, and global. Since different types of memories show different memory access performance, we propose a unique index architecture for the forwarding table by exploiting the memory architecture of the GPU system. Through a preliminary evaluation, we show that the forwarding table lookup using GPU can outperform the CPU only system.

Fast forwarding table lookup exploiting GPU memory architecture

Due to the advent of social networking services and other emerging applications, the Internet traffic is growing faster than ever. Thus, link or node failures in the Internet may generate a tremendous disaster to the services. To foster the fast recovery from the failures, various Fast Reroute schemes have been developed. Among them, Failure Insensitive Routing (FIR) is a simple and easy to implement scheme, which provides a guaranteed loop free routing when the number of link failures is not more than one. However, the backup paths of the FIR may generate high traffic load to the links close to the failure. In this paper, we introduce a new load balancing mechanism to FIR. For the load balancing objective, we try to minimize the maximum load among the links in the network. By reducing the maximum load, the network becomes less vulnerable to instant fluctuations of the network traffic. Through simulations,We show that our scheme reduces the maximum link utilization of FIR.

An efficient load balancing scheme in failure insensitive routing

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Mesh Router Placement Exploiting Obstacles for Mitigating Interference

Fast forwarding table lookup exploiting GPU memory architecture

An efficient load balancing scheme in failure insensitive routing