Being cheap and easy to deploy, dense WLANs are becoming the most popular solution to providing Internet access in locations where the population of users is large, such as on campuses, large enterprises, etc. The large density of access points (APs) comes from the need to have enough capacity to carry the traffic generated at peak hours although, in these scenarios, traffic varies a lot on a daily, weekly, or seasonal basis. During low or no traffic periods, APs are underutilized, even if they are consuming energy almost in the same amount as if they were fully loaded. Promising solutions to reducing this form of energy waste consist of activating only the number of APs that is strictly needed to carry the actual traffic; in other words, to make capacity dynamically adaptive through resource-on-demand (RoD) strategies. In this paper, we investigate the case of a portion of the dense WLAN on our campus. Through real trace analysis, we investigate users' behavior in accessing the WLAN and formulate a stochastic characterization of it. We propose a simple model that describes RoD strategies and use it to study the system performance that is evaluated in terms of AP activity and inactivity periods, AP switching frequency, and energy saving. Finally, we present some results obtained by experimenting with RoD strategies in a portion of the WLAN. Our results show that RoD strategies for dense WLANs are feasible and effective in trading-off the opposite needs to save some energy and to guarantee a smooth network operation and high quality of service.

A multistage software router (MSSR) architecture is composed of several personal computers (PCs) to overcome single PC-based software router scalability issues. Although the architecture scales almost linearly with the number of internal elements, energy consumption could be a threat to its scalability features when building a core router with many internal components. Assuming a known 24 hour traffic load, this paper proposes energy efficient multistage software router design approaches, which enhance the performance of energy saving algorithms that minimize the energy consumption by tailoring the architecture to match the current input traffic demand. Simulation results show that a multistage software router architecture defined by the newly proposed design approaches saves roughly 10% of energy with respect to existing algorithms for similar cost. The energy saving may reach 20% depending on the initial admissible budget.

We focus on a distributed multi-stage software router (MSSR) architecture internally composed by several personal computers (PCs) to overcome scalability and performance issues of software routers (SRs) based on a single PC. Sizing the internal architecture to sustain the peak load may lead to power inefficiency at low loads. This paper presents a power saving scheme to improve the power efficiency of the MSSR by dynamically adapting the size of its internal architecture to the offered load to reduce power needs. The off-line problem is defined as a mixed integer linear programming optimization model, shown to be NP-hard. We propose a differential on-line heuristic to solve the optimization problem when the traffic load changes. The heuristic avoids the complete MSSR reconfiguration of the optimal off-line solution that may lead to forwarding delay increase or service interruption. The performance evaluation shows that the proposed on-line algorithm, that gracefully modifies the internal MSSR configuration, preserves the load proportional power demand characteristics of the optimal offline solution.

A multi-stage software router overcomes scalability issues related to a single, PC-based, software router by introducing parallel forwarding paths. However, since the architecture includes different internal components, energy inefficiency at low loads may arise if the multi-stage internal architecture does not adapt to currently offered traffic. This paper presents an energy-saving scheme to improve energy efficiency of the multi-stage router architecture by focusing on the back-end stage and sizing it to the offered load to reduce energy needs. The problem is defined as a mixed integer linear programming model, shown to be NP-hard. We tackle the scalability issues of the optimal problem by defining a two-step heuristic which takes advantage of existing Bin Packing algorithms. Our results shows that the two-step solution is within 10% relative error with respect to the optimal solution for different realistic scenarios.

Multi-stage software router architectures permit to overcome several limitations of single-stage software routers, allowing to expand the number of available interfaces and to increase the overall throughput. However, a multi-stage software router, despite being composed by several internal elements, must externally appear as a single device. A control protocol called DIST was defined to solve this problem from the control plane point of view for a previously proposed multi-stage architecture. In this paper, we tackle the same problem from the network management point of view. We define a management architecture and a manager-agent communication model to coordinate the information residing on the single elements of the multi-stage router to present a unified view to the external network management station issuing SNMP requests. We analyze the different variable types contained in the SNMP MIB and divide them into different categories depending on how the response to a SNMP request is compiled. The handling methods used to create the proper response to SNMP request for the different types of MIB variables are described. Analytical computations shows that the proposed management architectures does not affect the multi-stage software router scalability.