Timestepped Stochastic Simulation of 802.11 WLANs

Performance evaluation of computer networks is primarily done using packet-level simulation because analytical methods typically cannot adequately capture the combination of state-dependent control mechanisms (such as TCP congestion control) and stochastic behavior exhibited by networks. However, packet-level simulation becomes prohibitively expensive as link speeds, workloads, and network size increase. Timestepped Stochastic Simulation (TSS) overcomes scalability problems of packet-level simulation by generating a sample path of the system state S(t) at time t=d,2d,... rather than at each packet transmission. In each timestep [t,t+d], the distribution Pr[S(t+d)|S(t)] is obtained analytically, and S(t+d) is sampled from it. This dissertation presents TSS for shared links, specifically, 802.11 WLAN links. Our method computes sample paths of instantaneous goodput N_i(t) for all stations "i" in a WLAN over timesteps of length "d". For accurate modeling of higher layer protocols, "d" should be lesser than their control timescales (e.g., TCP's round-trip time). At typical values of "d" (e.g, 50ms), N_i(t)'s are correlated across timesteps (e.g., a station with high contention window has low goodput for several timesteps) as well as across stations (since they share the same media). To model these correlations, we obtain, jointly with the N_i(t)'s, sample paths of the WLAN's state, which consists of a contention window and a backoff counter at each station. Comparisons with packet level simulations show that TSS is accurate and provides up to two orders of magnitude improvement in simulation runtime

Timestepped Stochastic Simulation of 802.11 WLANs

We present Timestepped Stochastic Simulation (TSS) for 802.11 WLANs. TSS overcomes scalability problems of packet-level simulation by generating a sample path of the system state $\mathbf{S}(t)$ at time $t = \delta, 2\delta, \cdots$, rather than at each packet transmission. In each timestep $[t,t+\delta]$, the distribution S(t+\delta)|S(t)} is obtained analytically and $S(t+\delta)$ is sampled from it. Our method computes sample paths of instantaneous goodput $N_i(t)$ for all stations $i$ in a WLAN over timesteps of length $\delta$. For accurate modeling of higher layer protocols, $\delta$ should be lesser than their control timescales (e.g., TCP's RTT).At typical values of $\delta$ (e.g, $50$ms), $N_i(t)$'s are correlated across both timesteps (e.g., a station with high contention window has low goodput for several timesteps) and stations (since they share the same media). To model these correlations, we obtain, jointly with the $N_i(t)$'s, sample paths of the WLAN's DCF state, which consists of a contention window and a backoff counter at each station. Comparisons with packet level simulations show that TSS is accurate and provides up to two orders of magnitude improvement in simulation runtime. Our transient analysis of 802.11 complements prior literature and also yields: (1) the distribution of the instantaneous aggregate goodput; (2) the distribution of instantaneous goodput of a tagged station conditioned on its MAC state; (3) quantification of short-term goodput unfairness conditioned on the DCF state; (4) efficient accurate approximation for the $n$-fold convolution of the distribution of the total backoff duration experienced by a tagged packet; and (5) a simple closed form expression and its logarithmic approximation for the collision probability as a function of the number of active stations

Specification and Analysis of the DCF Protocol in the 802.11 Standard using Systems of Communicating Machines

The 802.11 specification is an emerging standard for WLANs. In this paper, we propose a formal model for a section of the 802.11 MAC protocol using systems of communicating machines. We model the ad-hoc mode of the DCF, i.e., CSMA/CA protocol and the MACA\footnote{The 802.11 standard does not refer to RTS/CTS exchanges as MACA. However, the paper which introduced this idea called it the MACA, and we use this name.} using RTS/CTS sequences. Each station is modelled as a finite state machine which has a set of local variables, and the Wireless Medium is modelled as a shared variable. Analyses show that the 802.11 MAC CSMA/CA protocol and the MACA using RTS/CTS exchanges are free from state deadlocks and non-executable transitions. However, the MACA protocol has a potential livelock, though it is unlikely it will come to pass in normal operation. (Also UMIACS-TR-2002-37

An Empirical Characterization of Instantaneous Throughput in 802.11b WLANs

We present an empirical, i.e, measurement-based, characterization of the instantaneous throughput of a station in an 802.11b WLAN as a function of the number of competing stations sharing the access point. Our methodology is applicable to practically any wireless MAC protocol. Our findings show that as the number of stations increases, the overall throughput decreases and its variance increases. Furthermore, the per-station performance depends significantly on the wireless card implementation and does not depend as much on the station's processing capacity. UMIACS-TR-2002-6

An Empirical Characterization of Instantaneous Throughput in 802.11b WLANs

We present an empirical, i.e, measurementbased, characterization of the instantaneous throughput of a station in an 802.11b WLAN as a function of the number of competing stations sharing the access point. Our methodology is applicable to practically any wireless MAC protocol. Our findings show that as the number of stations increases, the overall throughput decreases and its variance increases. Furthermore, the per-station performance depends significantly on the wireless card implementation and does not depend as much on the station's processing capacity

Optimization for Infrastructure Cyber-Physical Systems

Cyber-physical systems (CPS) are systems where a decision making (cyber/control) component is tightly integrated with a physical system (with sensing/actuation) to enable real-time monitoring and control. Recently, there has been significant research effort in viewing and optimizing physical infrastructure in built environments as CPS, even if the control action is not in real-time. Some examples of infrastructure CPS include electrical power grids; water distribution networks; transportation and logistics networks; heating, ventilation, and air conditioning (HVAC) in buildings; etc. Complexity arises in infrastructure CPS from the large scale of operations; heterogeneity of system components; dynamic and uncertain operating conditions; and goal-driven decision making and control with time-bounded task completion guarantees. For control optimization, an infrastructure CPS is typically viewed as a system of semi-autonomous sub-systems with a network of sensors and uses distributed control optimization to achieve system-wide objectives that are typically measured and quantified by better, cheaper, or faster system performance. In this article, we first illustrate the scope for control optimization in common infrastructure CPS. Next, we present a brief overview of current optimization techniques. Finally, we share our research position with a description of specific optimization approaches and their challenges for infrastructure CPS of the future.Comment: 4 page