Sridhar Machiraju

Primary Users in Cellular Networks: A Large-Scale Measurement Study

Most existing studies of spectrum usage have been performed by actively sensing the energy levels in specific RF bands including cellular bands. In this paper, we provide a unique, complementary analysis of cellular primary usage by analyzing a dataset collected inside a cellular network operator. One of the key aspects of our dataset is its scale - it consists of data collected over three weeks at hundreds of base stations. We dissect this data along different dimensions to characterize and model primary usage as well as understand its temporal and spatial variations. Our analysis reveals several results that are relevant if dynamic spectrum access (DSA) approaches are to be deployed for cellular frequency bands. For instance, we find that call durations show significant deviations from the often- used exponential distribution, which makes call-based modeling more complicated. We also show that a random walk process, which does not use call durations, can often be used for modeling the aggregate cell capacity. Furthermore, we highlight some applications of our results to improve secondary usage of licensed spectrum.

Primary user behavior in cellular networks and implications for dynamic spectrum access

Dynamic spectrum access approaches, which propose to opportunistically use underutilized portions of licensed wireless spectrum such as cellular bands, are increasingly being seen as a way to alleviate spectrum scarcity. However, before DSA approaches can be enabled, it is important that we understand the dynamics of spectrum usage in licensed bands. Our focus in this article is the cellular band. Using a unique dataset collected inside a cellular network operator, we analyze the usage in cellular bands and discuss the implications of our results on enabling DSA in these bands. One of the key aspects of our dataset is its scale-it consists of data collected over three weeks at hundreds of base stations. We dissect this data along different dimensions to characterize if and when spectrum is available, develop models of primary usage, and understand the implications of these results on DSA techniques such as sensing.

Mobile call graphs: beyond power-law and lognormal distributions

We analyze a massive social network, gathered from the records of a large mobile phone operator, with more than a million users and tens of millions of calls. We examine the distributions of the number of phone calls per customer; the total talk minutes per customer; and the distinct number of calling partners per customer. We find that these distributions are skewed, and that they significantly deviate from what would be expected by power-law and lognormal distributions. To analyze our observed distributions (of number of calls, distinct call partners, and total talk time), we propose PowerTrack , a method which fits a lesser known but more suitable distribution, namely the Double Pareto LogNormal (DPLN) distribution, to our data and track its parameters over time. Using PowerTrack , we find that our graph changes over time in a way consistent with a generative process that naturally results in the DPLN distributions we observe. Furthermore, we show that this generative process lends itself to a natural and appealing social wealth interpretation in the context of social networks such as ours. We discuss the application of those results to our model and to forecasting.

Experiences in a 3G network: interplay between the wireless channel and applications

We present an experimental characterization of the physical and MAC layers in CDMA 1xEV-DO and their impact on transport layer performance. The 1xEV-DO network is currently the fastest mobile broadband cellular network, offering data rates of up to 3.1 Mbps for both stationary and mobile users. These rates are achieved by using novel capacity enhancement techniques at the lower layers. Specifically, 1xEV-DO incorporates rapid channel rate adaptation in response to signal conditions, and opportunistic scheduling to exploit channel fluctuations. Although shown to perform well in isolation, there is no comprehensive literature that examines the impact of these features on transport layer and application performance in real networks. We take the first step in addressing this issue through a large set of experiments conducted on a commercial 1xEV-DO network. Our evaluation includes both stationary and mobile scenarios wherein we transferred data using four popular transport protocols: TCPReno, TCP-Vegas, TCP-Westwood, and TCP-Cubic, and logged detailed measurements about wireless channel level characteristics as well as transport layer performance. We analyzed data from several days of experiments and inferred the properties of the physical, MAC and transport layers, as well as potential interactions between them. We find that the wireless channel data rate shows significant variability over long time scales on the order of hours, but retains high memory and predictability over small time scales on the order of milliseconds. We also find that loss-based TCP variants are largely unaffected by channel variations due to the presence of large buffers, and hence able to achieve in excess of 80% of the system capacity.

The SAHARA Model for Service Composition across Multiple Providers

Services are capabilities that enable applications and are of crucial importance to pervasive computing in next-generation networks. Service Composition is the construction of complex services from primitive ones; thus enabling rapid and flexible creation of new services. The presence of multiple independent service providers poses new and significant challenges. Managing trust across providers and verifying the performance of the components in composition become essential issues. Adapting the composed service to network and user dynamics by choosing service providers and instances is yet another challenge. In SAHARA, we are developing a comprehensive architecture for the creation, placement, and management of services for composition across independent providers. In this paper, we present a layered reference model for composition based on a classification of different kinds of composition. We then discuss the different overarching mechanisms necessary for the successful deployment of such an architecture through a variety of case-studies involving composition.

EigenSpokes: surprising patterns and scalable community chipping in large graphs

We report a surprising, persistent pattern in large sparse social graphs, which we term EigenSpokes We focus on large Mobile Call graphs, spanning about 186K nodes and millions of calls, and find that the singular vectors of these graphs exhibit a striking EigenSpokes pattern wherein, when plotted against each other, they have clear, separate lines that often neatly align along specific axes (hence the term “spokes”) Furthermore, analysis of several other real-world datasets e.g. Patent Citations, Internet, etc. reveals similar phenomena indicating this to be a more fundamental attribute of large sparse graphs that is related to their community structure. This is the first contribution of this paper Additional ones include (a) study of the conditions that lead to such EigenSpokes, and (b) a fast algorithm for spotting and extracting tightly-knit communities, called SpokEn, that exploits our findings about the EigenSpokes pattern.

Defending against sensor-sniffing attacks on mobile phones

Modern mobile phones possess three types of capabilities: computing, communication, and sensing. While these capabilities enable a variety of novel applications, they also raise serious privacy concerns. We explore the vulnerability where attackers snoop on users by sniffing on their mobile phone sensors, such as the microphone, camera, and GPS receiver. We show that current mobile phone platforms inadequately protect their users from this threat. To provide better privacy for mobile phone users, we analyze desirable uses of these sensors and discuss the properties of good privacy protection solutions. Then, we propose a general framework for such solutions and discuss various possible approaches to implement the frameworks components.

On optimal probing for delay and loss measurement

Packet delay and loss are two fundamental measures of performance. Using active probing to measure delay and loss typically involves sending Poisson probes, on the basis of the PASTA property (Poisson Arrivals See Time Averages), which ensures that Poisson probing yields unbiased estimates. Recent work, however, has questioned the utility of PASTA for probing and shown that, for delay measurements, i) a wide variety of processes other than Poisson can be used to probe with zero bias and ii) Poisson probing does not necessarily minimize the variance of delay estimates. In this paper, we determine optimal probing processes that minimize the mean-square error of measurement estimates for both delay and loss. Our contributions are twofold. First, we show that a family of probing processes, specifically Gamma renewal probing processes, has optimal properties in terms of bias and variance. The optimality result is general, and only assumes that the target process we seek to optimally measure via probing, such as a loss or delay process, has a convex auto-covariance function. Second, we use empirical datasets to demonstrate the applicability of our results in practice, specifically to show that the convexity condition holds true and that Gamma probing is indeed superior to Poisson probing. Together, these results lead to explicit guidelines on designing the best probe streams for both delay and loss estimation.

A scalable and robust solution for bandwidth allocation

We propose a novel architecture for providing bandwidth allocation and reservation that is both scalable and robust. Scalability is achieved by not requiring routers to maintain per-flow state on either the data or control planes. To achieve robustness, we develop two key techniques. First, we use an admission control mechanism based on lightweight certificates and random sampling to prevent malicious users from claiming reservations that were never allocated to them. Second, we use a recursive monitoring algorithm to detect misbehaving flows that exceed their reservations. We randomly divide the traffic into large aggregates, and then compare the data arrival rate of each aggregate to its reservation. If an aggregate misbehaves, i.e., its arrival rate is greater than its reservation, we split and monitor that aggregate recursively until we detect the misbehaving flow(s). These misbehaving flows are then policed separately. We conduct extensive simulations to evaluate our solutions. The results show that the proposed solution is very effective in protecting well-behaved flows when the fraction of misbehaving flows is limited.

Adding definition to active probing

Active probing techniques have overwhelmingly been based on a few key heuristics. To progress to the next level a more powerful approach is needed, which is capable of filtering noise effectively, designing (and defining) optimal probing strategies, and understanding fundamental limitations. We provide a probabilistic, queueing-theoretic treatment that contributes to this program in the single hop case. We provide an exact inversion method for cross traffic distributions, rigorous system identifiability results to help determine what active probing can and canâ t achieve, a new approach for treating queueing theoretic â noiseâ based on conditioning, and cross traffic estimators with enhanced properties