A major threat to the Internet infrastructure and, more broadly, to its culture is posed by DDoS attacks. To mitigate their impact, detection should preferably occur close to the attack origin, e.g., at home-routers. However, these devices typically have limited resources and an approach that relies on packet inspection does not bode well with such devices.We propose a lightweight approach for DDoS detection that solely employs network interface byte and packet counts. To detect attacks with such a limited amount of information, our key insight consists in training classifiers to make use of workload data from 1,823 home-users augmented with attacks generated in a controlled environment. In our experiments, we selected seven attack vectors generated using Mirai and BASHLITE malwares. We then conduct a device-agnostic detection of attacks vectors, obtaining F1 scores typically higher than 0.99. To cope with the evolving nature of DDoS attacks, we also report results indicating the detection power of the proposed methodology when different attack vectors are used for training and testing.

Swarming systems, such as BitTorrent, are one of the most common solutions for scalable, robust and inexpensive content distribution. Although the service capacity of swarming systems has been studied for decades through modeling and analysis, there is a lack of experimental evidence about how the throughput of such systems behaves in under-provisioned regimes. The aim of this paper is to fill this gap. In this paper, we consider a closed-loop model to assess the throughput of peer-to-peer systems. Then, we show through controlled experiments using BitTorrent clients that some analytical findings recently reported in the literature, such as the missing piece syndrome, occur in practice. In particular, we indicate that when seeds have a small effective service capacity, or when seeds are intermittent, the throughput saturates as the population size grows. Finally, we discuss the implications of such findings on the modeling and design of swarming systems.

SUMMARY Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage, and intelligent features such as automated fault detection, isolation, and restoration (FDIR) and demand response. In this paper, we present an analytical model and metrics for the survivability assessment of the distribution power grid network. The proposed metrics extend the system average interruption duration index, accounting for the fact that after a failure, the energy demand and supply will vary over time during a multi‐step recovery process. The analytical model used to compute the proposed metrics is built on top of three design principles: state space factorization, state aggregation, and initial state conditioning. Using these principles, we reduce a Markov chain model with large state space cardinality to a set of much simpler models that are amenable to analytical treatment and efficient numerical solution. In case demand response is not integrated with FDIR, we provide closed form solutions to the metrics of interest, such as the mean time to repair a given set of sections. Under specific independence assumptions, we show how the proposed methodology can be adapted to account for multiple failures. We have evaluated the presented model using data from a real power distribution grid, and we have found that survivability of distribution power grids can be improved by the integration of the demand response feature with automated FDIR approaches. Our empirical results indicate the importance of quantifying survivability to support investment decisions at different parts of the power grid distribution network. Copyright © 2014 John Wiley & Sons, Ltd.

Smart grids are fostering a paradigm shift in the realm of power distribution systems. Whereas traditionally different components of the power distribution system have been provided and analyzed by different teams through different lenses, smart grids require a unified and holistic approach that takes into consideration the interplay of communication reliability, energy backup, distribution automation topology, energy storage and intelligent features such as automated failure detection, isolation and restoration (FDIR) and demand response.

One of the most widely used techniques to obtain transient measures is the uniformization method. However, although uniformization has many advantages, the computational cost required to calculate transient probabilities is very large for stiff models. We study efficient solutions that can be applied to an approximate method developed for calculating transient state probabilities of Markov models and cumulative expected reward measures over a finite interval. Our work is based on a method that approximates the state probabilities at time t by the state probabilities calculated at a random time with Erlangian distribution. The original method requires an inversion of a matrix obtained from the state transition rate matrix that destroys special structures such as sparseness and banded matrices. This precludes the use of the technique for large models. In our work we propose efficient solutions that can take advantage of special structures. Finally, we present examples that show that the proposed technique is computationally very efficient for stiff models when compared with uniformization.

In this paper we study the behavior of a continuous time random walk (CTRW) on a stationary and ergodic time varying dynamic graph. We establish conditions under which the CTRW is a stationary and ergodic process. In general, the stationary distribution of the walker depends on the walker rate and is difficult to characterize. However, we characterize the stationary distribution in the following cases: i) the walker rate is significantly larger or smaller than the rate in which the graph changes (time-scale separation), ii) the walker rate is proportional to the degree of the node that it resides on (coupled dynamics), and iii) the degrees of node belonging to the same connected component are identical (structural constraints). We provide examples that illustrate our theoretical findings.

No abstract available.

No abstract available.

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of tasks execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system's architect. Finally, we have developed models of hardware and software reliability. We report on hardware reliability results based on the evaluation of the system architecture. As a topic for future work, we report on an approach that we recommend be applied to evaluate the system under study software reliability.

We develop a simple, yet robust, application-layer adaptation technique based on scalable video codecs. The proposed technique can adapt to the varying capacity available to a connection in order to deliver video with a superior quality. We take advantage of both scalable video properties and the characteristics of the packet dropping random process at the last link's wireless router's queue. By fine-tuning the inter-packet transmission time at the server side, we show that the resulting packet loss pattern yields a superior quality of experience (QoE) without any changes in the wireless router's packet scheduling or drop policies. In other words, by varying the per frame packet inter-spacing time while maintaining a given frame rate (i.e., by varying the transmission burstiness) one can control the QoE. Our proposal is founded on an analytical model to study the loss pattern of individual packets in a n-packet burst.

We present in this paper our experience with applying a new architecture review process that uses a globally distributed review team to assess architecture risk of a complex mission critical system. The new architecture review process uses aspects of the checklist-based architecture review process and the operational scenario-based architecture review process. We present the architecture review process approach, a summary of the architecture under review and the detailed analysis of the most important operational scenarios. We conclude by presenting a summary of the lessons we learned using the new process.

We consider a scenario in which users share an access point and are mainly interested in VoIP applications. Each user is allowed to adapt to varying network conditions by choosing the transmission rate at which VoIP traffic is received. We denote this adaptation process by end-user congestion control, our object of study. The two questions that we ask are: (1) what are the performance consequences of letting the users to freely choose their rates? and (2) how to explain the adaptation process of the users? We set a controlled lab experiment having students as subject to answer the first question, and we extend an evolutionary game-theoretic model to address the second. Our partial answers are the following: (1) free users with local information can reach an equilibrium which is close to optimal from the system perspective. However, the equilibrium can be unfair; (2) the adaptation of the users can be explained using a game theoretic model. We propose a methodology to parameterize the latter, which involves active network measurements, simulations and an artificial neural network to estimate the QoS perceived by the users in each of the states of the model.

In this work the behavior of a 3G wireless technology is evaluated. The analysis is done using a simulation tool, based on the Tangram-II platform, which incorporates a detailed propagation model and the relevant features of the cdma20001x EVDO system. Internet access through Web browsers was judged to be the key application to be provided. We are interested in analyzing the throughput, latency and fairness metrics for several scenarios, including cases considering only static users as well as cases where a fraction of terminals has mobility. Our experiments show that the introduction of mobile users severely compromises the fairness in respect to user's goodput and delay. A previous work of ours has shown that even if some parameters of the scheduling algorithm are varied, due to physical layer limitations it is difficult to improve performance of the technology with respect to fairness. In this article we show that the use of directional antennas can play an even more important role in improving EV-DO behavior for the envisioned application when some users are mobile.

The deployment of wireless LANs (WLANs) has been steadily increasing over the years and estimating the actual bit rate of a WLAN device is important for management and applications such as those that can adapt their transmission rates according to the network characteristics. We propose a simple and accurate active measurement technique to infer the bit rate of an IEEE802.11 device. The proposed method is based both on a recently proposed technique to infer the type of access network and on the packet pair approach, but adapted to take into account the overhead caused by the IEEE802.11 control and the existence of concurrent WLAN traffic. Furthermore, the technique does not require the WLAN to be the bottleneck link in the path from the measuring point to the end computer. Results from simulation and from measurements show that the approach is accurate to infer WLAN access point rates both in scenarios where the WLAN devices can adapt their rates as well as WLANs with fixed transmission rates.

Despite the emergence of voice over IP and videoconferencing services, recent studies have shown that the Internet still may not provide acceptable quality for interactive streaming applications. Among the major parameters that affect quality of service are packet loss rates and loss burst sizes. To mitigate the negative impact of losses, forward error correction (FEC) techniques can be employed. These work by adding extra information to the data stream. So, while FEC can potentially reduce the perceived effects of packet losses, it increases bandwidth requirements which in turn may increase the loss rate. Therefore, it is important to choose, in real time, the proper FEC scheme to provide the best performance to the application. We evaluate the performance of parity-based FEC schemes using an analytical loss model. Then, we develop an adaptive mechanism for FEC selection using a predictive model. We present the results of simulation experiments using real Internet measurements.

In this work the behavior of particular cellular technology for use in a wireless implementation of a bi-directional digital TV return channel is considered. The analysis is done using a simulation tool, based on the Tangram-II platform, that incorporates a detailed propagation model and the relevant features of the cdma2000 EV-DO system. Internet access through Web browsers was judged to be the key application to be provided. This paper presents both throughput and latency results with varying number of terminals in the system. It also identifies variations in the user quality of service due to their geographic position. The results show that even if some parameters of the scheduling algorithm are varied, it is difficult to improve the fairness performance of the technology. A very simple solution is proposed to enhance these fairness characteristics

In the past few years, several bandwidth sharing techniques for multimedia servers have been proposed in the literature. Among those, the patching technique has several advantages such as: simplicity of implementation and efficiency. Recently, a simple expression to estimate the average bandwidth required by the patching technique has been obtained in the literature. However, average values do not always provide an accurate estimation of the required bandwidth. We propose an analytical model to compute the distribution of the capacity required by the patching technique. We study the sensitivity of the distribution function of the capacity required by the method with respect to the size of the patching window. We also investigate the adequacy of using average parameter values to dimension the server.

The calculation of the distribution of a linear combination of order statistics from random variables that are uniformly distributed is considered. A simple recursion to compute this distribution is presented that, unlike previous methods, is numerically stable and efficient. As such, this should be the algorithm of choice when the linear combination distribution needs to be obtained.

Currently, many location update protocols have been proposed for mobile terminal tracking in cellular networks. These protocols, such as the time-based and the distance-based protocols, try to minimize the system overhead for locating a mobile user in the network. The contribution of this paper to develop a novel mathematical model to analyze these protocols and at the same time, make a general model to capture many important movement features. In this paper, we propose to use the transient analysis method to predict the location of a mobile user and the accuracy of the prediction of the location of a user can be enhanced. By applying the transient analysis to evaluate the time-based and the distance-based protocol, it gives us more insights in the performance of these protocols under different movement configurations. Last but not least, our model is general enough such that it provides a general framework to analyze other location update protocols in cellular networks.

Markov reward models have been employed to obtain performability measures of computer and communication systems. In these models, a continuous time Markov chain is used to represent changes in the system structure, usually caused by faults and repairs of its components, and reward rates are assigned to states of the model to indicate some measure of accomplishment at each structure. A procedure to calculate numerically the distribution of the reward accumulated over a finite observation period is presented. The development is based solely on probabilistic arguments, and the final recursion is quite simple. The algorithm has a low computational cost in terms of model parameters. In fact, the number of operations is linear in a parameter that is smaller than the number of rewards, while the storage required is independent of the number of rewards. We also consider the calculation of the distribution of cumulative reward for models in which impulse based rewards are associated with transitions.

Polling systems have long been the subject of study and are of particular interest in the analysis of high-speed communications networks. There are many options for the scheduling policies that can be used at each polling station (exhaustive, gated, customer limited, etc.). In addition, one can impose an upper bound on the total service time delivered to customers at a station per server visit. In the most common case the upper bound is a constant for each polling station, and the resulting system model is not Markovian even when service times and interarrival times are exponential. In the paper, a comprehensive solution is developed for the major scheduling policies with time limits for each polling station. The approach is based on studying the embedded Markov chain defined at the sequence of epochs when the server arrives at each polling station. The computation of transition probabilities for the embedded chain requires transient analysis of the Markov process describing the system evolution between epochs. Uniformization methods are used to develop efficient algorithms for the transition probabilities and for system performance measures. Example problems are solved using the techniques developed to illustrate the utility of the results.>

Performance and dependability analysis is usually based on Markov models. One of the main problems faced by the analyst is the large state space cardinality of the Markov chain associated with the model, which precludes not only the model solution, but also the generation of the transition rate matrix. However, in many real system models, most of the probability mass is concentrated in a small number of states in comparison with the whole state space. Therefore, performability measures may be accurately evaluated from these “high probable” states. In this paper, we present an algorithm to generate the most probable states that is more efficient than previous algorithms in the literature. We also address the problem of calculating measures of interest and show how bounds on some measures can be efficiently calculated.

A solution method is developed to analyze various scheduled maintenance policies for repairable computer systems. The analysis is applicable to systems with behavior (exclusive of the scheduled maintenance policy) that can be modeled by a continuous-time Markov process, and thus important characteristics can be included in the model. Furthermore, the assumption of perfect repair, which is unrealistic for most systems, is not used. Both transient and steady-state measures are obtained. The measures considered include expected availability, expected number of unscheduled repairs (repairs that are performed outside of the scheduled maintenance period), and the probability of an unscheduled repair. The solution approach is based on the randomization technique and possesses advantage such as numerical stability and ease of implementation.>

Linearizer is one of the best known approximation algorithms for obtaining numeric solutions for closed-product-form queueing networks. In the original exposition of Linearizer, the computational cost was stated to be O(MK/sup 3/) for a model with M queues and K job classes. It is shown that with some straightforward algebraic manipulation, Linearizer can be modified to require a cost that is only O(MK/sup 2/).>

Markov models are widely used for the analysis of availability of computer/communication systems. Realistic models often involve state space cardinalities that are so large that it is impractical to generate the transition rate matrix let alone solve for availability measures. Various state space reduction methods have been developed, particularly for transient analysis. In this paper we present an approximation technique for determining steady state availability. Of particular interest is that the method also provides bounds on the error. Examples are given to illustrate the method.

Repairable computer systems are considered, the availability behavior of which can be modeled as a homogeneous Markov process. The randomization method is used to calculate various measures over a finite observation period related to availability modeling of these systems. These measures include the distribution of the number of events of a certain type, the distribution of the length of time in a set of states, and the probability of a near-coincident fault. The method is then extended to calculate performability distributions. The method relies on coloring subintervals of the finite observation period based on the particular application, and then calculating the measure of interest using these colored intervals.

A new computational algorithm called distribution analysis by chain (DAC) is developed. This algorithm computes joint queue-length distributions for product-form queuing networks with single-server fixed rate, infinite server, and queue-dependent service centers. Joint distributions are essential in problems such as the calculation of availability measures using queuing network models. The algorithm is efficient since the cost to evaluate joint queue-length probabilities is of the same order as the number of these probabilities. This contrasts with the cost of evaluating these probabilities using previous algorithms. The DAC algorithm also computes mean queue lengths and throughputs more efficiently than the recently proposed RECAL and MVAC algorithms. Furthermore, the algorithm is numerically stable and its recursion is surprisingly simple.

A computational algorithm is developed for closed multichain product-form queueing networks. For networks that consist of only single-server fixed rate and infinite-server service centers, it involves only mean performance measures. The algorithm, called mean value analysis by chain (MVAC), is based on a recursion that is quite different in form from the recursion used in the well-known mean value analysis (MVA) algorithm and has quite different computational and storage costs. For networks with few service centers and many chains, MVAC typically has much lower costs than MVA, although it becomes more costly than MVA as the number of service centers increases. The MVAC recursion is similar in structure to a recursion involving normalizing constants that was derived by A.E. Conway and N.D. Georganas (1986). That recursion formed the basis for their recursion by chain (RECAL) algorithm for computing the normalizing constant and from it the mean performance measures. The computational and storage costs for MVAC are shown to be similar to those for RECAL.>

Markov models are widely used for the analysis of availability of computer/communication systems. Realistic models often involve state-space cardinalities that are so large that it is impractical to generate the transition-rate matrix let alone solve for availability measures. An approximation technique is presented for determining steady-state availability. Of particular interest is that the method also provides bounds on the error. Examples are given to illustrate the method.>

Obtains simple relationships among partial derivatives and moments of mean queue lengths in product form queueing network models. It is shown that the mean value analysis equations can be used to obtain recursive expressions which make it possible to easily evaluate these moments in a mean value analysis type recursion. These results are also considered important in applications such as optimization problems and sensitivity analysis. Finally, it is shown that some known mean value equations can be easily derived from the results proven.>

We consider repairable computer systems, those for which repair can be performed to put the system back in operation. The behavior of the system is assumed to be modeled as a homogeneous Markov process. We calculate numerically the distribution of cumulative operational time, which is the distribution of the total time during which the system was in operation over a finite observation period. The method is based on the randomization technique. The main advantages include the ability to specify error tolerances in advance, numerical stability, and simplicity of implementation. We also show that other quantities of interest can be calculated as a byproduct of the method without any significant extra computational effort.

The past few years have witnessed an increasing number of large distributed computer system implementations based on local area networks. In these systems a number of resources (CPU's, file servers, disks, etc.) are shared among jobs originating at different sites. Evaluating the performance of such large systems typically requires the solution of a queueing network model with a large number of closed chains, which precludes the use of exact solution techniques. Therefore, it is important to develop accurate and cost-effective methods for the approximate analysis of closed queueing networks with many chains. In this paper, we present an approach based on the clustering of chains and service centers. The method is applicable to queueing networks with single server fixed rate, infimite server and multiple server service centers. We present the results obtained when the method is used to solve large queueing network models. Extensive comparison of this method to existing approximation techniques indicates that the approach has better accuracy/cost characteristics.