Application-level Performance Metrics Research Articles

Defence against Side-Channel Attacks for Encrypted Network Communication Using Multiple Paths

As more network communication is encrypted to provide data privacy for users, attackers are focusing their attention on traffic analysis methods for side-channel attacks on user privacy. These attacks exploit patterns in particular features of communication flows such as interpacket timings and packet sizes. Unsupervised machine learning approaches, such as Hidden Markov Models (HMMs), can be trained on unlabelled data to estimate these flow attributes from an exposed packet flow, even one that is encrypted, so it is highly feasible for an eavesdropper to perform this attack. Traditional defences try to protect specific side channels by modifying the packet transmission for the flow, e.g., by adding redundant information (padding of packets or use of junk packets) and perturbing packet timings (e.g., artificially delaying packet transmission at the sender). Such defences incur significant overhead and impact application-level performance metrics, such as latency, throughput, end-to-end delay, and jitter. Furthermore, these mechanisms can be complex, often ineffective, and are not general solutions—a new profile must be created for every application, which is an infeasible expectation to place on software developers. We show that an approach exploiting multipath communication can be effective against HMM-based traffic analysis. After presenting the core analytical background, we demonstrate the efficacy of this approach with a number of diverse, simulated traffic flows. Based on the results, we define some simple design rules for software developers to adopt in order to exploit the mechanism we describe, including a critical examination of existing communication protocol behavior.

Analysis and Improvement of Reliability Through Coding for Safety Message Broadcasting in Urban Vehicular Networks

In this paper, we study the impact of obstacles (such as buildings) in the radio propagation in the urban vehicular network's environment. The impact has been realized not only by the network-level performance metrics, but also by the application-level performance metrics. A majority of the existing works consider perfect physical layer, which ignores the impact of path loss and shadowing effect in radio propagation caused by the urban canyon. First, we show that ignoring the impact of obstacles in radio propagation provides very high superficial network-level performance. Second, under obstacle shadowing, we perform analytical analysis and Ns-3 based realistic urban vehicular simulation of current dedicated short-range communication based safety message broadcasting to study the achievable performance of application-level performance metrics. This study reveals the answer of the key question, whether the achieved network-level performance metric is good enough for the safety-critical applications under the acute attenuation in radio propagation in urban environment. Third, we propose opportunistic vehicle-assisted or dedicated road side unit-assisted network coded relaying for improving reliability at road intersection. Simulation results show that the proposed approach improves more than 65% reliability for delay sensitive and around 25% reliability for less delay sensitive applications at intersections.

Towards detecting software performance anti-patterns using classification techniques

This paper presents a non-intrusive machine learning approach called Non-intrusive Performance Anti-pattern Detecter (NiPAD) for identifying and classifying software performance anti-patterns. NiPAD uses only system performance metrics-as opposed to analyzing application level performance metrics or source code and the design of a software application to identify and classify software performance anti-patterns within an application. The results of applying NiPAD to an example application show that NiPAD is able to predict the One Lane Bridge software performance anti-pattern within a software application with 0.94 accuracy.

Cross-Layer MIMO Transceiver Optimization for Multimedia Streaming in Interference Networks

In this paper, we consider dynamic precoder/decorrelator optimization for multimedia streaming in MIMO interference networks. We propose a truly cross-layer framework in the sense that the optimization objective is the application level performance metrics for multimedia streaming, namely the playback interruption and buffer overflow probabilities. The optimization variables are the MIMO precoders/decorrelators at the transmitters and the receivers, which are adaptive to both the instantaneous channel condition and the playback queue length. The problem is a challenging multi-dimensional stochastic optimization problem and brute-force solution has exponential complexity. By exploiting the underlying timescale separation and special structure in the problem, we derive a closed-form approximation of the value function based on continuous time perturbation. Using this approximation, we propose a low complexity dynamic MIMO precoder/decorrelator control algorithm by solving an equivalent weighted MMSE problem. We also establish the technical conditions for asymptotic optimality of the low complexity control algorithm. Finally, the proposed scheme is compared with various baselines through simulations and it is shown that significant performance gain can be achieved.

MAC and application level performance evaluation of beacon message dissemination in DSRC safety communication

In this paper, an analytic model is proposed for both MAC and application-level performance evaluation of periodic beacon message dissemination in the dedicated short range communications (DSRC) system on highways. Each vehicle can periodically generate beacon messages, which contain the information including position, velocity, direction, etc.. Out-dated message is replaced by the newly generated message. In order to develop a tractable analytic approximation, we first develop a semi-Markov process (SMP) model for the tagged vehicle to capture the periodic message generation, out-dated message replacement, channel contention and backoff behavior in IEEE 802.11 broadcast ad hoc networks. Further, an SMP model with an absorbing state is constructed to derive the message service time distribution. The fixed-point iteration method is utilized to obtain the converged solutions to resolve interactions between the SMPs of different vehicles. Both MAC-level and application-level performance metrics are derived. MAC-level analytic–numeric results are verified through extensive simulations. Application-level analytic–numerical results are evaluated subsequently to provide insights on network parameter settings. To ensure the quality of service (QoS) for a VANET application, application requirements in terms of application-level metrics are specified. Three typical safety-applications are evaluated in order to assess whether their application requirements can be satisfied or not.

Online Capacity Identification of Multitier Websites Using Hardware Performance Counters

Understanding server capacity is crucial to system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure server capacity offline in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the problem of online capacity measurement even more challenge. This paper presents an online measurement approach based on low-level hardware performance metrics such as instructions execution rate and cache access behavior. Such metrics together define a system internal running state. The measurement approach uses machine learning techniques to infer application-level performance at each tier from a set of selected hardware performance counters. A coordinated predictor is induced over individual tier-wide models to make global system performance prediction and identify the bottleneck when the system becomes overloaded. Experiments were conducted on a two-tier Tomcat/MySQL-configured website using TPC-W benchmarks. Experimental results demonstrated that this approach was able to achieve an overload prediction accuracy of higher than 90 percent for a priori known input traffic mix and over 85 percent accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5 percent runtime overhead for data collection and no more than 50 ms for each online decision making.

Benchmarking of high throughput computing applications on Grids

Grids constitute a promising platform to execute loosely coupled, high-throughput parameter sweep applications, which arise naturally in many scientific and engineering fields like bio-informatics, computational fluid dynamics, particle physics, etc. In spite of the simple computational structure of these applications, its efficient execution and scheduling are challenging because of the dynamic and heterogeneous nature of Grids. In this work, we propose a benchmarking methodology to analyze the performance of computational Grids in the execution of high throughput computing applications, that combines: (i) a representative benchmark included in the NAS Grid Benchmark suite; (ii) a performance model that provides a way to parametrize and compare different Grids; and (iii) a set of application-level performance metrics to analyze and predict the performance of this kind of applications. The benchmarking methodology will be applied to the performance analysis of a Globus-based research testbed that spans heterogeneous resources in five institutions.