Real-time content analysis is typically a bottleneck in Web filtering. To accelerate the filtering process, this work presents a simple, but effective early decision algorithm that analyzes only part of the Web content. This algorithm can make the filtering decision, either to block or to pass the Web content, as soon as it is confident with a high probability that the content really belongs to a banned or an allowed category. Experiments show the algorithm needs to examine only around one-fourth of the Web content on average, while the accuracy remains fairly good: 89% for the banned content and 93% for the allowed content. This algorithm can complement other Web filtering approaches, such as URL blocking, to filter the Web content with high accuracy and efficiency. Text classification algorithms in other applications can also follow the principle of early decision to accelerate their applications.

The computing of applications in embedded devices suffers tight constraints on computation and energy resources. Thus, it is important that applications running on these resource-constrained devices are aware of the energy constraint and are able to execute efficiently. The existing execution time and energy profiling tools could help developers to identify the bottlenecks of applications. However, the profiling tools need large space to store detailed profiling data at runtime, which is a hard demand upon embedded devices. In this article, a reconfigurable multi-resolution profiling (RMP) approach is proposed to handle this issue on embedded devices. It first instruments all profiling points into source code of the target application and framework. Developers can narrow down the causes of bottleneck by adjusting the profiling scope using the configuration tool step by step without recompiling the profiled targets. RMP has been implemented as an open source tool on Android systems. Experiment results show that the required log space using RMP for a web browser application is 25 times smaller than that of Android debug class, and the profiling error rate of execution time is proven 24 times lower than that of debug class. Besides, the CPU and memory overheads of RMP are only 5% and 6.53% for the browsing scenario, respectively.

The proportional delay differentiation (PDD) model provides consistent packet delay differentiation between classes of service. Currently, the present schedulers performing the PDD model cannot achieve desired delay proportion observed in short timescales under light/moderate load. Thus, we propose a Non-Work-Conserving (NWC) scheduler, which utilizes the pseudo-waiting time for an empty queue and forces each class to compare its priority with those of all other classes. Simulation results reveal that NWC outperforms all current schedulers in achieving the PDD model. However, NWC suspends the server from transmitting packets immediately if an empty class has the maximum priority, resulting in an idle server. Therefore, we further propose two approaches, which will serve a best-effort class during this idle time. Compared with other schedulers, the proposed approaches can provide more predictable and controllable delay proportion, accompanied with satisfactory throughput and average queuing delay.

Managing increasing traffic from Instant Messengers and P2P applications is becoming more important nowadays. We present an in-kernel architecture of management gateway, namely kP2PADM, built upon open-source packages with several modifications and design techniques. First, the in-kernel design streamlines the data path through the gateway. Second, the dual-queue buffer eliminates head-of-line blocking for multiple connections. Third, a connection cache reduces useless reconnection attempts from the peers. Fourth, a fast-pass mechanism avoids slowing down the TCP transmission. The in-kernel design approximately doubles the throughput of the design in the user space. The internal benchmarks also analyze the impact of each function on performance.

A buffer overflow attack occurs when a program writes data outside the allocated memory in an attempt to invade a system. Approximately forty percent of all software vulnerabilities over the past several years are attributed to buffer overflow. Taint tracking is a novel technique to prevent buffer overflow. Previous studies on taint tracking ran a victim's program on an emulator to dynamically instrument the code for tracking the propagation of taint data in memory and checking whether malicious code is executed. However, the critical problem of this approach is its heavy performance overhead. Analysis of this overhead shows that 60% of the overhead is from the emulator, and the remaining 40% is from dynamic instrumentation and taint information maintenance. This article proposes a new taint-style system called Embedded TaintTracker to eliminate the overhead in the emulator and dynamic instrumentation by compressing a checking mechanism into the operating system (OS) kernel and moving the instrumentation from runtime to compilation time. Results show that the proposed system outperforms the previous work, TaintCheck, by at least 8 times on throughput degradation, and is about 17.5 times faster than TaintCheck when browsing 1 KB web pages.

The issue of guaranteeing Quality of Services (QoS) in a network has emerged in recent years. The Proportional Delay Differentiated Model has been presented to provide the predictable and controllable queueing delay differentiation for different classes of connections. However, most related works have focused on providing this model for a wired network. This study proposes a novel scheduler to provide proportional delay differentiation in a wireless network that includes a multi-state link. This scheduler, Look-ahead Waiting-Time Priority (LWTP), offers proportional delay differentiation and a low queueing delay, by adapting to the location-dependent capacity of the wireless link and solving the head-of-line (HOL) blocking problem. The simulation results demonstrate that the LWTP scheduler actually achieves delay ratios much closer to the target delay proportion between classes and yields smaller queueing delays than past schedulers.

Most Android applications are written in JAVA and run on a Dalvik virtual machine. For smartphone vendors and users who wish to know the performance of an application on a particular smartphone but cannot obtain the source code, we propose a new technique, Dalvik Profiler for Applications (DPA), to profile an Android application on a Dalvik virtual machine without the support of source code. Within a Dalvik virtual machine, we determine the entry and exit locations of a method, log its execution time, and analyze the log to determine the performance of the application. Our experimental results show an error ratio of less than 5% from the baseline tool Traceview which instruments source code. The results also show some interesting behaviors of applications and smartphones: the performance of some smartphones with higher hardware specifications is 1.5 times less than the phones with lower specifications. DPA is now publicly available as an open source tool.