The Experience Sampling Method (ESM) is widely used to collect emotion self-reports to train machine learning models for emotion inference. However, as ESM studies are time-consuming and burdensome, participants often withdraw in between. This unplanned withdrawal compels the researchers to discard the dropout participants’ data, significantly impacting the quality and quantity of the self-reports. To address this problem, we leverage only the self-reporting similarity across participants (unlike prior works that apply different machine learning approaches on additional modalities) for missing self-report estimation. In specific, we propose a Multi-task Learning (MTL) framework, MUSE, that constructs the missing self-reports of the dropout participants. We evaluate MUSE in two in-the-wild studies (N1=24, N2=30) of 6-week and 8-week duration, during which the participants reported four emotions (happy, sad, stressed, relaxed) using a smartphone application. The evaluation reveals that MUSE estimates the missing emotion self-reports with an average AUCROC of 84% (Study I) and 82% (Study II). A follow-up evaluation of MUSE for an emotion inference (downstream) task reveals no significant difference in emotion inference performance when estimated self-reports are used. These findings underscore the utility of MUSE in estimating missing self-reports in ESM studies and the applicability of MUSE for downstream tasks (e.g., emotion inference).

Smartphone keyboard interaction based emotion detection systems are used widely to provide value-added services such as mental health monitoring, keyboard layout optimization, guided response generation. At the core of these services lie a machine learning model, which automatically infers emotion based on keyboard interaction pattern. To train these models, the emotion ground truth labels are typically collected as emotion self-report by conducting an Experience Sampling Method (ESM) based study. However, as responding to repetitive self-report probes is time-consuming and fatigue-inducing, efficient self-report collection approaches are essential that avoid probing at inopportune moments and reduce survey fatigue. To address this problem, we propose an active learning based framework, ALOE (Active Learning based Opportunistic Experience Sampling for Emotion Self-report Collection) that automatically decides to avoid probing at the unfavorable moments based on the typing signatures captured from smartphone keyboard interaction sessions. We bootstrap the framework with a few labeled instances (typing session) and allow the learner to probe (or query) the user only when it is least confident about an instance (typing session) and retrain accordingly. This way, we reduce the number of probes required (and therefore user engagement) and yet probe at the opportune moments. We evaluate ALOE in a 3-week in-the-wild study involving 18 participants, who record their smartphone keyboard interaction patterns and emotion self-reports during this period. The experimental results demonstrate that ALOE requires 56% less inopportune self-reports to train the probing moment detection learning model and yet detects the probing moments accurately with an average F-score of 93%.

Keyboard interaction patterns on a smartphone is the input for many intelligent emotion-aware applications, such as adaptive interface, optimized keyboard layout, automatic emoji recommendation in IM applications. The simplest approach, called the Experience Sampling Method (ESM), is to systematically gather self-reported emotion labels from users, which act as the ground truth labels, and build a supervised prediction model for emotion inference. However, as manual self-reporting is fatigue-inducing and attention-demanding, the self-report requests are to be scheduled at favorable moments to ensure high fidelity response. We, in this paper, perform fine-grain keyboard interaction analysis to determine suitable probing moments. Keyboard interaction patterns, both cadence, and latency between strokes, nicely translate to frequency and time domain analysis of the patterns. In this paper, we perform a 3-week in-the-wild study (N = 22) to log keyboard interaction patterns and self-report details indicating (in)opportune probing moments. Analysis of the dataset reveals that time-domain features (e.g., session length, session duration) and frequency-domain features (e.g., number of peak amplitudes, value of peak amplitude) vary significantly between opportune and inopportune probing moments. Driven by these analyses, we develop a generalized (all-user) Random Forest based model, which can identify the opportune probing moments with an average F-score of 93%. We also carry out the explainability analysis of the model using SHAP (SHapley Additive exPlanations), which reveals that the session length and peak amplitude have strongest influence to determine the probing moments.

Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus, a good ESM design must consider -probing frequency,timely self-report collection, andnotifying at opportune momentto ensure highresponse quality. We propose a two-phase ESM design, where the first phase (a) balances between probing frequency and self-report timeliness, and (b) in parallel, constructs a predictive model to identify opportune probing moments. The second phase uses this model to further improve response quality by eliminating inopportune probes. We use typing-based emotion detection in smartphone as a case study to validate proposed ESM design. Our results demonstrate that it reduces probing rate by 64 percent, samples self-reports timely by reducing elapsed time between self-report collection, and event trigger by 9 percent while detecting inopportune moments with an average accuracy of 89 percent. These design choices improve the response quality, as manifested by 96 percent valid response collection and a maximum improvement of 24 percent in emotion classification accuracy.

Characteristics of typing on smartphone keyboards among different individuals can elicit emotion, similar to speech prosody or facial expressions. Existing works on typing based emotion recognition rely on feature engineering to build machine learning models, while recent speech and facial expression based techniques have shown the efficacy of learning the features automatically. Therefore, in this work, we explore the effectiveness of such learning models in keyboard interaction based emotion detection. In this paper, we propose an end-to-end framework, which first uses a sequence-based encoding method to automatically learn the representation from raw keyboard interaction pattern and subsequently uses this representation to train a multi-task learning based neural network (MTL-NN)to identify different emotions. We carry out a 3-week in-the-wild study involving 24 participants using a custom keyboard capable of tracing users' interaction pattern during text entry. We collect interaction details like touch speed, error rate, pressure and self-reported emotions (happy, sad, stressed, relaxed) during the study. Our analysis on the collected dataset reveals that the representation learnt from the interaction pattern has an average correlation of 0.901 within the same emotion and 0.811 between different emotions. As a result, the representation is effective in distinguishing different emotions with an average accuracy (AUCROC)of 84%.

Typing based interfaces are common across many mobile applications, especially messaging apps. To reduce the difficulty of typing using keyboard applications on smartphones, smartwatches with restricted space, several techniques, such as auto-complete, auto-suggest, are implemented. Although helpful, these techniques do add more cognitive load on the user. Hence beyond the importance to improve the word recommendations, it is useful to understand the pattern of use of auto-suggestions during typing. Among several factors that may influence use of auto-suggest, the role of emotion has been mostly overlooked, often due to the difficulty of unobtrusively inferring emotion. With advances in affective computing, and ability to infer user's emotional states accurately, it is imperative to investigate how auto-suggest can be guided by emotion aware decisions. In this work, we investigate correlations between user emotion and usage of auto-suggest i.e. whether users prefer to use auto-suggest in specific emotion states. We developed an Android keyboard application, which records auto-suggest usage and collects emotion self-reports from users in a 3-week in-the-wild study. Analysis of the dataset reveals relationship between user reported emotion state and use of auto-suggest. We used the data to train personalized models for predicting use of auto-suggest in specific emotion state. The model can predict use of auto-suggest with an average accuracy (AUCROC) of 82% showing the feasibility of emotion-aware auto-suggestion.

Network traffic classification is an important tool for network administrators in enabling monitoring and service provisioning. Traditional techniques employed in classifying traffic do not work well for mobile app traffic due to lack of unique signatures. Encryption renders this task even more difficult since packet content is no longer available to parse. More recent techniques based on statistical analysis of parameters such as packet-size and arrival time of packets have shown promise; such techniques have been shown to classify traffic from a small number of applications with a high degree of accuracy. However, we show that when employed to a large number of applications, the performance falls short of satisfactory. In this paper, we propose a novel set of bit-sequence based features which exploit differences in randomness of data generated by different applications. These differences originating due to dissimilarities in encryption implementations by different applications leave footprints on the data generated by them. We validate that these features can differentiate data encrypted with various ciphers (89% accuracy) and key-sizes (83% accuracy). Our evaluation shows that such features can not only differentiate traffic originating from different categories of mobile apps (90% accuracy), but can also classify 175 individual applications with 95% accuracy.

A large fraction of video content providers have adopted adaptive bitrate streaming over HTTP. The client player typically runs an adaptive bitrate (ABR) algorithm to decide upon the most optimal quality for the next few seconds of video playback. State-of-the-art ABR algorithms attempt to achieve an optimal trade-off among the competing objectives of high bitrate, less rebuffering, and high smoothness, in the face of unpredictable bandwidth variability. However, optimal bandwidth utilization does not necessarily ensure high quality of experience (QoE). Different users have different content preferences even within the same video, due to differences in team loyalties (in sport), character preferences (in movies and soaps), and so on. In this work, we present HotDASH, a system which enables opportune prefetching of user-preferred temporal video segments (called hotspots). HotDASH implements a prefetch module in the open source DASH player dash.js, which is powered by an optimal prefetch and bitrate decision engine. The decision engine is designed as a cascaded reinforcement learning (RL) model, implemented using a state-of-the-art actor-critic RL algorithm over a neural network. We train the neural network using trace-driven simulations over a large variety of bandwidth conditions. HotDASH outperforms all baseline algorithms, with a 16.2% QoE improvement over the best-performing baseline, and achieves 14.31% better average bitrate due to its ability to prefetch opportunistically.

Typing characteristics on smartphones can provide clues for emotion detection. Collecting large volumes of typing data is also easy on smartphones. This motivates the use of Deep Neural Network (DNN) to determine emotion states from smartphone typing. In this work, we developed a DNN model based on typing features to predict four emotion states (happy, sad, stressed, relaxed) and investigate its performance on a smartphone. The evaluation of the model in a 3-week study with 15 participants reveals that it can reliably detect emotions with an average accuracy of 80% with peak CPU utilization less than 15%.

In Affective Computing, different modalities, such as speech, facial expressions, physiological properties, smart-phone usage patterns, and their combinations, are applied to detect the affective states of a user. Keystroke analysis i.e. study of the typing behavior in desktop computer is found to be an effective modality for emotion detection because of its reliability, non-intrusiveness and low resource overhead. As smartphones proliferate, typing behavior on smartphone presents an equally powerful modality for emotion detection. It has the added advantage to run in-situ experiments with better coverage than the experiments using desktop computer keyboards. This work explores the efficacy of smartphone typing to detect multiple affective states. We use a qualitative and experimental approach to answer the question. We conduct an online survey among 120 participants to understand the typing habits in smartphones and collect feedback on multiple measurable parameters that affect their emotion while typing. The findings lead us to design and implement an Android based emotion detection system, TapSense, which can identify four different emotion states (happy, sad, stressed, relaxed) with an average accuracy (AUCROC) of 73% (maximum of 94%) based on typing features only. The analysis also reveals that among different features, typing speed is the most discriminative one.

Computation offloading frameworks partition an application's execution between a cloud server and the mobile device to minimize its completion time on the mobile device. An important component of an offloading framework is the partitioning algorithm that decides which tasks to execute on mobile device or cloud server. The partitioning algorithm schedules tasks of a mobile application for execution either on mobile device or cloud server to minimize the application finish time. Most offloading frameworks partition parallel applications devices using an optimization solver which takes a lot of time. We show that by allowing duplicate execution of selected tasks on both the mobile device and the remote cloud server, a polynomial algorithm exists to determine a schedule that minimizes the completion time. We use simulation on both random data and traces to show the savings in both finish time and scheduling time over existing approaches. Our trace-driven simulation on benchmark applications shows that our algorithm reduces the scheduling time by 8 times compared to a standard optimization solver while guaranteeing minimum makespan.

Experience Sampling Method (ESM) is widely used in idiographic approaches to collect within-person patterns. Planning a suitable survey schedule while designing an ESM based experiment is challenging as it must balance between survey fatigue of users, and the timeliness and accuracy of the responses provided by users. Even with the proliferation of ESM experiments, survey scheduling typically remains confined to use of fixed schedules, where periodic probes are sent to user, or event-based schedules, where depending on the number of events, large number of probes may interrupt user frequently. We propose a novel survey scheduling scheme, Low-Interference High-fidelity (LIHF) ESM schedule, which is designed to reduce interference while retaining fidelity of user response. We integrated LIHF into an ESM application, called TapSense, that is used to infer user's emotion from typing characteristics on smartphone keypad. Conducting a 2-week field study involving 9 users, using proposed metrics we show that using LIHF there is 26% reduction in survey fatigue, 50% improvement in triggering survey probes in timely manner, and 8% improvement in predicting emotion states based on typing patterns compared to typical ESM scheduling techniques.

Among the mobile applications contributing to the surging Internet traffic, video applications are some of the biggest contributors. Most of these video applications use HTTP/HTTPS tunneling making it difficult to apply port based or packet data based identification of flows. This makes it challenging for network operators to enforce bandwidth regulation policies for app based service differentiation due to lack of flow identification mechanisms for mobile apps. We explore a packet data agnostic feature of video flows, namely packet-size, to identify the flows. We show that it is possible to train a classifier that can distinguish packets from streaming and interactive video apps with high accuracy. We design and implement a system, called MoViDiff, with this classifier at the core, that allows bandwidth regulation between video traffic of two different categories, streaming and interactive. We show that we can achieve an average accuracy of 96% in classifying the traffic, with the maximum accuracy reaching as high as 98%.

Typing based communication applications on smartphones, like WhatsApp, can induce emotional exchanges. The effects of an emotion in one session of communication can persist across sessions. In this work, we attempt automatic emotion detection by jointly modeling the typing characteristics, and the persistence of emotion. Typing characteristics, like speed, number of mistakes, special characters used, are inferred from typing sessions. Self reports recording emotion states after typing sessions capture persistence of emotion. We use this data to train a personalized machine learning model for multi-state emotion classification. We implemented an Android based smartphone application, called TapSense, that records typing related metadata, and uses a carefully designed Experience Sampling Method (ESM) to collect emotion self reports. We are able to classify four emotion states - happy, sad, stressed, and relaxed, with an average accuracy (AUCROC) of 84% for a group of 22 participants who installed and used TapSense for 3 weeks.

YouTube has emerged as the largest player among video streaming services, serving video content for users using DASH. Research studies on various aspects of YouTube, especially its streaming service, abound in the literature. However, these works study YouTube streaming from the periphery, and report results based on their understanding of general DASH recommendations. In this study, we explore in depth YouTube's implementation of the DASH client. We identify important parameters in YouTube's rate adaptation algorithm, and study their roles. In a departure from existing literature, we observe that YouTube opportunistically adapts segment length, in addition to quality level, in response to bandwidth fluctuations. We report that this scheme results in a much lower average data wastage ratio (0.82x10-6), than reported earlier. We also propose an analytical model, augmented with a machine learning based classifier (with average accuracy of 85.75%), to predict data consumption for a playback session in advance.

Mobile cloud computing is increasingly being used in recent times to offload parts of an application to the cloud to reduce its finish time. However, quality of offloading decisions depend on network conditions and hence many offloading solutions assume that MAC layer retransmissions will tackle transient frame errors. This can lead to suboptimal solutions, as well as, degrade service level guarantee of reducing finish time compared to execution without offloading. In this work, we propose an error-aware solution that uses run-time channel conditions to adapt the offloading decisions. We guarantee that given a failure rate bound (ϵ), offloading decisions will achieve application execution in less time than that of local execution with a probability of (1-ϵ) while operating in networks with unpredictable error characteristics. Simulation results show that at channel error rate of 20%, our heuristic provides 90% guarantee of better performance than on-device computation and reduces the mean finish time by 18% compared to execution without any offloading.

Semantic localization refers to the process of finding one's location with respect to visible or identifiable objects in the scene instead of finding position coordinates. In this research, we use textual signs and their geographical relationships inside a building as semantic signatures to design an infrastructureless indoor navigation system without a site survey. We propose a computer vision-based approach, which takes as an input a floor plan image and automatically infers the floor graph. The constructed graph is then used to estimate the shortest path, which is a sequence of store names, that the user needs to pass to reach her destination.

Mobile browsing habits are characteristically different from browsing on traditional devices. Mobile users often look for information snippets instead of complete web pages. Also, mobile devices are often constrained in terms of resource availability, such as battery, data plan limits and network bandwidth. Under such constraints, partial-loading of a web page, by loading the most relevant content snippets early, can satisfy the user and consume less resources. Mobile content adaptation middleware has traditionally focused on user factors, such as user feedbacks and user context. We believe that the content creator, with complete knowledge of the importance of each item in the web page, is well-suited to guide the adaptation process. Combining user choice, and ratings assigned by the content creator to different web page elements (items), enables delivering the most relevant items in an ordered manner in response to a page request. We present ScoDA, a cooperative content adaptation middleware framework, under resource constraints. We evaluate the effectiveness of page loading using ScoDA, on simulated complex web pages as well as real web pages.

In this paper, we present a new resource allocation scheme for cell-edge active users to achieve improved performance in terms of a higher system capacity and better quality-of-service (QoS) guarantee of the users, where we utilize the two-dimensional resource allocation flexibility of orthogonal frequency division multiple access (OFDMA) networks. Here, the mobile stations (MSs) at the cell-edge can maintain parallel connections with more than one base station (BS) when it is in their coverage area. A MS, before handoff to a new BS, seeks to utilize additional resources from the other BSs if the BS through which its current session is registered is not able to satisfy its requirements. The handoff procedure is termed as split handoff. The BSs participate in split handoff operation while guaranteeing that they are able to maintain QoS of the existing connections associated with them. In this study, first, we present the proposed shared resource allocation architecture and protocol functionalities in split handoff, and give a theoretical proof of concept of system capacity gain associated with the shared resource allocation approach. Then, we provide a differentiated QoS provisioning approach that accounts for the MS speed, its channel quality, as well as the loads at different BSs. Via extensive simulations in Qualnet, the benefits of the proposed class-based split handoff approach is demonstrated. The results also indicate traffic load balancing property of the proposed scheme in heavy traffic conditions.

We investigate the strength of patterns as secret signatures in Android's pattern based authentication mechanism. Parallelism of GPU is exploited to exhaustively search for the secret pattern. Typically, searching for a pattern, composed of a number of nodes and edges, requires an exhaustive search for the pattern. In this work, we show that the use of GPU can speed up the graph search, hence the pattern password, through parallelization. Preliminary results on cracking the Android pattern based passwords shows that the technique can be used as the basis to implement a tool that can check the strength of a pattern based password and thereby recommend strong patterns to the user.

No abstract available.

In Software-as-a-Service (SaaS) cloud delivery model, a hosting center deploys a Virtual Machine (VM) image template on a server on demand. Image templates are usually maintained in a central repository. With geographically dispersed hosting centers, time to transfer a large, often GigaByte sized, template file from the repository faces high latency due to low Internet bandwidth. An architecture that maintains a template cache, collocated with the hosting centers, can reduce request service latency. Since templates are large in size, caching complete templates is prohibitive in terms of storage space. In order to optimize cache space requirement, as well as, to reduce transfers from the repository, we propose a differential template caching technique, called DiffCache. A difference file or a patch between two templates, that have common components, is small in size. DiffCache computes an optimal selection of templates and patches based on the frequency of requests for specific templates. A template missing in the cache can be generated if any cached template can be patched with a cached patch file, thereby saving the transfer time from the repository at the cost of relatively small patching time. We show that patch based caching coupled with intelligent population of the cache can lead to a 90% improvement in service request latency when compared with caching only template files.

Provisioning a virtual server instance in cloud goes through an elaborate workflow, characterized by user request submission, search for the requested image template in image repository, transfer of the template file to compute hosts, followed by expansion and boot up. Optimizing any step in this workflow is crucial in reducing the service time. In this work, we focus on reducing average service time by masking the template file transfer time from repository, and preparing a VM instance beforehand to service a request instantaneously. We use a strategy of pre-provisioning multiple VM instances from different templates. The instances to be pre-provisioned are determined based on request history. We show the benefits of our method using request trace from an enterprise grade cloud environment. Simulation results show more than 50% improvement in reducing average service time while delivering a server instance.

Maintaining consistent views of components in complex IT environments is challenging due to frequent changes applied to the systems. Discovery tools, typically used for scanning the environment, can fail to gather up-to-date information. We present a technique to track changes by monitoring system events induced by a change. The system events are associated to entities in a configuration model. A change in a software component may also necessitate change to a dependent application. We track cross-product dependency by enhancing the configuration model of the service. We show the efficacy of our techniques by extending the configuration model of a complex IT environment.

With the growing use of virtual servers for hosting applications, management of large IT infrastructure faces new challenges. In order to reduce the capital expenditure at data centers, virtual server consolidation approaches has been the focus of attention. However, server consolidation inadvertently introduces a new point of failure, which is the physical resource on which the virtual machines are hosted. A common practice is that for a multi-tier application, different tiers of the application are hosted from a single physical machine. Although this is beneficial in maintaining low cost of ownership when the number of hosted applications is low, with increasing number of hosted applications this leads to a skewed distribution of the applications. We propose that spreading the virtual servers of different applications across the available physical resources can lead to a more resilient design for multi-tier application hosting. We formulate the problem of striping the virtual machines across multiple physical machines as an optimization problem. We propose two MIP based techniques, where the first solution is fast but may be infeasible in many cases, while the other one is better in terms of accuracy but takes more time. The solution is tested on data sets representing typical hosting environments.

Various studies have shown that OS jitter can degrade parallel program performance considerably at large processor counts. Most sources of system jitter fall broadly into 5 categories - user space processes, kernel threads, interrupts, SMT interference and hypervisor activity. Solutions to OS jitter typically consist of a combination of techniques such as synchronization of jitter across nodes (co-scheduling or gang scheduling) and use of microkernels. Both techniques present several drawbacks. Multicore and Multithreaded systems present opportunities to handle OS jitter. They have multiple cores and threads, some of which can be used for handling OS jitter, while the application threads run on remaining cores and threads. However, they are also prone to risks such as inter-thread cache interference and process migration. In this paper, we present a holistic approach that aims to reduce jitter caused by various sources of jitter by utilizing the additional threads or cores in a system. Our approach handles jitter through reduction of kernel threads, intelligent interrupt handling, and switching of hardware SMT thread priorities. This helps in reducing jitter experienced by application threads in the user space, at the kernel level, and at the hardware level. We make use of existing features available in the Linux kernel and Power Architecture as well make enhancements to the Linux kernel. We demonstrate the efficacy of our techniques by reducing jitter on two different platforms and operating system versions. In the first case our approach helps in reducing periodic jitter that improves both average and worst case performance of a simulated parallel application. In the second case our approach helps in reducing infrequent very large jitter that helps the worst case performance of a real parallel application. Our experimental results show up to 30% reduction in slowdown in the average case at 16K OS images and up to 50% reduction in slowdown in the worst case at 8 OS images using this approach as compared to a baseline configuration.

Network flows running on a wireless mesh network (WMN) may suffer from partial failures in the form of serious throughput degradation, sometimes to the extent of starvation, because of weaknesses in the underlying MAC protocol, dissimilar physical transmission rates or different degrees of local congestion. Most existing WMN transport protocols fail to take these factors into account. This paper describes the design, implementation and evaluation of a coordinated congestion control (C3L) algorithm that guarantees fair resource allocation under adverse scenarios and thus provides end-to-end max-min fairness among competing flows. The C3L algorithm features an advanced topology discovery mechanism that detects the inhibition of wireless communication links, and a general collision domain capacity re-estimation mechanism that effectively addresses such inhibition. A comprehensive ns-2-based simulation study as well as empirical measurements taken from an IEEE 802.11a-based multi-hop wireless testbed demonstrate that the C3L algorithm greatly improves inter-flow fairness, eliminates the starvation problem, and at the same time maintains high radio resource utilization efficiency.

Various studies have pointed out the debilitating effects of OS jitter on the performance of parallel applications on large clusters such as the ASCI Purple and the Mare Nostrum at Barcelona Supercomputing Center. These clusters use commodity OSes such as AIX and Linux respectively. The biggest hindrance in evaluating any technique to mitigate jitter is getting access to such large scale production HPC systems running a commodity OS. An earlier attempt aimed at solving this problem was to emulate the effects of OS jitter on more widely available and jitter-free systems such as BlueGene/L. In this paper, we point out the shortcomings of previous such approaches and present the design and implementation of an emulation framework that helps overcome those shortcomings by using innovative techniques. We collect jitter traces on a commodity OS with a given configuration, under which we want to study the scaling behavior. These traces are then replayed on a jitter-free system to predict scalability in presence of OS jitter. The application of this emulation framework to predict scalability is illustrated through a comparative scalability study of an off-the-shelf Linux distribution with a minimal configuration (runlevel 1) and a highly optimized embedded Linux distribution, running on the IO nodes of BlueGene/L. We validate the results of our emulation both on a single node as well as on a real cluster. Our results indicate that an optimized OS along with a technique to synchronize jitter can reduce the performance degradation due to jitter from 99% (in case of the off-the-shelf Linux without any synchronization) to a much more tolerable level of 6% (in case of highly optimized BlueGene/L IO node Linux with synchronization) at 2048 processors. Furthermore, perfect synchronization can give linear scaling with less than 1% slowdown, regardless of the type of OS used. However, as the jitter at different nodes starts getting desynchronized, even with a minor skew across nodes, the optimized OS starts outperforming the off-the-shelf OS.

Understanding the behavior and impact of various sources of Operating System Jitter (OS Jitter) is important not only for tuning a system for HPC applications, but also for the ongoing efforts to create light-weight versions of commercial operating systems such as Linux, that can be used on compute nodes of large scale HPC systems. In this paper, we present a tool that helps in identifying sources of OS Jitter in a commodity operating system such as Linux and measures the impact of OS Jitter through fine grained kernel instrumentation. Our methodology comprises of running a user-level micro-benchmark and measuring the latencies experienced by the benchmark. We then associate each latency to operating system daemons and interrupts using data obtained from kernel instrumentation. We present experimental results that help identify the biggest contributors to the total OS Jitter perceived by an application on a commodity operating system such as Linux. Our results revealed that while 63% of the total jitter comes from timer interrupts, the rest comes from various system daemons and interrupts, most of which can be easily eliminated. The tool presented in this paper can also be used to tune “out of the box” commodity operating systems as well as to detect new sources of operating system jitter that get introduced as software get installed and upgraded on a tuned system.

Economies of scale make IEEE 802.11 an attractive technology for building wireless mesh networks (WMNs). However, the IEEE 802.11 protocol exhibits serious link-layer unfairness when used in multi-hop networks. Existing fairness solutions either do not address this problem, or require proprietary MAC protocol to provide fairness. In this paper, we argue that an ideal transport protocol should be able to achieve fairness even on top of an unfair MAC layer such as 802.11. Towards this end, we propose a co-ordinatedcongestioncontrolalgorithm that performs global bandwidth allocation and provides end-to-end flow-level max-min fairness despite weaknesses in the MAC layer. The proposed algorithm features an advanced topology discovery mechanism that detects the inhibition of wireless communication links, and a general collision domain capacity re-estimation mechanism that effectively addresses such inhibition. Through an ns-2-based simulation study we demonstrate that the proposed algorithm substantially improves the fairness across flows, eliminates starvation problem, and simultaneously maintains a high overall network throughput.

An effective transport protocol for a wireless mesh network (WMN) must fairly and efficiently allocate the limited network resources among multiple flows sharing the network while minimizing the performance overhead it incurs. While many transport protocols have been proposed specifically for multi-hop wireless networks, most of them refrain from keeping state in the intermediate network nodes. In this paper, we focus on the other extreme of the design space:statefultransportprotocol, and study the research question of how much performance improvement is possible if intermediate network nodes could maintain as much state as needed. We present the design of a stateful transport protocol, namedlink-awarereliabletransportprotocol(LRTP), and examine how LRTP can fairly and efficiently allocate the network resources by accurately estimating the sending rate of each flow traversing the network using information about effective physical link capacity and the number of sharing flows. LRTP reduces the performance overhead associated with reliable packet delivery by leveraging the link-layer retransmission mechanism to eliminate per-packet end-to-end acknowledgments and unnecessary packet transmissions. Experiments conducted on anIEEE802.1la-basedmulti-channelwirelessmeshnetworktestbedas well asns-2simulationsdemonstrate that LRTP can achieve significant improvements in both overall network throughput and inter-flow fairness, especially on wireless networks with channel errors, when compared with the de facto Internet transport protocol TCP, and state-of-the-art MANET transport protocols such as ATP.

Limited fidelity of software-based wireless network simulations has prompted many researchers to build testbeds for developing and evaluating their wireless protocols and mobile applications. Since most testbeds are tailored to the needs of specific research projects, they cannot be easily reused for other research projects that may have different requirements on physical topology, radio channel characteristics or mobility pattern. In this paper, we describe the design, implementation and evaluation of MiNT-m, an experimentation platform devised specifically to support arbitrary experiments for mobile multi-hop wireless network protocols. In addition to inheriting the miniaturization feature from its predecessor MiNT [9], MiNT-m enables flexible testbed reconfiguration on an experiment-by-experiment basis by putting each testbed node on a centrally controlled untethered mobile robot. To support mobility and reconfiguration of testbed nodes, MiNT-m includes a scalable mobile robot navigation control subsystem, which in turn consists of a vision-based robot positioning module and a collision avoidance-based trajectory planning module. Further, MiNT-m provides a comprehensive network/experiment management subsystem that affords a user full interactive control over the testbed as well as real-time visualization of the testbed activities. Finally, because MiNT-m is designed to be a shared research infrastructure that supports 24x7 operation, it incorporates a novel automatic battery recharging capability that enables testbed robots to operate without human intervention for weeks.

Most mobile wireless networking research today relies on simulations. However, fidelity of simulation results has always been a concern, especially when the protocols being studied are affected by the propagation and interference characteristics of the radio channels. Inherent difficulty in faithfully modeling the wireless channel characteristics has encouraged several researchers to build wireless network testbeds. A full-fledged wireless testbed is spread over a large physical space because of the wide coverage area of radio signals. This makes a large-scale testbed difficult and expensive to set up, configure, and manage. This paper describes a miniaturized 802.11b-based, multi-hop wireless network testbed called MiNT. MiNT occupies a significantly small space, and dramatically reduces the efforts required in setting up a multi-hop wireless network used for wireless application/protocol testing and evaluation. MiNT is also a hybrid simulation platform that can execute ns-2 simulation scripts with the link, MAC and physical layer in the simulator replaced by real hardware. We demonstrate the fidelity of MiNT by comparing experimental results on it with similar experiments conducted on a non-miniaturized testbed. We also compare the results of experiments conducted using hybrid simulation on MiNT with those obtained using pure simulation. Finally, using a case study we show the usefulness of MiNT in wireless application testing and evaluation.

The ability to deliver digital video over wireless networks is an enabling technology for many useful applications, ranging from home entertainment and security monitoring to enterprise messaging and military reconnaissance, and thus represents the holy grail of wireless technology development. We describe a wireless video delivery system, WiVision, which uses IEEE 802.11 wireless LANs as the last mile for both real-time video distribution and on-demand video playback. WiVision can air both live events, such as on-campus seminars and sports activities, and prestored video streams, such as course lectures and financial analysis sessions, to mobile users, who can tune in to selected channels of their choice from their laptops or PDAs. An innovative feature of WiVision is the support for random video access based on keyword-search, where keywords are extracted from the closed-caption text embedded in TV programs. In addition, WiVision is able to broadcast video streams on the wireless link while seamlessly working with commercially available media players. The paper presents the implementation details of the real-time acquisition and network transport components of a fully operational WiVision prototype, and the results of a performance evaluation study on that prototype.

The prevailing practice for testing protocol implementations is direct code instrumentation to trigger specific states in the code. This leaves very little scope for reuse of the test cases. In this paper, we present the design, implementation, and evaluation of VirtualWire, a network fault injection and analysis system designed to facilitate the process of testing network protocol implementations. VirtualWire injects user-specified network faults and matches network events against anticipated responses based on high-level specifications written in a declarative scripting language. With VirtualWire, testing requires no code instrumentation and fault specifications can be reused across versions of a protocol implementation. We illustrate the effectiveness of VirtualWire with examples drawn from testing Linux's TCP implementation and a real-time Ethernet protocol called Rether. In each case, 10 to 20 lines of script is sufficient to specify the test scenario. VirtualWire is completely transparent to the protocols under test, and additional overhead in protocol processing latency it introduces is below 10% of the normal.