Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Baobao Chang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Effective incident management is pivotal for the smooth operation of Microsoft cloud services. In order to expedite incident mitigation, service teams gather troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to On-Call Engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs’ intervention. In addition, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-hire OCEs. In this work, we propose Nissist which leverages unstructured TSGs and incident mitigation history to provide proactive incident mitigation suggestions, reducing human intervention. Leveraging Large Language Models (LLM), Nissist extracts knowledge from unstructured TSGs and incident mitigation history, forming a comprehensive knowledge base. Its multi-agent system design enhances proficiency in precisely discerning OCE intents, retrieving relevant information, and delivering systematic plans consecutively. Through our user experiments, we demonstrate that Nissist significantly reduce Time to Mitigate (TTM) in incident mitigation, alleviating operational burdens on OCEs and improving service reliability. Our webpage is available at https://aka.ms/nissist.

Logging, which aims to determine the position of logging statements, the verbosity levels, and the log messages, is a crucial process for software reliability enhancement. In recent years, numerous automatic logging tools have been designed to assist developers in one of the logging tasks (e.g., providing suggestions on whether to log in try-catch blocks). These tools are useful in certain situations yet cannot provide a comprehensive logging solution in general. Moreover, although recent research has started to explore end-to-end logging, it is still largely constrained by the high cost of fine-tuning, hindering its practical usefulness in software development. To address these problems, this paper proposes UniLog, an automatic logging framework based on the in-context learning (ICL) paradigm of large language models (LLMs). Specifically, UniLog can generate an appropriate logging statement with only a prompt containing five demonstration examples without any model tuning. In addition, UniLog can further enhance its logging ability after warmup with only a few hundred random samples. We evaluated UniLog on a large dataset containing 12,012 code snippets extracted from 1,465 GitHub repositories. The results show that UniLog achieved the state-of-the-art performance in automatic logging: (1) 76.9% accuracy in selecting logging positions, (2) 72.3% accuracy in predicting verbosity levels, and (3) 27.1 BLEU-4 score in generating log messages. Meanwhile, UniLog requires less than 4% of the parameter tuning time needed by fine-tuning the same LLM.

Cloud providers often have resources that are not being fully utilized, and they may offer them at a lower cost to make up for the reduced availability of these resources. However, customers may be hesitant to use such offerings (such as spot VMs) as making trade-offs between cost and resource availability is not always straightforward. In this work, we propose Snape (Spot On-demand Perfect Mixture), an intelligent framework to optimize the cost and resource availability by dynamically mixing on-demand VMs with spot VMs. Through a detailed characterization based on real production traces, we verify that the eviction of spot VMs is predictable to some extent. Snape also leverages constrained reinforcement learning to adjust the mixture policy online. Experiments across different configurations show that Snape achieves 44% savings compared to using only on-demand VMs while maintaining 99.96% availability, which is 2.77% higher than using only spot VMs.

In cloud systems, incidents are potential threats to customer satisfaction and business revenue. When customers are affected by incidents, they often request customer support service (CSS) from the cloud provider by submitting a support ticket. Many tickets could be duplicate as they are reported in a distributed and uncoordinated manner. Thus, aggregating such duplicate tickets is essential for efficient ticket management. Previous studies mainly rely on tickets' textual similarity to detect duplication; however, duplicate tickets in a cloud system could carry semantically different descriptions due to the complex service dependency of the cloud system. To tackle this problem, we propose iPACK, an incident-aware method for aggregating duplicate tickets by fusing the failure information between the customer side (i.e., tickets) and the cloud side (i.e., incidents). We extensively evaluate iPACK on three datasets collected from the production environment of a large-scale cloud platform, Azure. The experimental results show that iPACK can precisely and comprehensively aggregate duplicate tickets, achieving an F1 score of 0.871~0.935 and outperforming state-of-the-art methods by 12.4%~31.2%.

In distributed systems and microservice applications, tracing is a crucial observability signal employed for comprehending their internal states. To mitigate the overhead associated with distributed tracing, most tracing frameworks utilize a uniform sampling strategy, which retains only a subset of traces. However, this approach is insufficient for preserving system observability. This is primarily attributed to the long-tail distribution of traces in practice, which results in the omission or rarity of minority yet critical traces after sampling. In this study, we introduce an observability-preserving trace sampling method, denoted as STEAM, which aims to retain as much information as possible in the sampled traces. We employ Graph Neural Networks (GNN) for trace representation, while incorporating domain knowledge of trace comparison through logical clauses. Subsequently, we employ a scalable approach to sample traces, emphasizing mutually dissimilar traces. STEAM has been implemented on top of OpenTelemetry, comprising approximately 1.6K lines of Golang code and 2K lines of Python code. Evaluation on four benchmark microservice applications and a production system demonstrates the superior performance of our approach compared to baseline methods. Furthermore, STEAM is capable of processing 15,000 traces in approximately 4 seconds.

Cloud systems have become increasingly popular in recent years due to their flexibility and scalability. Each time cloud computing applications and services hosted on the cloud are affected by a cloud outage, users can experience slow response times, connection issues or total service disruption, resulting in a significant negative business impact. Outages are usually comprised of several concurring events/source causes, and therefore understanding the context of outages is a very challenging yet crucial first step toward mitigating and resolving outages. In current practice, on-call engineers with in-depth domain knowledge, have to manually assess and summarize outages when they happen, which is time-consuming and labor-intensive. In this paper, we first present a large-scale empirical study investigating the way on-call engineers currently deal with cloud outages at Microsoft, and then present and empirically validate a novel approach (dubbed Oasis) to help the engineers in this task. Oasis is able to automatically assess the impact scope of outages as well as to produce human-readable summarization. Specifically, Oasis first assesses the impact scope of an outage by aggregating relevant incidents via multiple techniques. Then, it generates a human-readable summary by leveraging fine-tuned large language models like GPT-3.x. The impact assessment component of Oasis was introduced in Microsoft over three years ago, and it is now widely adopted, while the outage summarization component has been recently introduced, and in this article we present the results of an empirical evaluation we carried out on 18 real-world cloud systems as well as a human-based evaluation with outage owners. The results obtained show that Oasis can effectively and efficiently summarize outages, and lead Microsoft to deploy its first prototype which is currently under experimental adoption by some of the incident teams.

Logs are crucial to the management and maintenance of software systems. In recent years, log analysis research has achieved notable progress on various topics such as log parsing and log-based anomaly detection. However, the real voices from front-line practitioners are seldom heard. For example, what are the pain points of log analysis in practice? In this work, we conduct a comprehensive survey study on log analysis at Microsoft. We collected feedback from 105 employees through a questionnaire of 13 questions and individual interviews with 12 employees. We summarize the format, scenario, method, tool, and pain points of log analysis. Additionally, by comparing the industrial practices with academic research, we discuss the gaps between academia and industry, and future opportunities on log analysis with four inspiring findings. Particularly, we observe a huge gap exists between log anomaly detection research and failure alerting practices regarding the goal, technique, efficiency, etc. Moreover, data-driven log parsing, which has been widely studied in recent research, can be alternatively achieved by simply logging template IDs during software development. We hope this paper could uncover the real needs of industrial practitioners and the unnoticed yet significant gap between industry and academia, and inspire interesting future directions that converge efforts from both sides.

Log parsing, which extracts log templates and parameters, is a critical prerequisite step for automated log analysis techniques. Though existing log parsers have achieved promising accuracy on public log datasets, they still face many challenges when applied in the industry. Through studying the characteristics of real-world log data and analyzing the limitations of existing log parsers, we identify two problems. Firstly, it is non-trivial to scale a log parser to a vast number of logs, especially in real-world scenarios where the log data is extremely imbalanced. Secondly, existing log parsers overlook the importance of user feedback, which is imperative for parser fine-tuning under the continuous evolution of log data. To overcome the challenges, we propose SPINE, which is a highly scalable log parser with user feedback guidance. Based on our log parser equipped with initial grouping and progressive clustering,we propose a novel log data scheduling algorithm to improve the efficiency of parallelization under the large-scale imbalanced log data. Besides, we introduce user feedback to make the parser fast adapt to the evolving logs. We evaluated SPINE on 16 public log datasets. SPINE achieves more than 0.90 parsing accuracy on average with the highest parsing efficiency, which outperforms the state-of-the-art log parsers. We also evaluated SPINE in the production environment of Microsoft, in which SPINE can parse 30million logs in less than 8 minutes under 16 executors, achieving near real-time performance. In addition, our evaluations show that SPINE can consistently achieve good accuracy under log evolution with a moderate number of user feedback.

Logs provide first-hand information for engineers to diagnose failures in large-scale online service systems. Log parsing, which transforms semi-structured raw log messages into structured data, is a prerequisite of automated log analysis such as log-based anomaly detection and diagnosis. Almost all existing log parsers follow the general idea of extracting the common part as templates and the dynamic part as parameters. However, these log parsing methods, often neglect the semantic meaning of log messages. Furthermore, high diversity among various log sources also poses an obstacle in the generalization of log parsing across different systems. In this paper, we propose UniParser to capture the common logging behaviours from heterogeneous log data. UniParser utilizes a Token Encoder module and a Context Encoder module to learn the patterns from the log token and its neighbouring context. A Context Similarity module is specially designed to model the commonalities of learned patterns. We have performed extensive experiments on 16 public log datasets and our results show that UniParser outperforms state-of-the-art log parsers by a large margin. 1

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%-83.5%, which outperforms the state-of-the-art triage approach by 28.0%-29.7%.

The management of cloud service incidents (unplanned interruptions or outages of a service/product) greatly affects customer satisfaction and business revenue. After years of efforts, cloud enterprises are able to solve most incidents automatically and timely. However, in practice, we still observe critical service incidents that occurred in an unexpected manner and orchestrated diagnosis workflow failed to mitigate them. In order to accelerate the understanding of unprecedented incidents and provide actionable recommendations, modern incident management system employs the strategy of AIOps (Artificial Intelligence for IT Operations). In this paper, to provide a broad view of industrial incident management and understand the modern incident management system, we conduct a comprehensive empirical study spanning over two years of incident management practices at Microsoft. Particularly, we identify two critical challenges (namely, incomplete service/resource dependencies and imprecise resource health assessment) and investigate the underlying reasons from the perspective of cloud system design and operations. We also present IcM BRAIN, our AIOps framework towards intelligent incident management, and show its practical benefits conveyed to the cloud services of Microsoft.

In cloud service systems, customers will report the service issues they have encountered to cloud service providers. Despite many issues can be handled by the support team, sometimes the customer issues can not be easily solved, thus raising customer incidents. Quick troubleshooting of a customer incident is critical. To this end, a customer incident should be assigned to its responsible team accurately in a timely manner.

We present Travi-Navi-a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high-quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds shortcuts whenever possible. In this paper, we describe the key techniques to solve several practical challenges, including robust tracking, shortcut identification, and high-quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a four-step offset, and detect deviation events within nine steps. We also characterize the power consumption of Travi-Navi on various mobile phones.

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 × less energy in tracking compared to WiFi-based tracking.

Clock synchronization is critical for many WSNs due to the need of inter-node coordination and collaborative information processing. Existing protocols based on message passing achieve satisfactory clock synchronization accuracy, however, incur prohibitively high overhead especially in large-scale networks. In this paper, we propose a new clock synchronization approach called ROCS which exploits the radio data system (RDS) from FM radio stations. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40km2. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach.

We present Travi-Navi - a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds the most efficient shortcuts whenever possible. We encounter and solve several challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a 4-step offset, and detect deviation events within 9 steps.

Recent years have witnessed the pilot deployments of audio or low-rate video wireless sensor networks for a class of mission-critical applications including search-and-rescue, security surveillance, and disaster management. In this article, we report the design and implementation of Quality-aware Voice Streaming (QVS) for wireless sensor networks. QVS is built upon SenEar, a new sensor hardware platform we developed for high-bandwidth wireless audio communication. QVS comprises several novel components, which include an empirical model for online voice-quality evaluation and control, dynamic voice compression/duplication adaptation for lossy wireless links, and distributed stream admission control that exploits network capacity for rate allocation. We have extensively tested QVS on a 20-node network deployment. Our experimental results show that QVS delivers satisfactory voice quality under a range of realistic settings while achieving high network capacity utilization.

Indoor physical analytics calls for high-accuracy localization that existing indoor (e.g., WiFi-based) localization systems may not offer. By exploiting the ever increasingly wider adoption of LED lighting, in this paper, we study the problem of using visible LED lights for accurate localization. We identify the key challenges and tackle them through the design of Pharos. In particular, we establish and experimentally verify an optical channel model suitable for localization. We adopt BFSK and channel hopping to achieve reliable location beaconing from multiple, uncoordinated light sources over shared light medium. Preliminary evaluation shows that Pharos achieves the 90th percentile localization accuracy of 0.4m and 0.7m for two typical indoor environments. We believe visible light based localization holds the potential to significantly improve the position accuracy, despite few potential issues to be conquered in real deployment.

We present ViRi -- an intriguing system that enables a user to enjoy a frontal view experience even when the user is actually at a slanted viewing angle. ViRi tries to restore the front-view effect by enhancing the normal content rendering process with an additional geometry correction stage. The necessary prerequisite is effectively and accurately estimating the actual viewing angle under natural viewing situations and under the constraints of the device's computational power and limited battery deposit. We tackle the problem with face detection and augment the phone camera with a fisheye lens to expand its field of view so that the device can recognize its user even the phone is placed casually. We propose effective pre-processing techniques to ensure the applicability of face detection tools onto highly distorted fisheye images. To save energy, we leverage information from system states, employ multiple low power sensors to rule out unlikely viewing situations, and aggressively seek additional opportunities to maximally skip the face detection. For situations in which face detection is unavoidable, we design efficient prediction techniques to further speed up the face detection. The effectiveness of the proposed techniques have been confirmed through thorough evaluations. We have also built a straw man application to allow users to experience the intriguing effects of ViRi.

Low-power Wireless Networks (LWNs) have become increasingly available for mission-critical applications such as security surveillance and disaster response. In particular, emerging low-power wireless audio platforms provide an economical solution for ad hoc voice communication in emergency scenarios. In this paper, we develop a system called Adaptive Stream Multicast (ASM) for voice communication over multihop LWNs. ASM is composed of several novel components specially designed to deliver robust voice quality for multiple sinks in dynamic environments: 1) an empirical model to automatically evaluate the voice quality perceived at sinks based on current network condition; 2) a feedback-based Forward Error Correction (FEC) scheme where the source can adapt its coding redundancy ratio dynamically in response to the voice quality variation at sinks; 3) a Tree-based Opportunistic Routing (TOR) protocol that fully exploits the broadcast opportunities on a tree based on novel forwarder selection and coordination rules; and 4) a distributed admission control algorithm that ensures the voice quality guarantees when admitting new voice streams. ASM has been implemented on a low-power hardware platform and extensively evaluated through experiments on a test bed of 18 nodes. The experiment results show that ASM can achieve satisfactory multicast voice quality in dynamic environments while incurring low-communication overhead.

Clock synchronization is critical for Wireless Sensor Networks (WSNs) due to the need of inter-node coordination and collaborative information processing. Although many message passing protocols can achieve satisfactory clock synchronization accuracy, they incur prohibitively high overhead when the network scales to more than tens of nodes. An alternative approach is to take advantage of the global time reference induced by existing infrastructures including GPS, timekeeping radio stations, or power grid. However, high power consumption and geographic constraints present them from being widely adopted in WSNs. In this paper, we propose ROCS, a new clock synchronization approach exploiting the Radio Data System (RDS) of FM radios. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40 $km^2$. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS intelligently predicts the time error due to drift, and adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

(1) Institute of Software, Chinese Academy of Sciences, China; (2) Graduate University, Chinese Academy of Sciences, China; (3) Department of Computer Science and Engineering, Michigan State University, United States

In this paper, we propose a priority (or progressive) linear coding based opportunistic routing mechanism (OR-PLC) for H.264 video streaming over multi-hop ad hoc networks. OR-PLC assigns different error protection priorities to video packets according to their perceptual importance to mitigate error propagation problem so that the video quality is enhanced in receiver. Furthermore, OR-PLC exploits the broadcast feature of wireless medium to improve the bandwidth utility. Compared with other opportunistic routing schemes, OR-PLC reduces the delay by progressive encoding and decoding. The experiments show that our mechanism outperforms two state-of-the-art schemes, i.e., MORE and MP-RTP. It turns out that OR-PLC delivers more than 3.5 dB PSNR gains in average, while using less bandwidth.

Geographic message forwarding in vehicular ad hoc networks (VANET) has attracted much attention and become one of the most promising research areas recent years. In this paper, inspired with the intuition that drivers' route are with high regularity, we propose a prediction-based message forwarding strategy named Seer. Seer trains a 2nd-order Markov model based on long-term historic trip GPS data. Then probabilistic predictions about driving trend is made by looking at the intersections the driver just passed by. Seer can work without special service such as the traffic navigation systems and it can avoid leaking the position privacy of the driver. With extensive simulation in ONE, we show that Seer can achieve higher packet delivery ratio and lower delay, comparing with random or position-based message forwarding strategies.

Low-power Wireless Networks (LWNs) have become increasingly available for mission-critical applications such as security surveillance and disaster response. In particular, emerging low-power wireless audio platforms provide an economical solution for ad hoc voice communication in emergency scenarios. In this paper, we develop a system called Adaptive Stream Multicast (ASM) for voice communication over multi-hop LWNs. ASM is composed of several novel components specially designed to deliver robust voice quality for multiple sinks in dynamic environments: 1) an empirical model to automatically evaluate the voice quality perceived at sinks based on current network condition, 2) a feedback-based Forward Error Correction scheme where the source can adapt its coding redundancy ratio dynamically in response to the voice quality variation at sinks, 3) a Tree-based Opportunistic Routing (TOR) protocol that fully exploits the broadcast opportunities on a tree based on novel forwarder selection and coordination rules, and 4) a distributed admission control algorithm that ensures the voice quality guarantees when admitting new voice streams. ASM has been implemented on a low-power hardware platform and extensively evaluated through experiments on a testbed of 18 nodes.

Recent years have witnessed the pilot deployments of audio or low-rate video wireless sensor networks for a class of mission-critical applications including search and rescue, security surveillance, and disaster management. In this paper, we report the design and implementation of Quality-aware Voice Streaming (QVS) for wireless sensor networks. QVS is built upon SenEar, a new sensor hardware platform we developed for high-bandwidth wireless audio communication. QVS comprises several novel components, which include an empirical model for online voice quality evaluation and control, dynamic voice compression/duplication adaptation for lossy wireless links, and distributed stream admission control that exploits network capacity for rate allocation. We have extensively tested QVS on a 20-node network deployment. Our experimental results show that QVS delivers satisfactory voice quality under a range of realistic settings while achieving high network capacity utilization.

In the field of automated home video editing, exploring the dependence relations between who (character) and where (scene) makes great sense to end-users for content selection. However, such techniques have not been well developed in real applications due to their computational intensity. The emerging multi-core architectures provide an opportunity to speed up those compute expensive algorithms if shift from serial thinking to parallelism. This demonstration presents a scalable parallel system for home video editing. In a realtime processing speed, the system analyzes how many characters and scenes are captured and provides end-users with flexible preference customization. Through kernel module optimization and data-level parallelization, evaluations on a real 8-core machine indicates a near linear speed up could be achieved along with the increasing number of cores.