Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers' salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. These efforts can advance in-season monitoring and decision making to support adaptive management of sustainable wild salmon fisheries.

Virtualization has attained mainstream status in enterprise IT industry. Despite its widespread adoption, it is known that virtualization also introduces non-trivial overhead when tasks are executed on a virtual machine (VM). In particular, a combined effect from device virtualization overhead and CPU scheduling latency can cause performance degradation when computation intensive tasks and I/O intensive tasks are co-located on a VM. Such an interference also causes extra energy consumption. In this paper, we present Hylics, a novel solution that enables efficient data traverse paths for both I/O and computation intensive workloads. This is achieved with the provision of in-memory file system and network service at the hypervisor level. Several important design issues are pinpointed and addressed during our prototype implementation, including efficient intermediate data sharing, network service offloading, and QoS-aware memory usage management. Based on our real-world deployment on KVM, we show that Hylics can significantly improve computation and I/O performance for hybrid workloads. Moreover, this design also alleviates the existing virtualization overhead and naturally optimizes the overall energy efficiency.

While advancements in computing algorithms and hardware have enabled real-time stream analytics in videos, the information-rich audio bonded with video is still usually dropped and wasted. Processing both audio and video stream is not trivial as synchronizing multiple data streams creates much more difficulties than processing only one stream. In this paper, we designed and implemented a lightweight multimodal stream processing system that keeps both streams synchronized in processing and tested in a typical use case profanity filter. While it is inevitable to slow down certain processors to keep the steams synchronized, by careful butter setting, the overall latency is not affected (still 400-500ms). Besides achieving real-time processing, we located a core problem causing bursty audio latency and gave directions for further latency improvements.

Today's explosively growing Internet video traffics and viewers' ever-increasing quality of experience (QoE) demands for video streaming bring tremendous pressures to the backbone network. As a new network paradigm, mobile edge caching provides a promising alternative by pushing video content closer at the network edge rather than the remote CDN servers so as to reduce both content access latency and redundant network traffic. However, our large-scale trace analysis shows that different from CDN based caching, edge caching environment is much more complicated with massively dynamic and diverse request patterns, which renders that existing rule-based and model-based caching solutions may not well fit such complicated edge environments. Moreover, although cooperative caching has been proposed to better afford limited storage on each individual edge server, our trace analysis also shows that the request similarity among neighboring edges can be highly dynamic and diverse, which is drastically different from CDN based caching environment, and can easily compromise the benefits from traditional cooperative caching mostly designed based on CDN environment. In this paper, we propose MacoCache, an intelligent edge caching framework that is carefully designed to afford the massively diversified and distributed caching environment to minimize both content access latency and traffic cost. Specifically, MacoCache leverages a multi-agent deep reinforcement learning (MADRL) based solution, where each edge is able to adaptively learn its own best policy in conjunction with other edges for intelligent caching. The real trace-driven evaluation further demonstrates that MacoCache is able to reduce an average of 21% latency and 26% cost compared with the state-of-the-art caching solution.

Mobile augmented reality (MAR) augments a real-world environment (probably surrounding or close to the mobile user) by computer-generated perceptual information. Utilizing the emerging edge computing paradigm in MAR systems can reduce the power consumption and computation load for the mobile devices and improve responsiveness of the MAR service. Different from existing studies that mainly explored how to better enable the MAR services utilizing edge computing resources, our focus is to optimize the video generation stage of the edge-based MAR services-efficiently using the available edge computing resources to render and encode the augmented reality as video streams to the mobile clients. Specifically, for multi-party AR applications, we identify the advantages and disadvantages of two encoding schemes, namely colocated encoding and spilt encoding, and examine the trade-off between performance and scalability when the rendering and encoding tasks are colocated or split. Towards optimally placing AR video rendering and encoding in the edge, we formulate and solve the rendering and encoding task assignment problem for multi-party edge-based MAR services to maximize the QoS for the users and the edge computing efficiency. The proposed task assignment scheme is proved to be superior through extensive trace-driven simulations and experiments on our prototype system.

Data enrichment is the act of extending a local database with new attributes from external data sources. In this paper, we study a novel problem-how to progressively crawl the deep web (i.e., a hidden database) through a keyword-search API to enrich a local database in an e ective way. This is chal- lenging because these interfaces often limit the data access by enforcing the top-k constraint or limiting the number of queries that can be issued within a time window. In response, we propose SmartCrawl, a new framework to collect re- sults e ectively. Given a query budget b, SmartCrawl rst constructs a query pool based on the local database, and then iteratively issues a set of most bene cial queries to the hidden database such that the union of the query results can cover the maximum number of local records. The key technical challenge is how to estimate query bene t, i.e., the number of local records that can be covered by a given query. A simple approach is to estimate it as the query frequency in the local database. We nd that this is ine ective due to i) the impact of |ΔD|, where |ΔD| represents the number of local records that cannot be found in the hidden database, and ii) the top-k constraint enforced by the hidden database. We study how to mitigate the negative impacts of the two factors and propose e ective optimization techniques to improve performance. The experimental results show that on both simulated and real-world hidden databases, SmartCrawl signi cantly increases coverage over the local database as compared to the baselines.

With the prevalence of broadband network and wireless mobile network accesses, distributed interactive applications (DIAs) such as online gaming have attracted a vast number of users over the Internet. The deployment of these systems, however, comes with peculiar hardware/software requirements on the user consoles. Recently, such industrial pioneers as Gaikai, Onlive, and Ciinow have offered a new generation of cloud-based DIAs (CDIAs), which shifts the necessary computing loads to cloud platforms and largely relieves the pressure on individual user’s consoles. In this paper, we aim to understand the existing CDIA framework and highlight its design challenges. Our measurement reveals the inside structures as well as the operations of real CDIA systems and identifies the critical role of cloud proxies. While its design makes effective use of cloud resources to mitigate client’s workloads, it may also significantly increase the interaction latency among clients if not carefully handled. Besides the extra network latency caused by the cloud proxy involvement, we find that computation-intensive tasks (e.g., game video encoding) and bandwidth-intensive tasks (e.g., streaming the game screens to clients) together create a severe bottleneck in CDIA. Our experiment indicates that when the cloud proxies are virtual machines (VMs) in the cloud, the computation-intensive and bandwidth-intensive tasks may seriously interfere with each other. We accordingly capture this feature in our model and present an interference-aware solution. This solution not only smartly allocates workloads but also dynamically assigns capacities across VMs based on their arrival/departure patterns.

The ubiquity of 802.11 WiFi and the miniaturization as a result of Moore's law has recently enabled the success of IoT. From smart lightbulbs to smart toasters, many home appliances are now becoming both Internet-enabled and interconnected through WiFi. Soon, these futuristic smart homes will be able to run themselves, allowing the human operators to be fully in control of their homes -- or will they? Despite the physical advancements made since the '90s, the same cannot be said of the vulnerabilities of these smart devices. We analyze a set of common smart home appliances -- a lightbulb, power switch, motion sensor, security camera, and home assistant -- putting their vulnerabilities to the test to see what a 21st century home intruder could discover.

Entity resolution is defined as finding different records that refer to the same real-world entity. In this paper, we study deep entity resolution (DeepER) which aims to find pairs of records that describe the same entity between a local database and a hidden database. The local database can be accessed freely but the hidden database can only be accessed by a keyword-search query interface. To the best of our knowledge, we are the first to study this problem. We first show that straightforward solutions are inefficient because they fail to exploit the ideas of query sharing and local-database-aware crawling. In response, we propose SMARTCRAWL, a novel framework to overcome the limitations. Given a budget of b queries, SMARTCRAWL first constructs a query pool based on the local database and then iteratively issues b queries to the hidden database such that the union of the query results can cover the maximum number of records in the local database. Finally, it performs entity resolution between the local database and the crawled records. We find that query selection is the most challenging aspect, and we investigate how to select the query with the largest benefit at each iteration. SMARTCRAWL seeks to use a hidden database sample to estimate the query benefit. We propose unbiased estimators as well as biased estimators (with small biases) to achieve this goal, and devise efficient algorithms to implement them. We found that (1) biased estimators are much more effective than unbiased estimators, especially when the sample is small (e.g., 0.1%); (2) SMARTCRAWL is more robust to data errors than straightforward solutions. Experimental results over simulated and real hidden databases show that SMARTCRAWL can cover a large portion of the local database with a small budget, outperforming straightforward solutions by a factor of 2 − 7× in a large variety of situations.

Multiple network interface card (NIC) configuration is the key to achieving service isolation and link aggregation in enterprise networks. Despite the increasing deployments of multiple virtual NICs (multi-vNIC) in cloud environments, the performance and hidden cost of such multi-vNIC configurations are yet to be understood. Our measurements in both Xen and KVM environments reveal that the network workloads on one virtual NIC can negatively affect the transmission performance on other virtual NICs. Such a bottleneck however does not exist in conventional physical machines or to a much lower degree. We therefore explore the possible approaches to detect and resolve such a problem in the virtualization architecture design. In particular, a mutual information-based model is developed to monitor the stability of virtual NIC pairs. Based on this model, we further design a bottleneck avoidance mode to enhance the network performance. Different from the previous research that merely optimizes virtualized network I/O stack, our solution focuses on the entire application I/O workflow in the cloud environments. Extensive evaluations show that our enhancements can mitigate the performance degradation of multi-vNIC configuration, making it a practically workable solution for cloud environments.

Data scientists often spend more than 80% of their time on data preparation. Data enrichment, the act of extending a local database with new attributes from external data sources, is among the most time-consuming tasks. Existing data enrichment works are resource intensive: data-intensive by relying on web tables or knowledge bases, monetarily-intensive by purchasing entire datasets, or time-intensive by fully crawling a web-based data source. In this work, we explore a more targeted alternative that uses resources (in terms of web API calls) proportional to the size of the local database of interest. We build Deeper, a data enrichment system powered by the deep web. The goal of Deeper is to help data scientists to link a local database to a hidden database so that they can easily enrich the local database with the attributes from the hidden database. We find that a challenging problem is how to crawl a hidden database. This is different from a typical deep web crawling problem, whose goal is to crawl the entire hidden database rather than only the content relating to the data enrichment task. We demonstrate the limitations of straightforward solutions and propose an effective new crawling strategy. We also present the Deeper system architecture and discuss how to implement each component. During the demo, we will use Deeper to enrich a publication database and aim to show that (1) Deeper is an end-to-end data enrichment solution, and (2) the proposed crawling strategy is superior to the straightforward ones.

The past decade has witnessed significant breakthroughs in the world of computer vision. Recent deep learning-based computer vision algorithms exhibit strong performance on recognition, detection, and segmentation. While the development of vision algorithms elicits promising applications, it also presents immense computational challenge to the underlying hardware due to its complex nature, especially when attempting to process the data at line-rate. To this end we develop a highly scalable computer vision processing framework, which leverages advanced technologies such as Spark Streaming and OpenCV to achieve line-rate video data processing. To ensure the greatest flexibility, our framework is agnostic in terms of computer vision model, and can utilize environments with heterogeneous processing devices. To evaluate this framework, we deploy it in a production cloud computing environment, and perform a thorough analysis on the system's performance. We utilize existing real-world live video streams from Simon Fraser University to measure the number of cars entering our university campus. Further, the data collected from our experiments is being used for real-time predictions of traffic conditions on campus.

Networking and machine virtualization play critical roles in the success of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past, including the impact from network traffic. When it comes to virtual machines (VMs) in cloud data centers, the interplay between energy consumption and network traffic, however, becomes much more complicated. Through real-world measurement on both Xen- and KVM-based platforms, we show that these state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling network transactions, generating excessive interrupt requests with ceaseless context switching, which in turn, increases energy consumption. Even when a physical machine is in an idle state, its VM's network transactions will incur nontrivial energy consumption. More interestingly, the energy consumption significantly varies with traffic allocation strategies and virtual CPU affinity conditions, which was not seen in conventional physical machines. Looking closely into the virtualization architectures, we then pinpoint the root causes and examine that our measurement results can be extended for various network configurations. Moreover, we also provide initial solutions toward optimizing energy consumption in virtualized environments.

Combining advanced sensors and powerful processing capabilities smart-phone based augmented reality (AR) is becoming increasingly prolific. The increase in prominence of these resource hungry AR applications poses significant challenges to energy constrained environments such as mobile-phones.; AB@To that end we present a platform for offloading AR applications to powerful cloud servers. We implement this system using a thin-client design and explore its performance using the real world application Pokemon Go as a case study. We show that with careful design a thin client is capable of offloading much of the AR processing to a cloud server, with the results being streamed back. Our initial experiments show substantial energy savings, low latency and excellent image quality even at relatively low bit-rates.

Recent years have witnessed the rapid growth of new live broadcast services, represented by Twitch.tv and YouTube live events, where videos are crowdsourced from amateur users (e.g., game players), rather than from commercial and professional TV broadcaster or content providers. The viewers also actively contribute to the content through embedded open-chat channels. Such community interactions among viewers, or even between broadcasters and viewers, make content generation highly diversified and engaging, particularly for the young generation. In this context, cross-viewer synchronization is highly desirable; otherwise the viewers with shorter broadcast latency may act as spoilers, significantly affecting the user experience of other viewers. In this paper, we show that the end-to-end delay has a dramatically amplified impact on the broadcast latency for individual viewers. We suggest smart rate adaptation to achieve cross-viewer synchronization, and develop distributed algorithms based on dual decomposition. We further extend our solution to the cloud environment, and present the concept of ShadowCast, which moves broadcasters to the cloud to provide high-quality streams beyond broadcasters’ network bandwidth constraint. Its practicability and effectiveness is demonstrated by our implementation and test bed experiments.

chapter Share on Cloud gaming Authors: Kuan-Ta Chen Academia Sinica Academia SinicaView Profile , Wei Cai The University of British Columbia The University of British ColumbiaView Profile , Ryan Shea Simon Fraser University Simon Fraser UniversityView Profile , Chun-Ying Huang National Chiao Tung University National Chiao Tung UniversityView Profile , Jiangchuan Liu Simon Fraser University Simon Fraser UniversityView Profile , Victor C. M. Leung The University of British Columbia The University of British ColumbiaView Profile , Cheng-Hsin Hsu National Tsing Hua University National Tsing Hua UniversityView Profile Authors Info & Claims Frontiers of Multimedia ResearchDecember 2017 Pages 287–314https://doi.org/10.1145/3122865.3122877Published:19 December 2017Publication History 2citation279DownloadsMetricsTotal Citations2Total Downloads279Last 12 Months13Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Unmanned aerial vehicles (UAVs) are experiencing a major increase in popularity in both consumer and industrial markets as prices fall and the technology matures. No longer are drones limited to military purposes as manufacturers begin to mass produce civilian models, ushering in a new era of transportation technology. While consumer drones are still in their infancy stage, there is little in the way of rules and regulations regarding privacy issues of these new devices. In this paper, we design an energy efficient off-the-shelf hardware system capable of detecting and selectively disabling video feeds of WiFi based consumer drones if they enter a defended area.