Kunal Rao NEC Labs America

Kunal Rao

Researcher

Integrated Systems

Posts

LARA: Latency-Aware Resource Allocator for Stream Processing Applications

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One of the key metrics of interest for stream processing applications is “latency”, which indicates the total time it takes for the application to process and generate insights from streaming input data. For mission-critical video analytics applications like surveillance and monitoring, it is of paramount importance to report an incident as soon as it occurs so that necessary actions can be taken right away. Stream processing applications are typically developed as a chain of microservices and are deployed on container orchestration platforms like Kubernetes. Allocation of system resources like “cpu” and “memory” to individual application microservices has direct impact on “latency”. Kubernetes does provide ways to allocate these resources e.g. through fixed resource allocation or through vertical pod autoscaler (VPA), however there is no straightforward way in Kubernetes to prioritize “latency” for an end-to end application pipeline. In this paper, we present LARA, which is specifically designed to improve “latency” of stream processing application pipelines. LARA uses a regression-based technique for resource allocation to individual microservices. We implement four real-world video analytics application pipelines i.e. license plate recognition, face recognition, human attributes detection and pose detection, and show that compared to fixed allocation, LARA is able to reduce latency by up to ? 2.8X and is consistently better than VPA. While reducing latency, LARA is also able to deliver over 2X throughput compared to fixed allocation and is almost always better than VPA.

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Scale Up while Scaling Out Microservices in Video Analytics Pipelines

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Modern video analytics applications comprise multiple microservices chained together as pipelines and executed on container orchestration platforms like Kubernetes. Kubernetes automatically handles the scaling of these microservices for efficient application execution. There are two popular choices for scaling microservices in Kubernetes i.e. scaling Out using Horizontal Pod Autoscaler (HPA) and scaling Up using Vertical Pod Autoscaler (VPA). Both these have been studied independently, but there isn’t much prior work studying the joint scaling of these two. This paper investigates joint scaling, i.e., scaling up while scaling out (HPA) is in action. In particular, we focus on scaling up CPU resources allocated to the application microservices. We show that allocating fixed resources does not work well for different workloads for video analytics pipelines. We also show that Kubernetes’ VPA in conjunction with HPA does not work well for varying application workloads. As a remedy to this problem, in this paper, we propose DataX AutoScaleUp, which performs efficiently scaling up of CPU resources allocated to microservices in video analytics pipelines while Kubernetes’ HPA is operational. DataX AutoScaleUp uses novel techniques to adjust the allocated computing resources to different microservices in video analytics pipelines to improve overall application performance. Through real-world video analytics applications like Face Recognition and Human Attributes, we show that DataX AutoScaleUp can achieve up to 1.45X improvement in application processing rate when compared to alternative approaches with fixed CPU allocation and dynamic CPU allocation using VPA.

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AnB: Application-In-A-Box To Rapidly Deploy and Self-Optimize 5G Apps

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We present Application in a Box (AnB) product concept aimed at simplifying the deployment and operation of remote 5G applications. AnB comes pre-configured with all necessary hardware and software components, including sensors like cameras, hardware and software components for a local 5G wireless network, and 5G-ready apps. Enterprises can easily download additional apps from an App Store. Setting up a 5G infrastructure and running applications on it is a significant challenge, but AnB is designed to make it fast, convenient, and easy, even for those without extensive knowledge of software, computers, wireless networks, or AI-based analytics. With AnB, customers only need to open the box, set up the sensors, turn on the 5G networking and edge computing devices, and start running their applications. Our system software automatically deploys and optimizes the pipeline of microservices in the application on a tiered computing infrastructure that includes device, edge, and cloud computing. Dynamic resource management, placement of critical tasks for low-latency response, and dynamic network bandwidth allocation for efficient 5G network usage are all automatically orchestrated. AnB offers cost savings, simplified setup and management, and increased reliability and security. We’ve implemented several real-world applications, such as collision prediction at busy traffic light intersections and remote construction site monitoring using video analytics. With AnB, deployment and optimization effort can be reduced from several months to just a few minutes. This is the first-of-its-kind approach to easing deployment effort and automating self-optimization of the application during system operation.

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Elixir: A System To Enhance Data Quality For Multiple Analytics On A Video Stream

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IoT sensors, especially video cameras, are ubiquitously deployed around the world to perform a variety of computer vision tasks in several verticals including retail, health- care, safety and security, transportation, manufacturing, etc. To amortize their high deployment effort and cost, it is desirable to perform multiple video analytics tasks, which we refer to as Analytical Units (AUs), off the video feed coming out of every camera. As AUs typically use deep learning-based AI/ML models, their performance depend on the quality of the input video, and recent work has shown that dynamically adjusting the camera setting exposed by popular network cameras can help improve the quality of the video feed and hence the AU accuracy, in a single AU setting. In this paper, we first show that in a multi-AU setting, changing the camera setting has disproportionate impact on different AUs performance. In particular, the optimal setting for one AU may severely degrade the performance for another AU, and further the impact on different AUs varies as the environmental condition changes. We then present Elixir, a system to enhance the video stream quality for multiple analytics on a video stream. Elixir leverages Multi-Objective Reinforcement Learning (MORL), where the RL agent caters to the objectives from different AUs and adjusts the camera setting to simultaneously enhance the performance of all AUs. To define the multiple objectives in MORL, we develop new AU-specific quality estimator values for each individual AU. We evaluate Elixir through real-world experiments on a testbed with three cameras deployed next to each other (overlooking a large enterprise parking lot) running Elixir and two baseline approaches, respectively. Elixir correctly detects 7.1% (22,068) and 5.0% (15,731) more cars, 94% (551) and 72% (478) more faces, and 670.4% (4975) and 158.6% (3507) more persons than the default-setting and time-sharing approaches, respectively. It also detects 115 license plates, far more than the time-sharing approach (7) and the default setting (0).

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Content-aware auto-scaling of stream processing applications on container orchestration platforms

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Modern applications are designed as an interacting set of microservices, and these applications are typically deployed on container orchestration platforms like Kubernetes. Several attractive features in Kubernetes make it a popular choice for deploying applications, and automatic scaling is one such feature. The default horizontal scaling technique in Kubernetes is the Horizontal Pod Autoscaler (HPA). It scales each microservice independently while ignoring the interactions among the microservices in an application. In this paper, we show that ignoring such interactions by HPA leads to inefficient scaling, and the optimal scaling of different microservices in the application varies as the stream content changes. To automatically adapt to variations in stream content, we present a novel system called DataX AutoScaler that leverages knowledge of the entire stream processing application pipeline to efficiently auto-scale different microservices by taking into account their complex interactions. Through experiments on real-world video analytics applications, such as face recognition and pose classification, we show that DataX AutoScaler adapts to variations in stream content and achieves up to 43% improvement in overall application performance compared to a baseline system that uses HPA.

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DataX Allocator: Dynamic resource management for stream analytics at the Edge

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Serverless edge computing aims to deploy and manage applications so that developers are unaware of challenges associated with dynamic management, sharing, and maintenance of the edge infrastructure. However, this is a non-trivial task because the resource usage by various edge applications varies based on the content in their input sensor data streams. We present a novel reinforcement-learning (RL) technique to maximize the processing rates of applications by dynamically allocating resources (like CPU cores or memory) to microservices in these applications. We model applications as analytics pipelines consisting of several microservices, and a pipeline’s processing rate directly impacts the accuracy of insights from the application. In our unique problem formulation, the state space or the number of actions of RL is independent of the type of workload in the microservices, the number of microservices in a pipeline, or the number of pipelines. This enables us to learn the RL model only once and use it many times to improve the accuracy of insights for a diverse set of AI/ML engines like action recognition or face recognition and applications with varying microservices. Our experiments with real-world applications, i.e., face recognition and action recognition, show that our approach outperforms other widely-used alternative approaches and achieves up to 2.5X improvement in the overall application processing rate. Furthermore, when we apply our RL model trained on a face recognition pipeline to a different and more complex action recognition pipeline, we obtain a 2X improvement in processing rate, thus showing the versatility and robustness of our RL model to pipeline changes.

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APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning

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Cameras are increasingly being deployed in cities, enterprises and roads world-wide to enable many applications in public safety, intelligent transportation, retail, healthcare and manufacturing. Often, after initial deployment of the cameras, the environmental conditions and the scenes around these cameras change, and our experiments show that these changes can adversely impact the accuracy of insights from video analytics. This is because the camera parameter settings, though optimal at deployment time, are not the best settings for good-quality video capture as the environmental conditions and scenes around a camera change during operation. Capturing poor-quality video adversely affects the accuracy of analytics. To mitigate the loss in accuracy of insights, we propose a novel, reinforcement-learning based system APT that dynamically, and remotely (over 5G networks), tunes the camera parameters, to ensure a high-quality video capture, which mitigates any loss in accuracy of video analytics. As a result, such tuning restores the accuracy of insights when environmental conditions or scene content change. APT uses reinforcement learning, with no-reference perceptual quality estimation as the reward function. We conducted extensive real-world experiments, where we simultaneously deployed two cameras side-by-side overlooking an enterprise parking lot (one camera only has manufacturer-suggested default setting, while the other camera is dynamically tuned by APT during operation). Our experiments demonstrated that due to dynamic tuning by APT, the analytics insights are consistently better at all times of the day: the accuracy of object detection video analytics application was improved on average by ∼ 42%. Since our reward function is independent of any analytics task, APT can be readily used for different video analytics tasks.

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Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning

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In Video Analytics Pipelines (VAP), Analytics Units (AUs) such as object detection and face recognition running on remote servers critically rely on surveillance cameras to capture high-quality video streams in order to achieve high accuracy. Modern IP cameras come with a large number of camera parameters that directly affect the quality of the video stream capture. While a few of such parameters, e.g., exposure, focus, white balance are automatically adjusted by the camera internally, the remaining ones are not. We denote such camera parameters as non-automated (NAUTO) parameters. In this paper, we first show that environmental condition changes can have significant adverse effect on the accuracy of insights from the AUs, but such adverse impact can potentially be mitigated by dynamically adjusting NAUTO camera parameters in response to changes in environmental conditions. We then present CamTuner, to our knowledge, the first framework that dynamically adapts NAUTO camera parameters to optimize the accuracy of AUs in a VAP in response to adverse changes in environmental conditions. CamTuner is based on SARSA reinforcement learning and it incorporates two novel components: a light-weight analytics quality estimator and a virtual camera that drastically speed up offline RL training. Our controlled experiments and real-world VAP deployment show that compared to a VAP using the default camera setting, CamTuner enhances VAP accuracy by detecting 15.9% additional persons and 2.6%–4.2% additional cars (without any false positives) in a large enterprise parking lot and 9.7% additional cars in a 5G smart traffic intersection scenario, which enables a new usecase of accurate and reliable automatic vehicle collision prediction (AVCP). CamTuner opens doors for new ways to significantly enhance video analytics accuracy beyond incremental improvements from refining deep-learning models.

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Why is the video analytics accuracy fluctuating, and what can we do about it?

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It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos. In this paper, we show that this “leap of faith” that deep learning models that work well on images will also work well on videos is actually flawed. We show that even when a video camera is viewing a scene that is not changing in any human-perceptible way, and we control for external factors like video compression and environment (lighting), the accuracy of video analytics application fluctuates noticeably. These fluctuations occur because successive frames produced by the video camera may look similar visually but are perceived quite differently by the video analytics applications. We observed that the root cause for these fluctuations is the dynamic camera parameter changes that a video camera automatically makes in order to capture and produce a visually pleasing video. The camera inadvertently acts as an “unintentional adversary” because these slight changes in the image pixel values in consecutive frames, as we show, have a noticeably adverse impact on the accuracy of insights from video analytics tasks that re-use image-trained deep learning models. To address this inadvertent adversarial effect from the camera, we explore the use of transfer learning techniques to improve learning in video analytics tasks through the transfer of knowledge from learning on image analytics tasks. Our experiments with a number of different cameras, and a variety of different video analytics tasks, show that the inadvertent adversarial effect from the camera can be noticeably offset by quickly re-training the deep learning models using transfer learning. In particular, we show that our newly trained Yolov5 model reduces fluctuation in object detection across frames, which leads to better tracking of objects (∼40% fewer mistakes in tracking). Our paper also provides new directions and techniques to mitigate the camera’s adversarial effect on deep learning models used for video analytics applications.

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DataXc: Flexible and efficient communication in microservices-based stream analytics pipelines

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A big challenge in changing a monolithic application into a performant microservices-based application is the design of efficient mechanisms for microservices to communicate with each other. Prior proposals range from custom point-to-point communication among microservices using protocols like gRPC to service meshes like Linkerd to a flexible, many-to-many communication using broker-based messaging systems like NATS. We propose a new communication mechanism, DataXc, that is more efficient than prior proposals in terms of message latency, jitter, message processing rate and use of network resources. To the best of our knowledge, DataXc is the first communication design that has the desirable flexibility of a broker-based messaging systems like NATS and the high-performance of a rigid, custom point-to-point communication method. DataXc proposes a novel “pull” based communication method (i.e consumers fetch messages from producers). This is unlike prior proposals like NATS, gRPC or Linkerd, all of which are “push” based (i.e. producers send messages to consumers). Such communication methods make it difficult to take advantage of differential processing rates of consumers like video analytics tasks. In contrast, DataXc proposes a “pull” based design that avoids unnecessary communication of messages that are eventually discarded by the consumers. Also, unlike prior proposals, DataXc successfully addresses several key challenges in streaming video analytics pipelines like non-uniform processing of frames from multiple cameras, and high variance in latency of frames processed by consumers, all of which adversely affect the quality of insights from streaming video analytics. We report results on two popular real-world, streaming video analytics pipelines (video surveillance, and video action recognition). Compared to NATS, DataXc is just as flexible, but it has far superior performance: upto 80% higher processing rate, 3X lower latency, 7.5X lower jitter and 4.5X lower network bandwidth usage. Compared to gRPC or Linkerd, DataXc is highly flexible, achieves up to 2X higher processing rate, lower latency and lower jitter, but it also consumes more network bandwidth.

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