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As Cloud's adoption surges across industries, the limitations of its default scheduler, particularly on large scales or for jobs outside of its initial design scope, have become increasingly prominent. While the default schedulers in various cloud platforms were primarily engineered to focus on simple and predictable tasks, reinforcement learning (RL)-based schedulers are attracting attention as they can predict a larger and more diverse cloud environment. Nevertheless, there are practical constraints to the use of RL. Retraining for adaptation is necessary for each new environment, and exploration taken during each training may lead to unexpected performance degradation at runtime. To address these issues, this paper presents Dejavu which combines reinforcement learning with neural networks to learn and resolve scheduling problems more effectively. To tackle the extended training time and performance degradation by unexpected explorations, we apply pretraining using Demonstrations from existing heuristics. This guides the RL agent to explore in a safe and efficient manner. Furthermore, we design a robust reward function to push Dejavu to compete with and eventually outperform, the exploited heuristics and other baselines. The experimental results demonstrate the efficacy of Dejavu, showing remarkable improvements in key metrics. Compared to the default scheduler, it boosts resource utilization by 6 % and shortens scheduling time by 3% during the scheduling period. 

Today, video cameras are ubiquitously deployed. These cameras collect, stream, store, and analyze video footage for a variety of use cases, ranging from surveillance, retail analytics, architectural engineering, and more. At the same time, many citizens are becoming weary of the amount of personal data captured, along with the algorithms and datasets used to process video pipelines. This work investigates how users can opt-out of such pipelines by explicitly providing consent to be recorded. An ideal system should obfuscate or otherwise cleanse non-consenting user data, ideally before a user even enters the video processing pipeline itself. We present a system, called Consent-Box, that enables obfuscation of users without using complex or personally-identifying vision techniques. Instead, a user's location on a video frame is estimated via Wi-Fi localization of a user's mobile device. This estimation allows us to remove individuals from frames before those frames enter complex vision pipelines. 

In the past, researchers designed, deployed, and evaluated Wi-Fi based localization techniques in order to locate users and devices without adding extra or costly infrastructure. However, as infrastructure deployments change, one must reexamine the role of Wi-Fi localization. Today, cameras are becoming increasingly deployed, and therefore this work examines how contextual and vision data obtained from cameras can be integrated with Wi-Fi localization techniques. We present an approach called CALM that works on commodity APs and cameras. Our approach contains several contributions: a camera line fitting technique to restrict the search space of candidate locations, single AP and camera localization via a deprojection scheme inspired from 3D cameras, simple and robust AP weighting that analyzes the context of users via the camera, and a new virtual camera methodology to scale analysis. We motivate our scheme by analyzing real camera and AP topologies from a major vendor. Our evaluation over 9 rooms and 102,300 wireless readings shows CALM can obtain decimeter-level accuracy, improving performance over previous Wi-Fi techniques like FTM by 2.7× and SpotFi by 2.3×. 

Serverless computing is a rapidly growing paradigm that easily harnesses the power of the cloud. With serverless computing, developers simply provide an event-driven function to cloud providers, and the provider seamlessly scales function invocations to meet demands as event-triggers occur. As current and future serverless offerings support a wide variety of serverless applications, effective techniques to manage serverless workloads becomes an important issue. This work examines current management and scheduling practices in cloud providers, uncovering many issues including inflated application run times, function drops, inefficient allocations, and other undocumented and unexpected behavior. To fix these issues, a new quality-of-service function scheduling and allocation framework, called Sequoia, is designed. Sequoia allows developers or administrators to easily def ne how serverless functions and applications should be deployed, capped, prioritized, or altered based on easily configured, flexible policies. Results with controlled and realistic workloads show Sequoia seamlessly adapts to policies, eliminates mid-chain drops, reduces queuing times by up to 6.4X, enforces tight chain-level fairness, and improves run-time performance up to 25X. 

Wireless network management is important to ensure the performance, utilization, allocation, and robustness of the network is optimized. Until now, wireless network management has typically been dictated by in-band information, such as wireless measurements, client locations, or even device state. This position paper explores fundamental new ways to manage the network by utilizing out-of-band data provided by a rich deployment of sensors. Out-of-band data can capture information about the users, objects, or environments associated with a network device, meaning that richer contextual policies can be implemented in the network. We propose an architecture called SenseNet, which builds upon three recent trends: (1) the massive deployment of sensors today, (2) the existence of deep-learning algorithms to glean meaningful insights from the sensory data, and (3) the provisioning of edge computing resources to provide real-time actuation of new sensor-based policies. A brief evaluation shows the feasibility and motivates SenseNet and afterwards challenges towards practical deployment are discussed. 

We propose DeepFind, an on-device acceleration engine for mobile-native spatial exploratory applications that utilize deep-learning architectures such as CNNs. DeepFind leverages the fact that input frames exhibit high visual dynamicity, of which a dominant portion actually comes from the camera’s own motion. We develop lightweight sensor-based transformations and perspective-normalized global buffer management to replace expensive convolution operations, effectively speeding up CNN inference. DeepFind achieves a win-win – lightweight reusability determination coupled with avoiding a large amount of convolution operations, creating a virtuous cycle: smaller inter-frame differential leads to faster per-frame computation, yielding even smaller inter-frame differential and so on. 

Wireless network management is important to ensure the perfor- mance, utilization, allocation, and robustness of the network is optimized. Until now, wireless network management has typically been dictated by in-band information, such as wireless measurements, client locations, or even device state. This position paper explores fundamental new ways to manage the network by utilizing out-of-band data provided by a rich deployment of sensors. Out-of-band data can capture information about the users, objects, or environments associated with a network device, meaning that richer contextual policies can be implemented in the network. We propose an architecture called SenseNet, which builds upon three recent trends: (1) the massive deployment of sensors today, (2) the existence of deep-learning algorithms to glean meaningful insights from the sensory data, and (3) the provisioning of edge computing resources to provide real-time actuation of new sensor-based policies. A brief evaluation shows the feasibility and motivates SenseNet and afterwards challenges towards practical deployment are discussed.