Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking frameworks have two key limitations: 1) They are monolithic, proprietary, and lack the ability to rapidly incorporate sophisticated tracking models; and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera-network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance and the active camera-set size. We illustrate the concise expressiveness of the programming model for 4 tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance and quality trade-offs enabled by our dynamic tracking, batching and dropping strategies.
Aurora @cite_3 introduced the concept of , which is conceptually the same as data drops. They define QoS as a multidimensional function, including attributes such as response time, similar to our maximum tolerable latency, and tuple drops. Given this function, the objective is to maximize the QoS. Borealis @cite_19 extended this to a distributed setup. uses multiple drop points even within a task, which offers it fine-grained control and robustness. Features like do not drop'' and resilience to clock skews found in WAN resources are other domain and system specific optimizations.
@cite_19 @cite_3
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We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. A Data Stream Manager accepts push-based inputs from a set of data sources, processes these inputs with respect to a set of standing queries, and produces outputs based on Quality-of-Service (QoS) specifications. When input rates exceed system capacity, the system will become overloaded and latency will deteriorate. Under these conditions, the system will shed load, thus degrading the answer, in order to improve the observed latency of the results. This paper examines a technique for dynamically inserting and removing drop operators into query plans as required by the current load. We examine two types of drops: the first drops a fraction of the tuples in a randomized fashion, and the second drops tuples based on the importance of their content. We address the problems of determining when load shedding is needed, where in the query plan to insert drops, and how much of the load should be shed at that point in the plan. We describe efficient solutions and present experimental evidence that they can bring the system back into the useful operating range with minimal degradation in answer quality. In wireless ATM-based networks, admission control is required to reserve resources in advance for calls requiring guaranteed services. In the case of a multimedia call, each of its substreams (i.e., video, audio, and data) has its own distinct quality of service (QoS) requirements (e.g., cell loss rate, delay, jitter, etc.). The network attempts to deliver the required QoS by allocating an appropriate amount of resources (e.g., bandwidth, buffers). The negotiated QoS requirements constitute a certain QoS level that remains fixed during the call (static allocation approach). Accordingly, the corresponding allocated resources also remain unchanged. We present and analyze an adaptive allocation of resources algorithm based on genetic algorithms. In contrast to the static approach, each substream declares a preset range of acceptable QoS levels (e.g., high, medium, low) instead of just a single one. As the availability of resources in the wireless network varies, the algorithm selects the best possible QoS level that each substream can obtain. In case of congestion, the algorithm attempts to free up some resources by degrading the QoS levels of the existing calls to lesser ones. This is done, however, under the constraint of achieving maximum utilization of the resources while simultaneously distributing them fairly among the calls. The degradation is limited to a minimum value predefined in a user-defined profile (UDP). Genetic algorithms have been used to solve the optimization problem. From the user perspective, the perception of the QoS degradation is very graceful and happens only during overload periods. The network services, on the other hand, are greatly enhanced due to the fact that the call blocking probability is significantly decreased. Simulation results demonstrate that the proposed algorithm performs well in terms of increasing the number of admitted calls while utilizing the available bandwidth fairly and effectively. The enormous success of advanced wireless devices is pushing the demand for higher wireless data rates. Denser spectrum reuse through the deployment of more access points (APs) per square mile has the potential to successfully meet such demand. In principle, distributed multiuser multiple-input-multiple-output (MU-MIMO) provides the best approach to infrastructure density increase since several access points are connected to a central server and operate as a large distributed multiantenna access point. This ensures that all transmitted signal power serves the purpose of data transmission, rather than creating interference. In practice, however, a number of implementation difficulties must be addressed, the most significant of which is aligning the phases of all jointly coordinated APs. In this paper, we propose AirSync, a novel scheme that provides timing and phase synchronization accurate enough to enable distributed MU-MIMO. AirSync detects the slot boundary such that all APs are time-synchronous within a cyclic prefix (CP) of the orthogonal frequency-division multiplexing (OFDM) modulation and predicts the instantaneous carrier phase correction along the transmit slot such that all transmitters maintain their coherence, which is necessary for multiuser beamforming. We have implemented AirSync as a digital circuit in the field programmable gate array (FPGA) of the WARP radio platform. Our experimental testbed, comprising four APs and four clients, shows that AirSync is able to achieve timing synchronization within the OFDM CP and carrier phase coherence within a few degrees. For the purpose of demonstration, we have implemented two MU-MIMO precoding schemes, Zero-Forcing Beamforming (ZFBF) and Tomlinson-Harashima Precoding (THP). In both cases, our system approaches the theoretical optimal multiplexing gains. We also discuss aspects related to the MAC and multiuser scheduling design, in relation to the distributed MU-MIMO architecture. To the best of our knowledge, AirSync offers the first realization of the full distributed MU-MIMO multiplexing gain, namely the ability to increase the number of active wireless clients per time-frequency slot linearly with the number of jointly coordinated APs, without reducing the per client rate.