Regulated enterprises often seek to extend their workloads into the cloud, but are impeded by integration concerns relating to security, governance and compliance. Further, enterprises running mission-critical applica...
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ISBN:
(纸本)9798400711817
Regulated enterprises often seek to extend their workloads into the cloud, but are impeded by integration concerns relating to security, governance and compliance. Further, enterprises running mission-critical applications, face throughput and latency challenges due to cloud integration overheads. We present Hybrid Cloud Connector to accelerate on-prem to cloud integration by handling non-functional aspects in lieu of the application, reducing complexity, and centralizing administration via a policy-driven control point.
As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) increases, recent studies demonstrate that it can be beneficial to collaboratively run convolutional neural netw...
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ISBN:
(纸本)9781665481069
As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) increases, recent studies demonstrate that it can be beneficial to collaboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3x speedup compared to state-of-the-art methods.
The convergence of contemporary and cuttingedge technologies, including big data analytics and machine learning, is reshaping the scale and efficiency of healthcare systems. This work presents distinct approaches and ...
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ISBN:
(纸本)9798350354140;9798350354133
The convergence of contemporary and cuttingedge technologies, including big data analytics and machine learning, is reshaping the scale and efficiency of healthcare systems. This work presents distinct approaches and contributions, addressing specific challenges in healthcare and disease and diabetes analytics. The real-time healthcare for disease diabetes and the integration of big data analytics, machine learning, and real-time processing have paved the way for innovative solutions to address disease diabetes prediction and monitoring. It's explored innovative solutions to overcome challenges in healthcare analytics, offering real-time predictions and continuous monitoring for improved patient care. The realtime Healthcare-Diabetes dataset was analyzed using various machine learning models, and processing in real-time has led to innovative solutions to address diabetes prediction and monitoring. The Gradient Boosted Tree Classifier emerged as the most accurate model with an accuracy of 90.14%, followed by the Decision Tree Classifier at 84.62%, the Random Forest Classifier at 82.84%, the Linear Support Vector Classifier at 78.70%, and Logistic Regression at 64.69%. These results demonstrate the system's robustness and efficiency in real-time data collection, processing, and prediction. Leveraging Apache Spark and opensource big data technologies, specify data challenges and advocate for scalable, efficient, and cost-effective healthcare analytics. It contributes to the ongoing transformation of healthcare systems, demonstrating the effectiveness of advanced technologies in enhancing disease prediction, monitoring, and overall healthcare services.
We describe a simple deterministic O(epsilon(-1) log Delta) round distributed algorithm for (2 alpha+ 1) (1 + epsilon) approximation of minimum weighted dominating set on graphs with arboricity at most alpha. Here Del...
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ISBN:
(纸本)9781450392624
We describe a simple deterministic O(epsilon(-1) log Delta) round distributed algorithm for (2 alpha+ 1) (1 + epsilon) approximation of minimum weighted dominating set on graphs with arboricity at most alpha. Here Delta denotes the maximum degree. We also show a lower bound proving that this round complexity is nearly optimal even for the unweighted case, via a reduction from the celebrated KMW lower bound on distributed vertex cover approximation [Kuhn, Moscibroda, and Wattenhofer JACM'16]. Our algorithm improves on all the previous results (that work only for unweighted graphs) including a randomized O(alpha(2)) approximation in O(log n) rounds [Lenzen and Wattenhofer DISC'10], a deterministic O(alpha log Delta) approximation in O(log Delta) rounds [Lenzen and Wattenhofer DISC'10], a deterministic O(alpha) approximation in O(log(2) Delta) rounds [implicit in Bansal and Umboh IPL'17 and Kuhn, Moscibroda, and Wattenhofer SODA'06], and a randomized O(alpha) approximation in O(alpha log n) rounds [Morgan, Solomon and Wein DISC'21]. We also provide a randomized O(alpha log Delta) round distributed algorithm that sharpens the approximation factor to alpha (1 + o(1)). If each node is restricted to do polynomial-time computations, our approximation factor is tight in the first order as it is NP-hard to achieve alpha - 1 - epsilon approximation [Bansal and Umboh IPL'17].
Visual simultaneous localization and mapping (VSLAM) is a relevant solution for vehicle localization and mapping environments. However, it is computationally expensive because it demands large computational effort, ma...
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Visual simultaneous localization and mapping (VSLAM) is a relevant solution for vehicle localization and mapping environments. However, it is computationally expensive because it demands large computational effort, making it a non-real-time solution. The VSLAM systems that employ geometric reconstructions are based on the parallel processing paradigm developed in the Parallel Tracking and Mapping (PTAM) algorithm. This type of system was created for processors that have exactly two cores. The various SLAM methods based on the PTAM were also not designed to scale to all the cores of modern processors nor to function as a distributed system. Therefore, we propose a modification to the pipeline for the execution of well-known VSLAM systems so that they can be scaled to all available processors during execution, thereby increasing their performance in terms of processing time. We explain the principles behind this modification via a study of the threads in the SLAM systems based on PTAM. We validate our results with experiments describing the behavior of the original ORB-SLAM system and the modified version.
HyperShell is an elegant, cross-platform, high-performance computing utility for processing shell commands over a distributed, asynchronous queue. It is a highly scalable workflow automation tool for many-task scenari...
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ISBN:
(纸本)9781450391610
HyperShell is an elegant, cross-platform, high-performance computing utility for processing shell commands over a distributed, asynchronous queue. It is a highly scalable workflow automation tool for many-task scenarios. There are several existing tools that serve a similar purpose, but lack some aspect that HyperShell provides (e.g., distributed, detailed logging, automated retries, super scale). Novel aspects of HyperShell include but are not limited to (1) cross-platform, (2) client-server design, (3) staggered launch for large scales, (4) persistent hosting of the server, and optionally (5) a database in-the-loop for restarts and persisting task metadata. HyperShell was originally created to support researchers at Purdue University, out of a specific unmet need. It has been in use for several years now. With this next release, we've completely re-implemented HyperShell as both an application and a library to provide new features, scalability, flexibility, robustness, and wider support. (https://***/glentner/hyper-shell)
We present a simple algorithmic framework for designing efficient distributed algorithms for the fundamental symmetry breaking problem of Maximal Independent Set (MIS) in the sleeping model [Chatterjee et al, PODC 202...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
We present a simple algorithmic framework for designing efficient distributed algorithms for the fundamental symmetry breaking problem of Maximal Independent Set (MIS) in the sleeping model [Chatterjee et al, PODC 2020]. In the sleeping model, only the rounds in which a node is awake are counted for the awake complexity, while sleeping rounds are ignored. This is motivated by the fact that a node spends resources only in its awake rounds and hence the goal is to minimize the awake complexity. Our framework allows us to design distributed MIS algorithms that have O (polyloglog(n)) (worst-case) awake complexity in certain important graph classes which satisfy the so-called adjacency property. Informally, the adjacency property guarantees that the graph can be partitioned into an appropriate number of classes so that each node has at least one neighbor belonging to every class. Graphs that can satisfy the adjacency property are random graphs with large clustering coefficient such as random geometric graphs as well as line graphs of regular (or near regular) graphs. We first apply our framework to design two randomized distributed MIS algorithms for random geometric graphs of arbitrary dimension d (even non-constant). The first algorithm has O(polyloglog n) (worst-case) awake complexity with high probability, where n is the number of nodes in the graph.' This means that any node in the network spends only O(polyloglog n) awake rounds;this is almost exponentially better than the (traditional) time complexity of O(log n) rounds (where there is no distinction between awake and sleeping rounds) known for distributed MIS algorithms on general graphs or even the faster O(root log n/log log n ) rounds known for Erdos-Renyi random graphs. log log n However, the (traditional) time complexity of our first algorithm is quite large-essentially proportional to the degree of the graph. Our second algorithm has a slightly worse awake complexity of O(d polyloglog n), but achieves a sig
Globus Compute implements a hybrid Function as a Service (FaaS) model in which a single cloud-hosted service is used by users to manage execution of Python functions on user-owned and managed Globus Compute endpoints ...
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ISBN:
(纸本)9798400704192
Globus Compute implements a hybrid Function as a Service (FaaS) model in which a single cloud-hosted service is used by users to manage execution of Python functions on user-owned and managed Globus Compute endpoints deployed on arbitrary compute resources. Here we describe a new multi-user and multiconfiguration Globus Compute endpoint. This system, which can be deployed by administrators in a privileged account, enables dynamic creation of user endpoints that are forked as new processes in user space. The multi-user endpoint is designed to provide the security interfaces necessary for deployment on large, shared HPC clusters by, for example, restricting user endpoint configurations, enforcing various authorization policies, and via customizable identity-username mapping.
Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applie...
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ISBN:
(纸本)9781665473156
Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.
Deep Reinforcement learning has shown to be a powerful tool for developing policies in environments where an optimal solution is unclear. In this paper, we attempt to apply Twin Delayed Deep Deterministic Policy Gradi...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
Deep Reinforcement learning has shown to be a powerful tool for developing policies in environments where an optimal solution is unclear. In this paper, we attempt to apply Twin Delayed Deep Deterministic Policy Gradients to train a neural network to act as a velocity controller for a quadcopter. The quadcopter's objective is to quickly fly through a gate while avoiding crashing into the gate. We transfer our trained policy to the real world by deploying it on a quadcopter in a laboratory environment. Finally, we demonstrate that the trained policy is able to navigate the drone to the gate in the real world.
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