Incident-Supporting Visual Cloud Computing Utilizing Software-Defined Networking

Gargees, Rasha S.; Morago, Brittany; Pelapur, Rengarajan; Chemodanov, Dmitrii; Calyam, Prasad; Oraibi, Zakariya A.; Duan, Ye; Seetharaman, Guna; Palaniappan, Kannappan

doi:10.1109/tcsvt.2016.2564898

Cited by 35 publications

(19 citation statements)

References 45 publications

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“…The video analysis market is rapidly growing and is estimated to be worth more than $1.2 billion by the year 2017 [6]. A wide variety of applications such as improving public safety [21], aiding emergency response [14], and surveillance [28] may use the large volumes of visual data. The network cameras considered in this paper consist of both indoor and outdoor cameras including traffic cameras, cameras inside shopping malls, and other institutions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Mohan

Kaseb

et al. 2021

IEEE Trans. Cloud Comput.

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There has been tremendous growth in the amount of visual data available on the Internet in recent years. One type of visual data of particular interest is produced by network cameras providing real-time views. Millions of network cameras around the world continuously stream data to viewers connected to the Internet. This data may be used by a wide variety of applications such as enhancing public safety, urban planning, emergency response, and traffic management which are computationally intensive. Analyzing this data requires significant amounts of computational resources. Cloud computing can be a preferred solution for meeting the resource requirements for analyzing these data. There are many options when selecting cloud instances (amounts of memory, number of cores, locations, etc.). Inefficient provisioning of cloud resources may become costly in pay-per-use cloud computing. This paper presents a method to select cloud instances in order to meet the performance requirements for visual data analysis at a lower cost. We measure the frame rates when analyzing the data using different computer vision methods and model the relationships between frame rates and resource utilizations. We formulate the problem of managing cloud resources as a Variable Size Bin Packing Problem and use a heuristic solution. Experiments using Amazon EC2 validate the model and demonstrate that the proposed solution can reduce the cost up to 62% while meeting the performance requirements.

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Section: Introductionmentioning

confidence: 99%

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Mohan

Kaseb

et al. 2021

IEEE Trans. Cloud Comput.

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“…On the other hand, the Fog computing paradigm is defined to deploy computing resources closer to end users. Fog computing at the edge can rapidly compute and organize small instance processes locally and move relevant ondemand processing data flow from the incident geographical location to core platforms such as Amazon Web Services [5]. Moreover, some SDN-enabled switches that are located geographically near to the users are played edge switches (nodes or Fog Nodes) to address small-size flows with limited response time SLAs and deliver high user Quality of Experiences (QoEs) like [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

Joint failure recovery, fault prevention, and energy-efficient resource management for real-time SFC in fog-supported SDN

et al. 2019

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Middleboxes have become a vital part of modern networks by providing services such as load balancing, optimization of network traffic, and content filtering. A sequence of middleboxes comprising a logical service is called a Service Function Chain (SFC). In this context, the main issues are to maintain an acceptable level of network path survivability and a fair allocation of the resource between different demands in the event of faults or failures. In this paper, we focus on the problems of traffic engineering, failure recovery, fault prevention, and SFC with reliability and energy consumption constraints in Software Defined Networks (SDN). These types of deployments use Fog computing as an emerging paradigm to manage the distributed small-size traffic flows passing through the SDN-enabled switches (possibly Fog Nodes). The main aim of this integration is to support service delivery in real-time, failure recovery, and fault-awareness in an SFC context. Firstly, we present an architecture for Failure Recovery and Fault Prevention called FRFP; this is a multi-tier structure in which the real-time traffic flows pass through SDN-enabled switches to jointly decrease the network side-effects of flow rerouting and energy consumption of the Fog Nodes. We then mathematically formulate an optimization problem called the Optimal Fog-Supported Energy-Aware SFC rerouting algorithm (OFES) and propose a near-optimal heuristic called Heuristic OFES (HFES) to solve the corresponding problem in polynomial time. In this way, the energy consumption and the reliability of the selected paths are optimized, while the Quality of Service (QoS) constraints are met and the network congestion is minimized. In a reliability context, the focus of this work is on fault prevention; however, since we use a reallocation technique, the proposed scheme can be used as a failure recovery scheme. We compare the performance of HFES and OFES in terms of power consumption, average path length, fault probability, network side-effects, link utilization, and Fog Node utilization. Additionally, we analyze the computational complexity of HFES. We use a real-world network topology to evaluate our algorithm. The simulation results show that the heuristic algorithm is applicable to large-scale networks.

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“…The computing environment of disaster response networks is similar to general edge computing networks in that we have pervasive computing infrastructure with multi-modal, multi-dimensional, and geospatially dispersed data sources that rely on a wide range of services (e.g. pedestrian tracking, facial recognition, location services) [1]. Typically, the main challenge of edge computing is concerned with how to execute these services on resource constrained devices such as mobile phones or other IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Hyperprofile-Based Computation Offloading for Mobile Edge Networks

Crutcher¹,

Koch²,

Coleman

et al. 2017

2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.

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Incident-Supporting Visual Cloud Computing Utilizing Software-Defined Networking

Cited by 35 publications

References 45 publications

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Joint failure recovery, fault prevention, and energy-efficient resource management for real-time SFC in fog-supported SDN

Hyperprofile-Based Computation Offloading for Mobile Edge Networks

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