Dmitrii Chemodanov

Incident-Supporting Visual Cloud Computing Utilizing Software-Defined Networking

In the event of natural or man-made disasters, providing rapid situational awareness through video/image data collected at salient incident scenes is often critical to the first responders. However, computer vision techniques that can process the media-rich and data-intensive content obtained from civilian smartphones or surveillance cameras require large amounts of computational resources or ancillary data sources that may not be available at the geographical location of the incident. In this paper, we propose an incident-supporting visual cloud computing solution by defining a collection, computation, and consumption (3C) architecture supporting fog computing at the network edge close to the collection/consumption sites, which is coupled with cloud offloading to a core computation, utilizing software-defined networking (SDN). We evaluate our 3C architecture and algorithms using realistic virtual environment test beds. We also describe our insights in preparing the cloud provisioning and thin-client desktop fogs to handle the elasticity and user mobility demands in a theater-scale application. In addition, we demonstrate the use of SDN for on-demand compute offload with congestion-avoiding traffic steering to enhance remote user quality of experience in a regional-scale application. The optimization between fogs computing at the network edge with core cloud computing for managing visual analytics reduces latency, congestion, and increases throughput.

Energy-Aware Mobile Edge Computing for Low-Latency Visual Data Processing

New opportunities exist for applications such as disaster incident response that can benefit from the convergence of Internet of Things (IoT) and cloud computing technologies. Particularly, new paradigms such as Mobile Edge Computing (MEC) are becoming feasible to handle the data deluge occurring in the network edge to gain insights that assist in real-time decision making. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a face recognition application that is important in disaster incident response scenarios, we analyze the tradeoffs in computing policies that offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads, and their impact on energy consumption under different visual data consumption requirements (i.e., users with thick clients or thin clients). From our empirical results obtained from experiments with our face recognition application on a realistic edge and core cloud testbed, we show how MEC can provide flexibility to users who desire energy conservation over low-latency or vice versa in the visual data processing.

Wireless Mesh networking Protocol for sustained throughput in edge computing

It is critical to provide sustained data throughput in edge computing, where several sensor devices generate information that needs to be fused and used for decision making in e.g., disaster incident scenes. To this end, we compare the effectiveness of two protocols, Hybrid Wireless Mesh Protocol (HWMP) and Greedy Perimeter Stateless Routing (GPSR), based upon their ability to stream data in a mesh network. We model the two protocols using three topologies consisting of a sender, receiver and multiple Mesh Points to relay the data. We perform experiments varying the density, scale and failure rates of the topology. Finally, we evaluate the effectiveness of both protocols by comparing the total throughput from sender to receiver in each experiment. We show that geographic routing algorithms such as GPSR, given their potential for statelessness, can be more effective in delivering sustained data throughput than the 802.11s standard HWMP in high failure and large scale topology cases.

AGRA: AI-augmented geographic routing approach for IoT-based incident-supporting applications

Abstract Applications that cater to the needs of disaster incident response generate large amount of data and demand large computational resource access. Such datasets are usually collected in real-time at the incident scenes using different Internet of Things (IoT) devices. Hierarchical clouds, i.e., core and edge clouds, can help these applications’ real-time data orchestration challenges as well as with their IoT operations scalability, reliability and stability by overcoming infrastructure limitations at the ad-hoc wireless network edge. Routing is a crucial infrastructure management orchestration mechanism for such systems. Current geographic routing or greedy forwarding approaches designed for early wireless ad-hoc networks lack efficient solutions for disaster incident-supporting applications, given the high-speed and low-latency data delivery that edge cloud gateways impose. In this paper, we present a novel Artificial Intelligent (AI)-augmented geographic routing approach, that relies on an area knowledge obtained from the satellite imagery (available at the edge cloud) by applying deep learning. In particular, we propose a stateless greedy forwarding that uses such an environment learning to proactively avoid the local minimum problem by diverting traffic with an algorithm that emulates electrostatic repulsive forces. In our theoretical analysis, we show that our Greedy Forwarding achieves in the worst case a 3 . 291 path stretch approximation bound with respect to the shortest path, without assuming presence of symmetrical links or unit disk graphs. We evaluate our approach with both numerical and event-driven simulations, and we establish the practicality of our approach in a real incident-supporting hierarchical cloud deployment to demonstrate improvement of application level throughput due to a reduced path stretch under severe node failures and high mobility challenges of disaster response scenarios.

Application-Aware Infrastructure Clustering for Cloud Service Placement to Enhance User QoE

Cloud service placement can be suboptimal in certain cases due to inefficient network design, which in turn impacts user Quality of Experience (QoE). Consequently, Application Service Providers (ASPs) need to manage cloud network infrastructures with efficient designs that fully satisfy Service Level Objectives (SLOs) of their data/videointensive applications of users. To proactively avoid this problem, a straightforward solution used by ASPs is to have many replicas of their services by renting more resources from Infrastructure Providers (InPs) which can lead to an expensive service delivery proposition. In this paper, we present a novel possibilistic C-Means (PCM) approach to enhance user QoE in cloud service placement by clustering network infrastructure with awareness of user SLO satisfaction amidst network path constraints. Our evaluation results obtained using numerical simulations as well as in a real-world cloud testbed with actual users prove that our multi-constrained path aware PCM approach outperforms existing solutions. Specifically, we show how our proposed infrastructure clustering with the PCM approach allows ASPs to rent less resources from InPs that reduces user cost, while still delivering satisfactory user QoE.