1,720,963 research outputs found
Machine learning driven latency optimization for internet of things applications in edge computing
No full textEmerging Internet of Things (IoT) applications require faster execution time and response time to achieve optimal performance. However, most IoT devices have limited or no computing capability to achieve such stringent application requirements. To this end, computation offloading in edge computing has been used for IoT systems to achieve the desired performance. Nevertheless, randomly offloading applications to any available edge without considering their resource demands, inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation. We introduce Edge-IoT, a machine learning-enabled orchestration framework in this paper, which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency. We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources. Extensive experiments show the effectiveness and resource efficiency of the proposed approach
AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing
Get PDFEmerging edge computing (EC) systems are currently exploiting attaching portable edge devices on drones for
data processing close to the sources, to achieve high performance,
fast response times and real-time insights. To this end, existing EC
research has proposed several multiple drone-based edge deployments for various purposes, such as data caching, task offloading,
real-time video analytics, and computer vision. However, none of
them consider the ability of seamlessly integrating edge resources
running across multiple drones in a single pool, to holistically
manage and control these resources as well as to eliminate vendor
lock-in situations. This paper presents an intelligent resource
scheduling solution for a federated aerial EC system, called
AirEdge, which jointly considers task dependencies, heterogeneous resource demand and drones’ flight time. We propose a
multi-task execution time estimation and a dispatching policy, to
select the closest drone deployment having congruent flight time
and resource availability to execute ready tasks at any given time.
For the utilization of the drones’ attached edge resources, we propose a variant bin-packing optimization approach through gangscheduling of multi-dependent tasks that co-locates tasks tightly
on nodes to fully utilize available resources. Experiments on realworld data-trace from Alibaba cluster trace with information on
task dependencies (about 12,207,703 dependencies) and resource
demands show the effectiveness, fast executions, and resource
efficiency of our approac
Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system
No full textRecently, several edge deployment types, such as on-premise edge clusters, Unmanned Aerial Vehicles (UAV)-attached edge devices, telecommunication base stations installed with edge clusters, etc., are being deployed to enable faster response time for latency-sensitive tasks. One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization. Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks, inter-dependencies of tasks and edge resource availability. These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support, as well as provider lock-in. Therefore, we present EdgeColla, which is based on the integration of edge resources running across multi-edge deployments. EdgeColla leverages \textit{learning} techniques to intelligently dispatch multi-dependent tasks, and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them. Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes
EdgeDrones: Co-Scheduling of Drones for Multi-Location Aerial Computing Missions
Get PDFLow altitude platform (LAP) unmanned aerial vehicles (UAVs), also called drones, are currently being exploited by Edge computing (EC) systems to execute complex resource-hungry use cases, such as virtual reality, smart cities, autonomous vehicles, etc., by attaching portable edge devices on them. However, a typical drone has limited flight time, coupled with the resource-constrained attached edge device, which can jeopardize aerial computing missions if they are not holistically
taking into consideration. Moreover, the fundamental challenge is how to co-schedule multi-drone among multi-location where EC services are needed, such that drones are scheduled to maximize the utility from the activities while meeting computing resource and flight time constraints. Therefore, for a given fleet of drones and tasks across disjointed target locations in a city, we derive a machine learning (ML) linear regression model that estimates these tasks resource requirement and excution
time. Leveraging this estimation values, we jointly consider each drone’s flight time availability and its attached edge device resource capacity, and formulate a novel Multi-Location Capacitated Mission Scheduling Problem (MLCMSP) that selects suitable drones and co-schedules their flight routes with the least total distance to visit and execute tasks at the target locations. Then, we show that faster scheduling and execution of complex tasks at each location, while considering the inter-task
dependencies is important to achieve effective solution for our MLCMSP. Hence, we further propose EdgeDrones, a variant bin-packing optimization approach through gang-scheduling of inter-dependent
tasks that co-schedules and co-locates tasks tightly so as to achieve faster execution time, as well as to fully utilize available resources. Extensive experiments on Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) show that EdgeDrones achieves up to 73% higher resource utilization, up to 17.6 times faster executions, and up to 2.87 times faster flight travel time compared to the baseline approaches
Resource-Aware Multi-Task Offloading and Dependency-Aware Scheduling for Integrated Edge-Enabled IoV
Get PDFInternet of Vehicles (IoV) enables a wealth of modern vehicular applications, such as pedestrian detection, real-time video analytics, etc., that can help to improve traffic efficiency and driving safety. However, these applications impose significant resource demands on the in-vehicle resourceconstrained Edge Computing (EC) device installation. In this article, we study the problem of resource-aware offloading of these computation-intensive applications to the Closest roadside units
(RSUs) or telecommunication base stations (BSs), where on-site EC devices with larger resource capacities are deployed, and mobility of vehicles are considered at the same time. Specifically, we propose an Integrated EC framework, which can keep edge resources running across various invehicles, RSUs and BSs in a single pool, such that these resources can be holistically monitored from a single control plane (CP). Through the CP, individual in-vehicle, RSU or BS edge resource
availability can be obtained, hence applications can be offloaded concerning their resource demands. This approach can avoid execution delays due to resource unavailability or insufficient resource
availability at any EC deployment. This research further extends the state-of-the-art by providing intelligent multi-task scheduling, by considering both task dependencies and heterogeneous resource
demands at the same time. To achieve this, we propose FedEdge, a variant Bin-Packing optimization approach through Gang-Scheduling of multi-dependent tasks that co-schedules and co-locates multitask tightly on nodes to fully utilize available resources. Extensive experiments on real-world data trace from the recent Alibaba cluster trace, with information on task dependencies and resource demands,
show the effectiveness, faster executions, and resource efficiency of our approach compared to the existing approaches
Air-to-Air Collaborative Learning: A Multi-Task Orchestration in Federated Aerial Computing
Get PDF—Recent research on edge computing (EC) has proposed federated or collaborative learning technique, where machine learning models are shared among participating edge deployments, thereby benefiting from all available datasets without
exchanging them. In addition, EC systems are currently exploiting attaching portable edge devices on drones for data processing
close to the sources, to achieve high performance, fast response
times and real-time insights. Existing research lack the potential
to federate edge resources and manage corresponding service
entities running across multiple drones, thus resulting to suboptimal performance. Therefore, we introduce AerialEdge, a federated learning-based orchestration framework for a federated
aerial EC system. We propose a federated multi-output linear
regression models to estimate multi-task resource requirements
and execution time, to select the closest drone deployment having
congruent resource availability and flight time to execute ready
tasks at any given time. For better utilization of resources, we
propose a variant bin-packing optimization approach through
gang-scheduling of multi-dependent containerized tasks that coschedules and co-locates tasks tightly on nodes to fully utilize
available resources. Extensive experiments on real-world datatrace from Alibaba cluster trace with information on task
dependencies show the effectiveness, fast executions, and resource
efficiency of our approach
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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