scholarly journals A Survey of IoT Stream Query Execution Latency Optimization within Edge and Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak
Keyword(s):  
Query Processing ◽  
Streaming Data ◽  
Query Execution ◽  
Cloud Environment ◽  
Future Directions ◽  
Stream Query Processing ◽  
Execution Model ◽  
Iot Devices ◽  
Edge Based ◽  
Execution Models

IoT (Internet of Things) streaming data has increased dramatically over the recent years and continues to grow rapidly due to the exponential growth of connected IoT devices. For many IoT applications, fast stream query processing is crucial for correct operations. To achieve better query performance and quality, researchers and practitioners have developed various types of query execution models—purely cloud-based, geo-distributed, edge-based, and edge-cloud-based models. Each execution model presents unique challenges and limitations of query processing optimizations. In this work, we provide a comprehensive review and analysis of query execution models within the context of the query execution latency optimization. We also present a detailed overview of various query execution styles regarding different query execution models and highlight their contributions. Finally, the paper concludes by proposing promising future directions towards advancing the query executions in the edge and cloud environment.

XML Stream Query Processing

2010 ◽  
pp. 89-107
Author(s):  
Mingzhu Wei ◽  
Ming Li ◽  
Elke A. Rundensteiner ◽  
Murali Mani ◽  
Hong Su
Keyword(s):  
Query Processing ◽  
Input Data ◽  
Stream Processing ◽  
Query Execution ◽  
Data Semantics ◽  
Stream Query Processing ◽  
Xml Stream ◽  
And Algebra ◽  
Xml Streams ◽  
Compare And Contrast

Stream applications bring the challenge of efficiently processing queries on sequentially accessible XML data streams. In this chapter, the authors study the current techniques and open challenges of XML stream processing. Firstly, they examine the input data semantics in XML streams and introduce the state-of-the-art of XML stream processing. Secondly, they compare and contrast the automatonbased and algebra-based techniques used in XML stream query execution. Thirdly, they study different optimization strategies that have been investigated for XML stream processing – in particular, they discuss cost-based optimization as well as schema-based optimization strategies. Lastly but not least, the authors list several key open challenges in XML stream processing.

scholarly journals Hybrid-based Framework for COVID-19 Prediction via Federated Machine Learning Models

2021 ◽  
Author(s):  
Ameni Kallel ◽  
Molka Rekik ◽  
Mahdi Khemakhem
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Streaming Data ◽  
Cloud Environment ◽  
Short Term ◽  
The People ◽  
Cloud Framework ◽  
Iot Devices ◽  
New Framework

<div>The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. Therefore, a smart monitoring system that detects and tracks the suspected COVID-19 infected persons may improve the clinicians decision-making and consequently limit the pandemic spread. This paper entails a new framework integrating the Machine Learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, Lung UltraSound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a Service (federated-MLaaS); (i) the distributed batch-MLaaS, which is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream-MLaaS installed into a hybrid fog/cloud environment for a short-term decision-making. Stream-MLaaS use a shared federated prediction model stored into the cloud; whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream-ML algorithms from the Python’s libraries.</div>

scholarly journals Hybrid-based Framework for COVID-19 Prediction via Federated Machine Learning Models

2021 ◽  
Author(s):  
Ameni Kallel ◽  
Molka Rekik ◽  
Mahdi Khemakhem
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Streaming Data ◽  
Cloud Environment ◽  
Short Term ◽  
The People ◽  
Cloud Framework ◽  
Iot Devices ◽  
New Framework

<div>The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. Therefore, a smart monitoring system that detects and tracks the suspected COVID-19 infected persons may improve the clinicians decision-making and consequently limit the pandemic spread. This paper entails a new framework integrating the Machine Learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, Lung UltraSound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a Service (federated-MLaaS); (i) the distributed batch-MLaaS, which is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream-MLaaS installed into a hybrid fog/cloud environment for a short-term decision-making. Stream-MLaaS use a shared federated prediction model stored into the cloud; whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream-ML algorithms from the Python’s libraries.</div>

Interface Risk Reduction in an Integrated SURF-SPS Scope Execution

2021 ◽  
Author(s):  
Hemant Priyadarshi ◽  
Daniel Nickel ◽  
Seban Jose
Keyword(s):  
Risk Reduction ◽  
Qualitative Comparison ◽  
The Matrix ◽  
Execution Model ◽  
Execution Models

Abstract The paper provides a detailed estimation of the interfaces that exist in a split SURF-SPS execution model and provides a qualitative comparison to an integrated SURF-SPS execution model. A comprehensive matrix of dependencies between SURF and SPS is presented and is categorized into engineering, procurement, construction/fabrication and installation work packages. The matrix is used to illustrate the exact scope dependencies and thus, the sources of interfaces. A hypothetical greenfield development has been assumed to develop the interface matrix and to use it for comparison of the two execution models. The comparison also reveals how interfaces are naturally eliminated in an integrated SURF-SPS execution model. In each of the workstreams (E-P-C-I), top risks have been identified and monetary liability estimates for those risks have been provided. By transfer of these risks from company to contractor, monetary liability gets transferred to the contractor, thus, resulting in significant savings for operating companies. The following tangible results are provided in the paper: a) % of interface(s) reduced in the E-P-C-I areas; b) Risk reduction in monetary terms for operators – estimated values. This paper justifies the fact that there is a significant interface scope and risk reduction for operators, if they adopt an integrated SURF-SPS execution model.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals A Containerized Approach for Allocating Distributed Stream Queries to Fog Nodes

2021 ◽  
Author(s):  
Hamed Hasibi ◽  
Saeed Sedighian Kashi
Keyword(s):  
Fog Computing ◽  
Stream Processing ◽  
Stream Data ◽  
Process Data ◽  
Stream Query Processing ◽  
Tremendous Amount ◽  
Stream Processing Engines ◽  
Iot Devices ◽  
Distributed Stream Processing

Fog computing brings cloud capabilities closer to the Internet of Things (IoT) devices. IoT devices generate a tremendous amount of stream data towards the cloud via hierarchical fog nodes. To process data streams, many Stream Processing Engines (SPEs) have been developed. Without the fog layer, the stream query processing executes on the cloud, which forwards much traffic toward the cloud. When a hierarchical fog layer is available, a complex query can be divided into simple queries to run on fog nodes by using distributed stream processing. In this paper, we propose an approach to assign stream queries to fog nodes using container technology. We name this approach Stream Queries Placement in Fog (SQPF). Our goal is to minimize end-to-end delay to achieve a better quality of service. At first, in the emulation step, we make docker container instances from SPEs and evaluate their processing delay and throughput under different resource configurations and queries with varying input rates. Then in the placement step, we assign queries among fog nodes by using a genetic algorithm. The practical approach used in SQPF achieves a near-the-best assignment based on the lowest application deadline in real scenarios, and evaluation results are evidence of this goal.

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