scholarly journals SSK-DDoS: distributed stream processing framework based classification system for DDoS attacks

2022 ◽  
Author(s):  
Nilesh Vishwasrao Patil ◽  
C. Rama Krishna ◽  
Krishan Kumar
Keyword(s):  
Classification System ◽  
Stream Processing ◽  
Ddos Attacks ◽  
Distributed Stream Processing ◽  
Processing Framework

scholarly journals DART: Fast and Efficient Distributed Stream Processing Framework for Internet of Things

ETRI Journal ◽  
2017 ◽  
Vol 39 (2) ◽  
pp. 202-212 ◽  
Author(s):  
Jang-Ho Choi ◽  
Junyong Park ◽  
Hwin Dol Park ◽  
Ok-gee Min
Keyword(s):  
Internet Of Things ◽  
Stream Processing ◽  
Distributed Stream Processing ◽  
Processing Framework

Watershed-ng: an extensible distributed stream processing framework

2016 ◽  
Vol 28 (8) ◽  
pp. 2487-2502 ◽  
Author(s):  
Rodrigo Rocha ◽  
Bruno Hott ◽  
Vinícius Dias ◽  
Renato Ferreira ◽  
Wagner Meira ◽  
...  
Keyword(s):  
Stream Processing ◽  
Distributed Stream Processing ◽  
Processing Framework

Resource aware scheduler for distributed stream processing in cloud native environments

2021 ◽  
Author(s):  
Madushi Sarathchandra ◽  
Chulani Karandana ◽  
Winma Heenatigala ◽  
Miyuru Dayarathna ◽  
Sanath Jayasena
Keyword(s):  
Stream Processing ◽  
Resource Aware ◽  
Distributed Stream Processing

Predictable remote invocations for distributed stream processing

2020 ◽  
Vol 107 ◽  
pp. 716-729 ◽  
Author(s):  
P. Basanta-Val ◽  
N. Fernández-García ◽  
L. Sánchez-Fernández
Keyword(s):  
Stream Processing ◽  
Distributed Stream Processing

Evaluating Fault Tolerance of Distributed Stream Processing Systems

2020 ◽  
pp. 101-116
Author(s):  
Xiaotong Wang ◽  
Cheng Jiang ◽  
Junhua Fang ◽  
Ke Shu ◽  
Rong Zhang ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Stream Processing ◽  
Distributed Stream Processing

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.

scholarly journals A Systematic Study for Big Data Stream Processing Frameworks

2016 ◽  
Vol 2 (2) ◽  
pp. 4
Author(s):  
Ali Yazici ◽  
Ziya Karakaya ◽  
Mohammed Alayyoub
Keyword(s):  
Big Data ◽  
Systematic Study ◽  
Data Stream ◽  
Stream Processing ◽  
Systematic Mapping ◽  
Related Research ◽  
Research Outcomes ◽  
Research Questions ◽  
State Of Art ◽  
Processing Framework

The choice of the most effective stream processing framework (SPF) for Big Data has been an important issue among the researchers and practioners. Each of the SPFs has different cutting edge technologies in their steps of processing the data in motion that gives them a better advantage over the others. Even though, these technologies used in each SPF might better them, it is still difficult to conclude which framework berforms better under different scenarios and conditions. In this paper, we aim to show trends and differences about several SPFs for Big Data by using the so called Systematic Mapping (SM) approach using the related research outcomes. To achieve this objective, nine research questions (RQs) were raised, in which 91 studies that were conducted between 2010 and 2015 were evaluated. We present the trends by classifying the research on SPFs with respect to the proposed RQs which can direct researchers in getting an state-of-art overview of the field.

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