24 results on '"Barika, Mutaz"'
Search Results
2. A Taxonomy Study on Securing Blockchain-based Industrial Applications: An Overview, Application Perspectives, Requirements, Attacks, Countermeasures, and Open Issues
- Author
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Hameed, Khizar, Barika, Mutaz, Garg, Saurabh, Amin, Muhammad Bilal, and Kang, Byeong
- Subjects
Computer Science - Cryptography and Security - Abstract
Blockchain technology has taken on a leading role in today's industrial applications by providing salient features and showing significant performance since its beginning. Blockchain began its journey from the concept of cryptocurrency and is now part of a range of core applications to achieve resilience and automation between various tasks. With the integration of Blockchain technology into different industrial applications, many application designs, security and privacy challenges present themselves, posing serious threats to users and their data. Although several approaches have been proposed to address the specific security and privacy needs of targeted applications with functional parameters, there is still a need for a research study on the application, security and privacy challenges, and requirements of Blockchain-based industrial applications, along with possible security threats and countermeasures. This study presents a state-of-the-art survey of Blockchain-based Industry 4.0 applications, focusing on crucial application and security and privacy requirements, as well as corresponding attacks on Blockchain systems with potential countermeasures. We also analyse and provide the classification of different security and privacy techniques used in these applications to enhance the advancement of security features. Furthermore, we highlight some open issues in industrial applications that help to design secure Blockchain-based applications as future directions.
- Published
- 2021
3. Adaptive Scheduling for Efficient Execution of Dynamic Stream Workflows
- Author
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Barika, Mutaz, Garg, Saurabh, and Ranjan, Rajiv
- Subjects
Computer Science - Distributed, Parallel, and Cluster Computing - Abstract
Stream workflow application such as online anomaly detection or online traffic monitoring, integrates multiple streaming big data applications into data analysis pipeline. This application can be highly dynamic in nature, where the data velocity may change at runtime and therefore the resources should be managed overtime. To manage these changes, the orchestration of this application requires a dynamic execution environment and dynamic scheduling technique. For the former requirement, Multicloud environment is a visible solution to cope with the dynamic aspects of this workflow application. While for the latter requirement, dynamic scheduling technique not only need to adhere to end user's requirements in terms of data processing and deadline for decision making, and data stream sources location constraints, but also adjust provisioning and scheduling plan at runtime to cope with dynamic variations of stream data rates. Therefore, we propose a two-phase adaptive scheduling technique to efficiently schedule dynamic workflow application in Multicloud environment that can respond to changes in the velocity of data at runtime. The experimental results showed that the proposed technique is close to the lower bound and effective for different experiment scenarios., Comment: 17 pages, 11 figures
- Published
- 2019
4. Scheduling Algorithms for Efficient Execution of Stream Workflow Applications in Multicloud Environments
- Author
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Barika, Mutaz, Garg, Saurabh, Chan, Andrew, and Calheiros, Rodrigo N.
- Subjects
Computer Science - Distributed, Parallel, and Cluster Computing - Abstract
Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is stream workflow application, which integrates multiple streaming big data applications to support decision making. Each analytical component of these applications runs continuously and processes data streams whose velocity will depend on several factors such as network bandwidth and processing rate of parent analytical component. As a consequence, the execution of these applications on cloud environments requires advanced scheduling techniques that adhere to end user's requirements in terms of data processing and deadline for decision making. In this paper, we propose two Multicloud scheduling and resource allocation techniques for efficient execution of stream workflow applications on Multicloud environments while adhering to workflow application and user performance requirements and reducing execution cost. Results showed that the proposed genetic algorithm is an adequate and effective for all experiments., Comment: 17 pages, 15 figures
- Published
- 2019
5. A taxonomy study on securing Blockchain-based Industrial applications: An overview, application perspectives, requirements, attacks, countermeasures, and open issues
- Author
-
Hameed, Khizar, Barika, Mutaz, Garg, Saurabh, Amin, Muhammad Bilal, and Kang, Byeong
- Published
- 2022
- Full Text
- View/download PDF
6. Cost effective stream workflow scheduling to handle application structural changes
- Author
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Barika, Mutaz, Garg, Saurabh, and Ranjan, Rajiv
- Published
- 2020
- Full Text
- View/download PDF
7. IoTSim-SDWAN: A simulation framework for interconnecting distributed datacenters over Software-Defined Wide Area Network (SD-WAN)
- Author
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Alwasel, Khaled, Jha, Devki Nandan, Hernandez, Eduardo, Puthal, Deepak, Barika, Mutaz, Varghese, Blesson, Garg, Saurabh Kumar, James, Philip, Zomaya, Albert, Morgan, Graham, and Ranjan, Rajiv
- Published
- 2020
- Full Text
- View/download PDF
8. Scheduling Algorithms for Efficient Execution of Stream Workflow Applications in Multicloud Environments
- Author
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Barika, Mutaz, primary, Garg, Saurabh, additional, Chan, Andrew, additional, and Calheiros, Rodrigo N., additional
- Published
- 2022
- Full Text
- View/download PDF
9. Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows
- Author
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Barika, Mutaz, primary, Garg, Saurabh, additional, Zomaya, Albert Y., additional, and Ranjan, Rajiv, additional
- Published
- 2021
- Full Text
- View/download PDF
10. Detection of SLA Violation for Big Data Analytics Applications in Cloud
- Author
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Zeng, Xuezhi, primary, Garg, Saurabh, additional, Barika, Mutaz, additional, Bista, Sanat, additional, Puthal, Deepak, additional, Zomaya, Albert Y., additional, and Ranjan, Rajiv, additional
- Published
- 2021
- Full Text
- View/download PDF
11. SLA Management for Big Data Analytical Applications in Clouds
- Author
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Zeng, Xuezhi, primary, Garg, Saurabh, additional, Barika, Mutaz, additional, Zomaya, Albert Y., additional, Wang, Lizhe, additional, Villari, Massimo, additional, Chen, Dan, additional, and Ranjan, Rajiv, additional
- Published
- 2020
- Full Text
- View/download PDF
12. IoTSim-Stream: Modelling stream graph application in cloud simulation
- Author
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Barika, Mutaz, primary, Garg, Saurabh, additional, Chan, Andrew, additional, Calheiros, Rodrigo N., additional, and Ranjan, Rajiv, additional
- Published
- 2019
- Full Text
- View/download PDF
13. SLA Management for Big Data Analytical Applications in Clouds: A Taxonomy Study.
- Author
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XUEZHI ZENG, GARG, SAURABH, BARIKA, MUTAZ, ZOMAYA, ALBERT Y., LIZHE WANG, VILLARI, MASSIMO, DAN CHEN, and RANJAN, RAJIV
- Abstract
Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisionsmay be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLAmodel is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
14. Orchestrating Big Data Analysis Workflows in the Cloud
- Author
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Barika, Mutaz, primary, Garg, Saurabh, additional, Zomaya, Albert Y., additional, Wang, Lizhe, additional, Moorsel, Aad Van, additional, and Ranjan, Rajiv, additional
- Published
- 2019
- Full Text
- View/download PDF
15. Orchestrating Big Data AnalysisWorkflows in the Cloud: Research Challenges, Survey, and Future Directions.
- Author
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BARIKA, MUTAZ, GARG, SAURABH, ZOMAYA, ALBERT Y., LIZHE WANG, VAN MOORSEL, AAD, and RANJAN, RAJIV
- Abstract
Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies, and research outcomes. This has led to advancement in communication, programming, and processing technologies, including cloud computing services and technologies such as Hadoop, Spark, and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These big data workflows are vastly different in nature from traditional workflows. Researchers are currently facing the challenge of how to orchestrate and manage the execution of such workflows. In this article, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We also survey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
16. Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows
- Author
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Saurabh Garg, Albert Y. Zomaya, Rajiv Ranjan, Mutaz Barika, Barika, Mutaz, Garg, Saurabh, Zomaya, Albert Y, and Ranjan, Rajiv
- Subjects
IoT ,alpha-beta pruning ,020203 distributed computing ,Focus (computing) ,GA with random immigrants ,Computer science ,business.industry ,Distributed computing ,Big data ,cloud environments ,02 engineering and technology ,Dynamic priority scheduling ,dynamic scheduling ,Data modeling ,Scheduling (computing) ,stream workflow ,Workflow ,Computational Theory and Mathematics ,Hardware and Architecture ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,business ,Workflow application - Abstract
Many IoT applications and services such as smart parking and smart traffic control contain a network of different analytical components, which are composed in the form of a workflow to make better decisions. These workflows are also known as stream workflows. The focus of existing research works is on the streaming operator graph, which differs from stream workflow application as it involves heterogeneity, multiple data sources and multiple outputs. Considering the complexity and dynamism of stream workflow, meeting real-time data analysis requirements at deployment time is not the whole story as the velocity of data changes over time. This change is the most dynamic form of stream workflow that occurs frequently during the execution of this application. In this article, we propose a new dynamic scheduling technique that manages cloud resources over time to handle data velocity changes in stream workflow while maintaining user-defined real-time data analysis requirements and minimising execution cost. The efficiency of the proposed technique is evaluated, and experimental results showed that this technique outperformed its competitors and is close to the lower bound. Refereed/Peer-reviewed
- Published
- 2021
17. Cost effective stream workflow scheduling to handle application structural changes
- Author
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Mutaz Barika, Saurabh Garg, Rajiv Ranjan, Barika, Mutaz, Garg, Saurabh, and Ranjan, Rajiv
- Subjects
IoT ,Job shop scheduling ,Computer Networks and Communications ,business.industry ,Computer science ,Distributed computing ,cloud computing ,Big data ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,Dynamic priority scheduling ,pluggable technique ,dynamic scheduling ,Scheduling (computing) ,stream workflow ,Workflow ,Smart grid ,Hardware and Architecture ,Software deployment ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,business ,Software - Abstract
Stream workflow is a network of big data streaming applications that acts as key enabler for real-time analysis from Internet of Things data. Smart traffic management and smart grid are examples of stream workflow. The focus of existing work is on streaming operator graphs which differs from stream workflow and handling data fluctuations without significant consideration of different dynamic forms that could happen in the structure of data pipelines. This paper investigates the scheduling problem of stream workflow to support runtime alterations of stream workflow deployment, so that scheduling plans will be revised to handle stream workflow applications with continuously changing characteristics. It proposes a pluggable dynamic scheduling technique that accepts user-defined algorithms to handle stream workflow runtime changes. It also presents three different plug-in algorithms and methods to enable auto-scaling of this workflow in a Multicloud environment. The experimental results of the quality of the solution showed that the proposed plug-in optimisation technique is more efficient than baseline and dynamic fair-share techniques to handle runtime changes. Refereed/Peer-reviewed
- Published
- 2020
18. IoTSim-SDWAN: A simulation framework for interconnecting distributed datacenters over Software-Defined Wide Area Network (SD-WAN)
- Author
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Khaled Alwasel, Eduardo Hernández, Philip James, Rajiv Ranjan, Albert Y. Zomaya, Graham Morgan, Deepak Puthal, Blesson Varghese, Saurabh Garg, Devki Nandan Jha, Mutaz Barika, Alwasel, Khaled, Jha, Devki Nandan, Hernandez, Eduardo, Puthal, Deepak, Barika, Mutaz, Varghese, Blesson, Garg, Saurabh, James, Philip, Zomaya, Albert, Morgan, Graham, and Ranjan, Rajiv
- Subjects
internet of things (IoT) ,Computer Networks and Communications ,Computer science ,business.industry ,Distributed computing ,software-defined network (SDN) ,020206 networking & telecommunications ,SD-WAN ,Context (language use) ,Cloud computing ,02 engineering and technology ,Theoretical Computer Science ,OSI model ,Bandwidth allocation ,classical WAN ,Artificial Intelligence ,Hardware and Architecture ,Wide area network ,software-defined wide area network (SD-WAN) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Network performance ,business ,Software ,Edge computing - Abstract
Software-defined networking (SDN) has evolved as an approach that allows network administrators to program and initialize, control, change and manage networking components (mostly at L2-L3 layers) of the OSI model. SDN is designed to address the programmability shortcomings of traditional networking architectures commonly used in cloud datacenters (CDC). Deployment of SDN solutions have demonstrated significant improvements in areas such as flow optimization and bandwidth allocation in a CDC. However, the benefits are significantly less explored when considering Software-Defined Wide Area Networks (SD-WAN) architectures in the context of delivering solutions by networking multiple CDCs. To support the testing and bench-marking of data-driven applications that rely on data ingestion and processing (e.g., Smart Energy Cloud, Content Delivery Networks) across multiple cloud datacenters, this paper presents the simulator, IoTSim-SDWAN. To the best of our knowledge, IoTSim-SDWAN is the first simulator that facilitates the modeling, simulating, and evaluating of new algorithms, policies, and designs in the context of SD-WAN ecosystems and SDN-enabled multiple cloud datacenters. Finally, IoTSim-SDWAN simulator is evaluated for network performance and energy to illustrate the difference between classical WAN and SD-WAN environments. The obtained results show that SD-WAN surpasses the classical WAN in terms of accelerating traffic flows and reducing power consumption. Refereed/Peer-reviewed
- Published
- 2020
19. SLA Management for Big Data Analytical Applications in Clouds
- Author
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Massimo Villari, Xuezhi Zeng, Saurabh Garg, Dan Chen, Albert Y. Zomaya, Mutaz Barika, Rajiv Ranjan, Lizhe Wang, Zeng, Xuezhi, Garg, Saurabh, Barika, Mutaz, Zomaya, Albert Y, Wang, Lizhe, Villari, Massimo, Chen, Dan, and Ranjan, Rajiv
- Subjects
Service layer ,General Computer Science ,Computer science ,business.industry ,service layer ,Quality of service ,Online analytical processing ,Big data ,service level agreement ,020206 networking & telecommunications ,Cloud computing ,SLA metrics ,02 engineering and technology ,Data science ,Theoretical Computer Science ,Service-level agreement ,big data analytics application ,big data ,Service level ,0202 electrical engineering, electronic engineering, information engineering ,Information system ,020201 artificial intelligence & image processing ,SLA ,business - Abstract
Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLA model is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research. Refereed/Peer-reviewed
- Published
- 2020
20. IoTSim-Stream: Modelling stream graph application in cloud simulation
- Author
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Rajiv Ranjan, Rodrigo N. Calheiros, Saurabh Garg, Andrew Chan, Mutaz Barika, Barika, Mutaz, Garg, Saurabh, Chan, Andrew, Calheiros, Rodrigo N, and Ranjan, Rajiv
- Subjects
simulator ,Computer Networks and Communications ,Computer science ,business.industry ,Stream ,Distributed computing ,multicloud environment ,Big data ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,Stream processing ,stream graph applications ,internet of things (ioT) ,Workflow ,Hardware and Architecture ,Scalability ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,business ,Software ,stream processing - Abstract
In the era of big data, the high velocity of data imposes the demand for processing such data in real-time to gain real-time insights. Various real-time big data platforms/services (i.e. Apache Storm, Amazon Kinesis) allow to develop real-time big data applications to process continuous data to get incremental results. Composing those applications to form a workflow that is designed to accomplish certain goal is the becoming more important nowadays. However, given the current need of composing those applications into data pipelines forming stream workflow applications (aka stream graph applications) to support decision making, a simulation toolkit is required to simulate the behaviour of this graph application in Cloud computing environment. Therefore, in this paper, we propose an IoT Simulator for Stream processing on the big data (named IoTSim-Stream) that offers an environment to model complex stream graph applications in Multicloud environment, where the large-scale simulation-based studies can be conducted to evaluate and analyse these applications. The experimental results show that IoTSim-Stream is effective in modelling and simulating different structures of complex stream graph applications with excellent performance and scalability. Refereed/Peer-reviewed
- Published
- 2019
21. A Taxonomy Study on Securing Blockchain-based Industrial Applications: An Overview, Application Perspectives, Requirements, Attacks, Countermeasures, and Open Issues
- Author
-
Khizar Hameed, Mutaz Barika, Saurabh Garg, Muhammad Bilal Amin, Byeong Kang, Hameed, Khizar, Barika, Mutaz, Garg, Saurabh, Amin, Muhammad Bilal, and Kang, Byeong
- Subjects
FOS: Computer and information sciences ,Blockchain industrial applications ,Information Systems and Management ,Computer Science - Cryptography and Security ,security attacks ,security requirements ,privacy-preserving ,application requirements ,Cryptography and Security (cs.CR) ,countermeasures ,Industrial and Manufacturing Engineering - Abstract
Refereed/Peer-reviewed Blockchain technology has taken on a leading position in today's industrial applications by providing salient features and showing significant performance since its beginning. Blockchain began its journey from the concept of cryptocurrency and is now part of a range of core applications to achieve resilience and automation between various tasks. However, with the integration of Blockchain technology into different industrial applications, many application designs, security, and privacy challenges present themselves, posing serious threats to users and their data. Although several approaches have been proposed to address the specific application, security and privacy challenges of targeted applications with limited security enhancement solutions, there is still a need for a comprehensive research study on the application design, security and privacy challenges, and requirements of Blockchain-based industrial applications, along with possible security threats and countermeasures. This study presents a comprehensive and state-of-the-art survey of Blockchain-based Industry 4.0 applications, focusing on potential application design, security and privacy requirements, as well as corresponding attacks on Blockchain systems with potential countermeasures. We also analyse and provide the classification of security and privacy techniques used in these applications to enhance the advancement of security features. Furthermore, we highlight some open issues of integrating Blockchain technology into industrial applications that help design secure Blockchain-based applications as future directions.
- Published
- 2021
22. Detection of SLA Violation for Big Data Analytics Applications in Cloud
- Author
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Sanat Bista, Mutaz Barika, Deepak Puthal, Rajiv Ranjan, Saurabh Garg, Xuezhi Zeng, Albert Y. Zomaya, Zeng, Xuezhi, Garg, Saurabh, Barika, Mutaz, Bista, Sanat, Puthal, Deepak, Zomaya, Albert Y, and Ranjan, Rajiv
- Subjects
Service (systems architecture) ,Computer Hardware & Architecture ,neural network ,Computer science ,Big data ,service level agreement ,Cloud computing ,Context (language use) ,02 engineering and technology ,computer.software_genre ,Electronic mail ,Theoretical Computer Science ,Service-level agreement ,resampling ,big data ,0202 electrical engineering, electronic engineering, information engineering ,Service layer ,0803 Computer Software, 0805 Distributed Computing, 1006 Computer Hardware ,business.industry ,service layer ,SLA violation ,020202 computer hardware & architecture ,machine learning ,big data analytics application ,Computational Theory and Mathematics ,Hardware and Architecture ,Data mining ,Web service ,business ,computer ,Software - Abstract
SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this article, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers. Refereed/Peer-reviewed
- Published
- 2021
23. Orchestrating Big Data Analysis Workflows in the Cloud
- Author
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Albert Y. Zomaya, Saurabh Garg, Lizhe Wang, Aad van Moorsel, Rajiv Ranjan, Mutaz Barika, Barika, Mutaz, Garg, Saurabh, Zomaya, Albert Y, Wang, Lizhe, Van Moorsel, Aad, and Ranjan, Rajiv
- Subjects
General Computer Science ,Computer science ,Big data ,Public policy ,Cloud computing ,02 engineering and technology ,Theoretical Computer Science ,Open research ,big data ,0202 electrical engineering, electronic engineering, information engineering ,Orchestration (computing) ,computer.programming_language ,Cloud computing services ,business.industry ,cloud computing ,approaches ,020206 networking & telecommunications ,Data science ,research taxonomy ,Workflow ,SPARK (programming language) ,workflow orchestration ,020201 artificial intelligence & image processing ,techniques ,business ,computer - Abstract
Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies, and research outcomes. This has led to advancement in communication, programming, and processing technologies, including cloud computing services and technologies such as Hadoop, Spark, and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These big data workflows are vastly different in nature from traditional workflows. Researchers are currently facing the challenge of how to orchestrate and manage the execution of such workflows. In this article, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We also survey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area. Refereed/Peer-reviewed
- Published
- 2019
24. Scheduling Algorithms for Efficient Execution of Stream Workflow Applications in Multicloud Environments
- Author
-
Rodrigo N. Calheiros, Saurabh Garg, Andrew Chan, Mutaz Barika, Barika, Mutaz, Garg, Saurabh, Chan, Andrew, and Calheiros, Rodrigo N
- Subjects
FOS: Computer and information sciences ,Information Systems and Management ,Computer Networks and Communications ,Computer science ,business.industry ,Data stream mining ,Stream ,Quality of service ,Distributed computing ,Big data ,Cloud computing ,Computer Science Applications ,Scheduling (computing) ,stream workflow ,greedy algorithm ,Workflow ,Computer Science - Distributed, Parallel, and Cluster Computing ,Hardware and Architecture ,big data ,genetic algorithm ,Distributed, Parallel, and Cluster Computing (cs.DC) ,scheduling ,business ,Workflow application - Abstract
Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is stream workflow application, which integrates multiple streaming big data applications to support decision making. Each analytical component of these applications runs continuously and processes data streams whose velocity will depend on several factors such as network bandwidth and processing rate of parent analytical component. As a consequence, the execution of these applications on cloud environments requires advanced scheduling techniques that adhere to end user's requirements in terms of data processing and deadline for decision making. In this paper, we propose two Multicloud scheduling and resource allocation techniques for efficient execution of stream workflow applications on Multicloud environments while adhering to workflow application and user performance requirements and reducing execution cost. Results showed that the proposed genetic algorithm is an adequate and effective for all experiments., 17 pages, 15 figures
- Published
- 2019
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