Real-Time QoS Monitoring for Cloud-Based Big Data Analytics Applications in Mobile Environments

Alhamazani, Khalid; Ranjan, Rajiv; Jayaraman, Prem Prakash; Mitra, Karan; Wang, Meisong; Huang, Zhiqiang; Wang, Lizhe; Rabhi, Fethi A.

doi:10.1109/mdm.2014.74

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…For instance, if a consumer may host application at multiple clouds to ensure high availability, then switching between multiple clouds becomes easy with help of CMS. Effective use of cloud computing platforms, management of their high complexity and ensuring appropriate levels of Quality of Services (QoS) [79] require specialized CMS. Further, CMS supplies information like workload, QoS parameter, Key Performance Indicator and status of resource usage to both consumers and providers.…”

Section: Introductionmentioning

confidence: 99%

Cloud Monitoring System: Basics, Phases and Challenges

Birje¹,

Bulla²

2019

IJRTE

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Due to many economical benefits cloud computing has quickly become popular and is widely used for delivering services over the Internet. As the number of cloud users increases day by day, data centers are continuing to grow in terms of hardware resources, virtual resources and traffic volume; thus making cloud operation and management more intricate. To manage complex infrastructure of cloud, an efficient and effective Cloud Monitoring System (CMS) is needed to improve the overall performance of cloud. Cloud monitoring is the process of reviewing, controlling and managing the operational workflow and processes within a cloud-based IT asset or infrastructure. This paper deals with detailed study of CMSs based on their architecture, monitoring phases, properties and functions. It describes various phases of cloud monitoring activities and presents the comparative study of the state-of-theart works of theses phases. Usage of agents for monitoring of cloud activities is also described. Finally, various challenges/issues and possible future directions of cloud monitoring is discussed. This paper helps researchers, engineers and scientists to know the state-of-the-art works for monitoring cloud activities

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Section: Introductionmentioning

confidence: 99%

Cloud Monitoring System: Basics, Phases and Challenges

Birje¹,

Bulla²

2019

IJRTE

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“…To be precise, the processing performance of big data applications under massive data scenarios is its major selling point and breakthrough. According to the relevant literature, we can see that common performance optimization technologies for big data applications are generally divided into two parts [44,55,56,71,72,94]. The first one consists of hardware and system-level observations to find specific bottlenecks and make hardware or system-level adjustments.…”

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confidence: 99%

“…Velocity: With the flood of data generated quickly from smart phones and sensors, rapid analysis and processing of data need to be considered [71]. These data must be analyzed in time because the velocity of data generation is very quick [41,115].…”

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Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Cao

et al. 2020

Applied Sciences

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Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, such as functional error, failure and low performance. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: (1) the quality attributes that are focused for the quality of big data applications, including correctness, performance, availability, scalability and reliability, and the factors influencing them; (2) the existing implementation-specific QA technologies, including specification, architectural choice and fault tolerance, and the process-specific QA technologies, including analysis, verification, testing, monitoring and fault and failure prediction; (3) existing strengths and limitations of each kind of QA technology; (4) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications and can help developers of big data applications apply suitable QA technologies.

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“…Mean Opinion ScoreObjective QoE monitoring is based on the agent technology and QoS data can be retrieved by applied functionalities, which are provided by simple network management protocol (SNMP)[28]. SNMP is a popular protocol for network management.…”

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confidence: 99%

Quality of experience (QoE) and quality of service (QoS) in UAV systems

Laghari

Khan

et al. 2020

Imaging and Sensing for Unmanned Aircraft Systems: Volume 2: Deployment and Applications

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Cyber-Physical Systems (CPS) are integrations of computation and physical processes [11]. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. For operation, they require data. An Unnamed Aerial Vehicle (UAV) is an autonomous system which operates automatically without a human pilot; flight operates various degree of autonomy is controlled via computers [1] and can acquire unique data about physical process, thus allowing for real-time monitoring, management, control and actuation tasks in a CPS to be achieved. A UAV system also is known as a drone plane, which is commonly used for monitoring prohibited actives and target militants without risking the life of security forces. UAV was initially designed and developed for the military purpose and mostly used in remote sensing. Nowadays, UAV are used in much broader applications such as in agricultural, recreational, scientific, commercial as well as in applications such as policing, peacekeeping and surveillance, product deliveries, aerial photography, smuggling and drone racing [2,3]. UAV collects data such as image capturing, video recording, measure temperature, humidity, crop identification, forest and water management in remote areas, mountains and agriculture fields [4]. UAV provide better monitoring and image data capturing with high resolution compared to satellite remote sensing.There are many types of UAVs such as vertical takeoff and landing (VTOL) and fixed-wing UAVs developed for AggieAir, which are used for various application and research purpose. Figure 1.1 and 1.2 capture the AggieAir hexorotor VTOL and ying AggieAir Mnion separately [5]. Fixedwing UAVs are used in Fish tracking as shown in Figure 1.3 [6]. Figures 1.4, 1.5 & 1.6 show the application of UAVs in detection for Bear River Migratory Bird Refuge (MBR) [7], Precision agriculture [8] and Wetland detection [9].

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Real-Time QoS Monitoring for Cloud-Based Big Data Analytics Applications in Mobile Environments

Cited by 11 publications

References 5 publications

Cloud Monitoring System: Basics, Phases and Challenges

Cloud Monitoring System: Basics, Phases and Challenges

Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Quality of experience (QoE) and quality of service (QoS) in UAV systems

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