Design and Evaluation of an Autonomous Load Balancing System for Mobile Data Stream Processing Based On a Data Centric Publish Subscribe Approach

Vasconcelos, Rafael Oliveira; Endler, Markus; Gomes, Berto de Tácio Pereira; Silva, Francisco José da Silva e

doi:10.4018/ijaras.2014070101

Cited by 7 publications

(11 citation statements)

References 12 publications

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“…In respect to power management, the work Rahmani et al, 23 Cubo et al, 24 Balasubramanan and Stranieri 25 Beyond this comparison with other research groups initiatives, it is important to explain that the SDDL and M-Hub have been the subject of discussion and evaluation in some of our previously published work in opportunistic mobile sensing 16,29 and scalable data distribution fields. [9][10][11] However, these work were not motivated by Ambient Assisted Living scenarios. Here, we focus on healthcare applications, promoting a broad discussion about how the M-Hub and SDDL can be used to address several challenges related to patient monitoring in several AAL scenarios, including a case study that shows how M-Hub/SDDL was used to implement an AAL system for the human movement activity recognition.…”

Section: Discussionmentioning

confidence: 99%

“…11,13 The key concept of the solution is the Data Processing Slice (DPS) concept, the basic unit of data flow allocation. For balancing this workload of data sent by the mobile nodes to the processing nodes in the core network, SDDL uses a solution suitable for DDS-based systems called Data Processing Slice Load Balancing.…”

Section: Load Balancermentioning

confidence: 99%

“…For balancing this workload of data sent by the mobile nodes to the processing nodes in the core network, SDDL uses a solution suitable for DDS-based systems called Data Processing Slice Load Balancing. 11,13 The key concept of the solution is the Data Processing Slice (DPS) concept, the basic unit of data flow allocation. A DPS is a selection of (inbound) data items where one of its attributes satisfies a given filtering condition.…”

Section: Load Balancermentioning

confidence: 99%

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A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

Gomes

Muniz

Silva

et al. 2016

Concurrency and Computation

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Ambient Assisted Living (AAL) main goal is the development of health monitoring systems for patients with chronic diseases and elderly people through the use of body, home, and environmental sensors that increase their degree of independence and mobility. A comprehensive software infrastructure for AAL systems should be able to cover scenarios involving several patient mobility levels, locations, and physical and cognitive abilities. Cloud computing can provide to AAL systems the ability to extend the limited processing power of mobile devices, but its main role is to integrate all stakeholders through the storage and processing of health data and the orchestration of healthcare business logic. On the other hand, the Internet of Things (IoT) provides the ability to connect sensors and actuators, integrating and making them available through the Internet. This paper presents the Mobile-Hub/Scalable Data Distribution Layer, a middleware for AAL based on cloud computing and IoT. We discuss how this middleware can handle the requirements of the main health monitoring scenarios and present results that demonstrate the ability to opportunistically discover and connect with sensors in a timely manner and the scalability necessary for handling the connection and data processing of many connected patients. KEYWORDSambient assisted living, cloud computing, health monitoring systems, internet of things (IoT) 1 range of other applications for AAL systems, such as rescue and emer-gency response systems, fall detection, video surveillance systems, etc.Nowadays, AAL systems are regarded as a trend in a context of increasing awareness of how the Internet can be used to personal healthcare. Ambient Assisted Living systems are composed of several technologies: sensors and actuators, portable/wearable devices, heterogeneous wireless networks, medical applications executing on mobile devices (handhelds), personal computers, or in a cloud computing infrastructure. Among the variety of low-level sensors that can be applied in AAL systems, there are the wearable medical sensors, able to collect data from physiological signals (e.g., Electrocardiogram [ECG], Electromyogram, heart rate, and oxygen consumption) or data reflecting the body movement (e.g., accelerometer). Personal mobile devices, such as smartphones, are also usually equipped with motion and location sensors (e.g., accelerometer and GPS). Environmental sensors can also be used, as they collect information that helps determine if environmental conditions (e.g., temperature, light, humidity, and carbon dioxide levels) favor or not the patient's health. In addition to gathering data Concurrency Computat: Pract Exper. 2017;29:e4043. wileyonlinelibrary.com/journal/cpe

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Section: Discussionmentioning

confidence: 99%

Section: Load Balancermentioning

confidence: 99%

Section: Load Balancermentioning

confidence: 99%

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A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

Gomes

Muniz

Silva

et al. 2016

Concurrency and Computation

Self Cite

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“…While the model we are proposing, these needs can only be implanted or handled in each control loop defined, and the process can be performed in parallel, so this will affect the performance and better scalability. Vasconcelos [13] did the design and implementation of an autonomous mechanism for load balancing of mobile data streams, models can handle variable amount and great wireless connection and automatically adapt to variations in flow volume data. The approach adopted in this model also utilizes the concept of MAPE-K, but in the model that we are proposing, Keshvadi [14], introduced a scheduling strategy on VM load balancing by using multiple monitors and mobile agents, in this way can achieve the best load balancing and reduce or avoiding dynamic migration, thus completing the load balancing issues and migration costs are high.…”

Section: Related Jobsmentioning

confidence: 99%

Self-Adaptive Load Balancing System for Grid Computing

Darmawan

Aradea²

2019

Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

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Load balancing is necessary for computer network infrastructure services, with the aim of balancing computing resources to achieve optimal load processing time. There are many methods that can be used for load balancing, namely by adjusting the load on the computing resources that are provided. The strategy manages the coordination of each computing resources by looking at their ability. The problem that can arise here is that there is a diverse resource involved, yang. This is related to the problems of heterogeneity, scalability, and adaptability that is the fundamental issue in the process of determining the workload on the infrastructure of computing resources. When this has been many methods that can be used for load balancing techniques, namely by adjusting the load on the computing resources will be provided. The strategy does manage the coordination of each of computing resources by looking at their ability. The problem that can arise here is that there is diversity resource involved, as well as changes in workload that can happen quickly and in real time. This is related to the problems heterogeneity, scalability, and adaptability that is the fundamental issue in the process of determining the workload on the infrastructure of computing resources. This paper introduces the mechanism of load balancing approach, taking into account its ability to adapt to changing dynamic workload, as well as the diversity of computing resources involved. The strategy used is to integrate autonomic computing approach into the component-based software so that it can give birth to the ability of self-adaptive load balancing. Based on the experimental results, the proposed model can handle the workload factor of dynamism and diversity of resource through the description of system scalability. The strategy used is to integrate autonomic computing approach into the component-based software so that it can give birth to the ability of self-adaptive load balancing. Based on the experimental results, the proposed model can handle the workload factor of dynamism and diversity of resource through the description of system scalability. The strategy used is to integrate autonomic computing approach into the component-based software so that it can give birth to the ability of self-adaptive load balancing. Based on the experimental results, the proposed model can handle the workload factor of dynamism and diversity of resource through the description of system scalability.

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“…Aligned with the aforementioned requirements, applications in the field of data stream processing require continuous and timely processing of high-volume of data, originated from a myriad of distributed (and possibly mobile) sources, to obtain online notifications from complex queries over the steady flow of data items [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Dynamic and coordinated software reconfiguration in distributed data stream systems

Vasconcelos

Endler

2016

J Internet Serv Appl

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While many systems have to provide 24 × 7 services with no acceptable downtime, they have to be able to cope with changes in their execution environment and in the requirements that they must comply, in which data stream processing is one example of system that has to evolve during its execution. On one hand, dynamic reconfiguration (i.e., the capability of evolving on-the-fly) is a desirable feature. On the other hand, stream systems may suffer with the disruption and overhead caused by the reconfiguration. Due to these conflicting requirements, safe and non-disruptive reconfiguration is still an open problem. In this paper, we propose and validate a nondisruptive reconfiguration approach for distributed data stream systems that support stateful components and intermittent connections. We present experimental evidence that our mechanism supports safe distributed reconfiguration and has negligible impact on availability and performance.

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Design and Evaluation of an Autonomous Load Balancing System for Mobile Data Stream Processing Based On a Data Centric Publish Subscribe Approach

Cited by 7 publications

References 12 publications

A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things

Self-Adaptive Load Balancing System for Grid Computing

Dynamic and coordinated software reconfiguration in distributed data stream systems

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