A New Infrastructure Elasticity Control Algorithm for Containerized Cloud

Hanafy, Walid A.; Mohamed, Amr E.; Salem, Sameh A.

doi:10.1109/access.2019.2907171

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…In [21] an elasticity control algorithm for containerizedbased cloud infrastructure for augmenting the load balancing is introduced. Their proposed algorithm utilized two agents, namely the master agent for coordinating between hosts and the host agents for monitoring and predicting resource utilization using Autoregressive Moving Average (ARMA) prediction model.…”

Section: E Related Workmentioning

confidence: 99%

ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment

et al. 2019

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Cloud computing has been one of the most popular distributed computing paradigms. Elasticity is a crucial feature that distinguishes cloud computing from other distributed computing models. It considers the resource provisioning and allocation processes can be implemented automatically and dynamically. Elasticity feature allows cloud platforms to handle different loads efficiently without disrupting the normal behavior of the application. Therefore, providing a resource elasticity analytical model can play a significant role in cloud resource management. This paper presents Controlling Elasticity (ControCity) framework for controlling resources elasticity through using ''buffer management'' and ''elasticity management''. In the proposed framework, there are two essential components called buffer manager and elasticity manager in the application layer and middleware layer, respectively. The buffer management controls the input queue of the user's request and the elasticity management controls the elasticity of the cloud platform using learning automata technique. In the application layer, applications are received by cloud applications and, then, placed in the control of the buffer. Buffer manager controls the queue of requests, and elasticity manager of the middleware layer using the learning automata provides a solution for controlling the elasticity of the cloud platform. The experimental results indicate that the ControCity reduces the response time by up to 3.7%, and increases the resource utilization and elasticity by up to 8.4% and 5.4%, respectively, compared with the other approaches.

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Section: E Related Workmentioning

confidence: 99%

ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment

et al. 2019

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“…In the proactive elasticity, we highlight two works: Hanafy et al. [20] and Proliot [21]. Hanafy et al.…”

Section: Related Workmentioning

confidence: 99%

“…Hanafy et al. [20] proposed an elasticity control algorithm for a containerized cloud using two agents. The host agent monitors and predicts its utilization using Autoregressive Moving Average (ARMA) [22], while the master agent performs elasticity by handling failures in load interchange scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 and Table 2 presents a comparison of the collected papers, presenting some of their main characteristics, and pointing out some of their gaps. Based on the selected papers, we can identify that despite the elasticity being proposed for cloud computing, and being employed in reactive [37] and proactive [20,38] approaches, the same can also be employed in other areas such as energy [12], IoT [21] and C-RAN [19]. In this way, we can see the potential of elasticity to be extended to other contexts, such as human resources.…”

Section: Related Workmentioning

confidence: 99%

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Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals

Fischer

Righi

Costa

et al. 2019

Sensors

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Hospitals play an important role on ensuring a proper treatment of human health. One of the problems to be faced is the increasingly overcrowded patients care queues, who end up waiting for longer times without proper treatment to their health problems. The allocation of health professionals in hospital environments is not able to adapt to the demands of patients. There are times when underused rooms have idle professionals, and overused rooms have fewer professionals than necessary. Previous works have not solved this problem since they focus on understanding the evolution of doctor supply and patient demand, as to better adjust one to the other. However, they have not proposed concrete solutions for that regarding techniques for better allocating available human resources. Moreover, elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Based on this background, we introduce Elastic allocation of human resources in Healthcare environments (ElHealth) an IoT-focused model able to monitor patient usage of hospital rooms and adapt these rooms for patients demand. Using reactive and proactive elasticity approaches, ElHealth identifies when a room will have a demand that exceeds the capacity of care, and proposes actions to move human resources to adapt to patient demand. Our main contribution is the definition of Human Resources IoT-based Elasticity (i.e., an extension of the concept of resource elasticity in Cloud Computing to manage the use of human resources in a healthcare environment, where health professionals are allocated and deallocated according to patient demand). Another contribution is a cost–benefit analysis for the use of reactive and predictive strategies on human resources reorganization. ElHealth was simulated on a hospital environment using data from a Brazilian polyclinic, and obtained promising results, decreasing the waiting time by up to 96.4% and 96.73% in reactive and proactive approaches, respectively.

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“…thus, smart city applications need to be scalable, reliable, and easily deployable. Containerization provides a way of packaging and deploying applications in a consistent and efficient manner, allowing them to run reliably in different computing environments [40,41]. Containers are lightweight, isolated environments that contain all the necessary components for an application to run, including code, dependencies, and configuration files.…”

Section: Containerization For Smart City Iotmentioning

confidence: 99%

A Scalable Approach to Improve Security and Resilience of Smart City IoT Architectures

Zakurdaev

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The swift emergence of smart technologies, notably the Internet of Things (IoT), has revolutionized numerous industry sectors. However, outdated IoT architectures constrain innovation in smart cities due to scalability issues, compromised resilience, and security vulnerabilities. This thesis scrutinizes these challenges, advocating a contemporary approach to IoT system design. Prioritizing performance, scalability, security, and resilience, the research delves into the applicability of Platform-as-a-Service (PaaS) and Infrastructure-as-Code (IaC) methodologies, endorsing containerization and cloud-centric patterns for smart city IoT. The introduced model is contrasted with prevailing architectures, highlighting a trajectory for IoT progression. Merging empirical and theoretical insights, this study furnishes guidelines for the future of IoT in smart industries, underscoring the benefits of service-based architectures for efficiency, resilience, and security. iii This MASc thesis owes its existence to the guidance and encouragement of many. My appreciation goes to Dr. Chung-Horng Lung, whose keen insights sparked my interest in cloud technologies, one of the most fascinating focus areas of this research. I am also indebted to Mohammed Ismail, a long-time friend and academic cohort. His eagerness to explore the complexities of containerization technologies along with me, combined with his invaluable advice during the implementation and testing phases of my experiments, contributed significantly to the success of this work. My family, a constant source of strength, and my patient fiancé, who has endured my long working hours and has always stood by me, deserve heartfelt recognition. I would be remiss not to mention my two feline companions, whose incessant meows formed a harmonic and cheering soundtrack outside my office door. Finally, I attribute the greatest part of this journey's success to the unwavering guidance of my supervisor, Dr. Jason Jaskolka. His mentorship, from igniting my interest in cybersecurity to the detailed critiques of my work and consistent support, has been profoundly inspirational. His exemplary efficiency and dedication to his students' success were guiding lights during challenging times, for which I am endlessly grateful.

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A New Infrastructure Elasticity Control Algorithm for Containerized Cloud

Cited by 16 publications

References 23 publications

ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment

ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment

Towards Evaluating Proactive and Reactive Approaches on Reorganizing Human Resources in IoT-Based Smart Hospitals

A Scalable Approach to Improve Security and Resilience of Smart City IoT Architectures

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