A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Guerriero, Michele; Ciavotta, Michele; Gibilisco, Giovanni Paolo; Ardagna, Danilo

doi:10.1109/synasc.2015.60

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…In another study, the researchers introduced an integrated DevOps framework responsible for design-time modeling as well as optimization, and runtime control [24], so as to minimize the costs of implementing cloud applications that have embedded QoS guarantee. The modeling of the application chosen to evaluate included important aspects of cloud computing, such as variable workload, congestion due to multiple rents, and performance fluctuations.…”

Section: Related Workmentioning

confidence: 99%

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A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

Edinat

Al-Sayyed

Hudaib

2021

Int. J. Interact. Mob. Technol.

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Cloud computing is considered one of the most important techniques in the field of distributed computing which contributes to maintain increased scalability and flexibility in computer processing. This is achieved because it, using the Internet, provides different resources and shared services with minimum costs. Cloud service providers (CSPs) offer many different services to their customers, where the customers’ needs are met seeking the highest levels of quality at the lowest considerate prices. The relationship between CSPs and customers must be determined in a formal agreement, and to ensure how the QoS between them will be fulfilled, a clear Service Level Agreement (SLA) must be called for. Several previously-proposed models used in the literature to improve the QoS in the SLA for cloud computing and to face many of the challenges in the SLA are reviewed in this paper. We also addressed the challenges that are related to the violations of SLAs, and how overcoming them will enhance customers’ satisfaction. Furthermore, we proposed a model based on Deep Reinforcement Learning (DRL) and an enhanced DRL agent (EDRLA). In this model, and by optimizing the learning process in EDRLA, proposed agents would be able to have optimal CSPs by improving the learning process in EDRLA. This improvement will be reflected in the agent's performance and considerably affect it, especially in identifying cloud computing requirements based on the QoS metrics.

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Section: Related Workmentioning

confidence: 99%

“…In the following section, we review the proposed models in relation to the techniques and list most of the QoS parameters that have been used in prior related works. [12], [21], [24], [27], [29], [30], [31], [34], [35], [38], [39], [43], [44], [46], [48], [51], [52] 16%…”

Section: Related Workmentioning

confidence: 99%

A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

Edinat

Al-Sayyed

Hudaib

2021

Int. J. Interact. Mob. Technol.

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“…Goals are defined as transitions from original to destination state. Recently, the SPACE4CLOUD tool was extended to provide optimal runtime scaling decisions limited to replication DoF [34], while Moldovan et al [49] provided a cost model for resource replication that is more aligned with public Cloud offerings.…”

Section: Runtime Approachesmentioning

confidence: 99%

QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments

Cardellini

Grbac

Nardelli

et al. 2018

Lecture Notes in Computer Science

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With the emerging IoT and Cloud-based networked systems that rely heavily on virtualization technologies, elasticity becomes a dominant system engineering attribute for providing QoS-aware services to their users. Although the concept of elasticity can introduce significant QoS and cost benefits, its implementation in real systems is full of challenges. Indeed, nowadays systems are mainly distributed, built upon several layers of abstraction, and with centralized control mechanisms. In such a complex environment, controlling elasticity in a centralized manner might strongly penalize scalability. To overcome this issue, we can conveniently split the system in autonomous subsystems that implement elasticity mechanisms and run control policies in a decentralized manner. To efficiently and effectively cooperate with each other, the subsystems need to communicate among themselves to determine elasticity decisions that collectively improve the overall system performance. This new architecture calls for the development of new mechanisms and efficient policies. In this chapter, we focus on elasticity in IoT and Cloud-based systems, which can be geo-distributed also at the edge of the networks, and discuss its engineering perspectives along with various coordination mechanisms. We focus on the design choices that may affect the elasticity properties and provide an overview of some decentralized design patterns related to the coordination of elasticity decisions.

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“…DevOps is a software development culture that tries to eliminate the lack of collaboration between development and operations teams (Mahanta et al, 2016;Chen, 2019) by teaming them up to promote cooperation, collaboration and communication (Guerriero et al, 2016;Silva et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Agile information technology service management with DevOps: an incident management case study

Jo�ã

Faustino

Pereira

et al. 2020

IJASM

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This research aims to investigate how DevOps culture can be applied in the incident management process. The authors believe, based on experience as practitioners, that agile software development methodologies are fair enough to be used on Incident Management process, to quickly restore the business interruption. An application management team which solves incidents and applies DevOps practices was studied. Three data collection methods were used: interviews, document analysis and observation. This research provides novel findings supported by metrics and real experience implementing DevOps practices in incident management process. The novelty of the findings brings advantages for academics, and due to the exploratory nature of this research, it extends the body of knowledge. It also provides contributions for practitioners, by showing how these practices can be applied and the result of the implementation of these practices. Directions of future work are also presented.

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A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Cited by 9 publications

References 31 publications

A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments

Agile information technology service management with DevOps: an incident management case study

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