Decentralization of Control Loop for Self-Adaptive Software through Reinforcement Learning

Ganguly, Kishan Kumar; Sakib, Kazi

doi:10.1109/apsecw.2017.26

Cited by 2 publications

(9 citation statements)

References 14 publications

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“…Response time and responsiveness Filho et al [8], [9], [12], [18], [19] Response time and responsiveness Cardozo [4] X Wang et al [20]- [22] Responsiveness, availability, throughput, successability, reliability Younes [6] Hybrid (user's feedback) Mannava and Ramesh [23] Process CPU time, heap memory Ding et al [24] X Rosa et al [25] X Tan et al [26] Dependability and responsiveness Gonçalves et al [27] Responsiveness Yan et al [28] Responsiveness Belhaj et al [29] Availability, responsiveness, service calls Schneider et al [30], [31] Throughput, energy costs, efficiency Ganguly and Sakib [32] Failure rate, responsiveness Deshpande et al [33] Response time, availability, throughput, successability Kulkarni et al [34] Response time, model confidence, and CPU consumption Rainford et al [35] Response time, Resource utilization Silva et al [36] Resource utilization…”

Section: Approaches Non-functional Requirement Functional Requirement...mentioning

confidence: 99%

“…As seen in Table 3, most ESS approaches are based on nonfunctional adaptation goals, including response time [8], [9], [12], [15]- [19], [33], [35], responsiveness [20]- [22], [27]- [29], availability [20]- [22], [29], throughput [20]- [22], [30], [31], [33], successability [20]- [22], [33], reliability [20]- [22], process CPU time [23], [34], heap memory [23], dependability [26], service calls [29], energy cost [30], [31] and failure rate [32]. Some approaches only target one adaptation goal at a time such as [27], [28], while others tackle multiple adaptation goals.…”

Section: ) Non-functional Adaption Goalsmentioning

confidence: 99%

“…Learning time is classified as either online or offline. Online learning involves real-time data collection and utilization to proactively update the adaptation strategies [4], [8], [15], [32]. Conversely, offline learning updates adaptation strategies based on previously collected data, necessitaing pretraining [21], [24], [30].…”

Section: ) Learning Approaches Of Esssmentioning

confidence: 99%

“…Reinforcement Learning Others Porter et al [15] Thompson sampling Filho et al [8] Porter et al [16] Upper Confidence Bound (UCB1) Cardozo [4] Q-learning Ganguly and Sakib [32] Yan et al [28] Wang et al [21] Deep reinforcement learning * Schneider et al [30], [31] Younes [6] Reinforcement learning Wang et al [22] Wang et al [20] Multi-agent reinforcement learning Mannava and Ramesh [23] Association-rule-based learning Ding et al [24] Neural networks (petri nets learning) * Tan et al [26] Genetic algorithms Gonçalves et al [27] K-Nearest Neighbors (KNN) algorithm Filho et al [19] Thompson sampling and Bayesian regression * Kulkarni et al [34] K-means * Rainford et al [35] Genetic algorithms * Silva et al [36] Neural networks (Mac-Gyver Predictor)…”

Section: Approachesmentioning

confidence: 99%

See 3 more Smart Citations

The State of the Art of Emergent Software Systems

Shatnawi,

Faye,

Rima

et al. 2024

IEEE Access

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Emergent Software Systems (ESSs) are designed to reduce the initial effort in creating autonomous solutions and fully adaptive support systems that can autonomously learn the system's structure and operating environment without predefined knowledge. These models notably minimize/exclude the involvement of developers in the composition, maintenance, and evolution of software systems. Despite extensive research on self-adaptive systems, systematic reviews focusing specifically on ESSs are lacking. This paper addresses this gap by performing a systematic literature review on ESSs. Our goal is to equip researchers and industry practitioners with a comprehensive view of existing ESSs, enabling them to select approaches that meet their requirements and identify potential research avenues. The research questions are centered around knowing what ESSs are and identifying the set of activities essential for their creation. From an initial collection of 496 papers identified through search engines, 39 papers met our inclusion and exclusion criteria for retention and in-depth analysis. Finally, we build a taxonomy to categorize existing ESS approaches and dissect various ESS definitions to pinpoint their main characteristics. The taxonomy is structured around the goals, processes and usability of ESSs.

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Section: Approaches Non-functional Requirement Functional Requirement...mentioning

confidence: 99%

Section: ) Non-functional Adaption Goalsmentioning

confidence: 99%

Section: ) Learning Approaches Of Esssmentioning

confidence: 99%

Section: Approachesmentioning

confidence: 99%

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The State of the Art of Emergent Software Systems

Shatnawi,

Faye,

Rima

et al. 2024

IEEE Access

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“…These are applicable where static condition-action rules are insufficient. For self-adaptive systems, a large number of condition-action rules are required to support numerous goals and goal violation conditions [2]. Furthermore, these rules need to be altered when goals or the dynamics of the environment change.…”

Section: Introductionmentioning

confidence: 99%

An Environment Aware Learning-based SelfAdaptation Technique with Reusable Components

Ganguly¹,

Siddik²,

Islam³

et al. 2019

IJMECS

Self Cite

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Self-adaptive systems appeared in order to reduce the effort of manual software maintenance. Apart from software attributes, for example, different alternative software modules, self-adaptation decisions depend on environmental attributes, for example, service rate, bandwidth etc. Current well-known self-adaptation approaches can be further improved by incorporating environmental attributes. Moreover, reducing maintenance effort includes minimizing both operational and development effort. To reduce the effort of developing self-adaptive software, the constituent components should be reusable. This paper proposes a technique to incorporate environmental attributes to learning-based self-adaptation and to increase the reuse potential of self-adaptive system components. The environmental attributes are provided as a constraint to an optimization problem which results in an optimal software attribute selection. Design patterns for selfadaptive system components are proposed to improve its reusability. The proposed technique was validated on a news serving website called Znn.com. According to renowned reusability metrics such as Lines of Code (LOC), Message Passing Coupling (MPC) and Lack of Cohesion of Methods 4 (LCOM4), the proposed technique improved reuse potential. The website was further tested for adaptation effectiveness under two scenariosadaptation and without adaptation. According to our experiments, Adaptation gradually improved the main goal response time of the website where it performed poorly without adaptation.

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Decentralization of Control Loop for Self-Adaptive Software through Reinforcement Learning

Cited by 2 publications

References 14 publications

The State of the Art of Emergent Software Systems

The State of the Art of Emergent Software Systems

An Environment Aware Learning-based SelfAdaptation Technique with Reusable Components

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