Uncertainty-aware Decisions in Cloud Computing

Kabir, H M Dipu; Khosravi, Abbas; Mondal, Subrota Kumar; Rahman, Mustaneer; Nahavandi, Saeid; Buyya, Rajkumar

doi:10.1145/3447583

Cited by 42 publications

(10 citation statements)

References 141 publications

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“…In contrast to availability, reliability is defined in terms of a time interval instead of an instant in time. However, as highlighted in Reference 125, the reliability of a system is directly dependent on availability and security. Indeed, to ensure reliability, the chosen node on which a task is executed must not have availability issues.…”

Section: Discussionmentioning

confidence: 99%

Review on data replication strategies in single vs. interconnected cloud systems: Focus on data correlation‐aware strategies

Hamrouni

Mokadem

Khelifa

2023

Concurrency and Computation

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SummaryData replication is a well‐known technique in cloud systems for enhancing availability and performance. Various strategies and surveys have been proposed in this respect. These surveys include comprehensive analysis and classifications. However, to the best of the authors' knowledge, there is no survey concentrating on strategies designed for interconnected cloud systems. In this article, we provide an in‐depth analysis of existing data replication strategies in cloud systems, covering single and interconnected clouds. We also highlight data correlation‐aware strategies as well as their key steps. Furthermore, we examine the major strategies' features such as: () addressed replication issues, () orientation towards the provider and the consumer, () consideration of the service level agreement, () consideration of cost and economic aspects, and () evaluation tools. Finally, we provide a performance analysis through extensive simulations of several replication strategies dedicated for single and interconnected clouds.

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Section: Discussionmentioning

confidence: 99%

Review on data replication strategies in single vs. interconnected cloud systems: Focus on data correlation‐aware strategies

Hamrouni

Mokadem

Khelifa

2023

Concurrency and Computation

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“…The precise information of workload imparts prior reservation of resources to execute and manage the forthcoming workload effectively, reduce response time, SLA violations, over-provisioning, and under-provisioning problems, and improve resource utilization, reliability, service availability. [20][21][22][23].…”

Section: Motivationmentioning

confidence: 99%

Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud

Saxena,

Kumar,

Singh

et al. 2023

Preprint

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The precise estimation of resource usage is a complex and challenging issue due to the high variability and dimensionality of heterogeneous service types and dynamic workloads. Over the last few years, the prediction of resource usage and traffic has received ample attention from the research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper presents the first systematic survey cum performance analysis-based comparative study of diversified machine learning-driven cloud workload prediction models. The discussion initiates with the significance of predictive resource management followed by a schematic description, operational design, motivation, and challenges concerning these workload prediction models. Classification and taxonomy of different prediction approaches into five distinct categories are presented focusing on the theoretical concepts and mathematical functioning of the existing state-of-the-art workload prediction methods. The most prominent prediction approaches belonging to a distinct class of machine learning models are thoroughly surveyed and compared. All five classified machine learning-based workload prediction models are implemented on a common platform for systematic investigation and comparison using three distinct benchmark cloud workload traces via experimental analysis. The essential key performance indicators of state-of-the-art approaches are evaluated for comparison and the paper is concluded by discussing the trade-offs and notable remarks.

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“…Many studies have outlined relevant developments in SI-oriented applications but have only considered a small number of algorithms [ 8 , 9 ]. Some of the most important SI algorithms, such as routing algorithms based on particle-swarm optimization (PSO), are absent from existing reviews.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

Self Cite

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Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

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Uncertainty-aware Decisions in Cloud Computing

Cited by 42 publications

References 141 publications

Review on data replication strategies in single vs. interconnected cloud systems: Focus on data correlation‐aware strategies

Review on data replication strategies in single vs. interconnected cloud systems: Focus on data correlation‐aware strategies

Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud

Swarm Intelligence in Internet of Medical Things: A Review

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