An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach

Bhardwaj, Tushar; Sharma, S. C.

doi:10.1007/s00500-018-3587-x

Cited by 22 publications

(3 citation statements)

References 31 publications

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“…The fuzzy-based load balancing in cloud resource management involves transforming uncertain and fuzzy input into a well-defined decision-making process. It utilizes fuzzy logic to handle the ambiguity in decision variables, ultimately leading to effective VM allocation on terminal servers [56], [255]- [257].…”

Section: Fuzzy Methods-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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Section: Fuzzy Methods-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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“… Optimal resource provisioning : It is a major research gap that can help to predict and adjust the resources depending on the application type and current network scenario. In Reference 62, wireless body area networks (WBANs) based cloudlets are discussed to introduce a promising ervasive applications computing environment. The number of WBANs nodes need optimal computing resources for improved network with minimum data loss.…”

Section: Research Gaps Challenges and Future Research Directionsmentioning

confidence: 99%

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

Agrawal

2021

Trans Emerging Tel Tech

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The advancements and rapid adoption of service oriented architecture, utility computing, virtualization, etc., have emerged in a new computing paradigm called cloud computing, in the last few years. Cloud computing enables on-demand delivery of data, software, and hardware in the form of services over the Internet. But the growing scale of cloud-users and type-of-services urge for an autonomous environment to automate the highly complex cloud environment in a suitable way to improve the overall management of the cloud systems. To counter these automation related issues, autonomic computing (AC) emerges as a potential solution to optimize the performance of cloud computing.The merge of these two prominent technologies gives rise to a new technological domain called autonomic cloud computing (ACC). In this survey article, the existing AC assisted solutions for cloud computing is broadly categorized into two groups, namely features based and parameters based. Features based solutions are further classified into management (of services, workload, and resources) and security aware solutions. Similarly, parameters based solutions are grouped into quality-of-service aware and performance management solutions. Finally, the comparative analysis of state-of-the-art ACC approaches with respect to the approach referred, performance metrics, outcome, and the derived inference is also presented to have an in-depth understanding of the existing literature. Overall, the work excites the research community to design the effective autonomous solutions for ACC resources and services. Additionally, several research gaps and challenges are identified and discussed in this article based on the study with a focus on the future research directions.

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“…The conventional server‐side mechanisms have limitations in the adaptive allocation of the necessary computing resources to handle the variability of incoming users requests. The conventional algorithms are static in nature 4 . Hence, resource allocations may become either overloaded or underloaded and lead to poor resources utilization and poor response times.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

Kadarla

Sharma

2019

Concurrency and Computation

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The Internet of Things (IoT)-enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart-patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First-Come-First-Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT-enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios.

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An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach

Cited by 22 publications

References 31 publications

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

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