A Survey of the Workload Forecasting Methods in Cloud Computing

Yadav, Archana; Kushwaha, Shivam; Gupta, Jyoti; Saxena, Deepika; Singh, Ashutosh Kumar

doi:10.1007/978-981-19-2828-4_49

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In this article, we specifically address the issue of intelligent resource orchestration in distributed edge computing infrastructures by integrating forecasting techniques for predicting resource utilization [19,20] and optimization techniques for optimal resource allocation [21,22]. Resource utilization forecasting leverages historical and real-time data to predict future resource demands accurately.…”

Section: Edge Computing and Advanced 5g Networkmentioning

confidence: 99%

Intelligent Resource Orchestration for 5G Edge Infrastructures

Moreno-Vozmediano,

Montero,

Huedo

et al. 2024

Future Internet

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The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

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Section: Edge Computing and Advanced 5g Networkmentioning

confidence: 99%

Intelligent Resource Orchestration for 5G Edge Infrastructures

Moreno-Vozmediano,

Montero,

Huedo

et al. 2024

Future Internet

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“…The vulnerabilities and susceptibilities of virtual machines management layer and hypervisor facilitates malicious user to get access to target physical machine and compromise all the virtual machines hosted on it. Hence, the prime challenge for cloud datacentre is to secure user data which is distributed on the shared multi-tenant physical servers while provisioning higher scalability of resources along with high performance and cost optimization [42,[113][114][115][116][117][118][119][120][121][122][123][124][125].…”

Section: Introductionmentioning

confidence: 99%

A Review on Secure Cloud Resource Management

Chhabra¹,

Saxena²,

Rekha³

2023

Preprint

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<p>Security is always a prime concern of every cloud user. Many pioneer surveys have been done concerning security during data communication among owners, users, and stakeholders. However, there is a lack of literature surveys that address the concern of security during data processing or the workload execution within cloud data centers. In this context, we present a concise review of the existing methods that have addressed the security of workload or user data during load scheduling and execution within cloud environments. These methods have applied load-balancing strategies that helped to minimize the security attacks via co-residency or covert channels. The pandect comparative summary of these methods follows the corresponding review. The paper is concluded with limitations and future direction concerning secure load management.</p>

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“…Cloud computing has proven to be cost-effective, reliable, and scalable computing service choice for delivering and providing software solutions for various industrial applications [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. To take advantage of on-demand, elastic, and scalable resource provisioning, many companies have migrated to cloud data centers (CDCs) to save capital investment and keep their infrastructure in-house.…”

Section: Introductionmentioning

confidence: 99%

A Review on Sustainable Resource Management in Cloud Environment

Kumar¹,

Saxena²,

Rekha³

2023

Preprint

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<p>Cloud computing (CC) has become the best way to manage a large-scale computing paradigm. Resource management (RM) in the cloud environment is critical due to the heterogeneity of computing resource types and their inter-dependencies. Recently, significant research has been done on RM techniques focusing on efficiently sharing resources among multiple users. Machine learning techniques manage various resource management functions, such as VM consolidation, resource optimization, energy optimization, workload estimation, and task scheduling. This article contributes a comprehensive and concise review of RM approaches within cloud environments. Moreover, it suggests design goals and research challenges that must be considered while developing novel RM strategies.</p>

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A Survey of the Workload Forecasting Methods in Cloud Computing

Cited by 3 publications

References 14 publications

Intelligent Resource Orchestration for 5G Edge Infrastructures

Intelligent Resource Orchestration for 5G Edge Infrastructures

A Review on Secure Cloud Resource Management

A Review on Sustainable Resource Management in Cloud Environment

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