Revisiting VM performance and optimization challenges for big data

Nayyer, Muhammad Ziad; Raza, Imran; Hussain, Syed Asad

doi:10.1016/bs.adcom.2019.02.002

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…Cloud scaling reduces time to market and increases company agility and flexibility [26]. Machine learning can suggest appropriate elasticity models [5].…”

Section: Cloud Computingmentioning

confidence: 99%

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Study on the Design of Algorithm Based on Machine Learning to Improve Cloud Computing

Sultan

2023

Technium

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The on-demand availability of end-user resources, in particular data storage and processing power, without a direct or customer-defined organization is referred to as "cloud computing." Distributed computing is a term widely used yet may have different meanings to different people. Customers may access both public and private data using the cloud computing model. The potential of simultaneously requesting data from several clients of the same source, which slows down the source's response time, is the most significant security risk with cloud computing. Other security concerns with cloud computing include weaknesses in the client and connection. By reducing the delay between a client's request for data and the cloud source's answer, a method was developed in our recent research to enhance the performance of cloud computing. By requesting data from several clients from the same source at once or from multiple clients from the same source or from other sources at various times in the same network, four instances were shown. By testing request and response times while protecting data from loss and noise, the findings demonstrated the system's robustness.

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“…Cloud scaling reduces time to market and increases company agility and flexibility [26]. Machine learning can suggest appropriate elasticity models [5].…”

Section: Cloud Computingmentioning

confidence: 99%

“…Edge computing, a sort of cloud computing used to analyze time-sensitive data, gives application developers and service providers dispersed processing capacity [5]. Current edge processing uses virtualization to transfer and operate more edge servers.…”

Section: Introductionmentioning

confidence: 99%

Study on the Design of Algorithm Based on Machine Learning to Improve Cloud Computing

Sultan

2023

Technium

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“…The concept of virtualization is used to share the resources of a physical machine. Various service-oriented architectures are offered by the cloud computing paradigm namely, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) [1]. This paper considers the use case of the IaaS model where a task is bundled as a virtual machine (VM) and is placed on a physical server (cloudlet).…”

Section: Introductionmentioning

confidence: 99%

LBRO: Load Balancing for Resource Optimization in Edge Computing

et al. 2022

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Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resourcelimited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices.Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user's preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users' preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.

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“…Using the aggregated information, the optimal placement, load balancing, and resource sharing decisions are taken. The proposed model considers resource optimization as a nested resource optimization problem, where an already placed Virtual Machine (VM) [22,23] is also considered for re-optimization triggering a migration operation while enhancing the optimal solution space for new VM placement. Hence, both the placement and migration operations are considered as a part of the single bigger problem of resource optimization.…”

Section: Introductionmentioning

confidence: 99%

CFRO: Cloudlet Federation for Resource Optimization

2020

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A Cloud computing paradigm augments the limited resources of mobile devices resulting in increased distance, limited Internet bandwidth, and seamless connectivity challenges between a remote cloud and mobile devices. Cloudlet computing based solutions are widely used to address these challenges by bringing the computational facility closer to the user. The ever growing number of mobile devices, Internet of Things (IoT) sensors and Information Communication Technology (ICT) infrastructure used for smart cities demand more resources. The existing cloudlet based solutions are unable to manage the ever-increasing demand for power, storage, and computational resources, and therefore forward the resource extensive tasks to a remote cloud, limiting cloudlet computing benefits. We present the Cloudlet Federation for Resource Optimization (CFRO), a federated cloudlet model for resource optimization to address these resource scarcity challenges. The proposed model exerts the features of scalability, resource collaboration, and robustness. The underlying scheme for resource optimization has been modeled as a Nested Multi Objective Resource Optimization Problem (NMOROP) and a novel algorithm has been proposed to solve it. The detailed analysis and comparative results show that the proposed model offers improved performance and more resource elasticity as compared to the conventional cloudlet model.

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Revisiting VM performance and optimization challenges for big data

Cited by 6 publications

References 34 publications

Study on the Design of Algorithm Based on Machine Learning to Improve Cloud Computing

Study on the Design of Algorithm Based on Machine Learning to Improve Cloud Computing

LBRO: Load Balancing for Resource Optimization in Edge Computing

CFRO: Cloudlet Federation for Resource Optimization

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