A Memory Global Optimization Approach in Virtualized Cloud Computing Environments

Li, Yaqiong; Song, Ying; Huang, Yongbing

doi:10.3724/sp.j.1016.2011.00684

Cited by 13 publications

(6 citation statements)

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“…In cloud computing, physical memory is divided among multiple virtual machines, with each virtual machine being allocated a certain amount of physical memory. The quantity of physical memory allotted to a virtual machine is determined by the resource needs of the software and services that are running on it [4].…”

Section: Flexibilitymentioning

confidence: 99%

Double level Code Scanning Leveraging network virtualization to decrease risks of memory starvation and insecure programming

Albibrahim,

Hendaoui

2023

Preprint

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This study investigates the impact of a software-defined networking (SDN) controller on resource utilization, system stability, and performance in cloud environments, with a specific focus on the deployment of machine learning techniques. The SDN controller utilizes machine learning algorithms to dynamically detect and block vulnerable and memory-intensive cloudlet codes. The results demonstrate that the deployment of machine learning in the SDN controller significantly improves resource allocation, mitigates resource contention, and reduces the risk of memory exhaustion. By accurately identifying and preventing the execution of insecure cloudlets, the machine learning-enabled SDN controller enhances system security and maintains optimal resource utilization. Moreover, the study highlights the positive influence of the SDN controller on system stability, preventing disruptive operations and minimizing the likelihood of system crashes or slowdowns.

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Section: Flexibilitymentioning

confidence: 99%

Double level Code Scanning Leveraging network virtualization to decrease risks of memory starvation and insecure programming

Albibrahim,

Hendaoui

2023

Preprint

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“…Page reuse technology which is researched in [9,20] and dynamic adjusting memory mechanism in literature [3] has provided the basic support method, but they can not solve the problem of when and how to allocate the memory space. Literature [21] and literature [22] are absent of the global management system frame and assisted scheduling methods. In order to solve the above problems, Yaqiong Li et al [22] proposed a global memory optimization frame of cloud computing platform.…”

Section: Classification Of Memory Balancing Technologymentioning

confidence: 99%

“…Literature [21] and literature [22] are absent of the global management system frame and assisted scheduling methods. In order to solve the above problems, Yaqiong Li et al [22] proposed a global memory optimization frame of cloud computing platform. In this pattern, logic address and global address construct a two-layer address mapping.…”

Section: Classification Of Memory Balancing Technologymentioning

confidence: 99%

A Memory Configuration Method for Virtual Machine Based on User Preference in Distributed Cloud

2018

TIIS

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It is well-known that virtualization technology can bring many benefits not only to users but also to service providers. From the view of system security and resource utility, higher resource sharing degree and higher system reliability can be obtained by the introduction of virtualization technology in distributed cloud. The small size time-sharing multiplexing technology which is based on virtual machine in distributed cloud platform can enhance the resource utilization effectively by server consolidation. In this paper, the concept of memory block and user satisfaction is redefined combined with user requirements. According to the unbalanced memory resource states and user preference requirements in multi-virtual machine environments, a model of proper memory resource allocation is proposed combined with memory block and user satisfaction, and at the same time a memory optimization allocation algorithm is proposed which is based on virtual memory block, makespan and user satisfaction under the premise of an orderly physical nodes states also. In the algorithm, a memory optimal problem can be transformed into a resource workload balance problem. All the virtual machine tasks are simulated in Cloudsim platform. And the experimental results show that the problem of virtual machine memory resource allocation can be solved flexibly and efficiently.

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“…How to rationally allocate and schedule resources (such as CPU utilization, memory utilization, network load, etc. ), reduce energy consumption and improve the utilization of cloud computing resources has become a challenge for the industry [4]. Then, the research on forecasting a load of cloud computing resource is carried out.…”

Section: Introductionmentioning

confidence: 99%

Combined Forecasting Model of Cloud Computing Resource Load for Energy-Efficient IoT System

Zhang

et al. 2019

IEEE Access

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Cloud computing is generally considered as a special energy-efficient form for the Internet of Things (IoT) resource usage. Dedicated server systems for cloud services, better capacity utilization and economies of scale because of the use of larger and more energy-efficient data centers are the reasons why cloud solutions typically use less energy than traditional on-premise systems. To scientifically and rationally configure the hardware and software resources of the cloud computing, the research on forecasting a cloud computing resource load becomes a research focus. However, the widely-used single forecasting model cannot contain all the characteristics of the cloud computing resource load sequence, resulting in inaccurate forecasting results. In this paper, a combined forecasting approach of cloud computing resource load based on wavelet decomposition is proposed, which combined the grey model and cubic exponential smoothing model. It can well preserve details and reduce noise. Firstly, the cloud computing resource load sequence is decomposed into several frequencies by the wavelet decomposition method. The decomposed load sequences with different characteristics are divided into different resolution scale subspaces in deferent frequencies. The noise of the load sequences is reduced by the wavelet threshold denoising method. And then, the load sequences are reconstructed according to the wavelet coefficients. The reconstructed load sequence not only contains less noise but also reserves detailed information. Consequently, it is closer to the real data and more regular. Experimental results show that our proposed combined forecasting model with wavelet decomposition can provide more accurate forecasting results than each single forecasting model or the combined forecasting model without using the wavelet decomposition method. Thus, our proposal is demonstrated to be efficient for forecasting the cloud computing resource load and helping to reduce energy consumption.INDEX TERMS Cloud computing resource load, wavelet decomposition, combined forecasting model, grey model, cubic exponential smoothing model.

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A Memory Global Optimization Approach in Virtualized Cloud Computing Environments

Cited by 13 publications

References 0 publications

Double level Code Scanning Leveraging network virtualization to decrease risks of memory starvation and insecure programming

Double level Code Scanning Leveraging network virtualization to decrease risks of memory starvation and insecure programming

A Memory Configuration Method for Virtual Machine Based on User Preference in Distributed Cloud

Combined Forecasting Model of Cloud Computing Resource Load for Energy-Efficient IoT System

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