A Markov-Based Prediction Model for Host Load Detection in Live VM Migration

Melhem, Suhib Bani; Agarwal, Anjali; Goel, Nishith; Zaman, Musharraf

doi:10.1109/ficloud.2017.37

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…For the migration time selection problem, the prediction method is generally used to predict the resource utilization rate of the server in the future. For example, Melhem et al [12] proposed a host load detection algorithm to determine overload or light load and migrate VMs to achieve server consolidation or load balancing.…”

Section: Vm Placement Methodsmentioning

confidence: 99%

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A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks

Chen

Shi

Xiang

et al. 2021

Security and Communication Networks

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With the rapid development of Internet of Things technology, a large amount of user information needs to be uploaded to the cloud server for computing and storage. Side-channel attacks steal the private information of other virtual machines by coresident virtual machines to bring huge security threats to edge computing. Virtual machine migration technology is currently the main way to defend against side-channel attacks. VM migration can effectively prevent attackers from realizing coresident virtual machines, thereby ensuring data security and privacy protection of edge computing based on the Internet of Things. This paper considers the relevance between application services and proposes a VM migration strategy based on service correlation. This strategy defines service relevance factors to quantify the degree of service relevance, build VM migration groups through service relevance factors, and effectively reduce communication overhead between servers during migration, design and implement the VM memory migration based on the post-copy method, effectively reduce the occurrence of page fault interruption, and improve the efficiency of VM migration.

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Section: Vm Placement Methodsmentioning

confidence: 99%

“…return VM migration group G; (12) else (13) break; (14) end if (15) end if (16) end while (17) while(…”

Section: Priority Calculationmentioning

confidence: 99%

A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks

Chen

Shi

Xiang

et al. 2021

Security and Communication Networks

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“…Bezerra et al [20] presented a novel content movement approach based on multi-content duplication and fractional compressed to lower overall implementation complexity, migrations efficiency, total sample communication, and VM results of operations. Melhem et al [21] introduced the WS Clock unit due to the placement in combination with a type before a strategy to decrease the amount of the transmitted recollection area and overall migrations duration. They also looked at the factors affecting the success of route discovery.Melhem et al [22] cut the overall relocation rate in half by transmitting just a critical fraction of memory locations across the networking that wasn't on disc and downloading the deleted webpages in the meantime.…”

Section: Related Workmentioning

confidence: 99%

An Optimizable Conditional Random Field-Based Migration Strategy for Virtual Machines to Improve the Security and Privacy Issues in Cloud Computing

Chandrakalay¹,

Enireddy²

2022

INDJCSE

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Virtualization creates virtual OS, platform, network devices, storage, software, and hardware devices in cloud computing. Moreover, Virtual Machine (VM) technology has essential building blocks like cluster systems and data centre. The advancement is due to migrating, consolidating and isolating workloads. The VM migration seeks to enhance the security, performance, manageability and fault tolerance systems. In a virtual CC environment, some sets of tasks from various users are scheduled over the VMs, and load balancing turns out to be a crucial issue in achieving security and energy efficiency. Therefore, a novel optimization algorithm is initiated to resolve these issues and attain superior balancing with the influence of external resources. The Conditional Random Field-based Moth Algorithm (CRF-MA) considers the multi-objective functions by handling metrics like security, energy consumption, CPU utilization, makespan, migration, and resource cost. The performance of the CRF-MA is examined by determining the energy consumption, SLA violation, solution size and migration number. The simulation is done in CloudSim, and the proposed CRF-MA gives a better trade-off than other approaches.

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“…In [52] the authors propose a host CPU load detection algorithm called Median Absolute Deviation Markov Chain Host Detection algorithm (MADMCHD). The objective of the proposed algorithm is to minimize the SLA violation and to reduce the number of VMs migrations.…”

Section: Live Migration Cost Predictionmentioning

confidence: 99%

Virtual machines pre-copy live migration cost modeling and prediction: a survey

Elsaid

Abbas

Meinel

2021

Distrib Parallel Databases

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Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers’ research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments.

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A Markov-Based Prediction Model for Host Load Detection in Live VM Migration

Cited by 7 publications

References 13 publications

A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks

A Virtual Machine Migration Strategy Based on the Relevance of Services against Side-Channel Attacks

An Optimizable Conditional Random Field-Based Migration Strategy for Virtual Machines to Improve the Security and Privacy Issues in Cloud Computing

Virtual machines pre-copy live migration cost modeling and prediction: a survey

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