Live Migration in Bare-Metal Clouds

Fukai, Takaaki; Shinagawa, Takahiro; Kato, K.

doi:10.1109/tcc.2018.2848981

Cited by 16 publications

(6 citation statements)

References 26 publications

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“…Suppose the earlier backup sync with regular intervals. This procedure may be relatively quick, allowing the VM to uninterrupted services to the data center link [15]. A working operating system can be moved to another data center with minimal downtime with live migration.…”

Section: Related Workmentioning

confidence: 99%

Performance Framework for Virtual Machine Migration in Cloud Computing

Alyas¹,

Ghazal²,

Alfurhood³

et al. 2023

Computers, Materials &Amp; Continua

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In the cloud environment, the transfer of data from one cloud server to another cloud server is called migration. Data can be delivered in various ways, from one data centre to another. This research aims to increase the migration performance of the virtual machine (VM) in the cloud environment. VMs allow cloud customers to store essential data and resources. However, server usage has grown dramatically due to the virtualization of computer systems, resulting in higher data centre power consumption, storage needs, and operating expenses. Multiple VMs on one data centre manage share resources like central processing unit (CPU) cache, network bandwidth, memory, and application bandwidth. In multi-cloud, VM migration addresses the performance degradation due to cloud server configuration, unbalanced traffic load, resource load management, and fault situations during data transfer. VM migration speed is influenced by the size of the VM, the dirty rate of the running application, and the latency of migration iterations. As a result, evaluating VM migration performance while considering all of these factors becomes a difficult task. The main effort of this research is to assess migration problems on performance. The simulation results in Matlab show that if the VM size grows, the migration time of VMs and the downtime can be impacted by three orders of magnitude. The dirty page rate decreases, the migration time and the downtime grow, and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VM transfer is completed. All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

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Section: Related Workmentioning

confidence: 99%

Performance Framework for Virtual Machine Migration in Cloud Computing

Alyas¹,

Ghazal²,

Alfurhood³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In another article [15], authors reviewed real-time migration literature and analyzed various guidelines. Firstly, they classified real-time VM migration types (single, multi, and hybrid).…”

Section: Related Workmentioning

confidence: 99%

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Alyas¹,

Javed²,

Namoun³

et al. 2022

Computers, Materials &Amp; Continua

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Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in crossdata-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

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“…TLB shootdown is an operation that flushes a Translation Lookaside Buffer (TLB), a cache of address translation, on all CPU cores. The TLB shootdown on BitVisor was implemented by Fukai et al [10]; we used their implementation as it is. The appropriate interval is discussed in Section VI.…”

Section: Live-forensic Hypervisor For Collecting Memory Access Patternsmentioning

confidence: 99%

Machine Learning-based Ransomware Detection Using Low-level Memory Access Patterns Obtained From Live-forensic Hypervisor

Hirano,

Kobayashi

2022

Preprint

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Since modern anti-virus software mainly depends on a signature-based static analysis, they are not suitable for coping with the rapid increase in malware variants. Moreover, even worse, many vulnerabilities of operating systems enable attackers to evade such protection mechanisms. We, therefore, developed a thin and lightweight live-forensic hypervisor to create an additional protection layer under a conventional protection layer of operating systems with supporting ransomware detection using dynamic behavioral features. The developed live-forensic hypervisor collects low-level memory access patterns instead of highlevel information such as process IDs and API calls that modern Virtual Machine Introspection techniques have employed. We then created the low-level memory access patterns dataset of three ransomware samples, one wiper malware sample, and four benign applications. We confirmed that our best machine learning classifier using only low-level memory access patterns achieved an F1 score of 0.95 in detecting ransomware and wiper malware.

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Live Migration in Bare-Metal Clouds

Cited by 16 publications

References 26 publications

Performance Framework for Virtual Machine Migration in Cloud Computing

Performance Framework for Virtual Machine Migration in Cloud Computing

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Machine Learning-based Ransomware Detection Using Low-level Memory Access Patterns Obtained From Live-forensic Hypervisor

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