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

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ji-Ming Chen ◽  
Shi Chen ◽  
Xiang Wang ◽  
Lin Lin ◽  
Li Wang
Keyword(s):  
Internet Of Things ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Edge Computing ◽  
Security And Privacy ◽  
Side Channel ◽  
Virtual Machine Migration ◽  
Side Channel Attacks ◽  
Migration Strategy ◽  
Vm Migration

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.

scholarly journals A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yanbing Liu ◽  
Bo Gong ◽  
Congcong Xing ◽  
Yi Jian
Keyword(s):  
Time Series ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Virtual Machine Migration ◽  
Migration Strategy ◽  
Workload Prediction ◽  
Vm Migration ◽  
Host Machine

Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM) migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM), the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

scholarly journals Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning

2021 ◽  
Vol 11 (17) ◽  
pp. 7993
Author(s):  
Yu Dai ◽  
Qiuhong Zhang ◽  
Lei Yang
Keyword(s):  
Reinforcement Learning ◽  
Virtual Machine ◽  
Control Method ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Centralized Control ◽  
Virtual Machine Migration ◽  
Migration Strategy ◽  
Computing Power ◽  
Multi Agent

Mobile edge computing is a new computing model, which pushes cloud computing power from centralized cloud to network edge. However, with the sinking of computing power, user mobility brings new challenges: since it is usually unstable, services should be dynamically migrated between multiple edge servers to maintain service performance, that is, user-perceived latency. Considering that Mobile Edge Computing is a highly distributed computing environment and it is difficult to synchronize information between servers, in order to ensure the real-time performance of the migration strategy, a virtual machine migration strategy based on Multi-Agent Deep Reinforcement Learning is proposed in this paper. The method of centralized training and distributed execution is adopted, that is, the transfer action is guided by the global information during training, and only the local observation information is needed to obtain the transfer action. Compared with the centralized control method, the proposed method alleviates communication bottleneck. Compared with other distributed control methods, this method only needs local information, does not need communication between servers, and speeds up the perception of the current environment. Migration strategies can be generated faster. Simulation results show that the proposed strategy is better than the contrast strategy in terms of convergence and energy consumption.

scholarly journals Efficient Virtual Machine Memory Transfer in Datacenter with optimal downtime

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Flow Chart ◽  
Key Factors ◽  
Virtual Machine Migration ◽  
Memory Transfer ◽  
Vm Migration ◽  
Host Machine ◽  
Machine Memory

Cloud computing is a technology which relies onsharing various computing resources instead of having localservers to handle applications. Cloud computing is driven byvirtualization technology. Virtual machines need migration fromone host to anther due to the presence of error or over loading orslowness in the current running host machine. Live Virtualmachine migration is the transfer of running virtual machinefrom one host to another without stopping the current runningtask. During this live virtual machine migration Downtime is oneof the key factors that have to be considered and assessed.Here we present detailed survey on what are the importance oflive virtual machine migration in cloud computing technologyand various techniques to reduce the downtime during livevirtual machine migration. The flow chart showing the steps usedin Pre copy approach for VM migration. And also we presentthe result of the comparison between the two virtual machinemigration environments, VMWare and Xen Server.

scholarly journals Affinity based VM Migration: Taxonomy and Challenges

2019 ◽  
Vol 8 (12) ◽  
pp. 5799-5805
Keyword(s):  
Resource Management ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Paper ◽  
Cloud Environment ◽  
Virtual Machine Migration ◽  
Vm Migration ◽  
Dynamic Resource Management ◽  
Dynamic Resource

using virtualization many Virtual Machines can run parallel on the same Host. For dynamic resource management, virtual machines can be migrated from residing host to a different. But before starting the migration some questions need to be answered like when to start the virtual machine migration, which VM to migrated and where? The Virtual Machine migration methods on the Virtual cloud environment has already been researched at length, but very few studies have focused on affinity-relations among virtual machines during migration, hence the key objective of this research paper, is to explore the Affinity-aware VM migration in detail and propose Affinity-Aware VM migration algorithms for migration of a group of VMs with affinity to a destination Host with less capacity than required. This paper also provides a brief review of several virtual machine migration techniques

scholarly journals A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

2019 ◽  
Vol 8 (4S2) ◽  
pp. 430-435
Keyword(s):  
Cloud Computing ◽  
Evolutionary Algorithm ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Cloud Services ◽  
Virtual Machine Migration ◽  
Two Stage ◽  
Vm Migration ◽  
Hybrid Evolutionary Algorithm

The platform for cloud computing offers virtualization and a dynamic pool of resources to the consumers of the cloud. The acceptance and demand of cloud is growing on a regular basis. Cloud computing offers utility-based consumer services across the globe on a pay as you go strategy. Live Virtual Machine (VM) migration sets the basic foundation for cloud management. It hasakey role in reducing operating expenditure and revising quality of service without disruption of cloud services running in the VM. A lot of research has been done to yield better performance in live VM migration and has seen noteworthy development and accomplishment. However, some crucial problems require results and enhancement. With the growth of new cloud computing models like Mobile Edge Computing, certain problems related to optimization need to be addressed. The primary aim of this research work is to emphasize on optimum functioning of live migration. A migration algorithm to consolidate the computational resources, storage resources and network resources dynamically with a two-stage heuristic hybrid evolutionary algorithm is discussed. The resources are consolidated to cut down the energy and cost utilization depending upon the evolutionary Particle Swarm Optimization and the Ant Colony Optimization algorithms. These algorithms can rapidly identify the migrating virtual machines and locate their positions respectively.

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