Minimum-cost virtual machine migration strategy in datacenter

2015 ◽  
Vol 27 (17) ◽  
pp. 5177-5187 ◽  
Author(s):  
Xinyan Zhang ◽  
Keqiu Li ◽  
Yong Zhang
Keyword(s):  
Virtual Machine ◽  
Minimum Cost ◽  
Virtual Machine Migration ◽  
Migration Strategy

A Virtual Machine Migration Algorithm Based on Network Flow Balance

2014 ◽  
Vol 1044-1045 ◽  
pp. 1028-1034
Author(s):  
Yu Yang ◽  
Hua Zhou ◽  
Jun Hui Liu ◽  
Yun Feng
Keyword(s):  
Virtual Machine ◽  
Network Traffic ◽  
Network Flow ◽  
Virtual Machine Migration ◽  
Network Resources ◽  
Migration Strategy ◽  
Network Location ◽  
Flow Balance ◽  
The Difference ◽  
Communication Traffic

Current research of virtual machine migration strategy mainly focuses on how to reduce the delay of virtual machine migration process but does not pay much attention to the network flow problem caused by the virtual machine migration. Because of the difference caused by Infrastructure Operator's network location makes a different virtual machine migration strategy, which will result in large differences in network traffic. Infrastructure operator's network resources are scarce resources. Therefore, how to reduce the network flow of virtual machine migration is a problem to be studied. In order to reduce network traffic virtual machine migration, this paper proposes a virtual machine migration algorithm (NFBA) based on network flow balance to obtain the minimum scheduling cost. Experimental results show the migration strategy can effectively reduce the communication traffic between the virtual machine clusters within the system and reduce the burden of network and consider workload balance at the same time.

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.

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