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

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.

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

Efficient resource scheduling is one of the most critical issues for big datacenters in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

At what costs? A proposal for estimating migration costs in the Bangladesh-Malaysia corridor

This article provides a comprehensive proposal to figure out the full spectrum of costs borne by Bangladeshi workers on their journey to Malaysia. The article proposes to consider the differences based on skill level, employment sector, immigration status, gender, traveling routes as well as channels and funding sources in order to determine the true economic cost of migration. The article also proposes to take social cost of migration into account. It sheds light on the effects of migration on migrant households with a view to fully comprehend the overall migration cost scenario. Finally, the paper suggests to take into consideration the impact of COVID-19 pandemic on broader migration and repatriation context.

At what costs? A proposal for estimating migration costs in the Bangladesh-Malaysia corridor

This article provides a comprehensive proposal to figure out the full spectrum of costs borne by Bangladeshi workers on their journey to Malaysia. The article proposes to consider the differences based on skill level, employment sector, immigration status, gender, traveling routes as well as channels and funding sources in order to determine the true economic cost of migration. The article also proposes to take social cost of migration into account. It sheds light on the effects of migration on migrant households with a view to fully comprehend the overall migration cost scenario. Finally, the paper suggests to take into consideration the impact of COVID-19 pandemic on broader migration and repatriation context.

At what costs? A proposal for estimating migration costs in the Bangladesh-Malaysia corridor

This article provides a comprehensive proposal to figure out the full spectrum of costs borne by Bangladeshi workers on their journey to Malaysia. The article proposes to consider the differences based on skill level, employment sector, immigration status, gender, traveling routes as well as channels and funding sources in order to determine the true economic cost of migration. The article also proposes to take social cost of migration into account. It sheds light on the effects of migration on migrant households with a view to fully comprehend the overall migration cost scenario. Finally, the paper suggests to take into consideration the impact of COVID-19 pandemic on broader migration and repatriation context.