Xen Live Migration with Slowdown Scheduling Algorithm

Liu, Zhaobin; Qu, Wenyu; Liu, Weijiang; Li, Keqiu

doi:10.1109/pdcat.2010.88

Cited by 32 publications

(12 citation statements)

References 14 publications

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“…There are many other approaches to optimize memory migration process, such as memory compression [5], migration throttling [1], [2], [10], and dynamic self-ballooning [4]. These studies try to optimize migration without changing the precopy algorithm itself.…”

Section: Related Workmentioning

confidence: 99%

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Pre-Filter-Copy: Efficient and Self-Adaptive Live Migration of Virtual Machines

Ruan

Cao

Cui

2016

IEEE Systems Journal

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Live migration of virtual machines (VMs) is useful for resource management of data centers and cloud platforms. The precopy algorithm is widely used for memory migration. However, when encountered with write-intensive workloads, the precopy's straightforward iteration strategy will become inefficient. Worse still, it is hard to tune the performance with the existing parameters, unless load characteristics are known in advance. In this paper, we propose an improved pre-filter-copy (PFC) algorithm. The main target is to reduce migration time and bandwidth resource consumption of the precopy algorithm, while keeping downtime at the same level. We designed a novel data filter to achieve this goal. In each round of iteration, it forecasts the pages that will be subsequently dirtied and then filters them from the send list. Meanwhile, previously filtered pages will be reconsidered, to see if they can be added to the send list. This ensures that the downtime will not be increased. Furthermore, a new parameter is proposed to improve the adaptivity of the precopy algorithm. Experimental results show that the PFC algorithm significantly reduces migration time and the amount of migrated data, while keeping the downtime at the same level.

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Section: Related Workmentioning

confidence: 99%

“…L IVE migration of virtual machines (VMs) is a powerful tool that allows for the relocation of VM between different physical hosts [1], [4]- [10], [12], [17], [22], [26]- [28]. The whole software stack can be consistently transferred, while the continuous execution of the workload is guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Pre-Filter-Copy: Efficient and Self-Adaptive Live Migration of Virtual Machines

Ruan

Cao

Cui

2016

IEEE Systems Journal

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“…Virtualization is a key technique to improve QoS in could computing [24]; it provides a virtualized view of resources used to instantiate VMs. One important feature of virtualization is live VM migration, which can be employed to facilitate load balancing, fault management, and server maintenance [12,13,25]. Many problems about VM migration have been studied in traditional cloud systems.…”

Section: Vm Migration Problemmentioning

confidence: 99%

“…Another, Zhang et al [26] introduce an online migration algorithm that can maximize the number of overcommitted VMs, while minimizing the impact on the communication cost among VMs. Similarly, Liu et al [12] present a design and implementation of a novel slowdown scheduling algorithm for Xen live VM migration platform. In work [27], the authors consider the interdependencies and inter-communication patterns among VMs and propose an application-aware VM migration policy aimed to minimize the data center network traffic while satisfying all of the server-sides constrains.…”

Section: Vm Migration Problemmentioning

confidence: 99%

“…It allows simultaneous execution of diverse tenant's tasks over a shared hardware platform in the form of a virtual machine (VM). To facilitate load balancing, fault management, server maintenance, and so on, live VM migration is advocated in traditional cloud data center [12] [13]. However, the key differences of RSC-based VCC and traditional cloud computing platform are that (1) the cloud resources are highly distributed and (2) the users are highly dynamic.…”

Section: Introductionmentioning

confidence: 99%

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Migrate or not? Exploring virtual machine migration in roadside cloudlet‐based vehicular cloud

Yao

Bai

Zeng

et al. 2015

Concurrency and Computation

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SUMMARYVehicle Ad-Hoc Networks (VANET) enable all components in intelligent transportation systems to be connected so as to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance driving comfort. The vision of all vehicles connected poses a significant challenge to the collection, storage, and analysis of big traffic-related data. Vehicular cloud computing, which incorporates cloud computing into vehicular networks, emerges as a promising solution. Different from conventional cloud computing platform, the vehicle mobility poses new challenges to the allocation and management of cloud resources in roadside cloudlet. In this paper, we study a virtual machine (VM) migration problem in roadside cloudletbased vehicular network and unfold that (1) whether a VM shall be migrated or not along with the vehicle moving and (2) where a VM shall be migrated, in order to minimize the overall network cost for both VM migration and normal data traffic. We first treat the problem as a static off-line VM placement problem and formulate it into a mixed-integer quadratic programming problem. A heuristic algorithm with polynomial time is then proposed to tackle the complexity of solving mixed-integer quadratic programming. Extensive simulation results show that it produces near-optimal performance and outperforms other related algorithms significantly.

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