Efficient consolidation-aware VCPU scheduling on multicore virtualization platform

Wang, Bei; Cheng, Yuxia; Chen, Wenzhi; He, Qinming; Xiang, Yang; Hassan, Mohammad Mehedi; Alelaiwi, Abdulhameed

doi:10.1016/j.future.2015.08.007

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Wang et al [12] proposed a vCPU scheduling scheme that is applicable to multi-core virtualization environments. The proposed scheduling scheme tries to address the lock holder preemption problem in virtualized multicore systems, which may lead to significant wastage of CPU cycles resulting in degradation in VM performance.…”

Section: Related Workmentioning

confidence: 99%

Dynamic CPU scheduling for load balancing in virtualized environments

Moharana¹,

Samal²,

Swain³

et al. 2018

Turk J Elec Eng & Comp Sci

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In the modern era of computing, the cloud computing platform became popular with its on-demand resource scalability feature. Virtualization is the key technology to achieve resource scalability in the cloud environment. Virtual machine monitors (VMMs) like Xen and KVM are the enabling tools for virtualizing the resources in the cloud environment. The major role of VMMs is to map virtual CPUs of virtual machines to physical CPUs, popularly known as CPU scheduling. In this study, we analyzed the CPU scheduler of Xen VMM, called Credit CPU scheduler, with respect to CPU utilization. In order to maximize the physical CPU utilization in Xen VMM, the existing Credit CPU scheduling scheme distributes the physical CPU time among all the virtual CPUs available in virtual machines based on the current weight of the virtual machine. However, this scheduling approach wastes a considerable amount of CPU time in context switching due to random allocation of virtual CPUs to the real CPU cores. In addition to that, the Credit CPU scheduler in Xen is least concerned about load balancing at both virtual and physical CPUs level. Considering all these above issues, in this paper, a dynamic CPU scheduling algorithm is presented that will distribute both single and multithreaded load fairly among virtual and real CPUs, and also handle the issues related to context switching. The proposed CPU scheduling approach is implemented in Xen VMM using the virsh interface. The experimental results indicate that the proposed CPU scheduling approach distributes the available real CPU time evenly among virtual CPUs, which leads to balanced load in the Xen environment.

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Section: Related Workmentioning

confidence: 99%

Dynamic CPU scheduling for load balancing in virtualized environments

Moharana¹,

Samal²,

Swain³

et al. 2018

Turk J Elec Eng & Comp Sci

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“…Resources allocation is a central theme in Cloud computing, since it controls the way that resources and services are delivered to the end entities, at the same time it maintains an efficient utilization of computing resources [1], [3], [4]. Cloud providers intend to optimize the usage of underlying resources, through planned allocation of VMs in servers (hosts).…”

Section: Previous Work In Cloud Resources Allocation and Vms Consolidmentioning

confidence: 99%

“…VMs consolidation is a special problem that is mutually exclusive to resources allocation in virtualized hosted platforms like Cloud. It targets to consolidate maximum number of VMs onto minimum number of PMs, thus improving resources utilization [3], [18]. Frincu M. in [19] employed the Markov Decision Process (MDP) to generate long term precise migration decision which aims to improve the profit by avoiding the wrong decisions which may have an adverse effect on the total profit.…”

Section: Previous Work In Cloud Resources Allocation and Vms Consolidmentioning

confidence: 99%

“…Cloud computing is based on full virtualisation technology where a single physical machine is able to host several virtual machines which in turn are completely isolated. The net effect of having shared resource usage is having fewer physical servers with higher utilisation per server which will effectively minimise the hardware costs and the operational expenses [3], [4]. However, the flexibility enabled by virtualisation has produced new computational challenges of managing a shared pool of resources over the competing instances of applications or jobs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Capacity Combinatorial Ordering GA in Application to Cloud resources allocation and efficient virtual machines consolidation

Hallawi

Mehnen

2017

Future Generation Computer Systems

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Resource allocation is the process of mapping the available resources to competing jobs based on the individual job requirements [5]. Computing resources must be well-managed to prevent overloading and waste of bandwidth, processing unit, memory, etc. This waste relates directly to significant financial loss for large Cloud service providers with regards to energy, operational cost as well as dissatisfaction of the Cloud service user [6], [7]. Resources allocation systems control how multiple VMs share the underlying Physical Machines (PM). Fast and efficient resource allocation algorithms can help to save energy and cost while increasing customer satisfaction. Resource allocation is typically performed in two stages as shown in Figure 1. The first stage is the jobs assignment to the Virtual Machines: applications or jobs (both terms are used synonymously in the context of this paper) are executed on VMs. Each application has its own requirements of compute power, disk space and RAM space, communication bandwidth, priority, etc. (see [7]). Any VM must meet these requirements when resources are allocated.

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