Automatic memory-based vertical elasticity and oversubscription on cloud platforms

Moltó, Germán; Caballer, Miguel; Alfonso, Carlos

doi:10.1016/j.future.2015.10.002

Cited by 38 publications

(15 citation statements)

References 14 publications

(15 reference statements)

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“…According to authors, allocating a fixed amount of memory may lead to an underutilization of the resource, probably preventing the allocation of other VMs on the same host. The work presented in Reference and report the problem of memory ballooning when the memory allocated by the VM is not deallocated from the standpoint of the host operating system (OS). This problem is solved by monitoring the memory consumption within each VM and dynamically adjusting the allocated memory in accordance to the memory requirements of their running applications.…”

Section: Related Workmentioning

confidence: 99%

Static job scheduling for environments with vertical elasticity

Kloh

Rebello

Boeres

et al. 2020

Concurrency and Computation

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Summary In virtualized environments, such as Clouds, allocating a fixed amount of resources to a job a priori, may result in underutilization of the shared host. Meanwhile, vertical elasticity can be adopted to reduce the impact by resizing virtual machines (VMs) dynamically, in conjunction with suspension and/or migration before the host had been overloaded. In order to reduce the number of such events, but still avoid overloading the host, it is important to find an effective initial VM allocation. To achieve this, the scheduler must work in unison with the local host elasticity controllers to reduce interference. This work proposes and evaluates a framework for job scheduling in elastic memory managed virtualized environments. The memory elasticity management in clouds (MEMiC) framework is a two‐tier VM scheduler for batch jobs which attempts to predict the impact caused by competition for the memory of a host in shared Cloud‐like environments for harnessing VM allocation, suspension, and migration. Evaluations show that the MEMiC achieves a reduction of approximately 19% in the total time for jobs execution in comparison to other approaches, by reducing the degree of interference.

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Section: Related Workmentioning

confidence: 99%

Static job scheduling for environments with vertical elasticity

Kloh

Rebello

Boeres

et al. 2020

Concurrency and Computation

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“…• If a VM j is not using the memory requested during its creation, CloudVAMP will dynamically reduce its allocated memory without any downtime for the VM, according to the vertical elasticity rules described in [30].…”

Section: The Cloud Infrastructure Layermentioning

confidence: 99%

“…Concerning memory ballooning, CloudVAMP was configured to keep a minimum memory size per VM of 384 MB, required for the Operating System to properly function and allowing each node to maintain a 30% of free memory, which corresponds to a Memory Overprovisioning Percentage (MOP) slightly increased compared to the 20% value used in our earlier work in vertical elasticity [30]. Indeed, for this case study, the amount of cluster reconfigurations caused by adding additional nodes to the clusters introduced additional memory usage peaks which were better accommodated by these increased free memory safety margin.…”

Section: Case Studymentioning

confidence: 99%

Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures

et al. 2018

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Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the ability to dynamically power on the nodes of physical clusters as required. Therefore, this paper introduces an open-source framework to deploy elastic virtual clusters running on elastic physical clusters where the computing capabilities of the virtual clusters are dynamically changed to satisfy both the user application's computing requirements and to minimise the amount of energy consumed by the underlying physical cluster that supports an on-premises Cloud. For that, we integrate: i) an elasticity manager both at the infrastructure level (power management) and at the virtual infrastructure level (horizontal elasticity); ii) an automatic Virtual Machine (VM) consolidation agent that reduces the amount of powered on physical nodes using live migration and iii) a vertical elasticity manager to dynamically and transparently change the memory allocated to VMs, thus fostering enhanced consolidation. A case study based on real datasets executed over a production infrastructure is used to validate the proposed solution. The results show that a multi-elastic virtualized datacenter provides users with the ability to deploy customized scalable computing clusters while reducing its energy footprint.

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“…Modern platform virtualization offer sophisticated automatic memory allocation mechanisms to adjust the memory in use by the VMs they host, based on the current memory pressure regime , . Furthermore, other approaches (e.g., Liu et al, Schopp et al, Kim et al, Zhang et al, and Moltó et al just to name a few) aim at improving existing memory management techniques by overcoming their limitations and by reducing their overhead (and hence their impact) on the running VMs. However, all of these mechanisms are unaware of application performance and thus may lead to SLO violations, unless they are complemented by approaches like ours.…”

Section: Related Workmentioning

confidence: 99%

FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints

Anglano

Canonico

Guazzone

2016

Concurrency and Computation

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Summary Cloud providers (CPs) rely on server consolidation (the allocation of several virtual machines [VMs] on the same physical server) to minimize their costs. Maximizing the consolidation level is thus become 1 of the major goals of cloud providers. This is a challenging task because it requires the ability of estimating, in a resource contention scenario, multidimensional resource demands for multitier cloud applications that must meet service‐level agreements (SLAs) in face of nonstationary workloads. In this paper, we cope with the problem of jointly allocating CPU and memory capacity to (a) precisely estimate their capacity required by each VM to meet its SLAs and (b) coordinate their allocation to limit the negative effects due to the interactions of dynamic allocation mechanisms, which, if ignored, can lead to SLA violations. We tackle this problem by devising FCMS, a feedback fuzzy controller that is able to dynamically adjust the CPU and memory capacity allocated to each VM in a coordinated way, to precisely match the needs induced by the incoming workload. By means of an extensive experimental evaluation, we show that FCMS is able to achieve the above goals and works better than existing state‐of‐the‐art alternative solution in all the considered experimental scenarios.

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Automatic memory-based vertical elasticity and oversubscription on cloud platforms

Cited by 38 publications

References 14 publications

Static job scheduling for environments with vertical elasticity

Static job scheduling for environments with vertical elasticity

Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures

FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints

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