Augmenting Elasticity Controllers for Improved Accuracy

Rameshan, Navaneeth; Liu, Ying; Navarro, Leandro; Vlassov, Vladimir

doi:10.1109/icac.2016.60

Cited by 7 publications

(10 citation statements)

References 23 publications

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“…Similarly, predicting the runtime of VMs is also influenced by the type of workload hosted in the VM. In [22], the authors describe various approaches to accurate historical data if workloads differ. Further details on workload predictions can be found in [12], [16], [21], that offer reasonable accuracy for public clouds where workloads fluctuate more than private ones, significantly.…”

Section: Runtimes Predictionmentioning

confidence: 99%

“…Various studies suggest that performance of cloud applications or workloads perform quite differently due to: (i) CPU models [4], [24]; and (ii) resource contention [3], [22], [25]. Regarding (i), similar VMs (workloads) run quite strangely even on same CPU models; which may be related to either design (fabrication process), cache levels and/or memory churns.…”

Section: Performancementioning

confidence: 99%

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CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

Zakarya¹,

Gillam²,

Rana³

et al. 2021

Preprint

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In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users' costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this paper, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users' costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ~9.61% more energy and ~20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users' costs.<br>

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Section: Runtimes Predictionmentioning

confidence: 99%

Section: Performancementioning

confidence: 99%

CoLocateMe: Aggregation-based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

Zakarya¹,

Gillam²,

Rana³

et al. 2021

Preprint

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“…[3] proposes to use cost and round trip time metrics to decide whether to place latency-critical Internet of Things applications to edge or to the central data center. An approach based on the notion of technical debt was proposed in [4] to manage the elasticity of SaaS applications by establishing coalitions of applications sharing the cloud resources to fulfill the corresponding SLOs.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The proposed approach to vertical scaling was extended with probabilistic cloning of requests in [8], which improved SLO-compliance of running applications and reduced over-provisioning in comparison to [9]. The effects of 1 https://www.docker.com/ 2 https://kubernetes.io/ performance interference between co-located VMs on SLOs were thoroughly studied in [4], resulting in a technique that, depending on unmet demand and spare capacity, reconfigures the load balancer and adjusts the scaling actions.…”

Section: Background and Related Workmentioning

confidence: 99%

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Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing

Podolskiy

Mayo

Koey

et al. 2019

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented.

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