QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

Lu, Kuan; Yahyapour, Ramin; Wieder, Philipp; Kotsokalis, Constantinos; Yaqub, Edwin; Jehangiri, Ali Imran

doi:10.1109/cloud.2013.112

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…The evaluation system for defining security in a multi-tenant environment comprises confidentiality, integrity, and availability issues. The security objectives prevent unauthorized restrictions of cloud information [24].…”

Section: Less Reliable and Scalable Highly Reliable And Scalablementioning

confidence: 99%

Multi Objective Task Scheduling VM Placement Method in Cloud Computing Environment Using Resource Optimization Technique

RAMEGOWDA

2024

Preprint

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The single-tenant-based applications consume high bandwidth and energy for each client. The multi-tenancy process adopts Software as a Service (SaaS) capability that allows a single model executing the service provider's platform to be accessed by numerous clients simultaneously. The virtual machine-based optimization method is required in the cloud computing domain as a dynamic resource scheduling method situated on unanticipated workloads. The multi-tenancy method provides the access to hardware and computing resources using the Infrastructure as a Service (IaaS) model. This article proposes the Adaptive Particle Swarm Optimization (APSO) method in a multi-tenant environment. The paper defines virtual machine placement, allowing cloud providers to provide powerful resource utilization capabilities.

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Section: Less Reliable and Scalable Highly Reliable And Scalablementioning

confidence: 99%

Multi Objective Task Scheduling VM Placement Method in Cloud Computing Environment Using Resource Optimization Technique

RAMEGOWDA

2024

Preprint

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“…When pursuing energy efficiency through consolidation, a critical issue regards the violation of the Service Level Agreement (SLA), i.e., the quality of service guaranteed to users. In more details, when performing consolidation one has to take into account the following issues: (i) short reductions or contraventions of the SLA are unavoidable since the resources of VMs are not available during their migration [6]; (ii) too aggressive on/off cycles of PMs can increase hardware outages and operational costs; (iii) the time needed to wake-up a host from a sleeping state is nonnegligible [7]. The previous constraints impose a tradeoff among SLA violations, churning of PMs (i.e., on/off cycles) and energy savings.…”

Section: Introductionmentioning

confidence: 99%

A predictive control approach for energy-aware consolidation of virtual machines in cloud computing

Gaggero

Caviglione

2014

53rd IEEE Conference on Decision and Control

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The widespread diffusion of Infrastructure-as-aService and cloud computing paradigms requires large-scale data centers with thousands of running nodes and high energy demands, thus causing relevant economical and environmental costs. In this perspective, the paper presents an energy-aware consolidation strategy based on predictive control, in which virtual machines are migrated among nodes to reduce the number of active units. To describe a general cloud infrastructure, a discrete-time dynamic model is presented together with constraints. The migration strategies of virtual machines are obtained by solving finite-horizon optimal control problems involving integer variables. To reduce the computational effort, approximate solutions are searched for via Monte Carlo optimization. Besides power savings, the proposed method allows one to reduce violations of the service level agreement and aggressive on/off cycles of nodes. To showcase the effectiveness of the proposed approach, preliminary simulation results are provided.

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Multi-dimensional scheduling in cloud storage systems

Yao

Papapanagiotou

Callaway

2015

2015 IEEE International Conference on Communications (ICC)

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The increasing demand for elastic and scalable cloud block storage requires flexible and efficient ways to provision volumes. The scheduling of volume requests in physical storage nodes or virtualized storage pools is usually based on a single criterion, such as the available capacity or the number of volumes per backend. Those properties are exposed to the cloud block storage scheduler through drivers, and may vary based on the workload. Hence, most cloud storage providers refrain from describing Service Level Objectives (SLOs). In this paper, we present the design and implementation of a new scheduling algorithm for block storage systems that has the following advantages over the currently implemented scheduler in OpenStack. It provides guaranteed SLOs even in a dynamic workload, it increases the I/O throughput of the volumes that have been already provisioned in the backend systems, it can be scalable to a higher arrival rate for the volume requests, and finally it can minimize the number of active hosts (or else the energy consumption). The volume placement process is based on an APX-hard multi-dimensional Vector Bin Packing (V BP d ) algorithm. In order to reduce the complexity we propose a heuristic named Modified Vector Best Fit Decreasing (MVBFD). Our scheduler design for block storage systems is based on the principles of the OpenStack's Cinder scheduler; hence it can be deployed with only minor modifications to an OpenStack block storage deployment.

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QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

Cited by 13 publications

References 17 publications

Multi Objective Task Scheduling VM Placement Method in Cloud Computing Environment Using Resource Optimization Technique

Multi Objective Task Scheduling VM Placement Method in Cloud Computing Environment Using Resource Optimization Technique

A predictive control approach for energy-aware consolidation of virtual machines in cloud computing

Multi-dimensional scheduling in cloud storage systems

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