An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

Wang, Wenting; Chen, Haopeng; Chen, Xi

doi:10.1109/uic-atc.2012.31

Cited by 57 publications

(28 citation statements)

References 17 publications

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“…The nodes at bottom level are physical hosts where VMs are hosted. Wang et al were the first to provide a mathematical model to estimate the resulting availability from such a tree structure [36]. They calculate the availability of a single VM as the probability that neither the leaf itself, nor any of its ancestors fail.…”

Section: Level 3: Service Provisionmentioning

confidence: 99%

Traffic Management for Cloud Federation

Burakowski

Bęben

Berg

et al. 2018

Lecture Notes in Computer Science

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Abstract. The chapter summarizes activities of COST IC1304 ACROSS European Project corresponding to traffic management for Cloud Federation (CF). In particular, we provide a survey of CF architectures and standardization activities. We present comprehensive multilevel model for traffic management in CF that consists of five levels: Level 5 -Strategies for building CF, Level 4 -Network for CF, Level 3 -Service specification and provision, Level 2 -Service composition and orchestration, and Level 1 -Task service in cloud resources. For each level we propose specific methods and algorithms. The effectiveness of these solutions were verified by simulation and analytical methods. Finally, we also describe specialized simulator for testing CF solution in IoT environment.

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Section: Level 3: Service Provisionmentioning

confidence: 99%

Traffic Management for Cloud Federation

Burakowski

Bęben

Berg

et al. 2018

Lecture Notes in Computer Science

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“…Cloud customers pay for the requested resources. Cloud providers should pay for SLA penalty cost, rented hardware cost, software cost and action cost [31,32]. To minimize the cost for cloud customers or providers, a plenty of works have studied on scaling the application horizontally and/or vertically.…”

Section: Elasticity Managementmentioning

confidence: 99%

“…From the service providers' perspective, a few works [30][31][32] studied on application-to-VM-to-PM mappings to minimize the cost operating in a per-application level. For example, SmartSLA [32] horizontally and vertically tuned VMs according to the average SLA penalty cost predicted using machine learning to minimize SLA penalty cost, rented hardware cost, and action cost.…”

Section: Elasticity Managementmentioning

confidence: 99%

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Server Consolidation for Internet Applications in Virtualized Data Centers

Wang

Song²,

Sun³

et al. 2016

24th High Performance Computing Symposium

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Server consolidation based on virtualization technology simplifies system administration and improves energy efficiency by improving resource utilizations and reducing the physical machine (PM) number in contemporary service-oriented data centers. The elasticity of Internet applications changes the consolidation technologies from addressing virtual machines (VMs) to PMs mapping schemes which must know the VMs statuses, i.e. the number of VMs and the profiling data of each VM, into providing the application-to-VM-to-PM mapping. In this paper, we study on the consolidation of multiple Internet applications, minimizing the number of PMs with required performance. We first model the consolidation providing the application-to-VM-to-PM mapping to minimize the number of PMs as an integer linear programming problem, and then present a heuristic algorithm to solve the problem in polynomial time. Extensive experimental results show that our heuristic algorithm consumes less than 4.3% more resources than the optimal amounts with few overheads. Existing consolidation technologies using the input of the VM statuses output by our heuristic algorithm consume 1.06% more PMs.

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“…It has global load balancing support on multicore architecture. Dynamic Scaling of Cloud Applications proposes an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource [15].…”

Section: Related Workmentioning

confidence: 99%

CPU Resizing Vertical Scaling on Cloud

P¹,

Ellappan²,

Mehanathen³

2015

IJFCC

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Abstract-In a cloud computing, provisioned virtual machines are insufficient for computing the services it needs to be fine tuned by adding the additional resources or boosting the existing resources. A static method of resource allocation becomes inefficient under the different load. Based on the heavy load virtual machine can be adopted for dynamically changes of its behavior. There are two ways of scaling in the cloud scenario as horizontal scaling and vertical scaling. In a horizontal scaling, if the virtual machine resources are suffer from providing a service a new virtual machine was created and launched immediately. A threshold value has been maintained for scale out and scale down the virtual machine. Horizontal scaling is a traditional and well suitable approach for cloud computing environment. But the limitation of this approach is required separate load balancer to distribute the load between the virtual machines. In a vertical scaling resources are boosted by maximizing the virtual machine capabilities without shutting down the virtual machines. It's not like a traditional scaling approach to set the threshold and adding the resources. Here the resources are monitored by certain interval and based on the analysis resources are added to existing virtual machine. But lots of challenges are there in this approach. The limitation of this approach is we can't scale up the virtual machine up to the physical machine capability. Here we presented and analysis our approach for increasing the CPU capability of virtual machine. Likewise we can increase other resources like memory, vDisk and bandwidth. presented architecture resides on top of the virtual machine monitor and acts based on the scheduling algorithm.

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An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

Cited by 57 publications

References 17 publications

Traffic Management for Cloud Federation

Traffic Management for Cloud Federation

Server Consolidation for Internet Applications in Virtualized Data Centers

CPU Resizing Vertical Scaling on Cloud

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