Elastic Virtual Machine for Fine-Grained Cloud Resource Provisioning

Dawoud, Wesam; Takouna, Ibrahim; Meinel, Christoph

doi:10.1007/978-3-642-29219-4_2

Cited by 51 publications

(38 citation statements)

References 13 publications

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“…RUBBoS is a bulletin-board prototype website modeled after Slashdot and has also been used as a benchmark in cloud computing research [19,37,46,69].…”

Section: Methodsmentioning

confidence: 99%

Brownout: building more robust cloud applications

Klein

Maggio

Årzén

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

119

134

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Self-adaptation is a first class concern for cloud applications, which should be able to withstand diverse runtime changes. Variations are simultaneously happening both at the cloud infrastructure levelfor example hardware failures -and at the user workload levelflash crowds. However, robustly withstanding extreme variability, requires costly hardware over-provisioning.In this paper, we introduce a self-adaptation programming paradigm called brownout. Using this paradigm, applications can be designed to robustly withstand unpredictable runtime variations, without over-provisioning. The paradigm is based on optional code that can be dynamically deactivated through decisions based on control theory.We modified two popular web application prototypes -RUBiS and RUBBoS -with less than 170 lines of code, to make them brownout-compliant. Experiments show that brownout selfadaptation dramatically improves the ability to withstand flashcrowds and hardware failures.

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“…RUBBoS is a bulletin-board prototype website modeled after Slashdot and has also been used as a benchmark in cloud computing research [19,37,46,69].…”

Section: Methodsmentioning

confidence: 99%

Brownout: building more robust cloud applications

Klein

Maggio

Årzén

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

119

134

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“…While this is simpler to apply, compared to fine grain CPU and memory configuration, it may lead to wastage by over-allocating resources to workloads as well consume more power. In [33] the authors further argued that fine grain CPU and memory configuration reduces the provisioning overhead and mitigates SLA violations. Other proposals, particularly those focusing on maximising revenue, apply fine grain management of VM resources with CPU and memory configurations modified in discrete values using the Xen [34] hypervisor API.…”

Section: Vm Adaptation -Cpu and Memorymentioning

confidence: 99%

Adaptation in cloud resource configuration: a survey

2016

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With increased demand for computing resources at a lower cost by end-users, cloud infrastructure providers need to find ways to protect their revenue. To achieve this, infrastructure providers aim to increase revenue and lower operational costs. A promising approach to addressing these challenges is to modify the assignment of resources to workloads. This can be used, for example, to consolidate existing workloads; the new capability can be used to serve new requests or alternatively unused resources may be turned off to reduce power consumption. The goal of this paper is to highlight features, approaches and findings in the literature, in order to identify open challenges and facilitate future developments. We present a definition of cloud systems adaptation, a classification of the key features and a survey of adapting compute and storage configuration. Based on our analysis, we identify three open research challenges: characterising the workload type, accurate online profiling of workloads, and building highly scalable adaptation mechanisms.

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“…Some elasticity managements [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] applied one or the combination of vertical scaling (resizing a VM) and horizontal scaling (adding/deleting VMs) to provide the mapping between elastic applications and VMs leased from a public cloud for minimizing the rent cost in the perspective of cloud users. While they do not consider the placement of VMs on PMs, which is critical for improving energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

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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Elastic Virtual Machine for Fine-Grained Cloud Resource Provisioning

Cited by 51 publications

References 13 publications

Brownout: building more robust cloud applications

Brownout: building more robust cloud applications

Adaptation in cloud resource configuration: a survey

Server Consolidation for Internet Applications in Virtualized Data Centers

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