Improving Scheduling Performance Using a Q-Learning-Based Leasing Policy for Clouds

Fölling, Alexander; Hofmann, Matthias

doi:10.1007/978-3-642-32820-6_34

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Others build a model to estimate the costs of running workloads in the cloud but they assume a specific kind of workloads (e.g. master-slave [9]) or assume users are only satisfied by a certain amount of resources [10], [11].…”

Section: A Overcommitted Environmentsmentioning

confidence: 99%

“…Still, most works until now have been focused on finding resource allocations in the most efficient way for the provider [13], [26], [8], [10], usually based on energy and/or cooling and environmental costs. Few works consider that the customer accepts a negotiable performance during the workload execution.…”

Section: Related Workmentioning

confidence: 99%

“…Hz Memory (Gbytes) 10 2 1860 4 10 2 2660 4 The scheduling algorithms were implemented as extensions of the type that determines how a VM is assigned to a host (VmAllocationPolicy). It can use different matrices of partial utility classes and VM base prices, defined in the type that represents the partial utility-driven datacenter.…”

Section: Number Of Hosts Coresmentioning

confidence: 99%

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Flexible SLAs in the Cloud with a Partial Utility-Driven Scheduling Architecture

Simão

Veiga

2013

2013 IEEE 5th International Conference on Cloud Computing Technology and Science

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Current clouds SLAs include compensation for customers (i.e. resource renters) with credits when average availability drops below a certain point. However, this credit scheme is too inflexible because consumers lose a non measurable quantity of performance and are only compensated later (i.e. in the next charging cycle). We propose to schedule cloud isolation and execution units, i.e. virtual machines (VMs), driven by the partial utility of applying a certain amount of resources (CPU, memory or bandwidth) to a given VM. This partial utility metric, specified by the customer, allows the provider to transfer resources between VMs. This is particularly relevant for private clouds where resources are not so abundant. We have defined a cost model that incorporates the partial utility the client gives to a certain level of depreciation when VMs are allocated in an overcommit environment. CloudSim, a state of the art cloud simulator, was extended to support our partial utility-driven scheduling model. Using simulation scenarios with synthetic and real workloads, we show that our proposed scheduling strategy brings benefits to providers (i.e. revenue, resource utilization) and clients (i.e. workloads' execution time) by incorporating a SLAbased depreciation of computational power, allowing for more VMs to be allocated.

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Section: A Overcommitted Environmentsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Flexible SLAs in the Cloud with a Partial Utility-Driven Scheduling Architecture

Simão

Veiga

2013

2013 IEEE 5th International Conference on Cloud Computing Technology and Science

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“…The cloud-computing paradigm has changed the traditional way in which software systems are built by means of the introduction of a new model in which infrastructures, platforms, applications and services are served on demand [1]. The consolidation of this new approach in the industry as well as in research and academic environments has arisen the need to reconsider the way technological resources are used in organizations, integrating cloudcomputing along with these resources [2,3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Reducing the price of resource provisioning using EC2 spot instances with prediction models

Fabra

Ezpeleta

Álvarez

2019

Future Generation Computer Systems

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The increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptely terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on nonpublic Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed.

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“…Academia [1][2][3][4][5][6][7] and industry [8] are both increasingly using cloud resources as infrastructure to serve their users, due to the elastic, flexible, and pay-as-you-go features of Infrastructureas-a-Service (IaaS) clouds. Cloud brokers need to lease resources from IaaS clouds cheaply, yet execute the users' jobs in time.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

Shen

Deng

Iosup

et al. 2013

Euro-Par 2013 Parallel Processing

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Abstract. Deploying applications in leased cloud infrastructure is increasingly considered by a variety of business and service integrators. However, the challenge of selecting the leasing strategy -larger or faster instances? on-demand or reserved instances? etc.-and to configure the leasing strategy with appropriate scheduling policies is still daunting for the (potential) cloud user. In this work, we investigate leasing strategies and their policies from a broker's perspective. We propose, CoH, a family of Cloud-based, online Hybrid scheduling policies that minimizes rental cost by making use of both on-demand and reserved instances. We formulate the resource provisioning and job allocation policies as Integer Programming problems. As the policies need to be executed online, we limit the time to explore the optimal solution of the integer program, and compare the obtained solution with various heuristics-based policies; then automatically pick the best one. We show, via simulation and using multiple real-world traces, that the hybrid leasing policy can obtain significantly lower cost than typical heuristics-based policies.

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Improving Scheduling Performance Using a Q-Learning-Based Leasing Policy for Clouds

Cited by 5 publications

References 14 publications

Flexible SLAs in the Cloud with a Partial Utility-Driven Scheduling Architecture

Flexible SLAs in the Cloud with a Partial Utility-Driven Scheduling Architecture

Reducing the price of resource provisioning using EC2 spot instances with prediction models

Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

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