Adaptive resource provisioning for read intensive multi-tier applications in the cloud

Iqbal, Waheed; Dailey, Matthew N.; Carrera, David; Janecek, Paul

doi:10.1016/j.future.2010.10.016

Cited by 223 publications

(122 citation statements)

References 6 publications

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“…We choose to use extra-small VMs only because they offer the most cost-effective way to execute RUBiS workload. For example, the price of The delay settings for our RUBiS application are consistent with prior studies [51,122].…”

Section: Methodssupporting

confidence: 75%

Sustainable Resource Management for Cloud Data Centers

Mahmud¹

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In recent years, the demand for data center computing has increased significantly due to the growing popularity of cloud applications and Internet-based services. Today's large data centers host hundreds of thousands of servers and the peak power rating of a single data center may even exceed 100MW. The combined electricity consumption of global data centers accounts for about 3% of worldwide production, raising serious concerns about their carbon footprint. The utility providers and governments are consistently pressuring data center operators to reduce their carbon footprint and energy consumption.While these operators (e.g., Apple, Facebook, and Google) have taken steps to reduce their carbon footprints (e.g., by installing on-site/off-site renewable energy facility), they are aggressively looking for new approaches that do not require expensive hardware installation or modification.This dissertation focuses on developing algorithms and systems to improve the sustainability in data centers without incurring significant additional operational or setup costs. In the first part, we propose a provably-efficient resource management solution for a self-managed data center to cap and reduce the carbon emission while maintaining satisfactory service performance. Our solution reduces the carbon emission of a selfmanaged data center to net-zero level and achieves carbon neutrality. In the second part, we consider minimizing the carbon emission in a hybrid data center infrastructure that includes geographically distributed self-managed and colocation data centers. This segment vi identifies and addresses the challenges of resource management in a hybrid data center infrastructure and proposes an efficient distributed solution to optimize the workload and resource allocation jointly in both self-managed and colocation data centers. In the final part, we explore sustainable resource management from cloud service users' point of view. A cloud service user purchases computing resources (e.g., virtual machines) from the service provider and does not have direct control over the carbon emission of the service provider's data center. Our proposed solution encourages a user to take part in sustainable (both economical and environmental) computing by limiting its spending on cloud resource purchase while satisfying its application performance requirements.

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Section: Methodssupporting

confidence: 75%

Sustainable Resource Management for Cloud Data Centers

Mahmud¹

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“…The life cycle management of workloads can be categorised into two overlapping phases. Admission control [62], which is the decision to accept a new workload if it contributes to the current management objectives and resource adaptation [10], which reconfigures the infrastructure after a state change. Several proposals treat admission control as distinct phase and assume availability of free resources.…”

Section: Vm Adaptation -Cpu and Memorymentioning

confidence: 99%

“…In [29], the authors propose a proactive controller that provisions based on peak load seen in the last hour, with a reactive controller for sudden bursts, but it had no ability to scale down CPU/memory resources. In [26,62], the authors extend this approach and use a reactive controller for scaling up and a proactive controller for scaling down, by removing whole VMs. The authors claim this hybrid approach is able to cope with sudden bursts as well as being able to conserve energy by proactively switching nodes off.…”

Section: Adaptation Engagementmentioning

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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“…Carrera et al [12] presented a utilitybased web application placement approach to maximize application performance on clusters of PMs. Iqbal et al [24] proposed an approach for multi-tier web applications, which uses response time and CPU utilization metrics to determine the bottleneck tier and then scales it by provisioning a new VM. Calinescu et al [11] presented a tool-supported framework for QoS management and optimization of self-adaptive service-based systems.…”

Section: Vm Provisioning Approachesmentioning

confidence: 99%

Prediction-based VM provisioning and admission control for multi-tier web applications

2016

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We present a prediction-based, cost-efficient Virtual Machine (VM) provisioning and admission control approach for multi-tier web applications. The proposed approach provides automatic deployment and scaling of multiple web applications on a given Infrastructure as a Service (IaaS) cloud. It monitors and uses collected resource utilization metrics itself and does not require a performance model of the applications or the infrastructure dynamics. The approach uses the OSGi component model to share VM resources among deployed applications, reducing the total number of required VMs. The proposed approach comprises three sub-approaches: a reactive VM provisioning approach called ARVUE, a hybrid reactive-proactive VM provisioning approach called Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling (CRAMP), and a session-based adaptive admission control approach called adaptive Admission Control for Virtualized Application Servers (ACVAS). Performance under varying load conditions is guaranteed by automatic adjustment and tuning of the CRAMP and ACVAS parameters. The proposed approach is demonstrated in discrete-event simulations and is evaluated in a series of experiments involving synthetic as well as realistic load patterns.

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Adaptive resource provisioning for read intensive multi-tier applications in the cloud

Cited by 223 publications

References 6 publications

Sustainable Resource Management for Cloud Data Centers

Sustainable Resource Management for Cloud Data Centers

Adaptation in cloud resource configuration: a survey

Prediction-based VM provisioning and admission control for multi-tier web applications

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