Low level Metrics to High level SLAs - LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments

Emeakaroha, Vincent C.; Brandić, Ivona; Maurer, Michael; Dustdar, Schahram

doi:10.1109/hpcs.2010.5547150

Cited by 176 publications

(102 citation statements)

References 13 publications

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“…The authors define it as "the first attempt to combine SLA-based resource negotiations with virtualized resources in terms of on-demand service provision", and represents a first step in the line of automated SLA-aware Clouds systems. Further works deal with specific facets of SLA management, such as a system for the monitoring of low level metrics in distributed environments and its transformation to high level SLA parameters [19].…”

Section: Related Workmentioning

confidence: 99%

SLA-driven dynamic cloud resource management

Garcı́a

Espert

García

2014

Future Generation Computer Systems

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As the size and complexity of Cloud systems increases, the manual management of these solutions becomes a challenging issue as more personnel, resources and expertise are needed. Service Level Agreement (SLA)-aware autonomic cloud solutions enable managing large scale infrastructure management meanwhile supporting multiple dynamic requirement from users. This paper contributes to these topics by the introduction of Cloudcompaas, a SLA-aware PaaS Cloud platform that manages the complete resource lifecycle. This platform features an extension of the SLA specification WSAgreement, tailored to the specific needs of Cloud Computing. In particular, Cloudcompaas enables Cloud providers with a generic SLA model to deal with higher-level metrics, closer to end-user perception, and with flexible composition of the requirements of multiple actors in the computational scene. Moreover, Cloudcompaas provides a framework for general Cloud computing applications that could be dynamically adapted to correct the QoS violations by using the elasticity features of Cloud infrastructures. The effectiveness of this solution is demonstrated in this paper through a simulation that considers several realistic workload profiles, where Cloudcompaas achieve minimum cost and maximum efficiency, under highly heterogeneous utilization patterns.

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Section: Related Workmentioning

confidence: 99%

SLA-driven dynamic cloud resource management

Garcı́a

Espert

García

2014

Future Generation Computer Systems

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“…These relevant primitives are numerous and tend to be various in different cloud scenarios. Existing works (e.g., [1]- [12], [20,21]) however, have only focused on deterministic scenarios, where the consideration of CP is limited and of fixed dimension. Research has specifically looked at hardware CPs at the IaaS font.…”

Section: Introductionmentioning

confidence: 99%

“…However, existing QoS models and their sensitivity are either static [1,5,7] or semi-dynamic [2,3,4,6,8,9,10,11,12,20,21]. In particular, the semi-dynamic models have only focused on dynamic expressions of how the primitives correlate with QoS but their treatments for which and when have been static.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive and sensitivity-aware QoS modeling for the cloud

Chen

Bahsoon

2013

2013 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Abstract-Given the elasticity, dynamicity and on-demand nature of the cloud, cloud-based applications require dynamic models for Quality of Service (QoS), especially when the sensitivity of QoS tends to fluctuate at runtime. These models can be autonomically used by the cloud-based application to correctly self-adapt its QoS provision. We present a novel dynamic and self-adaptive sensitivity-aware QoS modeling approach, which is fine-grained and grounded on sound machine learning techniques. In particular, we combine symmetric uncertainty with two training techniques: Auto-Regressive Moving Average with eXogenous inputs model (ARMAX) and Artificial Neural Network (ANN) to reach two formulations of the model. We describe a middleware for implementing the approach. We experimentally evaluate the effectiveness of our models using the RUBiS benchmark and the FIFA 1998 workload trends. The results show that our modeling approach is effective and the resulting models produce better accuracy when compared with conventional models.

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“…Time is used to show the improvement of the SLA based on scheduling algorithm, expanding utilization of resources, increasing of the quality of service, scalable and elastic the infrastructures, improve the waiting time, and improving the processing time. In paper [37] incorporate time via a run-time monitor for mapping rules based on the services being provisioned. They introduce a framework to manage the mappings of the Low-level resource Metrics to High-level SLAs called LoM2HiS framework.…”

Section: B Resource Monitoringmentioning

confidence: 99%

A Survey on Resource Allocation and Monitoring in Cloud Computing

Mohamaddiah¹,

Abdullah²,

Subramaniam³

et al. 2014

IJMLC

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Abstract-The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing.Index Terms-Resource allocation, resource monitoring, cloud computing, resource management. I. INTRODUCTIONCloud Computing, a service model was introduced to provide computing resources such as computing power, storage and bandwidth to deliver the IT services to the organization. This service model is rapidly being adopted by many organizations because it offers lots of business opportunity especially in terms of financial investment and human capital. With cloud services, the organization may opt out to set up a data center for its IT infrastructure or to procure hardware and software for its business applications as they can lease the resources from the cloud service provider. Initial providers in cloud computing such as Amazon EC2 [1], Google Apps Engine [2] , Microsoft Azure [3], SalesForce.com [4] offers great business value to the interested organizations who want to subscribe the services with a concept pay-per-use, on-demand and a defined Service level agreements (SLAs). For example, Google Apps Engine offers monthly uptime percentage: 99.00% -< 99.95% for client for its covered services. For the downtime period, they offer a period of five consecutive minutes of downtime. This value is an attractive package for the client, because they manage to control the usage of resources and demands more, faster and reliable infrastructure. Manuscript received October 11, 2013; revised December 7, 2013 On the other hand, the resources on the cloud are pooled in order to serve multiple subscribers. The provider use multi-tenancy model where the resources (physical and virtual) are reassigned dynamically based on the tenant requirement [5]. The assigning of the resources will be based on the lease and SLA agreement, whereby different clients will need more or less amount of virtual resources. Subsequently, the growth of demands for cloud services is bringing more challenge for the provider to provide the resources to the client subscriber. Therefore, in this paper we provide a review on cloud computing which focus on resource management: allocation and monitoring. Our methodolo...

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Low level Metrics to High level SLAs - LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments

Abstract: ABSTRACT

Cited by 176 publications

References 13 publications

SLA-driven dynamic cloud resource management

SLA-driven dynamic cloud resource management

Self-adaptive and sensitivity-aware QoS modeling for the cloud

A Survey on Resource Allocation and Monitoring in Cloud Computing

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