SLA-Aware Application Deployment and Resource Allocation in Clouds

Emeakaroha, Vincent C.; Brandić, Ivona; Maurer, Michael; Breskovic, Ivan

doi:10.1109/compsacw.2011.97

Cited by 57 publications

(29 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, some of the researches proposed solutions for SLA violation which able to cope with all cloud layers. However, some of the researches consider the problem as an optimization problem in terms of minimizing violations (i.e., increase customer satisfaction) and maximize provider profit [19][20][21]. Other researchers considered SLA management as a market-based issue [22][23].…”

Section: Cloud Computingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on SLA Management for Cloud Computing and Cloud-Hosted Big Data Analytic Applications

Sahal¹,

Khafagy²,

Omara³

2016

IJDTA

View full text Add to dashboard Cite

show abstract

Section: Cloud Computingmentioning

confidence: 99%

“…In [19], a scheduling heuristic (i.e., optimization-based technique) has been proposed which considers multiple SLA parameters for application deployments in the cloud. The considered attributes (i.e., multiple SLA parameters) include CPU time, network bandwidth and storage capacity for deploying applications.…”

Section: Optimization-basedmentioning

confidence: 99%

A Survey on SLA Management for Cloud Computing and Cloud-Hosted Big Data Analytic Applications

Sahal¹,

Khafagy²,

Omara³

2016

IJDTA

View full text Add to dashboard Cite

show abstract

“…The earliest research efforts often focused on minimizing Cloud consumer's costs while taking into account maximum allowed execution time [7] while later approaches considered holistic Service Level Agreement (SLA) enforcement [5]. Recently, research efforts have paid special attention to the infrastructure perspective, i.e., the adherence to consumer-defined SLAs under the objective of profit maximization [14] or high resource utilization [9,13,16]. While most of these approaches apply threshold-based fixed rules to identify necessary actions (e.g., stop/start servers, move services), Li and Venugopal [16] make use of a learning-based approach to automatically scale an application up or down based on incoming workload.…”

Section: Related Workmentioning

confidence: 99%

Introducing the Vienna Platform for Elastic Processes

Schulte

Hoenisch

Venugopal

et al. 2013

Service-Oriented Computing

View full text Add to dashboard Cite

Abstract. Resource-intensive tasks are playing an increasing role in business processes. The emergence of Cloud computing has enabled the deployment of such tasks onto resources sourced on-demand from Cloud providers. This has enabled so-called elastic processes that are able to dynamically adjust their resource usage to meet varying workloads.Traditional Business Process Management Systems (BPMSs) do not consider the needs of elastic processes such as monitoring facilities, tracking the current and future system landscape, reasoning about optimally utilizing resources given Quality of Service constraints, and executing necessary actions (e.g., start/stop servers, move services). This paper introduces ViePEP, a research BPMS capable of handling the aforementioned requirements of elastic processes.

show abstract

“…Hence, building such a model of relations at the application-level can greatly improve application and service-level controllers. This is an area of particular relevance for other computation models as well, such as Cloud computing (Emeakaroha et al, 2011;Sironi et al, 2012;Yoo and Kim, 2013;Feng et al, 2012), Virtualization (Weng et al, 2011), and Map Reduce (Ibrahim et al, 2011), (Polo et al, 2011), where advanced resource and activity aware adaptive schedulers can be equipped with services activity and resources decomposition methods for better SLA and revenue awareness. An application level control and management is a widely adopted approach (Katchabaw et al, 1999;Chen et al, 2002), where control can be also done on a service level directly, with use of deeper instrumentation of the application running in ASM environment.…”

Section: Industry Practicementioning

confidence: 99%

Evolutionary approaches to signal decomposition in an application service management system

Sikora

Magoulas

2015

Soft Comput

View full text Add to dashboard Cite

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a-priori. In our approach, the Application Service Management System is treated as a black-or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queuebased computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers.

show abstract

SLA-Aware Application Deployment and Resource Allocation in Clouds

Cited by 57 publications

References 9 publications

A Survey on SLA Management for Cloud Computing and Cloud-Hosted Big Data Analytic Applications

A Survey on SLA Management for Cloud Computing and Cloud-Hosted Big Data Analytic Applications

Introducing the Vienna Platform for Elastic Processes

Evolutionary approaches to signal decomposition in an application service management system

Contact Info

Product

Resources

About