Resource Management in Clouds: Survey and Research Challenges

Jennings, Brendan; Stadler, Rolf

doi:10.1007/s10922-014-9307-7

Cited by 449 publications

(271 citation statements)

References 174 publications

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“…A cloud infrastructure is typically characterized by a pool of heterogeneous hardware and software resources that are shared by many application services with disparate performance objectives [ [135]; [136]]. The resulting resource contention and performance interference caused by resource sharing have significant impact on the performance of cloud services and systems [ [39]; [122]].…”

Section: Padbi Systems In the Cloud: Specific Requirementsmentioning

confidence: 99%

Performance Anomaly Detection and Bottleneck Identification

2015

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In order to meet stringent performance requirements, system administrators must e↵ectively detect undesirable performance behaviours, identify potential root causes and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in di↵erent system and application domains is well studied. In order to assess progress, research trends and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods.

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Section: Padbi Systems In the Cloud: Specific Requirementsmentioning

confidence: 99%

Performance Anomaly Detection and Bottleneck Identification

2015

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“…Amongst these problems, cloud elasticity (or dynamic cloud resource provisioning) approaches are covered either partially or in a limited capacity. For example, Singh and Chana [11] focused on autonomic computing with a particular emphasis on QoS-aware management of resources; Jennings and Stadler [12] used resource management functions as a classification method; Mustafa et al [13] reviewed the literature based on the metrics used and discussed the underlying research problems. Manvi and Shyam [14] classified the literature into problem specific categories such as resource provisioning and allocation; whereas Singh and Chana [15] targeted resource provisioning in general, wherein elasticity is considered as a trait of resource provisioning mechanism.…”

Section: Cloud Resource Managementmentioning

confidence: 99%

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

Ullah

Shen

et al. 2018

Cluster Comput

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The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the highlevel view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field.

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“…FORMULAÇÃO DO PROBLEMA Em um ambiente em nuvem, quando um mecanismo de provisionamento de recursos bem elaborado é utilizado, as aplicações podem operar mais eficientemente, com redução nos custos, melhor utilização da infraestrutura disponível e melhor desempenho em momentos de pico com variações na demanda de serviços. No entanto, o processo de provisionamento é complexo [28]. De acordo com [29], isto requer a definição das melhores configurações de software e hardware para garantir que os SLAs sejam cumpridos, além de maximizar a eficiência e a utilização do sistema.…”

Section: Tabela I Comparação Com Outros Trabalhos Relacionadounclassified

An Analysis of Optimization Algorithms designed to fully comply with SLA in Cloud Computing

Nakamura

Azevedo

Batista

et al. 2017

IEEE Latin Am. Trans.

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Abstract-Nowadays the access to a cloud computing environment is provided on-demand offering transparent services to customers. Although the cloud allows an abstraction of the behavior of the service providers in the infrastructure (involving logical and physical resources), it remains a challenge to fully comply with the Service Level Agreements (SLAs), because, depending on the service demand and system configuration, the providers may not be able to meet the requirements of the customers. There is a need for mechanisms that take account of load balancing algorithms to provide an efficient load distribution with the available resources. However, the studies in the literature do not effectively address the problem of the availability of resources to meet customers' requirements with analysis restricted to a limited set of objectives. This paper proposes algorithms to address the need for optimization when handling computational resources during the execution time. The methods optimize the efficient use of the resources available in the infrastructure aiming to comply with the service level agreements defined between client and provider.

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Resource Management in Clouds: Survey and Research Challenges

Cited by 449 publications

References 174 publications

Performance Anomaly Detection and Bottleneck Identification

Performance Anomaly Detection and Bottleneck Identification

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

An Analysis of Optimization Algorithms designed to fully comply with SLA in Cloud Computing

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