Autonomic Workload and Resources Management of Cloud Computing Services

Fargo, Farah; Tunc, Cihan; Al-Nashif, Youssif; Akoglu, Ali; Hariri, Salim

doi:10.1109/iccac.2014.36

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…The existing research works lack on providing an explanation on how a proposed method deals with such undesirable oscillatory behaviour. (vi) Resource usage analysis over-provisioning is used to avoid performance violation considering peak workload scenarios [110,111]. However, this results in the wastage of resources.…”

Section: Discussion Issues and Challengesmentioning

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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Section: Discussion Issues and Challengesmentioning

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 work proposed in [21] presents an autonomic power and performance management technique for cloud systems to dynamically match the application requirements with just-enough system resources at runtime which contributes to significant power reduction while meeting the QoS requirements of cloud applications. The proposed model delivers: 1) Real-time monitoring and describing of the workloads to specify the cloud resources that meet their QoS requirements.…”

Section: Autonomic Resource Management and Dynamic Resource Provisioningmentioning

confidence: 99%

Taxonomy of Autonomic Cloud Computing

Hamzeh¹,

Meacham²,

Virginas³

et al. 2018

IJCCE

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Cloud computing is a paradigm that has become popular in recent decade. The flexibility, scalability, elasticity, inexpensive and unlimited use of resources have made the cloud an efficient and valuable infrastructure for many organizations to perform their computational operations. Specifically, the elasticity feature of cloud computing leads to the increase of complexity of this technology. Considering the emergence of new technologies and user demands, the existing solutions are not suitable to satisfy the huge volume of data and user requirements. Moreover, certain quality requirements that have to be met for efficient resource provisioning such as Quality of Service (QoS) is an obstacle to scalability. Hence, autonomic computing has emerged as a highly dynamic solution for complex administration issues that goes beyond simple automation to self-learning and highly-adaptable systems. Therefore, the combination of cloud computing and autonomics known as Autonomic Cloud Computing (ACC) seems a natural progression for both areas. This paper is an overview of the latest conducted research in ACC and the corresponding software engineering techniques. Additionally, existing autonomic applications, methods and their use cases in cloud computing environment are also investigated.

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“…An example is re-evaluating the current mapping between virtual and physical hardware to reach higher-level objectives, such as improved application availability. Work in this area has been done by, e.g., Dautov et al [17], Karakostas [18], Fargo et al [19], Maurer et al [20], Wood et al [21], and Shrivastava et al [22]. At the service level, autonomic computing approaches can be used to manage the operations of the applications themselves.…”

Section: Autonomic Computingmentioning

confidence: 99%

Contextualization: dynamic configuration of virtual machines

et al. 2015

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New VM instances are created from static templates that contain the basic configuration of the VM to achieve elasticity with regards to capacity. Instance specific settings can be injected into the VM during the deployment phase through means of contextualization. So far this is limited to a single data source and data remains static throughout the lifecycle of the VM. We present a layered approach to contextualization that supports different classes of contextualization data available from several sources. The settings are made available to the VM through virtual devices. Inside each VM data from different classes are layered on top of each other to create a unified file hierarchy. Context data can be modified during runtime by updating the contents of the virtual devices, making our approach the first contextualization approach to natively support recontextualization. Recontextualization enables runtime reconfiguration of an executing service and can act as a trigger and key enabler of self-* techniques. This trigger provides a service with a mechanism to adapt or optimize itself in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are illustrated in an example with a distributed file system, demonstrating the feasibility of our approach.

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Autonomic Workload and Resources Management of Cloud Computing Services

Cited by 18 publications

References 29 publications

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

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

Taxonomy of Autonomic Cloud Computing

Contextualization: dynamic configuration of virtual machines

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