Adaptation in cloud resource configuration: a survey

Hummaida, Abdul Rahman; Paton, Norman W.; Sakellariou, Rizos

doi:10.1186/s13677-016-0057-9

Cited by 41 publications

(29 citation statements)

References 72 publications

(152 reference statements)

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“…Additionally, research on a scaling method that considers QoS, which is an index of the operational status and performance of services provided by the system, has been proposed [8][9][10]. Considering the actual state of services, it is possible to incorporate specific operational targets, such as a service level agreement (SLA) and service level objective (SLO), as a guide for scaling.…”

Section: Problem Of Observing the Dynamics Of Server Systemsmentioning

confidence: 99%

“…When scaling is executed based on the observation of the QoS, it is necessary to capture the relation between the amount of computing resources allocated to VMs and QoS, that is, the operational characteristics of the server system, in advance. Existing scaling methods have highlighted practical issues, such as restricting elasticity, which is an advantage of the cloud environment in terms of requiring prior knowledge or the offline profiling of specific services [8]. QoS is widely used in various systems as a means to capture the status of running systems.…”

Section: Problem Of Observing the Dynamics Of Server Systemsmentioning

confidence: 99%

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Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

et al. 2017

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Elasticity is one of the key features of cloud-hosted services built on virtualization technology. To utilize the elasticity of cloud environments, administrators should accurately capture the operational status of server systems, which changes constantly according to service requests incoming irregularly. However, it is difficult to detect and avoid in advance that operating services are falling into an undesirable state. In this paper, we focus on the management of server systems that include cloud systems, and propose a new method for detecting the sign of undesirable scenarios before the system becomes overloaded as a result of various causes. In this method, a measure that utilizes the fluctuation of the macroscopic operational state observed in the server system is introduced. The proposed measure has the property of drastically increasing before the server system is in an undesirable state. Using the proposed measure, we realize a function to detect that the server system is falling into an overload scenario, and we demonstrate its effectiveness through experiments.

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Section: Problem Of Observing the Dynamics Of Server Systemsmentioning

confidence: 99%

Section: Problem Of Observing the Dynamics Of Server Systemsmentioning

confidence: 99%

Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

et al. 2017

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“…The authors of [21] focused on strategy, action type and architecture perspective. Whereas, an adaptability view of computational resources with a larger scope including concepts like node adaptation and virtual machine (VM) migration is provided in [22]. They, however, used adaptation techniques as one of the dimensions to review the literature.…”

Section: Cloud Elasticitymentioning

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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“…,69 The forecasting results are, consequently, used in the second phase which involves making the scaling decisions, modifying the infrastructure accordingly. Our solution will benefit by forecasting techniques as scaling the infrastructure as per the incoming demand is key to meeting SLAs.…”

mentioning

confidence: 99%

A statistical approach to virtual server resource management

Kontoudis

Fouliras

2017

Concurrency and Computation

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Resource management is of key importance in a wide array of computer and network environments. Failure to effectively allocate resources necessary for smooth infrastructure operation may result in impediments in successful service provisioning. This is especially prominent in environments whose workloads present fluctuating resource demand, such as in Cloud-based infrastructures. The mathematical science of statistics offers an extensive theoretical and practical toolbox that can be of potential use in tackling such problems in the information technology domain. We propose a novel approach to resource management of virtualized data center components. We have worked toward creating a mechanism that allows for dynamic resource allocation, adapting to the changing demand patterns. Our system incorporates a resource controller, based on Statistical Process Control, which permits the online management of a virtual machine's processor and memory capacity through real-time analysis of its observed performance. We successfully demonstrate our approach, with real data, on an architecture running the core banking environment of a financial institution. The controller successfully manages the resource allocation of the virtual machine and stabilizes its performance while business workloads are being processed. Our approach can be extended to realize different workload models and to manage other types of hypervisor provisioned resources.

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Adaptation in cloud resource configuration: a survey

Cited by 41 publications

References 72 publications

Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

Analysis of the Macroscopic Behavior of Server Systems in the Internet Environment

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

A statistical approach to virtual server resource management

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