Availability in the cloud: State of the art

Nabi, Mina; Toeroe, Maria; Khendek, Ferhat

doi:10.1016/j.jnca.2015.11.014

Cited by 75 publications

(48 citation statements)

References 36 publications

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“…We endeavor to extend the modeling approach proposed in this paper to capture influences caused by different types of failures. Various availability solutions for cloud were proposed [21]. Future work also includes using our proposed modeling approach to analyze these solutions and compare from a model-based analysis point of view.…”

Section: Discussionmentioning

confidence: 99%

Model-based sensitivity analysis of IaaS cloud availability

Liu

Chang

Han

et al. 2018

Future Generation Computer Systems

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The increasing shift of various critical services towards Infrastructure-as-a-Service (IaaS) cloud data centers (CDCs) creates a need for analyzing CDCs' availability, which is affected by various factors including repair policy and system parameters. This paper aims to apply analytical modeling and sensitivity analysis techniques to investigate the impact of these factors on the availability of a large-scale IaaS CDC, which (1) consists of active and two kinds of standby physical machines (PMs), (2) allows PM moving among active and two kinds of standby PM pools, and (3) allows active and two kinds of standby PMs to have different mean repair times. Two repair policies are considered: (P1) all pools share a repair station and (P2) each pool uses its own repair station. We develop monolithic availability models for each repair policy by using Stochastic Reward Nets and also develop the corresponding scalable two-level models in order to overcome the monolithic model's limitations, caused by the large-scale feature of a CDC and the complicated interactions among CDC components. We also explore how to apply differential sensitivity analysis technique to conduct parametric sensitivity analysis in the case of interacting submodels. Numerical results of monolithic models and simulation results are used to verify the approximate accuracy of interacting sub-models, which are further applied to examine the sensitivity of the large-scale CDC availability with respect to repair policy and system parameters.

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Section: Discussionmentioning

confidence: 99%

Model-based sensitivity analysis of IaaS cloud availability

Liu

Chang

Han

et al. 2018

Future Generation Computer Systems

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“…The performance can be worse especially when the workload is not even. Others such as [27]- [41] used queuing theory to model the cloud system based on the arrival rate of requests and other parameters such as mean service time (i.e. response time) and CPU load.…”

Section: Dynamic System Reconfigurationmentioning

confidence: 99%

SLA Compliance Checking and System Runtime Reconfiguration — A Model Based Approach

Abbasipour¹,

Khendek²,

Toeroe³

2019

JSW

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Service providers aim at optimizing system resource utilization while ensuring the quality of service expressed in the Service Level Agreements (SLAs) is met. For this purpose, systems are reconfigured dynamically according to workload variations to satisfy the SLAs while using only the necessary resources. Whenever a dynamic reconfiguration is required because of low resource utilization or potential SLA violations, one or more triggers may be generated. These generated triggers invoke elasticity rules that define actions to be taken in each specific situation. The elasticity rules that are invoked at the same time may lead to actions that may impact each other. As a result, handling each trigger independently may threaten the stability of the system. In this paper, we propose a model-driven framework, which manages the compliance of SLAs and enables dynamic reconfiguration. We use UML models to describe all the artifacts in the framework. All SLA models are transformed into an SLA compliance model which is used at runtime to check SLA compliance and generate triggers when a dynamic reconfiguration is required. In this framework, the actions of the elasticity rules invoked simultaneously are correlated before their application. The proposed correlation is based on the relations between the triggers. We perform a preliminary evaluation of the approach.

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“…It is the combination of resource's accessibility, maintainability, reliability, securability and serviceability in cloud computing [133,134].…”

Section: Analysis Of Resources and Parameters Used In Current Studiesmentioning

confidence: 99%

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

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Availability in the cloud: State of the art

Cited by 75 publications

References 36 publications

Model-based sensitivity analysis of IaaS cloud availability

Model-based sensitivity analysis of IaaS cloud availability

SLA Compliance Checking and System Runtime Reconfiguration — A Model Based Approach

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

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