Effective Modeling Approach for IaaS Data Center Performance Analysis under Heterogeneous Workload

Chang, Xiaolin; Xia, Ruofan; Muppala, Jogesh K.; Trivedi, Kishor S.; Liu, Jiqiang

doi:10.1109/tcc.2016.2560158

Cited by 33 publications

(23 citation statements)

References 22 publications

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“…For large IaaS clouds that contain millions of heterogeneous computing nodes, analysis based on models is generally focused on identifying key factors without being tied to specific details or particular applications [4]. At present, many formal methods have been used to study the performance and availability of IaaS, for example, Markov chains [5] [6] , Petri nets [7] [8] [9], fault trees [ so on. However, there are two challenges not captured by previous IaaS availability models.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we use task size to denote the number of VM required by a job, and we will investigate the impact of task size on availability. Recently, [15] [9] considered the number of vCPUs requested by each customer job. However, these authors compute the instantaneous probability of a job departure by solving Markov chains [15] [9] [11].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, PMs in real cloud data centers may be from different manufacturers, or have been upgraded or replaced. They may have distinct features including different capacity, different configuration, different Mean Time To Repair (MTTR) and so on [9]. Thus the behaviors of PMs are heterogeneous due to their distinct features which may have an impact on availability analysis.…”

Section: Introductionmentioning

confidence: 99%

“…2) Addressing the challenge of determining the completion time of a super-task. In the prior research [9] [15] [5] [12], the task completion time is obtained by solving a Markov process, but the PMs are treated as identical i.e. the same kind of machine, by default.…”

Section: Introductionmentioning

confidence: 99%

“…3) Using attribute-based components, the heterogeneity of both task arrival patterns and PMs in IaaS can be modeled to reflect unique characteristics. Taking into account all features from capacity to execution mode rather than just the number of vCPUs [9] or the number of requested physical cores in VMs [15]. This is powerful enough to capture periodic tasks with different cycle lengths, burst tasks with different duration times, and PMs with an inherited ageing phenomenon.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Attribute-Based Availability Model for Large Scale IaaS Clouds with CARMA

Hillston

Piho

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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High availability is one of the core properties of Infrastructure as a Service (IaaS) and ensures that users have anytime access to on-demand cloud services. However, significant variations of workflow and the presence of super-tasks, mean that heterogeneous workload can severely impact the availability of IaaS clouds. Although previous work has investigated global queues, VM deployment, and failure of PMs, two aspects are yet to be fully explored: one is the impact of task size and the other is the differing features across PMs such as the variable execution rate and capacity. To address these challenges we propose an attribute-based availability model of large scale IaaS developed in the formal modeling language CARMA. The size of tasks in our model can be a fixed integer value or follow the normal, uniform or log-normal distribution. Additionally, our model also provides an easy approach to investigating how to arrange the slack and normal resources in order to achieve availability levels. The two goals of our work are providing an analysis of the availability of IaaS and showing that the use of CARMA allows us to easily model complex phenomena that were not readily captured by other existing approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Attribute-Based Availability Model for Large Scale IaaS Clouds with CARMA

Hillston

Piho

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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Availability modeling in redundant OpenStack private clouds

et al. 2021

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In cloud computing services, high availability is one of the quality of service requirements which is necessary to maintain customer confidence. High availability systems can be built by applying redundant nodes and multiple clusters in order to cope with software and hardware failures. Due to cloud computing complexity, dependability analysis of the cloud may require combining state-based and nonstate-based modeling techniques. This article proposes a hierarchical model combining reliability block diagrams and continuous time Markov chains to evaluate the availability of OpenStack private clouds, by considering different scenarios. The steady-state availability, downtime, and cost are used as measures to compare different scenarios studied in the article. The heterogeneous workloads are considered in the proposed models by varying the number of CPUs requested by each customer. Both hardware and software failure rates of OpenStack components used in the model are collected via setting up a real OpenStack environment applying redundancy techniques. Results obtained from the proposed models emphasize the positive impact of redundancy on availability and downtime. Considering the tradeoff between availability and cost, system providers can choose an appropriate scenario for a specific purpose. K E Y W O R D S availability modeling, cloud computing, continuous time Markov chains, hierarchical models, reliability block diagrams INTRODUCTIONCloud computing includes different resources of one or more data centers, such as storage, network and compute, shared through virtualization technology. 1 Infrastructure as a service (IaaS) is an important service model of the cloud computing paradigm in which providers allocate virtual computing resources as virtual machines (VMs) on physical machines (PMs) to customers over the internet. [2][3][4] According to Gartner, 5 the market for public cloud will reach over $306.9 billion at the end of 2021 and $364 billion at the end of 2022. Gartner expects the hyperscale providers, such as Amazon AWS, Google Cloud Platform, Alibaba, and Microsoft Azure, to account for 70% of the market by the end of 2021. 6 Large organization and government agencies usually like to develop their own cloud data centers for satisfying their needs. By contrast, small organizations usually rent cloud resources from cloud providers to solve their own problems. 7

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