Toward a high availability cloud: Techniques and challenges

Pham, Cuong; Cao, Phuong; Kalbarczyk, Zbigniew; Iyer, Ravishankar K.

doi:10.1109/dsnw.2012.6264687

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…State-of-the-art computing paradigms: Cloud computing has been accredited as a centralized computing paradigm successfully adopted in many online business services and applications in the past few years featured by its pricing models of pay-as-you-go and its service platforms of Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) [24,25]. Resource virtualization technologies along with fault-tolerance techniques at all levels of the cloud infrastructure help ease centralized operative management and resource scalability, thus maximize service availability, agility and adaptability to the non-uniform variation of service loads and user demands [26][27][28]. Nevertheless, the geographical centralization and the limited amount of huge cloud data centers which are often allocated in specific regions around the world for safety reasons cause various issues in real-time data transactions of latency-sensitive services and applications running in remote areas.…”

Section: Background On Internet Of Thingsmentioning

confidence: 99%

A Hierarchical Modeling and Analysis Framework for Availability and Security Quantification of IoT Infrastructures

2020

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Modeling a complete Internet of Things (IoT) infrastructure is crucial to assess its availability and security characteristics. However, modern IoT infrastructures often consist of a complex and heterogeneous architecture and thus taking into account both architecture and operative details of the IoT infrastructure in a monolithic model is a challenge for system practitioners and developers. In that regard, we propose a hierarchical modeling framework for the availability and security quantification of IoT infrastructures in this paper. The modeling methodology is based on a hierarchical model of three levels including (i) reliability block diagram (RBD) at the top level to capture the overall architecture of the IoT infrastructure, (ii) fault tree (FT) at the middle level to elaborate system architectures of the member systems in the IoT infrastructure, and (iii) continuous time Markov chain (CTMC) at the bottom level to capture detailed operative states and transitions of the bottom subsystems in the IoT infrastructure. We consider a specific case-study of IoT smart factory infrastructure to demonstrate the feasibility of the modeling framework. The IoT smart factory infrastructure is composed of integrated cloud, fog, and edge computing paradigms. A complete hierarchical model of RBD, FT, and CTMC is developed. A variety of availability and security measures are computed and analyzed. The investigation of the case-study’s analysis results shows that more frequent failures in cloud cause more severe decreases of overall availability, while faster recovery of edge enhances the availability of the IoT smart factory infrastructure. On the other hand, the analysis results of the case-study also reveal that cloud servers’ virtual machine monitor (VMM) and virtual machine (VM), and fog server’s operating system (OS) are the most vulnerable components to cyber-security attack intensity. The proposed modeling and analysis framework coupled with further investigation on the analysis results in this study help develop and operate the IoT infrastructure in order to gain the highest values of availability and security measures and to provide development guidelines in decision-making processes in practice.

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Section: Background On Internet Of Thingsmentioning

confidence: 99%

A Hierarchical Modeling and Analysis Framework for Availability and Security Quantification of IoT Infrastructures

2020

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“…Checkpointing is another method to provide fault tolerance [56]. We refer the reader to thorough surveys on cloud resiliency techniques [25,52].…”

Section: Related Workmentioning

confidence: 99%

Availability Knob

Shahrad

Wentzlaff

2016

Proceedings of the Seventh ACM Symposium on Cloud Computing

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Failure is inevitable in cloud environments. Finding the root cause of a failure can be very complex or at times nearly impossible. Different cloud customers have varying availability demands as well as a diverse willingness to pay for availability. In contrast to existing solutions that try to provide higher and higher availability in the cloud, we propose the Availability Knob (AK). AK provides flexible, user-defined, availability in IaaS clouds, allowing the IaaS cloud customer to express their desire for availability to the cloud provider. Complementary to existing high-reliability solutions and not requiring hardware changes, AK enables more efficient markets. This leads to reduced provider costs, increased provider profit, and improved user satisfaction when compared to an IaaS cloud with no ability to convey availability needs. We leverage game theory to derive incentive compatible pricing, which not only enables AK to function with no knowledge of the root cause of failure but also function under adversarial situations where users deliberately cause downtime. We develop a high-level stochastic simulator to test AK in large-scale IaaS clouds over long time periods. We also prototype AK in OpenStack to explore availability-API tradeoffs and to provide a grounded, real-world, implementation. Our results show that deploying AK leads to more than 10% cost reduction for providers and improves user satisfaction. It also enables providers to set variable profit margins based on the risk of not meeting availability guarantees and the disparity in availability supply/demand. Variable profit margins enable cloud providers to improve their profit by as much as 20%.

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“…The paper also examines failures that are not always noticeable to an end-user, such as 1-bit failures in DRAM. A thorough evaluation of failures and reliability at all levels of the CCS is found in [39].…”

Section: Cloud Computing Reliabilitymentioning

confidence: 99%

“…An AFR represents the estimated probability that a device will fail during a full year of use. In this study, all AFR values are derived from the work found in [36][37][38][39][40][41][42].…”

Section: Samplingmentioning

confidence: 99%

Evaluation and design of highly reliable and highly utilized cloud computing systems

et al. 2015

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Cloud computing paradigm has ushered in the need to provide resources to users in a scalable, flexible, and transparent fashion much like any other utility. This has led to a need for developing evaluation techniques that can provide quantitative measures of reliability of a cloud computing system (CCS) for efficient planning and expansion. This paper presents a new, scalable algorithm based on non-sequential Monte Carlo Simulation (MCS) to evaluate large scale cloud computing system (CCS) reliability, and it develops appropriate performance measures. Also, a new iterative algorithm is proposed and developed that leverages the MCS method for the design of highly reliable and highly utilized CCSs. The combination of these two algorithms allows CCSs to be evaluated by providers and users alike, providing a new method for estimating the parameters of service level agreements (SLAs) and designing CCSs to match those contractual requirements posed in SLAs. Results demonstrate that the proposed methods are effective and applicable to systems at a large scale. Multiple insights are also provided into the nature of CCS reliability and CCS design.

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Toward a high availability cloud: Techniques and challenges

Cited by 18 publications

References 12 publications

A Hierarchical Modeling and Analysis Framework for Availability and Security Quantification of IoT Infrastructures

A Hierarchical Modeling and Analysis Framework for Availability and Security Quantification of IoT Infrastructures

Availability Knob

Evaluation and design of highly reliable and highly utilized cloud computing systems

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