Why Does the Cloud Stop Computing?

Gunawi, Haryadi S.; Hao, Mingzhe; Suminto, Riza O.; Laksono, Agung; Satria, Anang D.; Adityatama, Jeffry; Eliazar, Kurnia J.

doi:10.1145/2987550.2987583

Cited by 127 publications

(13 citation statements)

References 19 publications

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“…We believe that our dual fault and timing assumptions reasonably reflect typical hybrid cloud deployment scenarios. In particular, the accuracy of this model finds confirmations in recent studies about performance and faults of public clouds [Gunawi et al 2016] and on-premise clusters [Cano et al 2016].…”

Section: System Modelsupporting

confidence: 71%

“…Besides security and trust concerns, storing data on a single cloud presents issues related to reliability [Gunawi et al 2016], performance, vendor lock-in [Armbrust et al 2010;Abu-Libdeh et al 2010], as well as consistency, since cloud storage services are notorious for typically providing only eventual consistency [Vogels 2009;Bermbach and Tai 2014]. To address these concerns, several research works considered storing data robustly in public clouds, by leveraging multiple cloud providers [Armbrust et al 2010;Vukolić 2010].…”

Section: Introductionmentioning

confidence: 99%

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Hybris

Viotti

Dobre²,

Vukolić

2017

ACM Trans. Storage

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Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources. Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data which is dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages, but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f + 1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3f + 1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds. We implemented Hybris in Java and evaluated it using a series of micro and macro-benchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.

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Section: System Modelsupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Hybris

Viotti

Dobre²,

Vukolić

2017

ACM Trans. Storage

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“…Whereas, reactive fault tolerance reduces the impact of failure on application execution when the failure efficaciously occurs. For scientific workflow systems, a study on fault tolerant mechanism [26] was proposed i.e., Cloud Outage Study (COS) in which it was argued that for scientific workflows mostly the resubmission mechanism is used as fault tolerance technique. Whenever a failed task is observed, it is resubmitted either to the same or to a separate resource at runtime.…”

Section: Related Workmentioning

confidence: 99%

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

2021

KSII TIIS

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Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through welldefined components and steps. A two way fault-tolerant mechanism i.e., task-based faulttolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

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“…Cloud systems are supposed to be always available. The research community is thus interested in the [10] and then in cloud service outage [11]. They report that logic specific bugs are the most frequent, partially caused by a gap between the system specifications and the resulting code.…”

Section: Related Workmentioning

confidence: 99%

Trustable virtual machine scheduling in a cloud

Hermenier

Henrio

2017

Proceedings of the 2017 Symposium on Cloud Computing

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In an Infrastructure As A Service (IaaS) cloud, the scheduler deploys VMs to servers according to service level objectives (SLOs). Clients and service providers must both trust the infrastructure. In particular they must be sure that the VM scheduler takes decisions that are consistent with its advertised behaviour. The difficulties to master every theoretical and practical aspects of a VM scheduler implementation leads however to faulty behaviours that break SLOs and reduce the provider revenues.We present SafePlace, a specification and testing framework that exhibits inconsistencies in VM schedulers. SafePlace mixes a DSL to formalise scheduling decisions with fuzz testing to generate a large spectrum of test cases and automatically report implementation faults.We evaluate SafePlace on the VM scheduler BtrPlace. Without any code modification, SafePlace allows to write test campaigns that are 3.83 times smaller than BtrPlace unit tests. SafePlace performs 200 tests per second, exhibited new non-trivial bugs, and outperforms the BtrPlace runtime assertion system. CCS CONCEPTS• Software and its engineering → Cloud computing; Software testing and debugging; Scheduling; KEYWORDS VM scheduling, software testing, specification language ACM Reference Format:

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Why Does the Cloud Stop Computing?

Cited by 127 publications

References 19 publications

Hybris

Hybris

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

Trustable virtual machine scheduling in a cloud

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