720 research outputs found
Optimization of Cloud Task Processing with Checkpoint-Restart Mechanism
International audienceIn this paper, we aim at optimizing fault-tolerance tech- niques based on a checkpointing/restart mechanism, in the context of cloud computing. Our contribution is three-fold. (1) We derive a fresh formula to compute the optimal num- ber of checkpoints for cloud jobs with varied distributions of failure events. Our analysis is not only generic with no assumption on failure probability distribution, but also at- tractively simple to apply in practice. (2) We design an adaptive algorithm to optimize the impact of checkpointing regarding various costs like checkpointing/restart overhead. (3) We evaluate our optimized solution in a real cluster en- vironment with hundreds of virtual machines and Berke- ley Lab Checkpoint/Restart tool. Task failure events are emulated via a production trace produced on a large-scale Google data center. Experiments confirm that our solution is fairly suitable for Google systems. Our optimized formula outperforms Young's formula by 3-10 percent, reducing wall- clock lengths by 50-100 seconds per job on average
Checkpointing as a Service in Heterogeneous Cloud Environments
A non-invasive, cloud-agnostic approach is demonstrated for extending
existing cloud platforms to include checkpoint-restart capability. Most cloud
platforms currently rely on each application to provide its own fault
tolerance. A uniform mechanism within the cloud itself serves two purposes: (a)
direct support for long-running jobs, which would otherwise require a custom
fault-tolerant mechanism for each application; and (b) the administrative
capability to manage an over-subscribed cloud by temporarily swapping out jobs
when higher priority jobs arrive. An advantage of this uniform approach is that
it also supports parallel and distributed computations, over both TCP and
InfiniBand, thus allowing traditional HPC applications to take advantage of an
existing cloud infrastructure. Additionally, an integrated health-monitoring
mechanism detects when long-running jobs either fail or incur exceptionally low
performance, perhaps due to resource starvation, and proactively suspends the
job. The cloud-agnostic feature is demonstrated by applying the implementation
to two very different cloud platforms: Snooze and OpenStack. The use of a
cloud-agnostic architecture also enables, for the first time, migration of
applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201
Intelligent Fault-Tolerant Mechanism for Data Centers of Cloud Infrastructure
Fault tolerance in cloud computing is considered as one of the most vital issues to deliver reliable services. Checkpoint/restart is one of the methods used to enhance the reliability of the cloud services. However, many existing methods do not focus on virtual machine (VM) failure that occurs due to the higher response time of a node, byzantine fault, and performance fault, and existing methods also ignore the optimization during the recovery phase. This paper proposes a checkpoint/restart mechanism to enhance reliability of cloud services. Our work is threefold: (1) we design an algorithm to identify virtual machine failure due to several faults; (2) an algorithm to optimize the checkpoint interval time is designed; (3) lastly, the asynchronous checkpoint/restart with log-based recovery mechanism is used to restart the failed tasks. The valuation results obtained using a real-time dataset shows that the proposed model reduces power consumption and improves the performance with a better fault tolerance solution compared to the nonoptimization method
Reliability
This entry contributes to enhancing the knowledge and skills needed for evaluating the reliability of assessments in second language acquisition. Different types of measurement error limit the effectiveness of assessments in yielding consistent and reproducible results, thereby reducing teachers\u27 ability to make instructional decisions accurately, to measure student progress, and to evaluate curriculum models, strategies, and programs for enhancing student development and achievement. The entry provides an overview of how to evaluate the reliability of language assessments and describes useful tools and strategies for ameliorating reliability issues
Autonomic Approach based on Semantics and Checkpointing for IoT System Management
Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur
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Optimising Fault Tolerance in Real-time Cloud Computing IaaS Environment
YesFault tolerance is the ability of a system to respond
swiftly to an unexpected failure. Failures in a cloud computing
environment are normal rather than exceptional, but fault
detection and system recovery in a real time cloud system is a
crucial issue. To deal with this problem and to minimize the risk
of failure, an optimal fault tolerance mechanism was introduced
where fault tolerance was achieved using the combination of the
Cloud Master, Compute nodes, Cloud load balancer, Selection
mechanism and Cloud Fault handler. In this paper, we proposed
an optimized fault tolerance approach where a model is designed
to tolerate faults based on the reliability of each compute node
(virtual machine) and can be replaced if the performance is not
optimal. Preliminary test of our algorithm indicates that the rate
of increase in pass rate exceeds the decrease in failure rate and it
also considers forward and backward recovery using diverse
software tools. Our results obtained are demonstrated through
experimental validation thereby laying a foundation for a fully
fault tolerant IaaS Cloud environment, which suggests a good
performance of our model compared to current existing
approaches.Petroleum Technology Development Fund (PTDF
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