12,035 research outputs found
Adaptive Fault Tolerance in Real Time Cloud Computing
International audienceWith the increasing demand and benefits of cloud computing infrastructure, real time computing can be performed on cloud infrastructure. A real time system can take advantage of intensive computing capabilities and scalable virtualized environment of cloud computing to execute real time tasks. In most of the real time cloud applications, processing is done on remote cloud computing nodes. So there are more chances of errors, due to the undetermined latency and loose control over computing node. On the other side, most of the real time systems are also safety critical and should be highly reliable. So there is an increased requirement for fault tolerance to achieve reliability for the real time computing on cloud infrastructure. In this paper, a fault tolerance model for real time cloud computing is proposed. In the proposed model, the system tolerates the faults and makes the decision on the basis of reliability of the processing nodes, i.e. virtual machines. The reliability of the virtual machines is adaptive, which changes after every computing cycle. If a virtual machine manages to produce a correct result within the time limit, its reliability increases. And if it fails to produce the result within time or correct result, its reliability decreases. A metric model is given for the reliability assessment. In the model, decrease in reliability is more than increase. If the node continues to fail, it is removed, and a new node is added. There is also a minimum reliability level. If any processing node does not achieve that level, the systems will perform backward recovery or safety measures. The proposed technique is based on the execution of design diverse variants on multiple virtual machines, and assigning reliability to the results produced by variants. The virtual machine instances can be of same type or of different types. The system provides both the forward and backward recovery mechanism, but main focus is on forward recovery. The main essence of the proposed technique is the adaptive behavior of the reliability weights assigned to each processing node and adding and removing of nodes on the basis of reliability
Fault-Tolerant Dynamic Deduplication for Utility Computing
Utility computing is an increasingly important paradigm, whereby computing resources are provided on-demand as utilities. An important component of utility computing is storage, data volumes are growing rapidly, and mechanisms to mitigate this growth need to be developed. Data deduplication is a promising technique for drastically reducing the amount of data stored in such system systems, however, current approachs are static in nature, using an amount of redundancy fixed at design time. This is inappropriate for truly dynamic modern systems. We propose a real-time adaptive deduplication system for Cloud and Utility computing that monitors in real-time for changing system, user, and environmental behaviour in order to fulfill a balance between changing storage efficiency, performance, and fault tolerance requirements. We evaluate our system through simulation, with experimental results showing that our system is both efficient and sclable. We also perform experimentation to evaluate the fault tolerance of the system by measuring Mean Time to Repair (MTTR), and using these values to calculate availability of the system. The results show that higher replication levels result in higher system availability, however, the number of files in the system also effects recovery time. We show that the tradeoff between replication levels and recovery time when the system overloads needs further investigation
Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)
In this tutorial paper, we will firstly review some basic simulation concepts
and then introduce the parallel and distributed simulation techniques in view
of some new challenges of today and tomorrow. More in particular, in the last
years there has been a wide diffusion of many cores architectures and we can
expect this trend to continue. On the other hand, the success of cloud
computing is strongly promoting the everything as a service paradigm. Is
parallel and distributed simulation ready for these new challenges? The current
approaches present many limitations in terms of usability and adaptivity: there
is a strong need for new evaluation metrics and for revising the currently
implemented mechanisms. In the last part of the paper, we propose a new
approach based on multi-agent systems for the simulation of complex systems. It
is possible to implement advanced techniques such as the migration of simulated
entities in order to build mechanisms that are both adaptive and very easy to
use. Adaptive mechanisms are able to significantly reduce the communication
cost in the parallel/distributed architectures, to implement load-balance
techniques and to cope with execution environments that are both variable and
dynamic. Finally, such mechanisms will be used to build simulations on top of
unreliable cloud services.Comment: Tutorial paper published in the Proceedings of the International
Conference on High Performance Computing and Simulation (HPCS 2011). Istanbul
(Turkey), IEEE, July 2011. ISBN 978-1-61284-382-
Fault Tolerant Adaptive Parallel and Distributed Simulation through Functional Replication
This paper presents FT-GAIA, a software-based fault-tolerant parallel and
distributed simulation middleware. FT-GAIA has being designed to reliably
handle Parallel And Distributed Simulation (PADS) models, which are needed to
properly simulate and analyze complex systems arising in any kind of scientific
or engineering field. PADS takes advantage of multiple execution units run in
multicore processors, cluster of workstations or HPC systems. However, large
computing systems, such as HPC systems that include hundreds of thousands of
computing nodes, have to handle frequent failures of some components. To cope
with this issue, FT-GAIA transparently replicates simulation entities and
distributes them on multiple execution nodes. This allows the simulation to
tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some
protection against Byzantine failures, since interaction messages among the
simulated entities are replicated as well, so that the receiving entity can
identify and discard corrupted messages. Results from an analytical model and
from an experimental evaluation show that FT-GAIA provides a high degree of
fault tolerance, at the cost of a moderate increase in the computational load
of the execution units.Comment: arXiv admin note: substantial text overlap with arXiv:1606.0731
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