Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHierarchical Failure detectors implementation using dual mode of heartbeat and interaction
تحقيق كاشف الأعطال الهرمي باستخدام النموذج المشترك النبضي و التفاعلي
932
0 7
0
(
0
)
Added by
Aِl-Baath University ورقة بحثية
Publication date
2016
fields
Informatics Engineering
and research's language is
العربية
Created by
Shamra Editor
Ask ChatGPT about the research
في هذا البحث سنقدم تطبيق لكاشف الأعطال ذو بنية هرمية حيث يعتمد التطبيق على تقسيم بيئة العمل العامة المكونة من مجموعة من العمليات إلى مجموعات فرعية و اختيار قائد للمجموعات يدعى بالعملية الرئيسية (المركزية) التي تقوم بتوزيع العمليات على المجموعات الفرعية و اختيار قائد لكل مجموعة فرعية و من ثم تطبيق كاشف الأعطال عند القادة الفرعيين و استخلاص النتائج و ارسالها إلى العملية الرئيسية.
In this paper, we propose an implementation of hierarchical failure detectors, which depends on dividing the processes into sub-groups and elect one leader called the main process . The main process then distributes the remaining processes into groups and chooses one leader for each one. Finally failure detector applied in the chosen leaders which send the results to the central process.
References used
T. D. Chandra and S. Toueg, “Unreliable failure detectors for reliable distributed systems,” Journal of the ACM, vol. 43,no. 2, pp. 225-267, 1996
S. Bansal, S. Sharma, and I. Trivedi, “Adaptive staircase multiple failure detector for parallel and distributed image processing” in Proceedings of the 1st International Conference on Recent Advances in Information Technology,Dhanbad,India, 2012, pp. 91-94
W. Chen, S. Toueg, and M. K. Aguilera, “On the quality of service of failure detectors,” IEEE Transactions on Computers, vol. 51, no. 5, pp. 561-580, 2002
rate research
Read More
Failure detection plays a central role in the engineering of
distributed systems. Furthermore, many applications have timing
constraints and require failure detectors that provide quality of
service (QoS) with some quantitative timeliness guarante
es.
Therefore, they need failure detectors that are fast and accurate.
Failure detectors are oracles that provide information about process
crashes , they are an important abstraction for fault tolerance in
distributed systems. Although current failure detectors theory
provides great generality and expressiveness, it also possess
significant challenges in developing a robust hierarchy of failure
detectors.
In this paper, we propose an implementation of failure detectors.
this implementation uses a dual model of heartbeat and interaction.
First, the heartbeat model is adopted to shorten the detection time.
if the detecting process does not receive the heartbeat message in
the expected time, the interaction model is then used to check the
process further.
We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using t
he permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scoresaround 0.85 on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction. We make the source code and dataset splits accessible.
Failure Mode and Effects Analysis (FMEA) is a risk assessment tool that explores, identifies,
and prioritizes the potential failure modes in a system, process, service or design.
The failure modes prioritizing technique used by FMEA has been criticized to have many
deficiencies, and various risk priority models have been proposed in the literature to enhance the
performance of FMEA.
While natural language understanding of long-form documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text
-- scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised interpretability' module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.
This paper focuses on securing zone routing protocol (ZRP) from
one of the most possible attacks such as bogus route attack and
black hole attack. This paper provides a solution against this attack
using identity based cryptography(IBC), We implement, evaluate
our proposal using NS-2 network Simulator.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
