An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble
Models
- URL: http://arxiv.org/abs/2008.05427v1
- Date: Wed, 12 Aug 2020 16:32:46 GMT
- Title: An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble
Models
- Authors: Kostas Kolomvatsos, Christos Anagnostopoulos
- Abstract summary: Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end users activities.
Data collected by numerous devices present in the IoT infrastructure can be hosted into a set of EC nodes becoming the subject of processing tasks for the provision of analytics.
We propose a meta-ensemble learning scheme that supports the decision making for the allocation of queries to the appropriate EC nodes.
- Score: 16.75218291152252
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The combination of Internet of Things (IoT) and Edge Computing (EC) can
assist in the delivery of novel applications that will facilitate end users
activities. Data collected by numerous devices present in the IoT
infrastructure can be hosted into a set of EC nodes becoming the subject of
processing tasks for the provision of analytics. Analytics are derived as the
result of various queries defined by end users or applications. Such queries
can be executed in the available EC nodes to limit the latency in the provision
of responses. In this paper, we propose a meta-ensemble learning scheme that
supports the decision making for the allocation of queries to the appropriate
EC nodes. Our learning model decides over queries' and nodes' characteristics.
We provide the description of a matching process between queries and nodes
after concluding the contextual information for each envisioned characteristic
adopted in our meta-ensemble scheme. We rely on widely known ensemble models,
combine them and offer an additional processing layer to increase the
performance. The aim is to result a subset of EC nodes that will host each
incoming query. Apart from the description of the proposed model, we report on
its evaluation and the corresponding results. Through a large set of
experiments and a numerical analysis, we aim at revealing the pros and cons of
the proposed scheme.
Related papers
- An Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge [5.4327243200369555]
We propose a scheme to be implemented by autonomous edge nodes concerning their identifications of the appropriate data to be migrated to particular locations within the infrastructure.
Our objective is to equip nodes with the capability to comprehend the access patterns relating to offloaded data-driven tasks.
It is evident that these tasks depend on the processing of data that is absent from the original hosting nodes.
To infer these data intervals, we utilize an ensemble approach that integrates a statistically oriented model and a machine learning framework.
arXiv Detail & Related papers (2024-10-12T19:09:16Z) - Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation [17.96176020727917]
We focus on the role of query outlining in scenarios that users request a specific range of information.
For $C2$ scenarios, we construct QTree, 10K sets of information-seeking queries with various perspectives on certain topics.
We analyze the effectiveness of generated outlines through automatic and human evaluation, targeting on retrieval-augmented generation (RAG)
arXiv Detail & Related papers (2024-07-01T10:26:19Z) - UQE: A Query Engine for Unstructured Databases [71.49289088592842]
We investigate the potential of Large Language Models to enable unstructured data analytics.
We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
arXiv Detail & Related papers (2024-06-23T06:58:55Z) - SQLNet: Scale-Modulated Query and Localization Network for Few-Shot
Class-Agnostic Counting [71.38754976584009]
The class-agnostic counting (CAC) task has recently been proposed to solve the problem of counting all objects of an arbitrary class with several exemplars given in the input image.
We propose a novel localization-based CAC approach, termed Scale-modulated Query and Localization Network (Net)
It fully explores the scales of exemplars in both the query and localization stages and achieves effective counting by accurately locating each object and predicting its approximate size.
arXiv Detail & Related papers (2023-11-16T16:50:56Z) - Improving Text Matching in E-Commerce Search with A Rationalizable,
Intervenable and Fast Entity-Based Relevance Model [78.80174696043021]
We propose a novel model called the Entity-Based Relevance Model (EBRM)
The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy.
We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
arXiv Detail & Related papers (2023-07-01T15:44:53Z) - Improved Representation Learning for Session-based Recommendation [0.0]
Session-based recommendation systems suggest relevant items to users by modeling user behavior and preferences using short-term anonymous sessions.
Existing methods leverage Graph Neural Networks (GNNs) that propagate and aggregate information from neighboring nodes.
We propose using a Transformer in combination with a target attentive GNN, which allows richer Representation Learning.
arXiv Detail & Related papers (2021-07-04T00:57:28Z) - Text Summarization with Latent Queries [60.468323530248945]
We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms.
Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time.
Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
arXiv Detail & Related papers (2021-05-31T21:14:58Z) - Data Synopses Management based on a Deep Learning Model [14.180331276028662]
We argue on the delivery of data synopses to EC nodes making them capable to take offloading decisions fully aligned with data present at peers.
Our approach involves a Deep Learning model for learning the distribution of calculated synopses and estimate future trends.
arXiv Detail & Related papers (2020-08-01T12:04:21Z) - Proactive Tasks Management based on a Deep Learning Model [9.289846887298852]
We propose an intelligent, proactive tasks management model based on the demand.
We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network.
We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.
arXiv Detail & Related papers (2020-07-25T05:28:14Z) - Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors.
It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features.
We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
arXiv Detail & Related papers (2020-06-26T17:03:06Z) - A Revised Generative Evaluation of Visual Dialogue [80.17353102854405]
We propose a revised evaluation scheme for the VisDial dataset.
We measure consensus between answers generated by the model and a set of relevant answers.
We release these sets and code for the revised evaluation scheme as DenseVisDial.
arXiv Detail & Related papers (2020-04-20T13:26:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.