159,787 research outputs found
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction
Federated Learning (FL) is a privacy-preserving machine learning (ML)
technology that enables collaborative training and learning of a global ML
model based on aggregating distributed local model updates. However, security
and privacy guarantees could be compromised due to malicious participants and
the centralized FL server. This article proposed a bi-level blockchained
architecture for secure federated learning-based traffic prediction. The bottom
and top layer blockchain store the local model and global aggregated parameters
accordingly, and the distributed homomorphic-encrypted federated averaging
(DHFA) scheme addresses the secure computation problems. We propose the partial
private key distribution protocol and a partially homomorphic
encryption/decryption scheme to achieve the distributed privacy-preserving
federated averaging model. We conduct extensive experiments to measure the
running time of DHFA operations, quantify the read and write performance of the
blockchain network, and elucidate the impacts of varying regional group sizes
and model complexities on the resulting prediction accuracy for the online
traffic flow prediction task. The results indicate that the proposed system can
facilitate secure and decentralized federated learning for real-world traffic
prediction tasks.Comment: Paper accepted for publication in IEEE Transactions on Services
Computing (TSC
A job response time prediction method for production Grid computing environments
A major obstacle to the widespread adoption of Grid Computing in both the scientific
community and industry sector is the difficulty of knowing in advance a job submission running
cost that can be used to plan a correct allocation of resources.
Traditional distributed computing solutions take advantage of homogeneous and open
environments to propose prediction methods that use a detailed analysis of the hardware and
software components. However, production Grid computing environments, which are large and
use a complex and dynamic set of resources, present a different challenge. In Grid computing
the source code of applications, programme libraries, and third-party software are not always
available. In addition, Grid security policies may not agree to run hardware or software analysis
tools to generate Grid components models.
The objective of this research is the prediction of a job response time in production Grid
computing environments. The solution is inspired by the concept of predicting future Grid
behaviours based on previous experiences learned from heterogeneous Grid workload trace
data. The research objective was selected with the aim of improving the Grid resource usability
and the administration of Grid environments. The predicted data can be used to allocate
resources in advance and inform forecasted finishing time and running costs before submission.
The proposed Grid Computing Response Time Prediction (GRTP) method implements
several internal stages where the workload traces are mined to produce a response time
prediction for a given job. In addition, the GRTP method assesses the predicted result against
the actual target job’s response time to inference information that is used to tune the methods
setting parameters.
The GRTP method was implemented and tested using a cross-validation technique to assess
how the proposed solution generalises to independent data sets. The training set was taken from
the Grid environment DAS (Distributed ASCI Supercomputer). The two testing sets were taken
from AuverGrid and Grid5000 Grid environments
Three consecutive tests assuming stable jobs, unstable jobs, and using a job type method to
select the most appropriate prediction function were carried out. The tests offered a significant
increase in prediction performance for data mining based methods applied in Grid computing
environments. For instance, in Grid5000 the GRTP method answered 77 percent of job
prediction requests with an error of less than 10 percent. While in the same environment, the most effective and accurate method using workload traces was only able to predict 32 percent of
the cases within the same range of error.
The GRTP method was able to handle unexpected changes in resources and services which
affect the job response time trends and was able to adapt to new scenarios. The tests showed
that the proposed GRTP method is capable of predicting job response time requests and it also
improves the prediction quality when compared to other current solutions
Monitoring and Optimization of ATLAS Tier 2 Center GoeGrid
The demand on computational and storage resources is growing along with the amount of infor-
mation that needs to be processed and preserved. In order to ease the provisioning of the digital
services to the growing number of consumers, more and more distributed computing systems and
platforms are actively developed and employed. The building block of the distributed computing
infrastructure are single computing centers, similar to the Worldwide LHC Computing Grid, Tier
2 centre GoeGrid. The main motivation of this thesis was the optimization of GoeGrid perfor-
mance by efficient monitoring. The goal has been achieved by means of the GoeGrid monitoring
information analysis. The data analysis approach was based on the adaptive-network-based
fuzzy inference system (ANFIS) and machine learning algorithm such as Linear Support Vector
Machine (SVM).
The main object of the research was the digital service, since availability, reliability and ser-
viceability of the computing platform can be measured according to the constant and stable
provisioning of the services. Due to the widely used concept of the service oriented architecture
(SOA) for large computing facilities, in advance knowing of the service state as well as the quick
and accurate detection of its disability allows to perform the proactive management of the com-
puting facility. The proactive management is considered as a core component of the computing
facility management automation concept, such as Autonomic Computing. Thus in time as well
as in advance and accurate identification of the provided service status can be considered as a
contribution to the computing facility management automation, which is directly related to the
provisioning of the stable and reliable computing resources.
Based on the case studies, performed using the GoeGrid monitoring data, consideration of the
approaches as generalized methods for the accurate and fast identification and prediction of the
service status is reasonable. Simplicity and low consumption of the computing resources allow
to consider the methods in the scope of the Autonomic Computing component
Green demand aware fog computing : a prediction-based dynamic resource provisioning approach
Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
Dynamic request management algorithms for Web-based services in cloud computing
Service providers of Web-based services can take advantage ofmany convenient features of cloud computing infrastructures, but theystill have to implement request management algorithms that are able toface sudden peaks of requests. We consider distributed algorithmsimplemented by front-end servers to dispatch and redirect requests amongapplication servers. Current solutions based on load-blind algorithms, orconsidering just server load and thresholds are inadequate to cope with thedemand patterns reaching modern Internet application servers. In thispaper, we propose and evaluate a request management algorithm, namelyPerformanceGain Prediction, that combines several pieces ofinformation (server load, computational cost of a request, usersession migration and redirection delay) to predict whether theredirection of a request to another server may result in a shorterresponse time. To the best of our knowledge, no other studycombines information about infrastructure status, user requestcharacteristics and redirection overhead for dynamic requestmanagement in cloud computing. Our results showthat the proposed algorithm is able to reduce the responsetime with respect to existing request management algorithmsoperating on the basis of thresholds
Navigation Recommender:Real-Time iGNSS QoS Prediction for Navigation Services
Global Navigation Satellite Systems (GNSSs), especially Global Positioning System (GPS), have become commonplace in mobile devices and are the most preferred geo-positioning sensors for many location-based applications. Besides GPS, other GNSSs under development or deployment are GLONASS, Galileo, and Compass. These four GNSSs are planned to be integrated in the near future. It is anticipated that integrated GNSSs (iGNSSs) will improve the overall satellite-based geo-positioning performance. However, one major shortcoming of any GNSS and iGNSSs is Quality of Service (QoS) degradation due to signal blockage and attenuation by the surrounding environments, particularly in obstructed areas. GNSS QoS uncertainty is the root cause of positioning ambiguity, poor localization performance, application freeze, and incorrect guidance in navigation applications.
In this research, a methodology, called iGNSS QoS prediction, that can provide GNSS QoS on desired and prospective routes is developed. Six iGNSS QoS parameters suitable for navigation are defined: visibility, availability, accuracy, continuity, reliability, and flexibility. The iGNSS QoS prediction methodology, which includes a set of algorithms, encompasses four modules: segment sampling, point-based iGNSS QoS prediction, tracking-based iGNSS QoS prediction, and iGNSS QoS segmentation. Given that iGNSS QoS prediction is data- and compute-intensive and navigation applications require real-time solutions, an efficient satellite selection algorithm is developed and distributed computing platforms, mainly grids and clouds, for achieving real-time performance are explored. The proposed methodology is unique in several respects: it specifically addresses the iGNSS positioning requirements of navigation systems/services; it provides a new means for route choices and routing in navigation systems/services; it is suitable for different modes of travel such as driving and walking; it takes high-resolution 3D data into account for GNSS positioning; and it is based on efficient algorithms and can utilize high-performance and scalable computing platforms such as grids and clouds to provide real-time solutions.
A number of experiments were conducted to evaluate the developed methodology and the algorithms using real field test data (GPS coordinates). The experimental results show that the methodology can predict iGNSS QoS in various areas, especially in problematic areas
An adaptive approach to better load balancing in a consumer-centric cloud environment
Pay-as-you-consume, as a new type of cloud computing paradigm, has become increasingly popular since a large number of cloud services are gradually opening up to consumers. It gives consumers a great convenience, where users no longer need to buy their hardware resources, but are confronted with how to deal effectively with data from the cloud. How to improve the performance of the cloud platform as a consumer-centric cloud computing model becomes a critical issue. Existing heterogeneous distributed computing systems provide efficient parallel and high fault tolerant and reliable services, due to its characteristics of managing largescale clusters. Though the latest cloud computing cluster meets the need for faster job execution, more effective use of computing resources is still a challenge. Presently proposed methods concentrated on improving the execution time of incoming jobs, e.g., shortening the MapReduce (MR) time. In this paper, an adaptive scheme is offered to achieve time and space efficiency in a heterogeneous cloud environment. A dynamic speculative execution strategy on real-time management of cluster resources is presented to optimize the execution time of Map phase, and a prediction model is used for fast prediction of task execution time. Combing the prediction model with a multi-objective optimization algorithm, an adaptive solution to optimize the performance of space-time is obtained. Experimental results depict that the proposed scheme can allocate tasks evenly and improve work efficiency in a heterogeneous cluster
Jeeva: Enterprise Grid-enabled Web Portal for Protein Secondary Structure Prediction
This paper presents a Grid portal for protein secondary structure prediction
developed by using services of Aneka, a .NET-based enterprise Grid technology.
The portal is used by research scientists to discover new prediction structures
in a parallel manner. An SVM (Support Vector Machine)-based prediction
algorithm is used with 64 sample protein sequences as a case study to
demonstrate the potential of enterprise Grids.Comment: 7 page
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