scholarly journals Autotuning of Exascale Applications With Anomalies Detection

2021 ◽  
Vol 4 ◽  
Author(s):  
Dragi Kimovski ◽  
Roland Mathá ◽  
Gabriel Iuhasz ◽  
Fabrizio Marozzo ◽  
Dana Petcu ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Search Space ◽  
Distributed Applications ◽  
Research Field ◽  
Novel Approach ◽  
Detection Approach ◽  
Parameter Variations ◽  
Hardware Failures ◽  
Time And Energy ◽  
Application Execution

The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and runtime parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objective functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.

scholarly journals MINC-NRL: An Information-Based Approach for Community Detection

Algorithms ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 20
Author(s):  
Yinan Chen ◽  
Chuanpeng Wang ◽  
Dong Li
Keyword(s):  
Community Detection ◽  
Structural Characteristics ◽  
Synthetic Data ◽  
Representation Learning ◽  
Search Space ◽  
Research Field ◽  
Data Sets ◽  
Detection Approach ◽  
Target Network ◽  
Overlapping Community

Complex networks usually consist of dense-connected cliques, which are defined as communities. A community structure is a reflection of the local characteristics existing in the network topology, this makes community detection become an important research field to reveal the internal structural characteristics of networks. In this article, an information-based community detection approach MINC-NRL is proposed, which can be applied to both overlapping and non-overlapping community detection. MINC-NRL introduces network representation learning (NRL) to represent the target network as vectors, then generates a community evolution process based on these vectors to reduce the search space, and finally, finds the best community partition in this process using mutual information between network and communities (MINC). Experiments on real-world and synthetic data sets verifies the effectiveness of the approach in community detection, both on non-overlapping and overlapping tasks.

RT-ZooKeeper: Taming the Recovery Latency of a Coordination Service

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-22
Author(s):  
Haoran Li ◽  
Chenyang Lu ◽  
Christopher D. Gill
Keyword(s):  
Fault Tolerant ◽  
Empirical Evaluation ◽  
Distributed Applications ◽  
Edge Computing ◽  
Challenges And Opportunities ◽  
Cloud Environments ◽  
Hand Coordination ◽  
Recovery Protocols ◽  
Recovery Latency

Fault-tolerant coordination services have been widely used in distributed applications in cloud environments. Recent years have witnessed the emergence of time-sensitive applications deployed in edge computing environments, which introduces both challenges and opportunities for coordination services. On one hand, coordination services must recover from failures in a timely manner. On the other hand, edge computing employs local networked platforms that can be exploited to achieve timely recovery. In this work, we first identify the limitations of the leader election and recovery protocols underlying Apache ZooKeeper, the prevailing open-source coordination service. To reduce recovery latency from leader failures, we then design RT-Zookeeper with a set of novel features including a fast-convergence election protocol, a quorum channel notification mechanism, and a distributed epoch persistence protocol. We have implemented RT-Zookeeper based on ZooKeeper version 3.5.8. Empirical evaluation shows that RT-ZooKeeper achieves 91% reduction in maximum recovery latency in comparison to ZooKeeper. Furthermore, a case study demonstrates that fast failure recovery in RT-ZooKeeper can benefit a common messaging service like Kafka in terms of message latency.

scholarly journals Privacy-preserving energy storage sharing with blockchain and secure multi-party computation

2021 ◽  
Vol 1 (1) ◽  
pp. 32-50
Author(s):  
Nan Wang ◽  
Sid Chi-Kin Chau ◽  
Yue Zhou
Keyword(s):  
Energy Storage ◽  
Energy Demand ◽  
Empirical Evaluation ◽  
Privacy Preserving ◽  
Third Party ◽  
User Privacy ◽  
External Energy ◽  
Novel Approach ◽  
Significant Cost Reduction ◽  
The Cost

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a majority of dishonest users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provided empirical evaluation in this paper 1 .

scholarly journals Scaling Up AND/OR Abstraction Sampling

2020 ◽  
Author(s):  
Kalev Kask ◽  
Bobak Pezeshki ◽  
Filjor Broka ◽  
Alexander Ihler ◽  
Rina Dechter
Keyword(s):  
Importance Sampling ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Search Space ◽  
Scaling Up ◽  
Sampling Scheme ◽  
Search Spaces

Abstraction Sampling (AS) is a recently introduced enhancement of Importance Sampling that exploits stratification by using a notion of abstractions: groupings of similar nodes into abstract states. It was previously shown that AS performs particularly well when sampling over an AND/OR search space; however, existing schemes were limited to ``proper'' abstractions in order to ensure unbiasedness, severely hindering scalability. In this paper, we introduce AOAS, a new Abstraction Sampling scheme on AND/OR search spaces that allow more flexible use of abstractions by circumventing the properness requirement. We analyze the properties of this new algorithm and, in an extensive empirical evaluation on five benchmarks, over 480 problems, and comparing against other state of the art algorithms, illustrate AOAS's properties and show that it provides a far more powerful and competitive Abstraction Sampling framework.

scholarly journals Multilingual Projection for Parsing Truly Low-Resource Languages

2016 ◽  
Vol 4 ◽  
pp. 301-312 ◽  
Author(s):  
Željko Agić ◽  
Anders Johannsen ◽  
Barbara Plank ◽  
Héctor Martínez Alonso ◽  
Natalie Schluter ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Upper Bounds ◽  
Low Resource ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Novel Approach ◽  
Cross Lingual ◽  
Test Languages ◽  
Speech Tagging ◽  
Parallel Texts

We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.

scholarly journals Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1409 ◽  
Author(s):  
Christina Baek ◽  
Sang-Woo Lee ◽  
Beom-Jin Lee ◽  
Dong-Hyun Kwak ◽  
Byoung-Tak Zhang
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Dna Nanotechnology ◽  
Search Space ◽  
Molecular Computing ◽  
Training Data ◽  
Molecular Systems ◽  
Novel Approach ◽  
Biological Substrates

Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.

Local Optima Avoidance in GA Biclustering using Map Reduce

2016 ◽  
Vol 6 (1) ◽  
pp. 37-47
Author(s):  
Gowri R. ◽  
Rathipriya R.
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Population Size ◽  
Optimal Solution ◽  
Search Space ◽  
Gene Expression Dataset ◽  
Map Reduce ◽  
Mapreduce Framework ◽  
Local Optima ◽  
Novel Approach

One of the prominent issues in Genetic Algorithm (GA) is premature convergence on local optima. This restricts the enhanced optimal solution searching in the entire search space. Population size is one of the influencing factors in Genetic Algorithm. Increasing the population size will improvise the randomized searching and maintains the diversity in the population. It also increases its computational complexity. Especially in GA Biclustering (GABiC), the search should be randomized to find more optimal patterns. In this paper, a novel approach for population setup in MapReduce framework is proposed. The maximal population is split into population sets, and these groups will proceed searching in parallel using MapReduce framework. This approach is attempted for biclustering the gene expression dataset in this paper. The performance of this proposed work seems promising on comparing its results with those obtained from previous hybridized optimization approaches. This approach will also handle data scalability issues and applicable to the big data biclustering problems.

scholarly journals WH-EA: An Evolutionary Algorithm for Wiener-Hammerstein System Identification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
J. Zambrano ◽  
J. Sanchis ◽  
J. M. Herrero ◽  
M. Martínez
Keyword(s):  
System Identification ◽  
Evolutionary Algorithm ◽  
Nonlinear System Identification ◽  
Search Space ◽  
Single Step ◽  
Fine Tuning ◽  
Hammerstein Model ◽  
Hammerstein System ◽  
Hammerstein Systems ◽  
Novel Approach

Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.

GENE THERAPY: A NOVEL APPROACH TO TREATMENT OF THE DEGENERATION OF INTERVERTEBRAL DISCS

2002 ◽  
Vol 06 (03n04) ◽  
pp. 181-187 ◽  
Author(s):  
Jiangwei Tan ◽  
Yougu Hu
Keyword(s):  
Gene Therapy ◽  
Back Pain ◽  
Disc Degeneration ◽  
Deep Understanding ◽  
Intervertebral Discs ◽  
Research Field ◽  
Clinical Approach ◽  
Novel Approach ◽  
Physiology And Biochemistry ◽  
Lowback Pain

The degeneration of intervertebral discs is one of the pathophysiological causes of lowback pain which has been seriously disturbing human health at the present, No clinical approach can provide prophylaxis or treatment for disc degeneration. But people are now thinking about gene transfer for the prevention of disc degeneration to reduce the incidence of back pain. This idea originated from the development of molecular biology; the deep understanding of the physiology and biochemistry of the intervertebral discs, and the growing scope of gene therapy. The increasing publications on gene therapy for disc degeneration have shown us the future of this research field, and revealed possible difficulties in the process of the study. This article focuses on several important aspects of gene therapy for disc degeneration.

AN EFFICIENT LOTOS-BASED FRAMEWORK FOR DESCRIBING AND SOLVING (TEMPORAL) CSPs

2009 ◽  
Vol 19 (06) ◽  
pp. 765-789
Author(s):  
SAMIRA SADAOUI ◽  
MALEK MOUHOUB ◽  
BO CHEN
Keyword(s):  
Constraint Satisfaction Problem ◽  
Combinatorial Problem ◽  
Experimental Tests ◽  
Search Space ◽  
Constraint Propagation ◽  
Combinatorial Problems ◽  
Temporal Constraints ◽  
Transition Systems ◽  
Time Performance ◽  
Novel Approach

Simulation of complex Lotos specifications is not always efficient due to the space explosion problem of their corresponding transition systems. To overcome this difficulty in practice, we present in this paper a novel approach which integrates constraint propagation techniques into the Lotos specifications. These solving techniques are used to reduce the size of the search space before and during the search for a solution to a given combinatorial problem under constraints. In order to do that, we first tackle the challenging task of describing combinatorial problems in Lotos using the Constraint Satisfaction Problem (CSP) framework. In this regard, we provide two generic Lotos templates for describing CSPs and temporal CSPs (CSPs involving temporal constraints). To evaluate the time performance of the framework we propose, we have conducted several experimental tests on instances of the N-Queens, the machine scheduling and randomly generated CSPs. The results of these experiments are promising and demonstrate the efficiency of Lotos simulation when CSP techniques are integrated.

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