Decomposition of portfolio risk into independent factors using an inductive causal search algorithm

Brian Deaton

doi:10.21314/jor.2016.341

A Causality Mining and Knowledge Graph Based Method of Root Cause Diagnosis for Performance Anomaly in Cloud Applications

Applied Sciences ◽

10.3390/app10062166 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2166 ◽

Cited By ~ 2

Author(s):

Juan Qiu ◽

Qingfeng Du ◽

Kanglin Yin ◽

Shuang-Li Zhang ◽

Chongshu Qian

Keyword(s):

Search Algorithm ◽

Knowledge Graph ◽

Computing Technology ◽

Related Services ◽

Root Cause ◽

Performance Metric ◽

Application Architecture ◽

Causal Search ◽

Cloud Applications ◽

Microservice Architecture

With the development of cloud computing technology, the microservice architecture (MSA) has become a prevailing application architecture in cloud-native applications. Many user-oriented services are supported by many microservices, and the dependencies between services are more complicated than those of a traditional monolithic architecture application. In such a situation, if an anomalous change happens in the performance metric of a microservice, it will cause other related services to be downgraded or even to fail, which would probably cause large losses to dependent businesses. Therefore, in the operation and maintenance job of cloud applications, it is critical to mine the causality of the problem and find its root cause as soon as possible. In this paper, we propose an approach for mining causality and diagnosing the root cause that uses knowledge graph technology and a causal search algorithm. We verified the proposed method on a classic cloud-native application and found that the method is effective. After applying our method on most of the services of a cloud-native application, both precision and recall were over 80%.

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Fast search algorithm for VQ-based recognition of isolated words

IEE Proceedings I Communications Speech and Vision ◽

10.1049/ip-i-2.1989.0059 ◽

1989 ◽

Vol 136 (6) ◽

pp. 391 ◽

Cited By ~ 15

Author(s):

S.H. Chen ◽

J.S. Pan

Keyword(s):

Search Algorithm ◽

Fast Search ◽

Fast Search Algorithm

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Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189134 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8125-8137

Author(s):

Jackson J Christy ◽

D Rekha ◽

V Vijayakumar ◽

Glaucio H.S. Carvalho

Keyword(s):

Intelligent Transportation System ◽

Search Algorithm ◽

Mac Protocol ◽

High Mobility ◽

Optimal Solution ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Traffic Density ◽

Scheduling Problem ◽

Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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