scholarly journals QoS-aware service composition based on context-free grammar and skyline in service function chaining using genetic algorithm

2021 ◽  
Vol 7 ◽  
pp. e603
Author(s):  
Pouya Khosravian ◽  
Sima Emadi ◽  
Ghasem Mirjalily ◽  
Behzad Zamani
Keyword(s):  
Genetic Algorithm ◽  
Service Composition ◽  
Service Providers ◽  
Task Force ◽  
Mobile Network ◽  
Search Space ◽  
Context Free Grammar ◽  
Service Function ◽  
Novel Approach ◽  
Context Free

Service function chaining (SFC) is a mechanism that allows service providers to combine various service functions and exploit the available virtual infrastructure. The best selection of virtual services in the network is essential for meeting user requirements and constraints. This paper proposes a novel approach to generate the optimal composition of the service functions. To this end, a genetic algorithm based on context-free grammar (CFG) that adheres to the Internet Engineering Task Force (IETF) standard and Skyline was developed to use in SFC. The IETF uses cases of the data center, security, and mobile network filtered out the invalid service chains, which resulted in reduced search space. The proposed genetic algorithm found the Skyline service chain instance with the highest quality. The genetic operations were defined to ensure that the service function chains generated in the algorithm process were standard. The experimental results showed that the proposed service composition method outperformed the other methods regarding the quality of service (QoS), running time, and time complexity metrics. Ultimately, the proposed CFG could be generalized to other SFC use cases.

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 Spurious Ambiguity and Focalization

2018 ◽  
Vol 44 (2) ◽  
pp. 285-327 ◽  
Author(s):  
Glyn Morrill ◽  
Oriol Valentín
Keyword(s):  
Mathematical Structure ◽  
Search Space ◽  
Categorial Grammar ◽  
Context Free Grammar ◽  
Ordered Trees ◽  
Proof Nets ◽  
Theoretic Technique ◽  
Context Free

Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize. In this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.

Cost efficiency of SDN-enabled service function chaining

Info ◽  
2016 ◽  
Vol 18 (5) ◽  
pp. 45-55 ◽  
Author(s):  
Nan Zhang ◽  
Heikki Hämmäinen ◽  
Hannu Flinck
Keyword(s):  
Cost Efficiency ◽  
Service Providers ◽  
Cost Savings ◽  
Mobile Network ◽  
Baseline Scenario ◽  
Lte Networks ◽  
Efficient Operation ◽  
Content Type ◽  
Service Function ◽  
The Cost

Purpose This paper models the cost efficiency of service function chaining (SFC) in software-defined LTE networks and compares it with traditional LTE networks. Design/methodology/approach Both the capital expenditure (CAPEX) and operational expenditure (OPEX) of the SFC are quantified using an average Finnish mobile network in 2015 as a reference. The modeling inputs are gathered through semi-structured interviews with Finnish mobile network operators (MNO) and network infrastructure vendors operating in the Finnish market. Findings The modeling shows that software-defined networking (SDN) can reduce SFC-related CAPEX and OPEX significantly for an average Finnish MNO in 2015. The analysis on different types of MNOs implies that a MNO without deep packet inspection sees the biggest cost savings compared to other MNO types. Practical implications Service function investments typically amount to 5-20 per cent of the overall MNO network investments, and savings in SFC may impact highly on the cost structure of a MNO. In addition, SFC acts as both a business interface, which connects the local MNOs with global internet service providers, and as a technical interface, where the 3GPP and IETF standards meet. Thus, the cost efficient operation of SFC may bring competitive advantages to the MNO. Originality/value The results show solid basis of network-related cost savings in SFC and contributes to MNOs making cost conscious investment decisions. In addition, the results act as a baseline scenario for further studies that combine SDN with virtualization to re-optimize network service functions.

Robust garden path parsing

2004 ◽  
Vol 10 (1) ◽  
pp. 1-24 ◽  
Author(s):  
BRIAN ROARK
Keyword(s):  
Ad Hoc ◽  
Search Space ◽  
Complete Coverage ◽  
Context Free Grammar ◽  
Garden Path ◽  
Empirical Results ◽  
Newspaper Text ◽  
Context Free ◽  
Probabilistic Context ◽  
Excellent Accuracy

This paper presents modifications to a standard probabilistic context-free grammar that enable a predictive parser to avoid garden pathing without resorting to any ad-hoc heuristic repair. The resulting parser is shown to apply efficiently to both newspaper text and telephone conversations with complete coverage and excellent accuracy. The distribution over trees is peaked enough to allow the parser to find parses efficiently, even with the much larger search space resulting from overgeneration. Empirical results are provided for both Wall St. Journal and Switchboard test corpora.

scholarly journals Unsupervised Grammar Induction with Depth-bounded PCFG

2018 ◽  
Vol 6 ◽  
pp. 211-224 ◽  
Author(s):  
Lifeng Jin ◽  
Finale Doshi-Velez ◽  
Timothy Miller ◽  
William Schuler ◽  
Lane Schwartz
Keyword(s):  
Parameter Space ◽  
Search Space ◽  
Grammar Induction ◽  
Context Free Grammar ◽  
Grammar Acquisition ◽  
Recent Interest ◽  
Category Labels ◽  
Context Free ◽  
Probabilistic Context

There has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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