Javid Taheri | Karlstad University (original) (raw)

Papers by Javid Taheri

Journal of Parallel and Distributed Computing, 2011

In recent years, the issue of energy consumption in parallel and distributed computing systems ha... more In recent years, the issue of energy consumption in parallel and distributed computing systems has attracted a great deal of attention. In response to this, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage–frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. Majority of these algorithms involve two passes: schedule generation and slack reclamation.

The Internet Measurement Conference 2019 Posters (IMC 2019 Posters) program, 2019

Internet services such as virtual reality, interactive cloud applications, and online gaming, hav... more Internet services such as virtual reality, interactive cloud applications, and online gaming, have a strict quality of service requirements (e.g., low-latency). However, the current Internet is not able to satisfy the low-latency requirements of these applications. This as the standard TCP induces high queuing delays when used by capacity-seeking traffic, which in turn results in unpredictable latency. The Low Latency Low Loss Scalable throughput (L4S) architecture aims to address this problem by combining scalable congestion controls (e.g., DCTCP) with early congestion signaling from the network. For incremental deployment, the L4S architecture defines a Dual Queue Coupled AQM that enables the safe coexistence of scalable and classic (e.g., Reno, Cubic, etc.) flows on the global Internet. The DualPI2 AQM is a Linux kernel implementation of a Dual Queue Coupled AQM. In this paper, we benchmark the DualPI2 AQM to validate experimental result(s) reported in previous works that demonstrate the coexistence of scalable and classic congestion controls, and its low-latency service. Our results validate the coexistence of scalable and classic flows using DualPI2 single queue AQM while the result with dual queue shows neither rate nor window fairness between the flows.

Journal of Multiple Valued Logic and Soft Computing, Jan 3, 2014

The study of protein-protein interactions (PPI) is an active area of research in biology because ... more The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods.

In this paper, we propose an analytical method to model the dependency between configuration para... more In this paper, we propose an analytical method to model the dependency between configuration parameters and total execution time of Map-Reduce applications. Our approach has three key phases: profiling, modeling, and prediction. In profiling, an application is run several times with different sets of MapReduce configuration parameters to profile the execution time of the application on a given platform. Then in modeling, the relation between these parameters and total execution time is modeled by multivariate linear regression. Among the possible configuration parameters, two main parameters have been used in this study: the number of Mappers, and the number of Reducers. For evaluation, two standard applications (WordCount, and Exim Mainlog parsing) are utilized to evaluate our technique on a 4-node MapReduce platform.

2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 2011

Abstract Single nucleotide polymorphisms (SNPs) studies have been an active topic of research in ... more Abstract Single nucleotide polymorphisms (SNPs) studies have been an active topic of research in the life sciences in recent years. Because SNPs are abundant, stable and sometimes can be related to specific diseases, they have been widely selected as biomarkers for multi-purpose research. As traditional methods for identifying SNPs are time-consuming and expensive, discovering SNPs from expressed sequence tags (ESTs) has became an alternative efficient way. As most EST databases do not store quality/trace files ...

Most of the biological functions are mediated by protein-protein interactions in the organism. If... more Most of the biological functions are mediated by protein-protein interactions in the organism. If one of these interactions behaves improperly, it may lead to a disease. Therefore, the study of protein-protein interactions i s very i mportant t o improve our understanding of diseases and ca n p rovide t he basi s for ne w therapeutic approaches. Although, there are no c oncrete properties in predicting protein-protein interactions, it is known from experimentally determined protein-protein interactions that interacting proteins have a high probability to share similar f unctions, cellu lar ro les and su b-cellular lo cations. If two p roteins h ave similar functions, they will theoretically share similar three-dimensional structures as well. Th erefore, it is believed that if two proteins have similar secondary structures, they will also have similar three-dimensional structures and c onsequently share sim ilar functions. As a result th ey will interact with each other. However, i f these proteins ha ve similar sequen ce, t hey do n ot al ways have sim ilar seconda ry st ructures and co nsequently similar t hree-dimensional st ructures an d f unctions. B ased on th ese theo ries, we pred ict th e in teracting proteins in Saccharomyces cerevisie (baker's yeast) from the information of their secondary structures using computational method. This paper proposes multiple independent fuzzy systems for predicting protein-protein interactions from the similarity of p rotein secondary st ructures. Our m ethod c onsists of t wo m ain st ages: (1) si milarity score computation, an d (2) sim ilarity classificatio n. Th e first stag e in volves th ree step s: (1 ) Mu ltiple-sequence alignment (MSA)-fi nding multiple-sequence alignment fo r ev ery family g roups of pro teins in Saccharomyces cerevisie , (2 ) Second ary str ucture p rediction ( SSP)-predicting secondary structure of aligned proteins seq uence usi ng seco ndary st ructure p rediction t ool cal led SSpr o, and ( 3) Si milarity measurement (Sim )-computing sim ilarity scores of predicted second ary st ructures fo r ev ery po ssible proteins pairs based on the number of three conformational states: helix (H), sheet (E), and coil (C).

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2012

Many vehicles rely on the Global Positioning System (GPS) to compute their locations. The inaccur... more Many vehicles rely on the Global Positioning System (GPS) to compute their locations. The inaccuracy of GPS devices means sometimes vehicles believe they are located in different lanes or roads altogether. Vehicular Ad Hoc Networks (VANETs) allow vehicles to communicate with each other using wireless means and thus connect them in a very dynamic wireless network. The algorithm VANET LOCation Improve (VLOCI), proposed in this work, uses VANETs and distance measurements taken by each vehicle to improve the ...

2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 2011

The study of protein-protein interactions (PPI) is an active area of research in biology because ... more The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods.

2011 IEEE 11th International Conference on Bioinformatics and Bioengineering, 2011

Abstract The study on protein-protein interactions is rapidly increasing; one of the most importa... more Abstract The study on protein-protein interactions is rapidly increasing; one of the most important findings of such study is the observation of hub proteins that play vital roles in all organisms. Identifying hub proteins may provide more information on essential proteins and lead to more efficient methods for their prediction. Here, we proposed a new network topological-based method for prediction of hub proteins in Saccharomyces cerevisiae (baker's yeast). The method, HP 3 NN (Hub Protein Prediction using Probabilistic Neural ...

Large Scale Network-Centric Distributed Systems, 2013

ABSTRACT The identification of essential proteins is theoretically and practically important as (... more ABSTRACT The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

2013 International Conference on Parallel and Distributed Computing, Applications and Technologies, 2013

2014 IEEE 6th International Conference on Cloud Computing Technology and Science, 2014

International journal of bioinformatics research and applications, 2015

Single nucleotide polymorphism studies have recently received significant amount of attention fro... more Single nucleotide polymorphism studies have recently received significant amount of attention from researchers in many life science disciplines. Previous researches indicated that a series of SNPs from the same chromosome, called haplotype, contains more information than individual SNPs. Hence, discovering ways to reconstruct reliable Single Individual Haplotypes becomes one of the core issues in the whole-genome research nowadays. However, obtaining sequence from current high-throughput sequencing technologies always contain inevitable sequencing errors and/or missing information. The SIH reconstruction problem can be formulated as bi-partitioning the input SNP fragment matrix into paternal and maternal sections to achieve minimum error correction; a problem that is proved to be NP-hard. In this study, we introduce a greedy approach, named RadixHap, to handle data sets with high error rates. The experimental results show that RadixHap can generate highly reliable results in most ca...

2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2012

This paper proposes a Parallel Genetic Algorithm (PGA) framework to solve the Knapsack problem in... more This paper proposes a Parallel Genetic Algorithm (PGA) framework to solve the Knapsack problem in the Cloud. Our PGA consists of several independent workers that cooperatively run in parallel to find optimal solutions for a Knapsack problem; we chose the Knapsack problem because it is known to be NP-Complete and can be used to motivate other cloud-based solutions for other combinatorial problems too. Although this problem has already been extensively studied in the literature, no cloud-based solution is ever presented for that -to the best of our knowledge. We used several benchmarks to validate our solutions against the optimal solution computed by dynamic programming. Results are very promising and illustrated reasonable scalability as well as speed up factor for our implementation using Microsoft Azure.

Journal of Parallel and Distributed Computing, 2011

In recent years, the issue of energy consumption in parallel and distributed computing systems ha... more In recent years, the issue of energy consumption in parallel and distributed computing systems has attracted a great deal of attention. In response to this, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage–frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. Majority of these algorithms involve two passes: schedule generation and slack reclamation.

The Internet Measurement Conference 2019 Posters (IMC 2019 Posters) program, 2019

Internet services such as virtual reality, interactive cloud applications, and online gaming, hav... more Internet services such as virtual reality, interactive cloud applications, and online gaming, have a strict quality of service requirements (e.g., low-latency). However, the current Internet is not able to satisfy the low-latency requirements of these applications. This as the standard TCP induces high queuing delays when used by capacity-seeking traffic, which in turn results in unpredictable latency. The Low Latency Low Loss Scalable throughput (L4S) architecture aims to address this problem by combining scalable congestion controls (e.g., DCTCP) with early congestion signaling from the network. For incremental deployment, the L4S architecture defines a Dual Queue Coupled AQM that enables the safe coexistence of scalable and classic (e.g., Reno, Cubic, etc.) flows on the global Internet. The DualPI2 AQM is a Linux kernel implementation of a Dual Queue Coupled AQM. In this paper, we benchmark the DualPI2 AQM to validate experimental result(s) reported in previous works that demonstrate the coexistence of scalable and classic congestion controls, and its low-latency service. Our results validate the coexistence of scalable and classic flows using DualPI2 single queue AQM while the result with dual queue shows neither rate nor window fairness between the flows.

Journal of Multiple Valued Logic and Soft Computing, Jan 3, 2014

The study of protein-protein interactions (PPI) is an active area of research in biology because ... more The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods.

In this paper, we propose an analytical method to model the dependency between configuration para... more In this paper, we propose an analytical method to model the dependency between configuration parameters and total execution time of Map-Reduce applications. Our approach has three key phases: profiling, modeling, and prediction. In profiling, an application is run several times with different sets of MapReduce configuration parameters to profile the execution time of the application on a given platform. Then in modeling, the relation between these parameters and total execution time is modeled by multivariate linear regression. Among the possible configuration parameters, two main parameters have been used in this study: the number of Mappers, and the number of Reducers. For evaluation, two standard applications (WordCount, and Exim Mainlog parsing) are utilized to evaluate our technique on a 4-node MapReduce platform.

2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 2011

Abstract Single nucleotide polymorphisms (SNPs) studies have been an active topic of research in ... more Abstract Single nucleotide polymorphisms (SNPs) studies have been an active topic of research in the life sciences in recent years. Because SNPs are abundant, stable and sometimes can be related to specific diseases, they have been widely selected as biomarkers for multi-purpose research. As traditional methods for identifying SNPs are time-consuming and expensive, discovering SNPs from expressed sequence tags (ESTs) has became an alternative efficient way. As most EST databases do not store quality/trace files ...

Most of the biological functions are mediated by protein-protein interactions in the organism. If... more Most of the biological functions are mediated by protein-protein interactions in the organism. If one of these interactions behaves improperly, it may lead to a disease. Therefore, the study of protein-protein interactions i s very i mportant t o improve our understanding of diseases and ca n p rovide t he basi s for ne w therapeutic approaches. Although, there are no c oncrete properties in predicting protein-protein interactions, it is known from experimentally determined protein-protein interactions that interacting proteins have a high probability to share similar f unctions, cellu lar ro les and su b-cellular lo cations. If two p roteins h ave similar functions, they will theoretically share similar three-dimensional structures as well. Th erefore, it is believed that if two proteins have similar secondary structures, they will also have similar three-dimensional structures and c onsequently share sim ilar functions. As a result th ey will interact with each other. However, i f these proteins ha ve similar sequen ce, t hey do n ot al ways have sim ilar seconda ry st ructures and co nsequently similar t hree-dimensional st ructures an d f unctions. B ased on th ese theo ries, we pred ict th e in teracting proteins in Saccharomyces cerevisie (baker's yeast) from the information of their secondary structures using computational method. This paper proposes multiple independent fuzzy systems for predicting protein-protein interactions from the similarity of p rotein secondary st ructures. Our m ethod c onsists of t wo m ain st ages: (1) si milarity score computation, an d (2) sim ilarity classificatio n. Th e first stag e in volves th ree step s: (1 ) Mu ltiple-sequence alignment (MSA)-fi nding multiple-sequence alignment fo r ev ery family g roups of pro teins in Saccharomyces cerevisie , (2 ) Second ary str ucture p rediction ( SSP)-predicting secondary structure of aligned proteins seq uence usi ng seco ndary st ructure p rediction t ool cal led SSpr o, and ( 3) Si milarity measurement (Sim )-computing sim ilarity scores of predicted second ary st ructures fo r ev ery po ssible proteins pairs based on the number of three conformational states: helix (H), sheet (E), and coil (C).

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2012

Many vehicles rely on the Global Positioning System (GPS) to compute their locations. The inaccur... more Many vehicles rely on the Global Positioning System (GPS) to compute their locations. The inaccuracy of GPS devices means sometimes vehicles believe they are located in different lanes or roads altogether. Vehicular Ad Hoc Networks (VANETs) allow vehicles to communicate with each other using wireless means and thus connect them in a very dynamic wireless network. The algorithm VANET LOCation Improve (VLOCI), proposed in this work, uses VANETs and distance measurements taken by each vehicle to improve the ...

2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 2011

The study of protein-protein interactions (PPI) is an active area of research in biology because ... more The study of protein-protein interactions (PPI) is an active area of research in biology because it mediates most of the biological functions in any organism. This work is inspired by the fact that proteins with similar secondary structures mostly share very similar three-dimensional structures, and consequently, very similar functions. As a result, they must interact with each other. In this study we used our approach, namely FIS-PNN, to predict the interacting proteins in yeast from the information of their secondary structures using hybrid machine learning algorithms. Two main stages of our approach are similarity score computation, and classification. The first stage is further divided into three steps: (1) Multiple-sequence alignment, (2) Secondary structure prediction, and (3) Similarity measurement. In the classification stage, several independent first order Sugeno Fuzzy Inference Systems and probabilistic neural networks are generated to model the behavior of similarity scores of all possible proteins pairs. The final results show that the multiple classifiers have significantly improved the performance of the single classifier. Our method, namely FIS-PNN, successfully predicts PPI with 96% of accuracy, a level that is significantly greater than all other sequence-based prediction methods.

2011 IEEE 11th International Conference on Bioinformatics and Bioengineering, 2011

Abstract The study on protein-protein interactions is rapidly increasing; one of the most importa... more Abstract The study on protein-protein interactions is rapidly increasing; one of the most important findings of such study is the observation of hub proteins that play vital roles in all organisms. Identifying hub proteins may provide more information on essential proteins and lead to more efficient methods for their prediction. Here, we proposed a new network topological-based method for prediction of hub proteins in Saccharomyces cerevisiae (baker's yeast). The method, HP 3 NN (Hub Protein Prediction using Probabilistic Neural ...

Large Scale Network-Centric Distributed Systems, 2013

ABSTRACT The identification of essential proteins is theoretically and practically important as (... more ABSTRACT The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

2013 International Conference on Parallel and Distributed Computing, Applications and Technologies, 2013

2014 IEEE 6th International Conference on Cloud Computing Technology and Science, 2014

International journal of bioinformatics research and applications, 2015

Single nucleotide polymorphism studies have recently received significant amount of attention fro... more Single nucleotide polymorphism studies have recently received significant amount of attention from researchers in many life science disciplines. Previous researches indicated that a series of SNPs from the same chromosome, called haplotype, contains more information than individual SNPs. Hence, discovering ways to reconstruct reliable Single Individual Haplotypes becomes one of the core issues in the whole-genome research nowadays. However, obtaining sequence from current high-throughput sequencing technologies always contain inevitable sequencing errors and/or missing information. The SIH reconstruction problem can be formulated as bi-partitioning the input SNP fragment matrix into paternal and maternal sections to achieve minimum error correction; a problem that is proved to be NP-hard. In this study, we introduce a greedy approach, named RadixHap, to handle data sets with high error rates. The experimental results show that RadixHap can generate highly reliable results in most ca...

2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2012

This paper proposes a Parallel Genetic Algorithm (PGA) framework to solve the Knapsack problem in... more This paper proposes a Parallel Genetic Algorithm (PGA) framework to solve the Knapsack problem in the Cloud. Our PGA consists of several independent workers that cooperatively run in parallel to find optimal solutions for a Knapsack problem; we chose the Knapsack problem because it is known to be NP-Complete and can be used to motivate other cloud-based solutions for other combinatorial problems too. Although this problem has already been extensively studied in the literature, no cloud-based solution is ever presented for that -to the best of our knowledge. We used several benchmarks to validate our solutions against the optimal solution computed by dynamic programming. Results are very promising and illustrated reasonable scalability as well as speed up factor for our implementation using Microsoft Azure.