We present in this paper an algorithm for finding degenerated common features by multiple comparison of a set of biological sequences (nucleic acids or proteins). The features that are of interest to us are words in the sequences. The algorithm uses the concept of a model we introduced earlier for locating these features. A model can be seen as a generalization of a consensus pattern as defined by Waterman [42]. It is an object against which the words in the sequences are compared and which serves as an identifier for the groups of similar ones. The algorithm given here innovates in relation to our previous work in that the models are defined over what we call a weighted combinatorial cover. This is a collection of sets among all possible subsets of the alphabet Σ of nucleotides or amino acids, including the wild card {Σ}, with a weight attached to each of these sets indicating the number of times it may appear in a model. In this way, we explore both the space of models and that of alphabets. The words that are related to a model defined over such a combinatorial cover, and thus considered to be similar, are then the ones that either belong to the model or present at most a certain number of errors with a nearest element of it. We use two algorithmic ideas that allow us to deal with such double combinatorics, one concerns a left-to-right minimality of the sets composing a model, the other involves making a sketch of the solution space before exploring it in detail.

A Double Combinatorial Approach to Discovering Patterns in Biological Sequences

Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.

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Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

Scheduling of scientific workflows on hybrid cloud architecture, which contains private and public clouds, is a challenging task because schedulers should be aware of task inter-dependencies, underlying heterogeneity, cost diversity, and virtual machine (VM) variable configurations during the scheduling process. On the one side, reaching a minimum total execution time or makespan is a favorable issue for users whereas the cost of utilizing quicker VMs may lead to conflict with their budget on the other side. Existing works in the literature scarcely consider VM’s monetary cost in the scheduling process but mainly focus on makespan. Therefore, in this paper, the problem of scientific workflow scheduling running on hybrid cloud architecture is formulated to a bi-objective optimization problem with makespan and monetary cost minimization viewpoint. To address this combinatorial discrete problem, this paper presents a hybrid bi-objective optimization based on simulated annealing and task duplication algorithms (BOSA-TDA) that exploits two important heuristics heterogeneous earliest finish time (HEFT) and duplication techniques to improve canonical SA. The extensive simulation results reported of running different well-known scientific workflows such as LIGO, SIPHT, Cybershake, Montage, and Epigenomics demonstrate that proposed BOSA-TDA has the amount of 12.5%, 14.5%, 17%, 13.5%, and 18.5% average improvement against other existing approaches in terms of makespan, monetary cost, speed up, SLR, and efficiency metrics, respectively.

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Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms

A hybrid bi-objective scheduling algorithm for execution of scientific workflows on cloud platforms with execution time and reliability approach

An efficient workflow scheduling can potentially exploit heterogeneity of resources in heterogeneous cloud computing (HCC) platform commensurate with variable requirement of dependent tasks in a given workflow. Minimizing the total scheduling length, m a k e s p a n , is essential for application performance in heterogeneous computing systems especially in cloud computing environment. The problem of scheduling a set of different dependent tasks onto a set of heterogeneous computational resources is a well-known NP-Hard problem. Therefore, no polynomial scheduling algorithm for computing the optimal solution exists. For approximating a solution to this problem many algorithms have been proposed, but majority of them have low efficiency. In this paper, a novel hybrid heuristic-based list scheduling (HH-LiSch) algorithm is presented for solving the dependent task scheduling in HCC systems in a bounded number of the fully connected virtual machines (VMs). The novelty of the current paper is to present the new task priority strategy, find appropriate VM's slot time, and utilize task duplication technique. Two novel task priority strategies are applied to prioritize tasks in an efficient ordered list. Then, during the scheduling process an insertion-based procedure is called to find an appropriate potential slot time for performing task duplication technique. If it works, the task duplication is added to rudimentary scheduling scheme. In this way, the final scheduling is gradually generated. To validate the work, the experiments are based on six real-world scientific workflows and a random task graph (RTG); then, the performance is evaluated in terms of makespan, Schedule Length Ratio (SLR), speedup and efficiency. The simulation results prove a significant improvement against other counterparts in literature.

A novel hybrid heuristic-based list scheduling algorithm in heterogeneous cloud computing environment for makespan optimization

A hybrid meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment

Discovery of Sequential pattern mining is an important data mining mission with wide applications There is no model that used multi coring techniques for parallel mining of closed sequential patterns The parallelization of a prefixspan method to discover DNAs is proposed in this study The prefixspan method is used to extract the frequent pattern from a sequence database This system requires the use of multiple computers connected in local area network This algorithm includes multi-coring to achieve communication between a master process and multiple slave processes This algorithm applies dynamic scheduling to avoid tasks idling Moreover we employ a technique, called selective sampling We implement this algorithm with using a 4G memory and AMD phenomX4 Our experimental results show that this algorithm attains good efficiencies on motifs extraction

Discovered DNAs of Protein with Using Parallel Prefixspan Method

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/pdf/modeling-sequence-diagram-in-fuzzy-uml-to-fuzzy-petri-net-2mp25511za.pdf

Reza Noorian Talouki

Papers

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

A novel hybrid heuristic-based list scheduling algorithm in heterogeneous cloud computing environment for makespan optimization

A hybrid meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment

Discovered DNAs of Protein with Using Parallel Prefixspan Method

Modeling Sequence Diagram in Fuzzy Uml to Fuzzy Petri-Net for Calculating Reliability Parameter