Mikhail Melnik

Hybrid Evolutionary Workflow Scheduling Algorithm for Dynamic Heterogeneous Distributed Computational Environment

The optimal workflow scheduling is one of the most important issues in heterogeneous distributed computational environment. Existing heuristic and evolutionary scheduling algorithms have their advantages and disadvantages. In this work we propose a hybrid algorithm based on Heterogeneous Earliest Finish Time heuristic and genetic algorithm that combines best characteristics of both approaches. We also experimentally show its efficiency for variable workload in dynamically changing heterogeneous computational environment.

Hybrid scheduling algorithm in early warning systems

Abstract The development of an efficient Early Warning System (EWS) is essential for the prediction and prevention of imminent natural hazards. In addition to providing a computationally intensive infrastructure with extensive data transfer, high-execution reliability and hard-deadline satisfaction are important requirements of EWS scenario processing. This is due to the fact that EWS has a limited window of opportunity to discern if a scene shows signs of an impending natural disaster. In this paper, the scheduling component of the EWS scenario is investigated and an efficient hybrid algorithm for the urgent workflows scheduling is proposed. The developed algorithm is based on traditional heuristic and meta-heuristic approaches along with state-of-the-art cloud computing principles.

Workflow Scheduling Algorithms for Hard-deadline Constrained Cloud Environments

Cloud computational platforms today are very promising for execution of scientific applications since they provide ready to go infrastructure for almost any task. However, complex tasks, which contain a large number of interconnected applications, which are usually called workflows, require efficient tasks scheduling in order to satisfy user defined QoS, like cost or execution time (makespan). When QoS has some restrictions limited cost or deadline scheduling becomes even more complicated. In this paper we propose heuristic algorithm for scheduling workflows in hard-deadline constrained clouds Levelwise Deadline Distributed Linewise Scheduling (LDD-LS) which, in combination with implementation of IC-PCP algorithm, is used for initialization of proposed metaheuristic algorithm Cloud Deadline Coevolutional Genetic Algorithm (CDCGA). Experiments show high efficiency of CDCGA, which makes it potentially applicable for scheduling in cloud environments.

Metaheuristic coevolution workflow scheduling in cloud environment

Today technological progress makes scientific community to challenge more and more complex issues related to computational organization in distributed heterogeneous environments, which usually include cloud computing systems, grids, clusters, PCs and even mobile phones. In such environments, traditionally, one of the most frequently used mechanisms of computational organization is the Workflow approach. Taking into account new technological advantages, such as resources virtualization, we propose new coevolution approaches for workflow scheduling problem. The approach is based on metaheuristic coevolution that evolves several diverse populations that influence each other with final positive effect. Besides traditional population, that optimizes tasks execution order and tasks map to the computational resources, additional populations are used to change computational environment to gain more efficient optimization. As a result, proposed scheduling algorithm optimizes both computation tasks to computation environment and computation environment to computation tasks, making final execution process more efficient than traditional approaches can provide.

Coevolutionary Workflow Scheduling in a Dynamic Cloud Environment

In this paper, we present a new coevolutionary algorithm for workflow scheduling in a dynamically changing environment. Nowadays, there are many efficient algorithms for workflow execution planning, many of which are based on the combination of heuristic and metaheuristic approaches or other forms of hybridization. The coevolutionary genetic algorithm (CGA) offers an extended mechanism for scheduling based on two principal operations: task mapping and resource configuration. While task mapping is a basic function of resource allocation, resource configuration changes the computational environment with the help of the virtualization mechanism. In this paper, we present a strategy for improving the CGA for dynamically changing environments that has a significant impact on the final dynamic CGA execution process.

Dynamic Resources Configuration for Coevolutionary Scheduling of Scientific Workflows in Cloud Environment

Modern composite scientific applications, also called scientific workflows, require large processing capacities. Cloud environments provide high performance and flexible infrastructure, which can be easily employed for workflows execution. Since cloud resources are paid in the most cases, there is a need to utilize these resources with maximal efficiency. In this paper we propose dynamic resources coevolutionary genetic algorithm, which extends previously developed coevolutionary genetic algorithm for dynamic cloud environment by changing computational capacities of execution nodes on runtime. This method along with using two types of chromosomes – mapping of tasks on resources and resources configuration – allows to greatly extend the search space of the algorithm. Experimental results demonstrate that developed algorithm is able to generate solutions better than other scheduling algorithms for a variety of scientific workflows.

Performance-aware scheduling of streaming applications using genetic algorithm

Abstract The main objective of Decision Support Systems is detection of critical states and response on them in time. Such systems can be based on constant monitoring of continuously incoming data. Stream processing is carried out on the basis of computing infrastructure and specialized frameworks such as Apache Storm, Flink, Spark Streaming. However, to provide the necessary system performance at high load incoming data, additional data processing mechanisms are required. In particular, the efficient scheduling of streaming applications plays an important role in the data stream processing. Therefore, this paper is devoted to investigation of genetic algorithm to improve the performance of data stream processing system. The proposed genetic algorithm is developed and integrated into Apache Storm platform, and its efficiency is compared with heuristic algorithm for scheduling of Storm streaming applications.

Graphical framework for scientific papers clustering

Data visualization traditionally is the most powerful tool for demonstration and analysis of scientific results and mathematical models in particular. In this paper we introduce the graphical framework for citation graph clustering. Furthermore, we discuss ways to detect factors responsible for scientific groups formation. Two datasets of scientific papers related to different fields were used in this work. Firstly we applied scientometric analysis to our data with the view to determine the most influential keywords. After that, we used two different ways for data clustering - graphic clustering method comprising N-body communication graph and a keyword-based hierarchical clustering. As a result of our studies we propose method for dynamic visualization of scientific papers clusters, built using open-access data.