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1

Decentralized job scheduling in the cloud based on a spatially generalized Prisoner’s Dilemma game

Gąsior J., Seredyński F.

International Journal of Applied Mathematics and Computer Science

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2015

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Vol. 25, no. 4

737--751

EN

We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of Spatial Prisoner’s Dilemma, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.

2

Image reconstruction with the use of evolutionary algorithms and cellular automata

Seredyński F., Skaruz J., Piraszewski A.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

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2012

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Vol. 12, no. 4

39--49

EN

In the paper we present a new approach to the image reconstruction problem based on evolution algorithms and cellular automata. Two-dimensional, nine state cellular automata with the Moore neighbourhood perform reconstruction of an image presenting a human face. Large space of automata rules is searched through efficiently by the genetic algorithm (GA), which finds a good quality rule. The experimental results show that the obtained rule allows to reconstruct an image with even 70% damaged pixels. Moreover, we show that the rule found in the genetic evolution process can be applied to the reconstruction of images of the same class but not presented during the evolutionary one.

3

LCS Approach to Tasks Scheduling Problem in the Two Processor System

Wasielewska K., Seredyński F.

Studia Informatica : systems and information technology

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2007

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Vol. 2(9)

29--39

EN

In this paper we propose an approach to solve multiprocessor scheduling problem with use of rule-based learning machine - Learning Classifier System (LCS). LCS combines reinforcement learning and evolutionary computing to produce adaptive systems. We interpret the multiprocessor scheduling problem as multi-step problem, where a feedback is given after some number steps. We show that LCS is able to solve scheduling tasks of a parallel program in the two processor system.

4

Game Theoretical Model Applied to Scheduling in Grid Computing

Świtalski P., Seredyński F.

Studia Informatica : systems and information technology

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2007

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Vol. 2(9)

19--27

EN

We consider a grid computational model which consist of a number of computation nodes and a number of users. Each user generates a computation load (jobs) requesting computational and communication resources. A deadline for each job is also defined. We propose a scheduling algorithm which is based on Iterated Prisoner's Dilemma (IPD) under the Random Pairing game, where nodes (players) of the grid system decide about their behavior: cooperate or defect. In this game players play a game with randomly chosen players and receive payoffs. Each player has strategies which define its decision. Genetic algorithm (GA) is used to evolve strategies to optimize a criterion related to scheduling problem. In this paper we show that GA is able to discover a strategy in the IPD model providing a cooperation between node-players, which permits to solve scheduling problem in grid.

5

LCS and GP Approaches to Multiplexer’s Problem

Wasielewska K., Bagiński M., Seredyński F.

Studia Informatica : systems and information technology

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2006

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Vol. 1(7)

195--206

EN

In this paper we present the use of learning classifier systems and genetic programming to solving multiplexer’s problem. The function of multiplexer is the popular apparatus of researches which is used to investigate the effectiveness of systems based on evolutionary algorithms. It turns out that the eXtended Classifier System (XCS) learns the problem of multiplexer effectively and Genetic Programming (GP) finds the form of function of multiplexer correctly.

6

Function optimization using metaheuristics

Pilski M., Seredyński F.

Studia Informatica : systems and information technology

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2006

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Vol. 1(7)

77--91

EN

The paper presents the results of comparison of three metaheuristics that currently exist in the problem of function optimization. The first algorithm is Particle Swarm Optimization (PSO) - the algorithm has recently emerged. The next one is based on a paradigm of Artificial Immune System (AIS). Both algorithms are compared with Genetic Algorithm (GA). The algorithms are applied to optimize a set of functions well known in the area of evolutionary computation. Experimental results show that it is difficult to unambiguously select one best algorithm which outperforms other tested metaheuristics.

7

Cryptography based upon Cellular Automata

Seredyński F., Bouvry P., Szaban M.

Studia Informatica : systems and information technology

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2003

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Vol. 2(2)

73--85

EN

New results concerning application of cellular automata (CAs) to secret key cryptography is described in this paper. One dimensional nonuniform CAs are considered for generating pseudorandom number sequences used in a secret key cryptographic system. The quality of PNSs highly depends on set of applied CA rules. The search of rules relies on an evolutionary technique called cellular programming. Different rule sizes are considered. As the result of collective behavior of discovered set of CA rules very high quality PNSs are generated. Indeed the quality of PNSs outerforms the quality of known one dimensional CA-based PNS generators used for secret key cryptography. The extended set of CA rules proposed in this article makes the cryptography system much more resistant on attacks.

8

Cellular automata approach to task scheduling

Święcicka A., Seredyński F.

Studia Informatica : systems and information technology

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2003

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Vol. 1(1)

37--46

EN

In this paper we propose using cellular automata (CAs) to perform distributed scheduling tasks of a parallel program in the two processor system. We consider a program graph as a CA with elementaty cells interacting locally according to a certain rule which must be found. Effective rules for a CA are discovered by a genetic algorithm (GA). With these rules, CA-based scheduler is able to find allocations which minimize the total execution time of the program in the two processor system. We also show a possibility of reusing knowledge gained during solving instances of the scheduling problem. Keywords: cellular automata, genetic algorithms, scheduling problem.

9

New trends in parallel and distributed evolutionary computing

Seredyński F.

Fundamenta Informaticae

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1998

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Vol. 35, nr 1-4

211-230

EN

The paper overviews existing models and new tendencies observed in the current literature concerning parallel and distributed evolutionary computation. Sources of parallelism, ways of exploring it and forms of a distributed control of execution of parallel evolutionary algorithms are discussed. Some taxonomy of parallel and distributed computation is proposed. The most known or promising computational schemes are overviewed.