In complex systems like Large-Scale Distributed Systems (LSDSs) the optimization of resource control is an open issue. The large number of resources and multicriteria optimization requirements make the optimization problem a complex one. The importance of resource control increases with the need of use for industrial process and manufacturing, being a key solution for QoS assuring. This paper presents different solutions for multiobjective decentralized control models for tasks assignment in LSDS. The transaction in real-time complex system is modeled in simulation by tasks which will be scheduled and executed in a distributed system, so a set of specifications and requirements are known. The paper presents a critical analysis of existing solutions and focuses on a genetic-based algorithm for optimization. The contribution of the algorithm is the fitness function that includes multiobjective criteria for optimization in different way. Several experimental scenarios, modeled using simulation, were considered to offer a support for analysis of near-optimal solution for resource selection.

The resources control in complex systems requires information about resources and tasks. A near-optimal assignment could be made based on some criterion function, such as minimum execution time or load balancing. There has been a steadily increasing interest, supported by advanced technological and economic developments, into dealing with very complex (dynamical) systems describing natural phenomena or manufacturing processes [

The scheduling process is considered to be the core of resources control in complex systems. Due to the NP-complete nature of scheduling algorithms, current research directions are focused on finding suboptimal (near-optimal) solutions, which can be further divided into the following two general categories: approximate and heuristic algorithms. At global level, two-phase scheduling solution comprised of a set of heuristic subalgorithms to achieve optimized scheduling performance over the scope of overall resources is a new research subject in the present [

Today, engineers face an increasing challenge in advanced applications with different requirements and constrains. Innovative developments for efficient mathematical approaches focused on approximate algorithms, heuristics-based methods, and bio-inspired models. The approximate algorithms use formal computational models, but instead of searching the entire solution space for an optimal solution, they are satisfied when a solution that is sufficiently

The rest of the paper is structured as follows. Section

Optimization methods for resource control use heuristic (multiobjective) approaches. The allocation problem considers a set of

In this model, a complex system has a number of

To evaluate the efficiency o resource utilization a resource is considered to be

For optimization, there are

Another important aspect of scheduling optimization considers real-time systems. These type of systems are defined as those systems in which the correctness of the system depends not only on the logical result of computation, but also on the time at which the results are produced. If the timing constraints of the system are not met, system failure is said to have occurred. Hence, it is essential that the timing constraints of the system are guaranteed to be met.

The Opportunistic Load Balancing heuristic selects the task

The heuristic assigns each task selected arbitrarily to the machine with the least expected execution time for that task [

The heuristic assigns each task selected in arbitrary order to the machine with the minimum expected completion time for that task [

The heuristic begins with the set

The heuristic is very similar to min-min, but considers

The heuristic is a combination of the min-min and max-min heuristics. The heuristic performs both of the min-min and max-min heuristics and used the better solution [

They are used for searching large solution spaces with multiple possible schedule of tasks. Each possible schedule is modeled by a chromosome that has a fitness value, which is the result of an objective function designed in accordance with the performance criteria of the problem (

It is an iterative technique that considers only one possible solution (mapping) for each task at a time [

Heuristic is a search technique that has been applied in various task allocation problems. The

Guaranteeing timing behavior requires that the system could be predicted. Predictability means that when a task is activated it should be possible to determine its execution time with certainty. It is also desirable that the system attains a high degree of utilization while satisfying the timing constraints of the system [

A complex system is said to be

Task

Task

The set of real-time tasks

The following goals should be considered in scheduling a real-time system: (i) meeting the timing constraints of the system; (ii) preventing simultaneous access to shared resources and devices; (iii) attaining a high degree of utilization while satisfying the timing constraints of the system; (iv) reducing the cost of context switches caused by preemption; (v) reducing the communication cost in real-time distributed systems. In addition, the following criteria are considered in advanced real-time systems: (vi) considering a combination of hard, firm, and soft real-time activities, which implies the possibility of applying dynamic scheduling policies that respect the optimality criteria; (vii) task scheduling for a real-time system whose behavior is dynamically adaptive, reconfigurable, reflexive, and intelligent; (viii) covering reliability, security, and safety. Basically, the scheduling problem is to determine a schedule for the execution of the task so that they are all completed before the overall deadline [

Multidimensional optimization methods are useful when the search space is likely to have many local optima, making it hard to locate the global optimum. In low-dimensional or constrained problems it may be enough to apply a local optimizer starting at a set of possible start points, generated either randomly or systematically (for instance, at systems locations), and choose the best result. However this approach is less likely to locate the true optimum as the ratio of volume of the search region to number of starting points increases. The application of different multidimensional optimization method proves that finding the global optimum is a hard problem.

Scheduling of vehicles in the container terminal is often studied as a static problem in the literature, where all information, including the number of task, their arrival time, and so forth, is known beforehand. The objective is generally minimizing the total traveling and/or waiting times of the vehicles. When the situation changes, for example, new jobs arrive or a section of the terminal is blocked, new solutions are generated from scratch.

A parallel approach of a modular simulated annealing (MSA) algorithm, a shortened SA algorithm, applied to classical job-shop scheduling (JSS) problems is presented. The JSS problems tackled are very well-known difficult benchmarks, which are considered to measure the quality of such systems.

GAs are developed for solving the machine-component grouping problem required for example a cellular manufacturing systems. GA provides a collection of satisfactory solutions for a two-objective environment (minimizing cell load variation and minimizing volume of inter cell movement), allowing the decision maker to then select the best alternative.

In [

Main actions of proposed algorithm.

A user requests that one or more tasks are scheduled.

The input is processed as a “batch of tasks” (group of tasks). The batch of tasks is broadcast to all the resources in the cluster.

The resources receive the group of tasks to be scheduled. The tasks are inserted-sorted in a queue according to a sorting criteria like arriving time (

On each resource, a tool keeps an up-to-date status of the computers in the LSDS on which tasks are sent for execution, by constantly interrogating a monitoring system.

The resources in the cluster run the GA. Each resource starts with a different, specific initialization of the genetic algorithm. The subsequent steps of the GA are similar for all the nodes in the cluster, and so is the fitness formula. The clients will compute different optimum from which the best one will be chosen.

The migration of the best current solutions is performed after each step of the GA, thus ensuring that the population finds a better optimum. The resources exchange the fittest individuals and insert them into the next generation.

The reproduction process stops after a finite, predefined number of steps. Each resource in the cluster computes its optimal individual.

Each resource sends its optimum to all the other nodes in the cluster and the final optimal individual is decided.

The scheduling obtained is saved in a history file on each resource in the cluster of resources.

The

Due to the complexity of the LSDS, involving many resources and many jobs being concurrently executed in heterogeneous environments, there are not many simulation tools to address the general problem of LSDS computing. The simulation instruments tend to narrow the range of simulation scenarios to specific subjects, such as scheduling or data replication. The simulation model provided by MONARC is more generic than others, as demonstrated in [

MONARC simulation tool: the Regional center model for LSDS control.

The maturity of the simulation model was demonstrated in previous work. For example, a number of data replications experiments were conducted in [

It is quite difficult to make a comparison among different control systems for LSDS, since each of them is suitable for different situations. For different control systems, the class of targeted applications and LSDS resource configurations may differ significantly. The adequate evaluation criteria for LSDS control systems are as follows. (i)

When designing the control infrastructure for LSDSs, these criteria are expected to receive careful consideration. Emphasis may be laid on different concerns among these evaluation criteria according to practical needs in real situations. The performance of scheduling algorithms for LSDS control is usually estimated using a certain number of standard parameters, like total time or schedule length. In the tests performed we used the following evaluation parameters [

total schedule length (SL)—

convergences time—the number of generations needed to obtain performances better then a certain threshold;

load balancing (where

The load balancing of system, for a given schedule, converges to 1 when all resources have approximately the same utilization rate, equal to makespan. In these conditions the square deviation

The test case considered task dependencies containing 38 tasks. The processors’ topology contained 8 processors connected in a full mesh. The results presented in Figure

Makespan comparison for the scheduling of 38 tasks on 8 processors.

In order to analyze the schedule length of different dependent task scheduling algorithms, it has been used a processor topology containing 8 processors connected in a full mesh. Two tests have been run for DAGs containing 90 and 506 tasks (see Figure

Makespan comparison for the following scenarios: 90 tasks + 8 resources, 506 tasks − 38 resources.

For the first test (90 tasks—left side of the figure), the best result, 263 time units, was provided by the proposed GA. On the second place came the GA without the initialization phase with the value of makespan equal to 266. From the classic scheduling algorithm, Duplex provided the best solution equal to 281, while the result of SA was the worst equal to 292. The test containing 506 tasks was an extreme test. The best result, 1073 time units, was offered by GA, proving once more the importance of the proposed algorithm. The worst solution was given by SA. The other compared algorithm is min-min [

Processor utilization overview for 90 tasks.

Memory is another important factor since it is the characteristic that controls most of the allocation algorithms and also since it cannot be oversubscribed. As can be seen, the memory allocator gets to the maximum memory value slower and thus allows for better performance. Also this allocator is the first to leave the maximum value barrier when the load is decreased (see Figure

Memory usage for Best Fit allocation.

We present in this paper an algorithm for controling the resources allocation for special tasks type (transitions) in LSDS (considered to be a complex one). The novelty of the proposed algorithm is represented by the multicriteria optimization fitness function for special tasks with specific requirements and constrains. The process was modulated using a genetic scheduling algorithm. The paper analyzed the existing methods for control optimization in LSDS. The multidimensional optimization criteria were considered with the real-time behavior introducing two measures for evaluation: penalty and reward. In accordance with this behavior, the convergence of proposed control method is very good in terms of convergence, solution cost, and memory usage.

The most important contribution of this paper is the innovative method for the optimization of dependent task scheduling control in LSDS. Inspired from the natural models, this algorithm evolves an initial population of chromosomes in order to achieve a good average fitness for the population. The experimental results have proven that the proposed algorithm offers the best solutions in most cases. For comparison were used several classical algorithms such as SA, Duplex, and min-min.

The research presented in this paper is supported by the Romanian Project: