A genetic approach using direct representation of solution for the parallel task scheduling problem

In scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program can be conceived as a set of parallel components (tasks) which can be executed according to some precedence relationship. In this case efficient scheduling of tasks permits to take full advantage of the computational power provided by a multiprocessor or a multicomputer system. This kind of planning involves the assignment of partially ordered tasks onto the system architecture processing components. This paper shows the problem of allocating a number of non-identical tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. The well-known Graham’s list scheduling algorithm (LSA) is contrasted with an evolutionary approach using a direct representation of solutions.Eje: Computación evolutivaRed de Universidades con Carreras en Informática (RedUNCI

Evolutionary approaches for the parallel task scheduling problem : the representation issue

The problem of how to find a schedule on m > 2 processors of equal capacity that minimises the whole processing time of independent tasks has been shown as belonging to the NP-complete class (Horowitz and Sahni [12]). Evolutionary Algorithms (EAs) have been used in the past to implement the allocation of the components (tasks) of a parallel program to processors [12], [13], [14], [16], [17]. Those approaches showed their advantages when contrasted against conventional approaches and different chromosome representations were proposed. This paper shows four algorithms to solve the problem of allocating a number of non-identical related tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. Three evolutionary algorithms, using an indirect-decode representation, are contrasted with the well-known Graham’s [11] list scheduling algorithm (LSA). All of them use the conventional Single Crossover Per Couple (SCPC) approach and indirectdecode representation but they differ in what is represented by the decoders. In the first representation scheme, decoders represent processor dispatching priorities, in the second decoders represent tasks priority lists, and in the third decoders represent both processor dispatching priorities and tasks priority lists in a bipartite chromosome. Chromosome structure, genetic operators, experiments and results are discussed.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

Solving unrestricted parallel machine scheduling problems via evolutionary algorithms

Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model involving m machines and n independent jobs is the foundation of more complex models. Here, the jobs are allocated according to resource availability following some allocation rule. The completion time of the last job to leave the system, known as the makespan (Cmax), is one of the most important objective functions to be minimized, because it usually implies high utilization of resources, but other important objectives must be also considered. These problems are known in the literature [9, 11] as unrestricted parallel machine scheduling problems. Many of these problems are NP-hard for 2≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. This presentation shows the problem of allocating a number of non-identical independent tasks in a production system. The model assumes that the system consists of a number of identical machines and only one task may execute on a machine at a time. All schedules and tasks are non-preemptive. A set of well-known conventional heuristics will be contrasted with evolutionary approaches using multiple recombination and indirect representations.Eje: Informática de GestiónRed de Universidades con Carreras en Informática (RedUNCI

A genetic approach using direct representation of solution for the parallel task scheduling problem

In scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program can be conceived as a set of parallel components (tasks) which can be executed according to some precedence relationship. In this case efficient scheduling of tasks permits to take full advantage of the computational power provided by a multiprocessor or a multicomputer system. This kind of planning involves the assignment of partially ordered tasks onto the system architecture processing components. This paper shows the problem of allocating a number of non-identical tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. The well-known Graham’s list scheduling algorithm (LSA) is contrasted with an evolutionary approach using a direct representation of solutions.Eje: Computación evolutivaRed de Universidades con Carreras en Informática (RedUNCI

Evolutionary approaches for the parallel task scheduling problem : the representation issue

The problem of how to find a schedule on m > 2 processors of equal capacity that minimises the whole processing time of independent tasks has been shown as belonging to the NP-complete class (Horowitz and Sahni [12]). Evolutionary Algorithms (EAs) have been used in the past to implement the allocation of the components (tasks) of a parallel program to processors [12], [13], [14], [16], [17]. Those approaches showed their advantages when contrasted against conventional approaches and different chromosome representations were proposed. This paper shows four algorithms to solve the problem of allocating a number of non-identical related tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. Three evolutionary algorithms, using an indirect-decode representation, are contrasted with the well-known Graham’s [11] list scheduling algorithm (LSA). All of them use the conventional Single Crossover Per Couple (SCPC) approach and indirectdecode representation but they differ in what is represented by the decoders. In the first representation scheme, decoders represent processor dispatching priorities, in the second decoders represent tasks priority lists, and in the third decoders represent both processor dispatching priorities and tasks priority lists in a bipartite chromosome. Chromosome structure, genetic operators, experiments and results are discussed.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

Primary recovery of hyaluronic acid produced in Streptococcus equi subsp. zooepidemicus using PEG-citrate aqueous two-phase systems

Given its biocompatibility, rheological, and physiological properties, hyaluronic acid (HA) has become a biomaterial of increasing interest with multiple applications in medicine and cosmetics. In recent decades, microbial fermentations have become an important source for the industrial production of HA. However, due to its final applications, microbial HA must undergo critical and long purification processes to ensure clinical and cosmetic grade purity. Aqueous two-phase systems (ATPS) have proven to be an efficient technique for the primary recovery of high-value biomolecules. Nevertheless, their implementation in HA downstream processing has been practically unexplored. In this work, polyethylene glycol (PEG)–citrate ATPS were used for the first time for the primary recovery of HA produced with an engineered strain of Streptococcus equi subsp. zooepidemicus. The effects of PEG molecular weight (MW), tie-line length (TLL), volume ratio (VR), and sample load on HA recovery and purity were studied with a clarified fermentation broth as feed material. HA was recovered in the salt-rich bottom phase, and its recovery increased when a PEG MW of 8000 g mol−1 was used. Lower VR values (0.38) favoured HA recovery, whereas purity was enhanced by a high VR (3.50). Meanwhile, sample load had a negative impact on both recovery and purity. The ATPS with the best performance was PEG 8000 g mol−1, TLL 43% (w/w), and VR 3.50, showing 79.4% HA recovery and 74.5% purity. This study demonstrated for the first time the potential of PEG–citrate ATPS as an effective primary recovery strategy for the downstream process of microbial HA

An evolutionary approach to the parallel task scheduling problem

A parallel program, when running, can be conceived a set of parallel components (tasks) which can be executed according to sorne precedence relationship. In this case efficient scheduling of tasks permits to take full.advantage of the computational power provided by a multiprocessor or a multicomputer system. This involves the assignment of partially ordered tasks onto the system architecture processing components. Ihis work shows the problem of allocating a number of nonidentical tasks in a multiprocessor or multicomputer system. Ihe model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are nonpreeptive. Ihe well-known Grabam's [8] list scheduling algorithm (LSA) is contrasted with an evolutionary approach using the indirect-decode representation.Eje: Redes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

An evolutionary approach to the parallel task scheduling problem

A parallel program, when running, can be conceived a set of parallel components (tasks) which can be executed according to sorne precedence relationship. In this case efficient scheduling of tasks permits to take full.advantage of the computational power provided by a multiprocessor or a multicomputer system. This involves the assignment of partially ordered tasks onto the system architecture processing components. Ihis work shows the problem of allocating a number of nonidentical tasks in a multiprocessor or multicomputer system. Ihe model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are nonpreeptive. Ihe well-known Grabam's [8] list scheduling algorithm (LSA) is contrasted with an evolutionary approach using the indirect-decode representation.Eje: Redes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

A genetic approach using direct representation of solution for parallel task scheduling problem

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.Facultad de Informátic