Genetic-based Scheduling Research Articles

New Hybrid Genetic Based Approach for Real-Time Scheduling of Reconfigurable Embedded Systems

This journal article deals with the problem of real-time scheduling of operating systems (OS) tasks by a hybrid genetic-based scheduling algorithm. Indeed, most of real-time systems are framed with aid of priority-based scheduling algorithms. Nevertheless, when such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated at run-time. In contrast, most of the applications of real-time systems are based on timing constraints, i.e. OS tasks should be scheduled properly to finish their execution within the time specified by the real-time systems. For this reason, the authors propose in their article, a hybrid genetic-based scheduling approach that automatically checks the systems feasibility after any reconfiguration scenario was applied to an embedded system. A benchmark example is given, and the experimental results demonstrate the effectiveness of the originally proposed genetic-based scheduling approach over other such classical genetic algorithmic approaches.

Data and Task Scheduling in Distributed Computing Environments

Data-aware scheduling in today’s large-scale heterogeneous environments has become a major research and engineering issue. Data Grids (DGs), Data Clouds (DCs) and Data Centers are designed for supporting the processing and analysis of massive data, which can be generated by distributed users, devices and computing centers. Data scheduling must be considered jointly with the application scheduling process. It generates a wide family of global optimization problems with the new scheduling criteria including data transmission time, data access and processing times, reliability of the data servers, security in the data processing and data access processes. In this paper, a new version of the Expected Time to Compute Matrix (ETC Matrix) model is defined for independent batch scheduling in physical network in DG and DC environments. In this model, the completion times of the computing nodes are estimated based on the standard ETC Matrix and data transmission times. The proposed model has been empirically evaluated on the static grid scheduling benchmark by using the simple genetic-based schedulers. A simple comparison of the achieved results for two basic scheduling metrics, namely makespan and average flowtime, with the results generated in the case of ignoring the data scheduling phase show the significant impact of the data processing model on the schedule execution times.

GENIUS – A genetic scheduling algorithm for high-performance switches

The most important switching systems in the Internet are the routers. Current routers employ input-queued crossbar switches that require sophisticated scheduling techniques for packet transmission. The performance of the router then directly depends on the scheduling algorithm, considering its throughput and complexity. In this paper, a new genetic-based scheduling algorithm, called GENIUS, is developed and tested for high-performance Internet switches operation. The GENIUS approach presents low complexity and the results show a performance close to the optimal.

Artificial Neural Network support to monitoring of the evolutionary driven security aware scheduling in computational distributed environments

Monitoring of the system performance in highly distributed computing environments is a wide research area. In cloud and grid computing, it is usually restricted to the utilization and reliability of the resources. However, in today’s Computational Grids (CGs) and Clouds (CCs), the end users may define the special personal requirements and preferences in the resource and service selection, service functionality and data access. Such requirements may refer to the special individual security conditions for the protection of the data and application codes. Therefore, solving the scheduling problems in modern distributed environments remains still challenging for most of the well known schedulers, and the general functionality of the monitoring systems must be improved to make them efficient as schedulers supporting modules.In this paper, we define a novel model of security-driven grid schedulers supported by an Artificial Neural Network (ANN). ANN module monitors the schedule executions and learns about secure task–machine mappings from the observed machine failures. Then, the metaheuristic grid schedulers (in our case—genetic-based schedulers) are supported by the ANN module through the integration of the sub-optimal schedules generated by the neural network, with the genetic populations of the schedules.The influence of the ANN support on the general schedulers’ performance is examined in the experiments conducted for four types of the grid networks (small, medium, large and very large grids), two security scheduling modes—risky and secure scenarios, and six genetic-based grid schedulers. The generated empirical results show the high effectiveness of such monitoring support in reducing the values of the major scheduling criteria (makespan and flowtime), the run times of the schedulers and the grid resource failures.

Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment

Task scheduling and resource allocation are the key rationale behind the computational grid. Distributed resource clusters usually work in different autonomous domains with their own access and security policies that have a great impact on the successful task execution across the domain boundaries. Heuristics and metaheuristics are the effective technologies for scheduling in grids due to their ability to deliver high quality solutions in reasonable time.In this paper, we develop a Hierarchic Genetic Scheduler (HGS-Sched) for improving the effectiveness of the single-population genetic-based schedulers in the dynamic grid environment. The HGS-Sched enables a concurrent exploration of the solution space by many small dependent populations. We consider a bi-objective independent batch job scheduling problem with makespan and flowtime minimized in hierarchical mode (makespan is a dominant criterion). The empirical results show the high effectiveness of the proposed method in comparison with the mono-population and hybrid genetic-based schedulers.

A beam search heuristics to solve the parcel hub scheduling problem

In this paper, a beam search scheduling heuristic (BSSH) is presented to solve the parcel hub scheduling problem (PHSP), which is a scheduling problem that is common in the parcel delivery industry (PDI). Companies in the PDI include the United States Postal Service, United Parcel Services, Federal Express, and Deutsche Post. Together, these companies move more than one trillion dollars of the United States’ Gross Domestic Product. The PHSP involves scheduling a set of inbound trailers each containing a set of heterogeneous parcels to a set of unload docks. At the unload docks, the parcels are unloaded, sorted, and moved to the appropriate outbound trailers at the load docks. At the load docks, the parcels are loaded onto the outbound trailers. The objective is to minimize the timespan of the transfer operation at the transshipment terminal. The BSSH is compared to various scheduling approaches: random scheduling algorithm (RSA), genetic-based scheduling algorithm (GBSA), and simulation-based scheduling algorithm (SBSA). While GBSA and SBSA offer solutions that are superior to BSSH for smaller size problems, BSSH outperforms these algorithms on larger size problems requiring much less computational time. The results show that for larger size problems the BSSH is able to produce solutions that are from 4% to 8% of the known optimum solutions. In contrast, GBSA and SBSA, respectively offer solutions from 23% to 38% and from 6% to 47% of the known optimum solutions. The contribution of this paper is a scheduling heuristic to solve the PHSP.

Integration of task abortion and security requirements in GA-based meta-heuristics for independent batch grid scheduling

Scheduling and resource allocation in large scale distributed environments, such as Computational Grids (CGs), arise new requirements and challenges not considered in traditional distributed computing environments. Among these new requirements, task abortion and security become needful criteria for Grid schedulers. The former arises due to the dynamics of the Grid systems, in which resources are expected to enter and leave the system in an unpredictable way. The latter requirement appears crucial in Grid systems mainly due to a multi-domain nature of CGs. The main aim of this paper is to develop a scheduling model that enables the aggregation of task abortion and security requirements as additional, together with makespan and flowtime, scheduling criteria into a cumulative objective function. We demonstrate the high effectiveness of genetic-based schedulers in finding near-optimal solutions for multi-objective scheduling problem, where all criteria (objectives) are simultaneously optimized. The proposed meta-heuristics are experimentally evaluated in static and dynamic Grid scenarios by using a Grid simulator. The obtained results show the fast reduction of the values of basic scheduler performance metrics, especially in the dynamic case, that confirms the usefulness of the proposed approach in real-life scenarios.

Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population

Independent Job Scheduling is one of the most useful versions of scheduling in grid systems. It aims at computing efficient and optimal mapping of jobs and/or applications submitted by independent users to the grid resources. Besides traditional restrictions, mapping of jobs to resources should be computed under high degree of heterogeneity of resources, the large scale and the dynamics of the system. Because of the complexity of the problem, the heuristic and meta-heuristic approaches are the most feasible methods of scheduling in grids due to their ability to deliver high quality solutions in reasonable computing time. One class of such meta-heuristics is Hierarchic Genetic Strategy (HGS). It is defined as a variant of Genetic Algorithms (GAs) which differs from the other genetic methods by its capability of concurrent search of the solution space. In this work, we present an implementation of HGS for Independent Job Scheduling in dynamic grid environments. We consider the bi-objective version of the problem in which makespan and flowtime are simultaneously optimized. Based on our previous work, we improve the HGS scheduling strategy by enhancing its main branching operations. The resulting HGS-based scheduler is evaluated under the heterogeneity, the large scale and dynamics conditions using a grid simulator. The experimental study showed that the HGS implementation outperforms existing GA-based schedulers proposed in the literature.

Genetic-based scheduling to solve the parcel hub scheduling problem

The parcel hub scheduling problem (PHSP) is scheduling problem that is common in the parcel delivery industry. The PHSP consists of scheduling a set of inbound trailers containing a batch of heterogeneous parcels to a set of unload docks with the objective of minimizing the time span of the transfer operation. The transfer operation involves unloading the inbound trailers and sorting and loading the parcels onto outbound trailers. The unload schedule can have a significant impact on the amount of time required to complete the transfer operation. In this paper, a mathematical model is proposed to solve small-size problems and a genetic algorithm is presented to solve the large-size problems with minimum computational time compared to other competing approaches.