620 research outputs found
A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments
The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. In this paper we describe an ant colony optimisation (ACO) algorithm that, when combined with local and tabu search, can find shorter schedules on benchmark problems than other techniques found in the literature
Hybrid ant colony optimization for grid computing
A hybrid ant colony optimization technique to solve the stagnation problem in grid computing is proposed in this paper.The proposed algorithm combines the techniques from Ant Colony System and Max – Min Ant System and focused on local pheromone trail update and trail limit.The agent concept is also integrated in this proposed technique for the purpose of updating the grid resource table.This facilitates the hybrid ant colony optimization technique in solving the stagnation problem in two ways within one cycle, thus minimize the total computational time of the jobs
Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
Grid computing is a distributed system with heterogeneous infrastructures. Resource
management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration
mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms
will enhance ACS in terms of exploration mechanism and solution refinement by
implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing
environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment,
performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan
The Application of Ant Colony Optimization
The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance
Hybrid Meta-heuristic Algorithms for Static and Dynamic Job Scheduling in Grid Computing
The term ’grid computing’ is used to describe an infrastructure that connects geographically
distributed computers and heterogeneous platforms owned by multiple organizations
allowing their computational power, storage capabilities and other resources to be selected
and shared. Allocating jobs to computational grid resources in an efficient manner is one
of the main challenges facing any grid computing system; this allocation is called job
scheduling in grid computing. This thesis studies the application of hybrid meta-heuristics
to the job scheduling problem in grid computing, which is recognized as being one of
the most important and challenging issues in grid computing environments. Similar to
job scheduling in traditional computing systems, this allocation is known to be an NPhard
problem. Meta-heuristic approaches such as the Genetic Algorithm (GA), Variable
Neighbourhood Search (VNS) and Ant Colony Optimisation (ACO) have all proven their
effectiveness in solving different scheduling problems. However, hybridising two or more
meta-heuristics shows better performance than applying a stand-alone approach. The new
high level meta-heuristic will inherit the best features of the hybridised algorithms, increasing
the chances of skipping away from local minima, and hence enhancing the overall
performance. In this thesis, the application of VNS for the job scheduling problem in grid
computing is introduced. Four new neighbourhood structures, together with a modified
local search, are proposed. The proposed VNS is hybridised using two meta-heuristic
methods, namely GA and ACO, in loosely and strongly coupled fashions, yielding four
new sequential hybrid meta-heuristic algorithms for the problem of static and dynamic
single-objective independent batch job scheduling in grid computing. For the static version
of the problem, several experiments were carried out to analyse the performance of the
proposed schedulers in terms of minimising the makespan using well known benchmarks.
The experiments show that the proposed schedulers achieved impressive results compared
to other traditional, heuristic and meta-heuristic approaches selected from the bibliography.
To model the dynamic version of the problem, a simple simulator, which uses
the rescheduling technique, is designed and new problem instances are generated, by
using a well-known methodology, to evaluate the performance of the proposed hybrid
schedulers. The experimental results show that the use of rescheduling provides significant
improvements in terms of the makespan compared to other non-rescheduling approaches
A WOA-based optimization approach for task scheduling in cloud Computing systems
Task scheduling in cloud computing can directly
affect the resource usage and operational cost of a system. To
improve the efficiency of task executions in a cloud, various
metaheuristic algorithms, as well as their variations, have been
proposed to optimize the scheduling. In this work, for the
first time, we apply the latest metaheuristics WOA (the whale
optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that
basis, we propose an advanced approach called IWC (Improved
WOA for Cloud task scheduling) to further improve the optimal
solution search capability of the WOA-based method. We present
the detailed implementation of IWC and our simulation-based
experiments show that the proposed IWC has better convergence
speed and accuracy in searching for the optimal task scheduling
plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource
utilization, in the presence of both small and large-scale tasks
Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments
Los Experimentos de Barrido de Parámetros (PSEs) permiten a los científicos llevar a cabo simulaciones mediante la ejecución de un mismo código con diferentes entradas de datos, lo cual genera una gran cantidad de trabajos intensivos en CPU que para ser ejecutados es necesario utilizar entornos de cómputo paralelos. Un ejemplo de este tipo de entornos son las Infraestructura como un Servicio (IaaS) de Cloud, las cuales ofrecen máquinas virtuales (VM) personalizables que son asignadas a máquinas físicas disponibles para luego ejecutar los trabajos. Además, es importante planificar correctamente la asignación de las máquinas físicas del Cloud, y por lo tanto es necesario implementar estrategias eficientes de planificación para asignar adecuadamente las VMs en las máquinas físicas. Una planificación eficiente constituye un desafío, debido a que es un problema NP-Completo. En este trabajo describimos y evaluamos un planificador Cloud basado en Optimización por Enjambre de Partículas (PSO). Las métricas principales de rendimiento a estudiar son el número de usuarios que el planificador es capáz de servir exitosamente y el número total de VMs creadas en un escenario online (no por lotes). Además, en este trabajo se evalúa el número de mensajes enviados a través de la red. Los experimentos son realizados mediante el uso del simulador CloudSim y datos de trabajos de problemas científicos reales. Los resultados muestran que nuestro planificador logra el mejor rendimiento respecto de las métricas estudiadas con respecto a una asignación random y algoritmos genéticos. En este trabajo también evaluamos el rendimiento, a través de las métricas propuestas, cuando se provee al planificador información cualitativa relacionada a la longitud de los trabajos o no se provee la misma.Parameter Sweep Experiments (PSEs) allow scientists to perform simulations by running the same code with different input data, which results in many CPU-intensive jobs, and hence parallel computing environments must be used. Within these, Infrastructure as a Service (IaaS) Clouds offer custom Virtual Machines (VM) that are launched in appropriate hosts available in a Cloud to handle such jobs. Then, correctly scheduling Cloud hosts is very important and thus efficient scheduling strategies to appropriately allocate VMs to physical resources must be developed. Scheduling is however challenging due to its inherent NP-completeness. We describe and evaluate a Cloud scheduler based on Particle Swarm Optimization (PSO). The main performance metrics to study are the number of Cloud users that the scheduler is able to successfully serve, and the total number of created VMs, in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performedusing CloudSim and job data from real scientific problems show that our scheduler achieves better performance than schedulers based on Random assignment and Genetic Algorithms. We also study the performance when supplying or not job information to the schedulers, namely a qualitative indication of job length.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto de Tecnologías de la Información y las Comunicaciones; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto de Tecnologías de la Información y las Comunicaciones; Argentin
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are required or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high. This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. The proposed algorithm can overcome stagnation problem, minimize processing time, match jobs with suitable resources, and balance entire resources in grid environment. This research follows the experimental research methodology that consists of problem analysis, developing the proposed framework, constructing the simulation environment, conducting a set of experiments and evaluating the results. There are three new mechanisms in this proposed framework that are used to organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone. This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid job scheduling. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. Experimental results show that EACO produced better grid resource management solution compared to other algorithms
Parallel Asynchronous Particle Swarm Optimization For Job Scheduling In Grid Environment
Grid computing is a new, large and powerful self managing virtual computer out of large collection of connected heterogeneous systems sharing various combination of resources and it is the combination of computer resources from multiple administrative domains applied to achieve a goal, it is used to solve scientific, technical or business problem that requires a great number of processing cycles and needs large amounts of data. One primary issue associated with the efficient utilization of heterogeneous resources in a grid environment is task scheduling. Task Scheduling is an important issue of current implementation of grid computing. The demand for scheduling is to achieve high performance computing. If large number of tasks is computed on the geographically distributed resources, a reasonable scheduling algorithm must be adopted in order to get the minimum completion time. Typically, it is difficult to find an optimal resource allocation for specific job that minimizes the schedule length of jobs. So the scheduling problem is defined as NP-complete problem and it is not trivial. Heuristic algorithms are used to solve the task scheduling problem in the grid environment and may provide high performance or high throughput computing or both. In this paper, a parallel asynchronous particle swarm optimization algorithm is proposed for job scheduling. The proposed scheduler allocates the best suitable resources to each task with minimal makespan and execution time. The experimental results are compared which shows that the algorithm produces better results when compared with the existing ant colony algorithm
A Hybrid Bacterial Swarming Methodology for Job Shop Scheduling Environment
Optimized utilization of resources is the need of the hour in any manufacturing system. A properly planned schedule is often required to facilitate optimization. This makes scheduling a significant phase in any manufacturing scenario. The Job Shop Scheduling Problem is an operation sequencing problem on multiple machines with some operation and machine precedence constraints, aimed to find the best sequence of operations on each machine in order to optimize a set of objectives. Bacterial Foraging algorithm is a relatively new biologically inspired optimization technique proposed based on the foraging behaviour of E.coli bacteria. Harmony Search is a phenomenon mimicking algorithm devised by the improvisation process of musicians. In this research paper, Harmony Search is hybridized with bacterial foraging to improve its scheduling strategies. A proposed Harmony Bacterial Swarming Algorithm is developed and tested with benchmark Job Shop instances. Computational results have clearly shown the competence of our method in obtaining the best schedule
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