Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems

To exploit the power of modern heterogeneous multiprocessor embedded platforms on partitioned applications, the designer usually needs to efficiently map and schedule all the tasks and the communications of the application, respecting the constraints imposed by the target architecture. Since the problem is heavily constrained, common methods used to explore such design space usually fail, obtaining low-quality solutions. In this paper, we propose an ant colony optimization (ACO) heuristic that, given a model of the target architecture and the application, efficiently executes both scheduling and mapping to optimize the application performance. We compare our approach with several other heuristics, including simulated annealing, tabu search, and genetic algorithms, on the performance to reach the optimum value and on the potential to explore the design space. We show that our approach obtains better results than other heuristics by at least 16% on average, despite an overhead in execution time. Finally, we validate the approach by scheduling and mapping a JPEG encoder on a realistic target architecture

Reinforcement learning based multi core scheduling (RLBMCS) for real time systems

Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system

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 Real-Time Task Scheduling Algorithm in Overload Situation for Multiprocessor System

Real-time systems are reactive systems which should meet major constraints in scheduling tasks like time limitation and resources allocation for scheduling the task effectively when the system in overloaded condition. Failure of system in scheduling tasks when system is overloaded can result in catastrophic impacts. The goal of this research is to propose a task scheduling algorithm that able to perform better than traditional Earliest Deadline First (EDF) and minimize the overall completion time when the system in overloaded condition. The proposed scheduling algorithm is built based on three new improved scheduling algorithms namely: (1) Hybrid Particle Swarm Optimization (PSO) and Hybrid Invasive Weed Optimization (HPIO), (2) Enhanced Initial Swarm (EIS), and (3) Hybrid EDF, EIS and HPIO Optimization (HEDFPIO). The author proves that more successful tasks is scheduled by using HPIO in multiprocessor system in over loaded situation among PSO and ACO. The author uses EIS algorithm in order to improve local search in HPIO and have fair load balance among processors. Finally the author presents a new hybrid algorithm that combines HPIO, EIS and EDF which is called HEDFPIO, It is observed that we could achieve higher successful ratio in task scheduling and with shorter calculation time in overloaded situation

A Tabu Search Algorithm for Scheduling Independent Jobs in Computational Grids

The efficient allocation of jobs to grid resources is indispensable for high performance grid-based applications, and it is a computationally hard problem even when there are no dependencies among jobs.We present in this paper a new tabu search (TS) algorithm for the problem of batch job scheduling on computational grids. We define it as a bi-objective optimization problem, consisting of the minimization of the makespan and flowtime. Our TS is validated versus three other algorithms in the literature for a classical benchmark. We additionally consider some more realistic benchmarks with larger size instances in static and dynamic environments. We show that our TS clearly outperforms the compared algorithms

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms