Constrained Optimization via Quantum Genetic Algorithm for Task Scheduling Problem. Task scheduling is one of the most important issues on heterogeneous multiprocessor systems. In this paper, the problem is defined as performanceconstrained energy optimization. It is a commonly used constrained optimization problem (COP) in practice. Task scheduling for constrained optimization problem is NP problem. It is usually handled by heuristics or metaheuristics method. Classic quantum genetic algorithm is an excellent metaheuristics algorithm, but they are hardly ever used to handle COPs because quantum rotation gate can only deal with single objective problem. Moreover, it is difficult to model the task scheduling problems so as to be handled by quantum genetic algorithm. To handles COPs in task scheduling on heterogeneous multiprocessor systems, we propose a new quantum genetic algorithm. In our algorithm, the chromosome consists of task sequence part and mapping part. Task sequence part is generated by list scheduling algorithm which can improve the parallel of the tasks. The mapping part indicates the correspondence between the tasks and the processors which they will run on. The mapping part will be transferred to quantum bits and take part in the evolvement guided by quantum genetic algorithm. Beside, we adopt an adaptive penalty method which belongs to constrainthandling technique to transfer COP into single objective problem. The results in simulations show the superiority of our method compared with stateoftheart algorithms.

Scheduling Periodic and Aperiodic Tasks with Time, Energy Harvesting and Precedence Constraints on Multicore Systems Abstract   This paper deals with the realtime scheduling problem of multicore systems powered by renewable energy harvested from environment. They handle two types of software tasks which are mapped to cores statically and not allowed to migrate. A task can be periodic which may depend on other tasksxe2x80x99 results, or aperiodic which is added to the system to cope with external interruptions. The uncertainty of energy availability in energy harvesting systems makes realtime scheduling more challenging because energy constraints can be violated to ensure realtime performance. A novel scheduling strategy is proposed to effectively compute deadlines allowing for tasks and messages to meet related constraints. This method consists of two phases, i) the first one defines different time slots each of which is characterized by energy and frequency parameters to cope with the energy availability issue, and ii) the second one calculates the deadlines ensuring realtime system feasibility by considering the invocation of aperiodic task execution and task precedence constraints. The originality of the current work compared with related studies is that it deals with multicore, periodic and aperiodic tasks, dependency, energy harvesting, and realtime aspects simultaneously.