Scheduling tasks in multiprocessor systems using evolutionary strategies

The scheduling of real-time tasks with primary-backup based fault-tolerant requirements has been an important problem for several years. Most of the known scheduling schemes are non-adaptive in nature meaning that they do not adapt to the dynamics of faults and task’s parameters in the system. In this paper,we propose an adaptive faulttolerant scheduling scheme that has a mechanism to control the overlap interval between the primary and backup versions of tasks such that the overall performance of the system is improved. The overlap interval is determined based on the estimated primary fault probability and task’s soft laxity. We also propose a new performance index that integrates schedulability (S) and reliability (R) into a single metric, called SR index. To evaluate the proposed scheme, we have conducted analytical and simulation studies under different fault and deadline scenarios, and found that the proposed adaptive scheme adapts to system dynamics and offers better SR index than that of the non-adaptive schemes.KeywordsSchedule SchemeProcessor UtilizationFault RateFault ProbabilityExecution IntervalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A distributed protocol is proposed for the synchronization of real-time tasks that have variable resource requirements. The protocol is simple to implement and is intended for large-scale distributed or parallel systems in which processes communicate by message passing. Critical sections, even when nested, may be executed on any processor. Thus, given an adequate number of processors, the execution of critical sections can be completely distributed. More significantly, since the protocol enables the distributed allocation of critical sections, the benefits of various allocations can be analyzed and the system optimized to provide minimal blocking. This has important application in global optimization techniques for allocating large numbers of hard real-time tasks in multiprocessor systems.

Static task scheduling in multiprocessor frameworks is one of the well-defined NP hard problem. Due to optimal utilization of processors and in addition investing less time, the scheduling of tasks in multiprocessor frameworks is of extraordinary significance. To solve NP hard problem using traditional strategies takes reasonable measures of time. Over the time, various heuristic procedures were presented for comprehending it. Therefore, heuristic methods such as genetic algorithms are appropriate methods for task scheduling in multiprocessor system. In this paper, a new GA for static task scheduling in multiprocessor systems has been presented whose priority of tasks’ execution is based on the height of task in graph and other mentioned parameters and then scheduling is performed. This proposed method is simulated and then compared with basic genetic algorithm.

Scheduling tasks in a multiprocessor system is found to be a NP-hard problem and a considerable amount of time is used up when it is solved using conventional techniques. Therefore, evolutionary algorithms like Genetic Algorithms (GA) have been explored for scheduling tasks in a multiprocessor system. GA can be implemented in various manners. This paper investigates the performance of GA with two different selection operators. This paper also studies how introducing elitism effects the performance of GA. Extensive simulations have been carried out in order to find the better candidate among the two selection operators. The decision is made depending on stability of the GA output, the rate of convergence of output and the ability of GA to give an output which is as close as possible to the actual output.

Efficient maximum reaction time analysis for data chains of real-time tasks in multiprocessor systems

Different task models have been proposed to represent the parallel structure of real-time tasks executing on manycore platforms: fork/join, synchronous parallel, DAG-based, etc. Despite different schedulability tests and resource augmentation bounds are available for these task systems, we experience difficulties in applying such results to real application scenarios, where the execution flow of parallel tasks is characterized by multiple (and nested) conditional structures. When a conditional branch drives the number and size of sub-jobs to spawn, it is hard to decide which execution path to select for modeling the worst-case scenario. To circumvent this problem, we integrate control flow information in the task model, considering conditional parallel tasks (cp-tasks) represented by DAGs composed of both precedence and conditional edges. For this task model, we identify meaningful parameters that characterize the schedulability of the system, and derive efficient algorithms to compute them. A response time analysis based on these parameters is then presented for different scheduling policies. A set of simulations shows that the proposed approach allows efficiently checking the schedulability of the addressed systems, and that it significantly tightens the schedulability analysis of non-conditional (e.g., Classic DAG) tasks over existing approaches.

Task scheduling in real-time systems is a concept by which we can schedule the tasks according to their priorities. In hard real-time systems, the task scheduling is more important due to the chance of system failure. We discuss EDF task scheduling which is an optimal technique for the uniprocessor systems. The existing result of the QPA algorithm was applied on the uniprocessor system to check the schedulability of the tasks in uniprocessor system. We have extended the QPA algorithm to check the schedulability of the task sets in multiprocessor systems. Our algorithm is based on QPA algorithm and the utilization balanced algorithm. The proposed algorithm checks the absolute deadlines much faster than the existing QPA algorithm for uniprocessor real-time systems.

Communication-aware task scheduling and voltage selection for total energy minimization in a multiprocessor system using Ant Colony Optimization

The objective of the scheduling soft real-time tasks is to minimize total tardiness and the scheduling these tasks on multiprocessor system is NP-hard problem. In this chapter, scheduling algorithms for soft real-time tasks using genetic algorithm (GA) are introduced. GA has been known to offer significant advantages against conventional heuristics by using simultaneously several search principles and heuristics. The objective of this study is to propose reasonable solutions for NP-hard scheduling problem which much less difficulties than those of traditional mathematical methods. A continuous task scheduling, real-time task scheduling on homogeneous system and real-time task scheduling on heterogeneous system are included in this chapter.

Task scheduling is essential for the proper functioning of parallel processor systems. Scheduling of tasks onto networks of parallel processors is an interesting problem that is well-defined and documented in the literature. However, most of the available techniques are based on heuristics that solve certain instances of the scheduling problem very efficiently and in reasonable amounts of time. This paper investigates an alternative paradigm, based on genetic algorithms, to efficiently solve the scheduling problem without the need to apply any restricted assumptions that are problem-specific, such is the case when using heuristics. Genetic algorithms are powerful search techniques based on the principles of evolution and natural selection. The performance of the genetic approach will be compared to the well-known list scheduling heuristics. The conditions under which a genetic algorithm performs best will also be highlighted. This will be accompanied by a number of examples and case studies.

Fault tolerant scheduling of hard real-time tasks on multiprocessor system using a hybrid genetic algorithm

The design and analysis of software which implements digital signal processing (DSP) algorithms on multiprocessor systems is examined. The DSP algorithms that are considered exhibit certain characteristics which are common of DSP algorithms used in communication and control systems. The analysis methods developed use linear complexity algorithms to analyze the software that implements the DSP algorithms. The first step involves finding the repeat factors associated with a basic cycle of the algorithm. Using these repeat factors it is straightforward to (1) find the precedence relationships between tasks and finally a feasible schedule for the multiprocessor, and (2) coordinate and organize the efforts to optimize the software. >

This paper describes a real-time robotic system. It runs on a heterogeneous multiprocessor system and the system communicates with the robot via RS232 link or infrared link. The multiprocessing system is a IBM personal computer and a i860 add-on card. There are several tasks required for a high performance real-time robotics system: (1) An efficient decision-making task to fulfil the real-time requirement such as supervision, coordination and local control of the robot (2) A real-time graphics task to display the robot's location and its environment (3) A real-time alerting task to alert the system software to perform efficient recovery when the robot is not in its controlling environment (4) Other nonreal-time tasks such as auditing and scheduling (5) Tasks for communication between the robot and the host. In this paper, a multiprocessing distributed system is designed such that the tasks 1 to 4 is handled on the IBM, and the graphics display system is handled by the i860 because of its extremely good graphics performance. The software and hardware design for the system to have a good real-time performance is also discussed. >

Aiming at edge computing nodes' limited computing and storage capacity, a two-layer task scheduling model based on blockchain and task cache was proposed. The high-similarity task results were cached in the edge cache pool, and the blockchain-assisted task caching model was combined to enhance system security. The genetic evolution algorithm was used to solve the minimum cost that the optimal scheduling model can obtain. The genetic algorithm’s initialization and mutation operations were adjusted to improve the convergence rate. Compared with algorithms without cache pooling and blockchain, the proposed joint blockchain and task caching task scheduling model reduced the cost by 9.4% and 14.3%, respectively. As the capacity space of the cache pool increased, the system cost gradually decreased. Compared with the capacity space of 3GB, the system cost of 10Gbit capacity space was reduced by 10.6%. The system cost decreased as the computing power of edge nodes increased. Compared with edge nodes with a computing frequency of 8GHz, the nodes cost at 18GHz was reduced by 36.4%. Therefore, the proposed edge computing task scheduling model ensures the security of task scheduling based on reducing delay and control costs, providing a foundation for modern industrial task scheduling.

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.