Task Scheduling In Multiprocessor Systems Research Articles

EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis

With the significant growth of multiprocessor systems (MPS) to deal with complex tasks and speed up their execution, the energy generated as a result of this growth becomes one of the significant limits to that growth. Although several traditional techniques are available to deal with this challenge, they don’t deal with this problem as multi-objective to optimize both energy and makespan metrics at the same time, in addition to expensive cost and memory usage. Therefore, this paper proposes a multi-objective approach to tackle the task scheduling for MPS based on the modified sine-cosine algorithm (MSCA) to optimize the makespan and energy using the Pareto dominance strategy; this version is abbreviated as energy-aware multi-objective MSCA (EA-M2SCA). The classical SCA is modified based on dividing the optimization process into three phases. The first phase explores the search space as much as possible at the start of the optimization process, the second phase searches around a solution selected randomly from the population to avoid becoming trapped into local minima within the optimization process, and the last searches around the best-so-far solution to accelerate the convergence. To further improve the performance of EA-M2SCA, it was hybridized with the polynomial mutation mechanism in two effective manners to accelerate the convergence toward the best-so-far solution with preserving the diversity of the solutions; this hybrid version is abbreviated as EA-MHSCA. Finally, the proposed algorithms were compared with a number of well-established multi-objective algorithms: EA-MHSCA is shown to be superior in most test cases.

Energy-aware whale optimization algorithm for real-time task scheduling in multiprocessor systems

The growth of Multiprocessing Systems (MPS) has become a necessity for dealing with complex tasks and speeding up their execution. Increasing the number of processing cores on a single chip produces a vast processing power, but the biggest obstacle is the energy generated from these cores. The traditional techniques guarantee to get the optimal schedule, but they are costly in terms of time and memory storage. In this paper, we propose an Improved Whale Algorithm (IWA) to allocate the dependent tasks in MPS with two objectives minimizing the energy consumption and the makespan. The processing cores are assumed to support Dynamic Voltage and Frequency Scaling (DVFS) as an effective technique to reduce energy. The allocation of tasks in MPS is an NP-hard problem. Inadequate scheduling of tasks can result in consuming energy. Also, the failure to complete the tasks before their predetermined deadlines is a critical issue for real-time applications. We consider three different sources of energy coming from the communication, idle, and active states of the processing cores. We use an initialization procedure to produce a population of candidate schedules that respects the precedence among tasks. In IWA, we employ two different discretization methods to map the continuous values into discrete ones. Two specialized crossover operations are adopted to boost the quality of the candidate schedules while respecting the dependencies among the tasks. IWA implements the Load Balancing Improvement (LBI) strategy to alleviate the load of tasks from heavy cores. LBI plays a vital role in decreasing the static energy consumption. We compare IWA with the other algorithms, and the results show the superiority of IWA.

A Novel Scheduler for Task scheduling in Multiprocessor System using Machine Learning approach

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

A variable neighborhood search approach for the vertex bisection problem

The Vertex Bisection Problem (VBP) belongs to the family of well-known graph partitioning problems, where the main goal is to find a partition of the vertices maximizing or minimizing a given objective function. These optimization problems have relevant application in the context of scientific computing, VLSI design circuit, or task scheduling in multi-processor systems. This family of problems has gained importance due to its application in clustering and detection of cliques in social, pathological, and biological networks.In this paper we use Basic Variable Neighborhood Search (BVNS) methodology to solve the VBP. In particular, we propose three constructive procedures and six improvement methods. We introduce a novel scheme for calculating the objective function which substantially reduces the computing time as compared with the direct implementation. After a set of preliminary experiments, the best BVNS design is compared with the state-of-the-art over the same set of instances obtaining better results for both, quality of the solutions and execution time. These results are further confirmed by non-parametric statistical tests.

Problems in Task Scheduling in Multiprocessor System

Problems in Task Scheduling in Multiprocessor System

An Efficient Task Scheduling of Multiprocessor Using Genetic Algorithm Based on Task Height

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.

Task Scheduling in Parallel Systems using Genetic Algorithm

The common problem of multiprocessor scheduling can be defined as allocating a task graph in a multiprocessor system so that schedule length can be improved. Task scheduling in multiprocessor system is a NP-complete problem. A number of heuristic methods have been cultivated that achieve partial solutions in less than the minimum computing time. Genetic algorithms have obtained much awareness as they are robust and provide a good solution. In this paper, genetic algorithm based on the principles of evolution to obtain an optimal solution for task scheduling is developed. Genetic algorithm is based on three operators: Natural Selection, Crossover and Mutation. The simulation results prove that the method proposed generates better results.

Heuristic Based Task Scheduling in Multiprocessor Systems with Genetic Algorithm by Choosing the Eligible Processor

In multiprocessor systems, one of the main factors of systems' performance is task scheduling. The well the task be distributed among the processors the well be the performance. Again finding the optimal solution of scheduling the tasks into the processors is NP-complete, that is, it will take a lot of time to find the optimal solution. Many evolutionary algorithms (e.g. Genetic Algorithm, Simulated annealing) are used to reach the near optimal solution in linear time. In this paper we propose a heuristic for genetic algorithm based task scheduling in multiprocessor systems by choosing the eligible processor on educated guess. From comparison it is found that this new heuristic based GA takes less computation time to reach the suboptimal solution.

A Novel Approach for Task Scheduling in Multiprocessor System

In multiprocessor system, scheduling of tasks to assigned on the number of processors. The major objective of task scheduling is to find minimum execution time of a program. It is well known that the complexity of a general scheduling problem is NP-Complete [9], there are number of heuristic have been developed. Each of which may either find optimal or near optimal scheduling under the different conditions. The task scheduling is represented by a directed acyclic graph (DAG). In this paper, we present a new scheduling algorithm which is called Task Scheduling based on Breath First Search(TSB). The TSB is queue based approach to schedule parallel tasks on the homogenous parallel multiprocessor system. Its performance is evaluated in comparison with Highest Level First with Estimate Time (HLFET) algorithm, Modified Critical Path (MCP) algorithm, Earliest Time First (ETF) algorithm and Dynamic Level Scheduling (DLS) algorithm in terms of Speedup, Efficiency, Load Balance and Normalized Scheduling Length (NSL).

A novel intelligent method for task scheduling in multiprocessor systems using genetic algorithm

In the multiprocessor systems, scheduling is a major issue in their operation, which is also an important problem in other area such as manufacturing, process control, economics, operation research and, etc. An efficient scheduling onto the processes that minimizes the entire run time and also average of response time is vital for achieving a high performance. Solving this problem is very hard and many attempts have been made to solve the problem, using classical algorithms and intelligent methods. In fact in all researches including intelligent methods, the classical algorithm is the basic part of the solution. Even in intelligent methods, which genetic algorithm has been used, when a final chromosome is produced after some generation, a classical algorithm is used to produce an optimal scheduling based on this chromosome. In this paper a novel intelligent solution has been proposed based on genetic algorithm and chromosome background tree without using any classical algorithm. In this method the genetic algorithm presents the optimal scheduling, directly from the produced chromosome in final generation. The time of transferring data between processes is considered, and also the method not only minimizes the entire run time, but also minimizes the average of the response time of all processes.

Genetic-algorithm-based real-time task scheduling with multiple goals

This paper presents and evaluates a new method for real-time task scheduling in multiprocessor systems. Its objectives are to minimize the number of processors required and the total tardiness of tasks. The minimization is carried out through a multiobjective genetic algorithm (GA), because the problem has non-commensurable and competing objectives to be optimized. The experimental results showed that when compared to five methods used previously, such as list-scheduling algorithms and a specific GA, the performance of our algorithm was comparable or better for 178 out of 180 randomly generated task graphs. Also shown is the impact of the sparsity of a task graph on the performance of our algorithm.

Fault-tolerant LPT task scheduling in multiprocessor systems

Minimizing the finish time of a set of independent tasks executed on a multiprocessor system, where no faults occur, is a well-known NP-complete problem. However, an approximated schedule can be found efficiently by means of the so called largest processing time (LPT) rule. This scheduling problem when extended to faulty environments may seem daunting. However, this paper shows how to extend the LPT rule to a fault-tolerant non-uniform memory access (NUMA) multiprocessor employing a primary copy and a backup copy for each task in order to handle processor failures. The concept of expected schedule length is also introduced to evaluate the performance of the resulting schedule in case of processor failures. Simulation studies are used to reveal interesting trade-offs associated with the scheduling algorithm.