A Performance Evaluation of Multiprocessor Scheduling with Genetic Algorithm

In job shop scheduling problems, the previously used nonpreemptive schedules are semiactive, active, and non-delay schedules, and they are initially designed for traditional scheduling performances and do not take energy efficiency into consideration. To fill the gap, this article presents a novel class of nonpreemptive schedules by investigating the special properties of job shop scheduling problems. Then, an energy-efficient generation procedure of the novel nonpreemptive schedules and the standby policy are proposed. An improved genetic algorithm is introduced with three superior features, including the elitist selection, the improved generation alternation model, and the operator decision mechanism. The Friedman test and the Holm multiple comparison test are conducted on experimental results to determine the appropriate parameter for the proposed genetic algorithm. Finally, the computation results and two-sample t-tests show that both energy-efficient generation procedure and standby policy help to reduce energy consumption without worsening production efficiency.

In global supply chain, the scheduling problems of large products such as ships, airplanes, space shuttles, assembled constructions, and/or automobiles are complicated by nature. New scheduling systems are often developed in order to reduce inherent computational complexity. As a result, a problem can be decomposed into small sub-problems, problems that contain independently small scheduling systems integrating into the initial problem. As one of the authors experienced, DAS (Daewoo Shipbuilding Scheduling System) has adopted a two-layered hierarchical architecture. In the hierarchical architecture, individual scheduling systems composed of a high-level dock scheduler, DAS-ERECT and low-level assembly plant schedulers, DAS-PBS, DAS-3DS, DAS-NPS, and DAS-A7 try to search the best schedules under their own constraints. Moreover, the steep growth of communication technology and logistics enables it to introduce distributed multi-nation production plants by which different parts are produced by designated plants. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. No standard coordination mechanism of multiple scheduling systems exists, even though there are various scheduling systems existing in the area of scheduling research. Previous research regarding the coordination mechanism has mainly focused on external conversation without capacity model. Prior research has heavily focuses on agent-based coordination in the area of agent research. Yet, no scheduling domain has been developed. Previous research regarding the agent-based scheduling has paid its ample attention to internal coordination of scheduling process, a process that has not been efficient. In this study, we suggest a general framework for agent-based coordination of multiple scheduling systems in global supply chain. The purpose of this study was to design a standard coordination mechanism. To do so, we first define an individual scheduling agent responsible for their own plants and a meta-level coordination agent involved with each individual scheduling agent. We then suggest variables and values describing the individual scheduling agent and meta-level coordination agent. These variables and values are represented by Backus-Naur Form. Second, we suggest scheduling agent communication protocols for each scheduling agent topology classified into the system architectures, existence or nonexistence of coordinator, and directions of coordination. If there was a coordinating agent, an individual scheduling agent could communicate with another individual agent indirectly through the coordinator. On the other hand, if there was not any coordinating agent existing, an individual scheduling agent should communicate with another individual agent directly. To apply agent communication language specifically to the scheduling coordination domain, we had to additionally define an inner language, a language that suitably expresses scheduling coordination. A scheduling agent communication language is devised for the communication among agents independent of domain. We adopt three message layers which are ACL layer, scheduling coordination layer, and industry-specific layer. The ACL layer is a domain independent outer language layer. The scheduling coordination layer has terms necessary for scheduling coordination. The industry-specific layer expresses the industry specification. Third, in order to improve the efficiency of communication among scheduling agents and avoid possible infinite loops, we suggest a look-ahead load balancing model which supports to monitor participating agents and to analyze the status of the agents. To build the look-ahead load balancing model, the status of participating agents should be monitored. Most of all, the amount of sharing information should be considered. If complete information is collected, updating and maintenance cost of sharing information will be increasing although the frequency of communication will be decreasing. Therefore the level of detail and updating period of sharing information should be decided contingently. By means of this standard coordination mechanism, we can easily model coordination processes of multiple scheduling systems into supply chain. Finally, we apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly- plant-scheduling agents, and a meta-level coordination agent. A series of experiments using the real world data are used to empirically examine this mechanism. The results of this study show that the effect of agent-based platform on coordinated scheduling is evident in terms of the number of tardy jobs, tardiness, and makespan.

The work undertaken in this thesis is about the integration of two well-known methodologies: Petri net (PN) model Ii ng/analysis of industrial production processes and Artificial Intelligence (AI) optimisation search techniques. The objective of this integration is to demonstrate its potential in solving a difficult and widely studied problem, the scheduling of Flexible Manufacturing Systems (FIVIS). This work builds on existing results that clearly show the convenience of PNs as a modelling tool for FIVIS. It addresses the problem of the integration of PN and Al based search methods. Whilst this is recognised as a potentially important approach to the scheduling of FIVIS there is a lack of any clear evidence that practical systems might be built. This thesis presents a novel scheduling methodology that takes forward the current state of the art in the area by: Firstly presenting a novel modelling procedure based on a new class of PN (cb-NETS) and a language to define the essential features of basic FIVIS, demonstrating that the inclusion of high level FIVIS constraints is straight forward. Secondly, we demonstrate that PN analysis is useful in reducing search complexity and presents two main results: a novel heuristic function based on PN analysis that is more efficient than existing methods and a novel reachability scheme that avoids futile exploration of candidate schedules. Thirdly a novel scheduling algorithm that overcomes the efficiency drawbacks of previous algorithms is presented. This algorithm satisfactorily overcomes the complexity issue while achieving very promising results in terms of optimality. Finally, this thesis presents a novel hybrid scheduler that demonstrates the convenience of the use of PN as a representation paradigm to support hybridisation between traditional OR methods, Al systematic search and stochastic optimisation algorithms. Initial results show that the approach is promising.

In this thesis, we study the theoretical approach on energy-efficient scheduling problems arising in demand response management in the modern electrical smart grid. Consumers send in power requests with flexible feasible timeslots during which their requests can be served. The grid controller, upon receiving power requests, schedules each request within the specified interval. The electricity cost is measured by a convex function of the load in each timeslot. The objective is to schedule all requests with the minimum total electricity cost. We study the smart grid scheduling problem in different models. For the offline model, we prove the problem is NP-hard for the general case. We propose a polynomial time algorithm for special input where jobs have unit power request and unit time duration. By adapting the polynomial time algorithm for unit-size jobs, we propose an approximation algorithm for more general input. On the other hand, we also present an exact algorithm to find the optimal schedule for the problem with general input. For the online model, we propose an online algorithm for jobs with jobs with arbitrary power request, arbitrary time duration, and arbitrary contiguous feasible intervals. We also show a lower bound of the competitive ratio for the smart grid scheduling problem with unit height and arbitrary width. For special cases, we design different online algorithms with better competitive ratios. Finally, we look at other optimization problems and show how to solve them by adapting our techniques. We prove that our online algorithm can solve the machine minimization problem with an asymptotically optimal competitive ratio. We also show that our exact algorithm can be adapted to solve other demand response management problems.

This paper presents the Tempus real-time middleware, which supports real-time CORBA 2.0's distributable threads (DTs) as an end-to-end programming abstraction for distributed real-time systems. DTs in Tempus can have time constraints, including time/utility functions (TUFs), can have resource constraints, including mutual exclusion, and can be scheduled according to utility accrual (UA) disciplines. Tempus propagates the scheduling parameters of DT's as they transit objects and hence perhaps node boundaries. Node-local instances of a UA scheduling algorithm use the propagated parameters to construct local schedules and resolve resource dependencies for local timeliness optimization, toward approximate, system-wide timeliness optimality. Tempus uses an application-level scheduling framework for node-local TUF/UA scheduling on real-time POSIX-compliant operating systems. Our experimental measurements demonstrate the effectiveness of the middleware in scheduling DTs.

As a main distributed computing system, Spark has been used to solve problems with more and more complex tasks. However, the native scheduling strategy of Spark assumes it works on a homogenized cluster, which is not so effective when it comes to heterogeneous cluster. The aim of this study is looking for a more effective strategy to schedule tasks and adding it to the source code of Spark. After investigating Spark scheduling principles and mechanisms, we developed a stratifying algorithm and a node scheduling algorithm are proposed in this paper to optimize the native scheduling strategy of Spark. In this new strategy, the static level of nodes is calculated, the dynamic factors such as the length of running tasks, and CPU usage of work nodes are considered comprehensively. And through a series of comparative experiments in alienation cluster, the new strategy costs less running time and lower CPU usage rate than the original Spark strategy, which verifies that the new schedule strategy is more effective one.

We consider the problem of scheduling with renewable speed-up resources. Given m identical machines, n jobs and c different discrete resources, the task is to schedule each job non-preemptively onto one of the machines so as to minimize the makespan. In our problem, a job has its original processing time, which could be reduced by utilizing one of the resources. As resources are different, the amount of the time reduced for each job is different depending on the resource it uses. Once a resource is being used by one job, it can not be used simultaneously by any other job until this job is finished, hence the scheduler should take into account the job-to-machine assignment together with the resource-to-job assignment. We observe that, the classical unrelated machine scheduling problem is actually a special case of our problem when m=c, i.e., the number of resources equals the number of machines. Extending the techniques for the unrelated machine scheduling, we give a 2-approximation algorithm when both m and c are part of the input. We then consider two special cases for the problem, with m or c being a constant, and derive PTASes (Polynomial Time Approximation Schemes) respectively. We also establish the relationship between the two parameters m and c, through which we are able to transform the PTAS for the case when m is constant to the case when c is a constant. The relationship between the two parameters reveals the structure within the problem, and may be of independent interest.

Scheduling task graphs under hard (end-to-end) timing constraints is an extensively studied NP-hard problem of critical importance for predictable software mapping on Multiprocessor System-on-chip (MPSoC) platforms. In this work we focus on an off-line (design-time) version of this problem, where the target task graph is known before execution time. We address the issue of scheduling robustness, i.e. providing hard guarantees that the schedule will meet the end-to-end deadline in presence of bounded variations of task execution times expressed as min-max intervals known at design time. We present a robust scheduling algorithm that proactively inserts sequencing constraints when they are needed to ensure that execution will have no inserted idle times and will meet the deadline for any possible combination of task execution times within the specified intervals. The algorithm is complete, i.e. it will return a feasible graph augmentation if one exists. Moreover, we provide an optimization version of the algorithm that can compute the shortest deadline that can be met in a robust way.

The wafer probing scheduling problem (WPSP) is a variation of the parallel-machine problem, which carries with the objective to minimize the total workload to enhance the utilization rate of machine capacity. However, to minimize the maximum completion time for the wafer probing scheduling problem (WPSP) is very important for equally utilizing the capacity of parallel-machine, while satisfying the requirements of product types, product family, sequence dependent setup time, product-type dependent processing time, due dates of jobs, and capacities of machines. To solve such a complicated problem efficiently and effectively, we adopt the hybrid genetic algorithm approach, which generates initial population with the insertion and savings algorithms for WPSP and provide a new crossover operator to generate better solutions. Considering the disturbance of due dates of jobs in scheduling, we design a new crossover which makes each machine keep the critical sub-schedule in the generation of offspring. The performance of the proposed hybrid genetic algorithm is evaluated by two real-world problems and the solution quality of the proposed approach is analyzed under five levels of mutation rates and four levels of number of generations. Computational results reveal that the hybrid genetic algorithm can efficiently solve the considered problem and the reduction of makespan is related to the mutation rates and the number of generations.

The authors describe two multiprocessor compiler techniques that generate synchronous multiprocessor schedules for multiple-rate algorithms represented by fully specified flow graphs (FSFGs) which contain up-samplers and down-samplers. The first scheduling technique is based on a modified multi-cyclic multiprocessor compiler that finds globally optimal solutions. The second technique generates near-optimal solutions by trading global optimality for a lower scheduling complexity. These scheduling techniques are illustrated with examples.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The General Scheduling Problem (GSP) generalizes scheduling problems with sum of cost objectives such as weighted flow time and weighted tardiness. Given a set of jobs with processing times, release dates, and job dependent cost functions, we seek to find a minimum cost preemptive schedule on a single machine. The best known algorithm for this problem and also for weighted flow time/tardiness is an O(loglog P)-approximation (where P denotes the range of the job processing times), while the best lower bound shows only strong NP-hardness. When release dates are identical there is also a gap: the problem remains strongly NP-hard and the best known approximation algorithm has a ratio of e+\epsilon (running in quasi-polynomial time). We reduce the latter gap by giving a QPTAS if the numbers in the input are quasi-polynomially bounded, ruling out the existence of an APX-hardness proof unless NP\subseteq DTIME(2^polylog(n)). Our techniques are based on the QPTAS known for the UFP-Cover problem, a particular case of GSP where we must pick a subset of intervals (jobs) on the real line with associated heights and costs. If an interval is selected, its height will help cover a given demand on any point contained within the interval. We reduce our problem to a generalization of UFP-Cover and use a sophisticated divide-and-conquer procedure with interdependent non-symmetric subproblems. We also present a pseudo-polynomial time approximation scheme for two variants of UFP-Cover. For the case of agreeable intervals we give an algorithm based on a new dynamic programming approach which might be useful for other problems of this type. The second one is a resource augmentation setting where we are allowed to slightly enlarge each interval.

This research introduces a new approach to restoring an image disrupted by salt and pepper (SPN) noise using a genetic algorithm (GA) at all densities, called Enhanced GA (EGA). The key contribution of the proposed algorithm is to merge the genetic algorithm with imaging approaches that are embedded into the population in order to achieve rapid convergence. The concept is to turn a group of entities into a variety of iterations using crossover and mutation operators. This method evolves a series of images rather than a series of filter parameters. Experimental simulation results on various images using a peak signal-to-noise ratio (PSNR), structural similarity index parameter (SSIM), demonstrate that the suggested algorithm outperforms other methods for eliminating SPN where the noise density is moderate and high. Keywords: Enhanced genetic algorithm, Genetic algorithm, Noise removal, Salt and pepper.

in this paper, routing optimization model that vehicles in rescue centre are less than the number of stricken area has been established. It adopts the hill climbing method to improve the traditional genetic algorithm, analyze production of the initial population, definition of adaptive function, Selection of coding method and genetic operator. In addition, Algorithm running process is presented in the paper, Computer simulations indicated that, compared with the basic algorithm, improved genetic algorithm has better path selection, and distribution of the time.

This paper presents a parallel hybrid genetic algorithm (GA) for solving the sum-of-pairs multiple protein sequence alignment. A new chromosome representation and its corresponding genetic operators are proposed. A multi-population GENITOR-type GA is combined with local search heuristics. It is then extended to run in parallel on a multiprocessor system for speeding up. Experimental results of benchmarks from the BAliBASE show that the proposed method is superior to MSA, OMA, and SAGA methods with regard to quality of solution and running time. It can be used for finding multiple sequence alignment as well as testing cost functions.

In many real-time systems, continuous operation can raise processor temperature, potentially leading to system failure, bodily harm to users, or a reduction in the functional lifetime of a system. Static power dominates the total power consumption, and is also directly proportional to the operating temperature. This reduces the effectiveness of frequency scaling and necessitates the use of sleep states. In this work, we explore the relationship between energy savings and system temperature in the context of fixed-priority energy-saving schedulers, which utilize a processor’s deep-sleep state to save energy. We derive insights from a well-known thermal model, and are able to identify proactive design choices which are independent of system constants and can be used to reduce processor temperature. Our observations indicate that, while energy savings are key to lower temperatures, not all energy-efficient solutions yield low temperatures. Based on these insights, we propose the SysSleep and ThermoSleep algorithms, which enable a thermally-effective sleep schedule. We also derive a lower bound on the optimal temperature achievable by energy-saving schedulers. Additionally, we discuss partitioning and task phasing techniques for multi-core processors, which require all cores to synchronously transition into deep sleep, as well as those which support independent deep-sleep transitions. We observe that, while energy optimization is straightforward in some cases, the dependence of temperature on partitioning and task phasing makes temperature minimization non-trivial. Evaluations show that compared to the existing purely energy-efficient design methodology, our proposed techniques yield lower temperatures along with significant energy savings.