Makespan Improvement Research Articles

SHIELD: A Secure Heuristic Integrated Environment for Load Distribution in Rural-AI

The increasing adoption of edge computing in rural areas is leading to a substantial rise in data generation, necessitating the need for development of advanced load balancing algorithms. This is particularly important in applications that utilise existing, though limited, computational and data communication infrastructures. Furthermore, rural communities have growing concerns regarding the privacy, security, and ownership of the data produced within their agricultural fields. Load distribution in rural edge devices can enhance agricultural practices by improving resource usage, decision-making, and addressing network connectivity challenges. Managing resource utilisation in this way also improves economic investments made in managing and deploying edge devices in rural environments. In this work, we propose SHIELD, a security-aware load balancing framework, primarily designed for edge-based systems in rural areas. For handling environments with limited connectivity, SHIELD efficiently manages tasks and computational resources by categorising them into restricted, public and private, shared respectively. It also allocates tasks considering key performance factors such as completion time, resource utilisation, failure rate, and security. The framework is evaluated on a weed detection scenario in precision agriculture, using three federated learning (FL) variants (local model training, global model aggregation, and model prediction) with the ResNet-50 model trained on the DeepWeeds image classification dataset. The proposed framework also integrates encryption and task replication techniques for data confidentiality, integrity, and availability. Experimental results show that SHIELD demonstrates an average of 23% (using Parsl), 29% (using OpenWhisk) improvement in failure rate and 18 s (Parsl), 13 s (OpenWhisk) average improvement in makespan compared to other task allocation approaches, such as secure variants of random, round robin, and least loaded.

Powder bed fusion factory productivity increases using discrete event simulation and genetic algorithm

AbstractPowder bed fusion is importance is growing with uses across industries in both polymer and metallic components, particularly in mass individualization. However, due to the relatively slow mass deposition speed compared to conventional methods, scheduling and production planning play a crucial role in scaling up additive manufacturing productivity to higher volumes. This paper introduces a framework combining discrete event simulation and a genetic algorithm showing makespan improvement opportunities for multiple powder bed fusion factories varying workers, jobs and available equipment. The results show that bottlenecks move among workstations based on worker and capital equipment availability, which depend on the size of the facility indicating a resource-driven constraint for makespan. A makespan reduction of 78% is achieved in the simulation. This shows the trade-off of worker and capital equipment to achieve makespan improvements. The addition of personnel or equipment increases production with further gains achieved by scheduling optimization. Two levels of job demands are analyzed showing productivity gains of 45% makespan improvement when adding the first worker and additional savings with scheduling optimization using a genetic algorithm up to 11%. Most research on additive manufacturing production has focused on the quality of produced parts and printing technology rather than factory level management. This is the first application of this methodology to varying sizes of these potential factories. The method developed here will help decision-makers to determine the appropriate number of resources to meet their customer demand on time, additionally, finding the optimal route for jobs before starting the production process.

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation. This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%.

Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds

A cloud is made up of many data centers, with its own set of data and resources. The reasons for employing several cloud sites to operate a workflow are that the data is already dispersed, the required resources surpass the constraints of a single site. This paper presents a hybrid multi-objective optimization algorithm denoted as HSOS-SOA, achieved by combining the Symbiotic Organisms Search and Seagull Optimization Algorithm. The HSOS-SOA uses chaotic maps to generate random numbers and performs a good trade-off between exploration and exploitation, resulting in a higher convergence rate. HSOS-SOA is used to solve scientific workflow scheduling problems in multisite cloud computing by taking into consideration elements such as makespan, cost, and reliability. A solution is chosen from the Pareto front using the knee-point approach in this approach. Extensive analyses are performed out in Microsoft Azure multisite cloud and the results exhibited that the HSOS-SOA can outperform other algorithms in terms of metrics such as IGD, Coverage Ratio, and so on. Experimental results of experiments reveal that the results in makespan improvement in the range of 5.72–28.61%, cost in the range of 5.16–45.16%, and reliability in the range of 3.11–25% over well-known metaheuristic algorithms.

Multi objective trust aware task scheduling algorithm in cloud computing using whale optimization

Task Scheduling is an enormous challenge in cloud computing model as to map diverse tasks arises from various sources there should be an efficient scheduling mechanism which provision resources dynamically to users based on their corresponding requests. Ineffective scheduling leads to increase in makespan, energy consumption and violates SLA made between cloud user and service provider thereby quality of service will be degraded and trust on the cloud service provider will be degraded. Trust typically based on quality-of-service parameters such as Availability of virtual resources, Success rate of tasks, Turnaround efficiency of tasks which are included in SLA. In this paper, we designed a Multi objective trust aware scheduler which takes priority of tasks, VMs and schedule tasks to appropriate virtual resources while minimizing makespan, energy consumption. Whale Optimization algorithm used to model our task scheduler. Entire simulation carried out on Cloudsim. Workload used in this simulation is of both fabricated and real-time worklogs captured from HPC2N and NASA. Our proposed approach compared against existing metaheuristic approaches i.e., ACO, GA, PSO approaches. From Simulation results, we observed that there is a significant improvement in makespan, Energy consumption, total running time and trust parameters i.e., Availability, Success rate, Turnaround efficiency.

MONWS: Multi-Objective Normalization Workflow Scheduling for Cloud Computing

Cloud computing is a prominent approach for complex scientific and business workflow applications in the pay-as-you-go model. Workflow scheduling poses a challenge in cloud computing due to its widespread applications in physics, astronomy, bioinformatics, and healthcare, etc. Resource allocation for workflow scheduling is problematic due to the computationally intensive nature of the workflow, the interdependence of tasks, and the heterogeneity of cloud resources. During resource allocation, the time and cost of execution are significant issues in the cloud-computing environment, which can potentially degrade the service quality that is provided to end users. This study proposes a method focusing on makespan, average utilization, and cost. The authors propose a task’s dynamic priority for workflow scheduling using MONWS, which uses the min-max algorithm to minimize the finish time and maximize resource utilization by calculating the dynamic threshold value for scheduling tasks on virtual machines. When the experimental results were compared to existing algorithms, MONWS achieved a 35% improvement in makespan, an 8% increase in maximum average cloud utilization, and a 4% decrease in cost.

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.

Distributed Fleet Management in Noisy Environments via Model-Predictive Control

We consider dynamic route planning for a fleet of Autonomous Mobile Robots (AMRs) doing fetch and carry tasks on a shared factory floor. In this paper, we propose Stochastic Work Graphs (SWG) as a formalism for capturing the semantics of such distributed and uncertain planning problems. We encode SWGs in the form of a Euclidean Markov Decision Process (EMDP) in the tool Uppaal Stratego, which employs Q-Learning to synthesize near-optimal plans. Furthermore, we deploy the tool in an online and distributed fashion to facilitate scalable, rapid replanning. While executing their current plan, each AMR generates a new plan incorporating updated information about the other AMRs positions and plans. We propose a two-layer Model Predictive Controller-structure (waypoint and station planning), each individually solved by the Q-learning-based solver. We demonstrate our approach using ARGoS3 large-scale robot simulation, where we simulate the AMR movement and observe an up to 27.5% improvement in makespan over a greedy approach to planning. To do so, we have implemented the full software stack, translating observations into SWGs and solving those with our proposed method. In addition, we construct a benchmark platform for comparing planning techniques on a reasonably realistic physical simulation and provide this under the MIT open-source license.

Scheduling in IaaS Cloud Computing Environment using Sailfish Optimization Algorithm

Due to the exceptional benefits of cloud computing, it has magnetized IT leaders and entrepreneurs at all levels. The cloud's popularity is attributed to various technologies like the Internet of Things (IoT), mobile computing, Fog, etc. Scheduling in cloud computing is still a challenging issue due to its NP-Hard nature. In recent years, many techniques have been proposed for optimal scheduling that can subsequently improve efficient Quality of Service (QoS). This paper has developed and analysed a novel Sailfish Optimization-based Scheduling Algorithm. SOSA is implemented on 2 data-sets; a real-world data-set from NASA workload, and a randomly generated data-set. SOSA exhibited 13.71 and 7.81 % average performance improvement in makespan compared to GA and PSO and 11.30 and 30.78 % average improvement in execution cost compared to GA and PSO.
 HIGHLIGHTS
 
 Scheduling in Cloud computing has NP -Hard Problem nature that may degrades Quality of Service to users
 A Novel Sailfish optimization based scheduling algorithm (SOSA) technique is proposed to increase Quality of Service in terms of Makespan and Execution Cost
 Simulation has been performed in CloudSim on NASA workload and synthetic dataset
 Proposed SOSA techniques proves to be more effective to provide Quality of Service and proves to be more significant as compared to GA and PSO

A new approach for task managing in the fog-based medical cyber-physical systems using a hybrid algorithm

PurposeThis paper aims to offer a hybrid genetic algorithm and the ant colony optimization (GA-ACO) algorithm for task mapping and resource management. The paper aims to reduce the makespan and total response time in fog computing- medical cyber-physical system (FC-MCPS).Design/methodology/approachSwift progress in today’s medical technologies has resulted in a new kind of health-care tool and therapy techniques like the MCPS. The MCPS is a smart and reliable mechanism of entrenched clinical equipment applied to check and manage the patients’ physiological condition. However, the extensive-delay connections among cloud data centers and medical devices are so problematic. FC has been introduced to handle these problems. It includes a group of near-user edge tools named fog points that are collaborating until executing the processing tasks, such as running applications, reducing the utilization of a momentous bulk of data and distributing the messages. Task mapping is a challenging problem for managing fog-based MCPS. As mapping is an non-deterministic pol ynomial-time-hard optimization issue, this paper has proposed a procedure depending on the hybrid GA-ACO to solve this problem in FC-MCPS. ACO and GA, that is applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as the main approach and GA as the local search. GA-ACO is a memetic algorithm using GA as the main approach and ACO as local search.FindingsMATLAB is used to simulate the proposed method and compare it to the ACO and MACO algorithms. The experimental results have validated the improvement in makespan, which makes the method a suitable one for use in medical and real-time systems.Research limitations/implicationsThe proposed method can achieve task mapping in FC-MCPS by attaining high efficiency, which is very significant in practice.Practical implicationsThe proposed approach can achieve the goal of task scheduling in FC-MCPS by attaining the highest total computational efficiency, which is very significant in practice.Originality/valueThis research proposes a GA-ACO algorithm to solve the task mapping in FC-MCPS. It is the most significant originality of the paper.

Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.

Efficient load balancing techniques for multi-datacenter cloud milieu

Load balancing is a challenge in the cloud network for proper resource utilization and improvement of makespan. To overcome the problem of load balancing various static and dynamic scheduling methods come into existence. In this paper, we have proposed two multi-datacenter two-phase load adjustment techniques for load balancing and apposite task scheduling. Our proposed methods allocate user jobs to different virtual machines present in different datacenters based on better makespan. Here we have considered communication delay and bandwidth requirement for inter-datacenter task migration. The simulation result specifies that proposed techniques give a significant improvement in makespan and resource utilization when compared with existing algorithms.

Exact and efficient reliability and performance optimization of synchronous task graphs

SRAM-based FPGAs have found many applications in modern computer systems. In these systems, high-performance computing applications are executed as task graphs in which reliability and performance are crucial constraints. In this paper, an exact method is presented to efficiently optimize the reliability and performance of synchronous task graphs running on SRAM-based FPGAs in harsh environments. Solving this optimization problem leads to the generation of a true Pareto set of Fault Tolerance (FT) techniques. Each solution of this set determines FT techniques of the tasks and leads to specific reliability and makespan. Thus, this solution set trades off between reliability and makespan, and one of the solutions that best meets system requirements can be applied to the running tasks to optimize the reliability and performance. The proposed technique is novel as it obtains the true Pareto set of FT techniques of the whole task graph by partitioning the task graph into its segments, optimizing different segments separately, and joining the obtained solutions. This partitioning strategy leads to reduce the computation time significantly. In this paper, it is mathematically proved that the proposed partitioning strategy generates global optima from the local ones without losing any optimal solutions. The experiments show that the proposed technique improves the MTTF of real-world and random task graphs by 46.30% on average without any negative effects on the performance. Then, the efficiency of the computation time of the proposed technique is demonstrated by conducting several experiments on small-, medium-, and large-size synchronous task graphs and comparing the results with other exact and evolutionary optimization methods. Finally, supplementary experiments in dynamic environments show that the proposed technique outperforms adaptive state-of-the-art FT techniques in terms of reliability and makespan improvement.

Multi-criteria HPC task scheduling on IaaS cloud infrastructures using meta-heuristics

With the rapid increase in the use of cloud computing systems, an efficient task scheduling policy, which deals with the assignment of tasks to resources, is required to obtain maximum performance. Cloud task scheduling (CTS) is an established NP-Hard optimization problem that can be effectively tackled with meta-heuristic algorithms. The cuckoo search (CS) algorithm is a powerful swarm-intelligence meta-heuristic that has been successfully applied over a wide-range of real-life optimization problems, including task scheduling problems. Besides its strong exploration ability, the CS algorithm suffers from insufficient local search, lack of solution diversity towards the end, and slow convergence problem. These drawbacks produce inefficient cloud task schedules resulting in sub-optimal performance. In this manuscript, an improved CS-based scheduling algorithm called CSDEO is introduced, which combines the features of the Opposition-based learning (OBL) method, Cuckoo search, and Differential evolution (DE) algorithms to optimize workload makespan and energy consumption of the cloud resources. Our CSDEO algorithm firstly uses the OBL method to produce an optimal initial population by providing solutions across the entire solution space. Then, the CSDEO uses an effective way of switching between the CS exploration phase and the DE exploitation phase, depending on each solution's fitness. Experiments are conducted on the CloudSim simulator by using the CEA-Curie and HPC2N supercomputing workloads. The observations show that in the case of CEA-Curie workloads, the proposed CSDEO algorithm achieves makespan improvement in the range of 6.29–29.76% and energy consumption improvement in the range of 3.76–201.98% over well-known scheduling algorithms. In the case of HPC2N workloads, the improvement ranges of the CSDEO approach for the makespan and energy consumption metrics are 9.86–281.69% and 6.12–233.3%, respectively compared to the tested scheduling algorithms.

A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing

Recent developments in cloud computing have made it a powerful solution for executing large-scale scientific problems. The complexity of scientific workflows demands efficient utilization of cloud resources to satisfy user requirements. Scheduling of scientific workflows in a cloud environment is a challenge for researchers. The problem is considered as NP-hard. Some constraints such as a heterogeneous environment, dependencies between tasks, quality of service and user deadlines make it difficult for the scheduler to fully utilize available resources. The problem has been extensively studied in the literature. Different researchers have targeted different parameters. This article presents a multi-objective scheduling algorithm for scheduling scientific workflows in cloud computing. The solution is based on genetic algorithm that targets makespan, monetary cost, and load balance. The proposed algorithm first finds the best solution for each parameter. Based on these solutions, the algorithm finds the superbest solution for all parameters. The proposed algorithm is evaluated with benchmark datasets and comparative results with the standard genetic algorithm, particle swarm optimization, and specialized scheduler are presented. The results show that the proposed algorithm achieves an improvement in makespan and reduces the cost with a well load balanced system.

Process planning and scheduling optimisation with alternative recipes

Abstract This paper considers an application of a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes, to a real-world chemical plant. The problem is optimised using a multi-objective genetic algorithm with customised mutation and elitism operators that minimises both the total production time and the produced commodity surplus. The algorithm evaluation is performed with both random and historic manufacturing orders. The latter demonstrated that the proposed system can lead to more than 10 % makespan improvements in comparison with human operators.

A prefetch-aware scheduling for FPGA-based multi-task graph systems

In partially run-time reconfigurable (PRR) FPGAs, hardware tasks should be configured before their execution. The configuration delay imposed by the reconfiguration process increases the total execution time of the hardware tasks and task graphs. In this paper, a new technique named forefront-fetch is presented to improve the makespan of hardware task graphs running on PRR FPGAs via alleviating the adverse effects of the configuration delays. In this technique, which is applied to a sequence of task graphs, the configuration of some tasks is carried out within the execution phase of the previous task graph. This strategy leads to hide the configuration delay of the forefront-fetched tasks that as a result improves the execution time. The proposed solution modifies the schedules of the task graphs at design time to obtain a set of schedule pairs for the run-time environment. Experiments on actual and synthesized task graphs demonstrate the ability of the proposed technique in improving the makespan of hardware task graphs. The obtained results show that for a set of task graphs running on Xilinx™ Virtex-5 XUPV5LX110T FPGA, makespan is improved by 37.81% on average. Moreover, the proposed solution outperforms state-of-the-art prefetch-aware scheduling strategies by 14.78% makespan improvement.

An Improved Ant Colony Optimized Tabu Search Algorithm for Makespan Improvement in Job Shop

In industries, the completion time of job problems is increased drastically in the production unit. In many existing kinds of research, the completion time i.e. makespan of the job is minimized using straight paths which is time-consuming. In this paper, we addressed this problem using an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm by identifying the fault occurrence position exactly to rollback. Also, we used a short term memory-based rollback recovery technique to roll back to its own short term memory to reduce the completion time of the job. Short term memory is used to visit the recent movements in Tabu search. Our proposed ACOTS-Cmax approach is efficient and consumed less completion time compared to the ACO algorithm.

Digital twin-based cyber physical production system architectural framework for personalized production

Personalized production is a manufacturing concept relevant to the fourth industrial revolution, which can satisfy various customer needs inexpensively. There are three main hurdles to the efficient implementation of this concept: access, cost, and performance. This paper proposes a digital twin-based cyber physical production system (CPPS) architectural framework that overcomes the performance hurdle. The proposed architectural framework comprises five services that are solutions to the performance hurdle of personalized production, and it operates on information based on the proposed product, process, plan, plant, and resource (P4R) information model. This information model is manufacturing abstraction for personalized production and is presented on a detailed level with object-orientation and the “type and instance” concept. Whereas previous studies on the digital twin concept considered one or several facilities and focused on the development of independent applications, this study focuses on the digital twin as a core technological element of the entire system and analyzes CPPS design and its operation from the system-of-systems perspective. The application of the digital twin-based CPPS to a micro smart factory (MSF) provides an advanced solution for the personalized production of various products. An average makespan improvement of ~ 26.87% was achieved using the implemented CPPS services in the MSF.

A History-Based Resource Manager for Genome Analysis Workflows Applications on Clusters with Heterogeneous Nodes

Bioinformatics workflows require large amounts of resources and are commonly executed in clusters. Determining the adequate amount of resources for bioinformatics applications is a tricky matter, since the resource usage of a single application might vary substantially from one execution to the next. Resource management systems in clusters don’t consider these variations and subsequent needs. As a result, the computing power offered by clusters is not harnessed properly, compromising both application performance and resource efficiency. To tackle these issues, we propose a History-Based Resource Manager for bioinformatics workflows applications running on clusters with heterogeneous nodes. The proposed resource manager features a prediction model that generates multiple performance predictions for each job under different combinations of cluster resources. Furthermore, the proposed resource manager includes a scheduling algorithm that considers the degree of multiprogramming of the nodes, scheduling combinations of applications for simultaneous same-node execution upon their compatibility. To test the proposed resource manager, we process two workloads formed by different amounts of workflows made up by common bioinformatics applications. Results prove that for the given cases, the proposed resource manager improves the performance obtained with SLURM, using First Come First Served policy. The proposal shows an average workflow makespan improvement range between 28 and 35%, averaging 32%, an average workflow efficiency improvement range between 75 and 83%, averaging 79%, and an average resource usage improvement range between 96 and 101%, averaging 99%. Furthermore, the proposed scheduling algorithm can improve the average workflow makespan by a range of values between 26 and 36%, averaging 31%, compared to Max–Min and Min–Min algorithms.