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We correct, extend and improve recent algorithmic and computational complexity results for the identical parallel machine scheduling problem with preemptions and setup times preceding uninterrupted job parts. The objective is to minimise the makespan. Our results include a proof of strong NP-hardness for an arbitrary number of machines, and a pseudo-polynomial algorithm and an FPTAS for the two machines case. [ABSTRACT FROM AUTHOR]

Over the last decades, scheduling theory has been put into practice so as to tackle production process as a combinatorial optimisation problem and specifically as a PFSP (Permutation Flowshop Scheduling Problem). Often, NP-completeness of PFSP leads numerous research towards suggesting heuristic algorithms. The objective is to propose an optimal or near-to-optimal order of n jobs processing on m machines with a minimum completion time of all jobs. In this paper, a two-phase heuristic algorithm will be presented, named IFRS (Improved FRS). FRS, within the first phase, is going to be used to find a superior order of jobs out of Taillard's instances; then the order of jobs will be improved again in phase 2. While running IFRS, a completely new idea, named Pixelation of time matrices, will be used for the very first time and provide a final pattern, which can be used in any heuristic algorithm with makespan criterion. After using the hard benchmark instances of Taillard, the superiority of IFRS over 20 algorithms is going to be shown by tables and statistical graphics. Due to its low makespan values, IFRS benefits flow production, as does NEH. [ABSTRACT FROM AUTHOR]

We study scheduling problems on parallel dedicated machines. Thus, each job can be processed on one specific machine only. The option of job-rejection is considered, and the total permitted rejection cost of all the jobs is bounded. Six scheduling problems are solved: ( $ i $ i) minimising makespan, ( $ ii $ ii) minimising makespan with release-dates, ( $ iii $ iii) minimising total completion time, ( $ iv $ iv) minimising total weighted completion time, ( $ v $ v) minimising total load, and ( $ vi $ vi) minimising maximum tardiness. Pseudo-polynomial dynamic programming algorithms are introduced for all these NP-hard problems. [ABSTRACT FROM AUTHOR]

In modern manufacturing and service industries, urgent orders and service tasks are common, and the speed of handling such urgent tasks is an important indicator of production and service efficiency. In this study, we consider scheduling jobs on two parallel machines with the random arrival of an emergency job. The objective is to minimise the makespan, subject to a given maximum waiting time of the emergency job. We first show that the worst-case ratio of the existing algorithm LPT $ _l $ l -SPT $ _{m-l} $ m − l is at least 3/2 when m = 2. We then analyse some properties of the optimal schedule and derive lower bounds on the optimal makespan. Finally, we present an improved approximation algorithm with a tight worst-case ratio of 4/3. We also provide numerical results showing that our proposed algorithm outperforms algorithm LPT $ _l $ l -SPT $ _{m-l} $ m − l . [ABSTRACT FROM AUTHOR]

This paper addresses a serial distributed permutation flowshop scheduling problem (SDPFSP) inspired by a printed circuit board assembly process that contains two production stages linked by a transportation stage, where the scheduling problem in each production stage can be seen as a distributed permutation flowshop scheduling problem (DPFSP). A sequence-based mixed-integer linear programming model is established. A solution representation consisting of two components, one component per stage, is presented and a makespan calculation method is given for the representation. Two suites of accelerations based on the insertion neighbourhood are proposed to reduce the computational complexity. A cooperative iterated greedy (CIG) algorithm is developed with two subloops, each of which optimises a component of the solution. A collaboration mechanism is used to conduct the collaboration of the two subloops effectively. Problem-specific operators including the NEH-based heuristics, destruction, reconstruction and three local search procedures, are designed. Extensive computational experiments and statistical analysis verify the validity of the model, the effectiveness of the proposed CIG algorithm and the superiority of the proposed CIG over the existing methods for solving the problem under consideration. [ABSTRACT FROM AUTHOR]

This work presents four mixed integer linear programming (MILP) models for the assembly flow shop problem in order to minimize the makespan. This production environment has two stages: production and assembly. The first stage consists of different machines designed to manufacture parts of a product. The second stage is intended for a final assembly. The performance measure considered is highly essential for industries from different segments, as it focuses on the best use of the time available for production. Statistical analysis with different tools was used to assess the performance and efficiency of mathematical models, emphasizing the analysis of performance profiles. Results showed that mathematical models are efficient, and the position-based model presented the best results for small and large instances during computational experimentation. All mathematical models can be used as direct tools in decision-making for the production sequencing problem in the approached environment. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of scheduling jobs on identical machines for minimising the maximum completion time (makespan). Each job requires a sequence-independent setup time, which represents the time needed to prepare the machines for job execution. Then, we introduce a dynamic programme to solve the case with two machines, and show that this problem admits a fully polynomial time approximation scheme. For the case of m machines, we propose heuristics and an adapted genetic algorithm. Some numerical experiments are done to evaluate the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with a two-machine flow shop problem with shortening jobs. A shortening job means that the job's processing time is a decreasing function of its starting time. The aim is to find a sequence that minimizes the makespan of all the jobs. several dominance properties, some lower bounds, and an initial upper bound are derived, which are applied to propose a branch-and-bound algorithm to solve the problem. We also propose some heuristics and mathematical programming. Computational experiments are conducted to evaluate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments. [ABSTRACT FROM AUTHOR]

Powder 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. [ABSTRACT FROM AUTHOR]

Cloud computing represents an evolved form of cluster, client server, and grid computing, enabling users to seamlessly access resources over the internet. The quality and reliability of the cloud computing services are depends on the specific tasks undertaken by the users. Task Scheduling emerges as a pivotal factor in enhancing the efficiency and reliability of a cloud environment, aiming to optimize resource utilization. Furthermore, efficient task scheduling holds a prime importance in achieving superior performance, minimizing response time, reducing energy consumption and maximizing throughput. Assigning work to essential resources is a challenging process to achieve better performance. However, this paper plans to propose a novel workload prioritization and optimal task scheduling in the cloud with two steps. At first, the ranks are allotted to the tasks with Analytical Hierarchy Process based ranking process that uses a k-means clustering strategy to group the workloads. Then, the tasks are scheduled under the consideration of constraints like makespan, utilization cost, and migration cost and risk probability; based on priority. Accordingly, the task scheduling is done optimally by the proposed hybrid optimization Blue Updated Jellyfish Search Optimization that combines algorithms like Blue Monkey Optimization and Jelly fish Search Optimization algorithms. The performance of the proposed scheduling process is validated and proved over the conventional methods. [ABSTRACT FROM AUTHOR]

The distributed permutation flow shop scheduling problem (DPFSP) has received increasing attention in recent years. The iterated greedy algorithm (IGA) serves as a powerful optimizer for addressing such a problem because of its straightforward, single-solution evolution framework. However, a potential draw-back of IGA is the lack of utilization of historical information, which could lead to an imbalance between exploration and exploitation, especially in large-scale DPFSPs. As a consequence, this paper develops an IGA with memory and learning mechanisms (MLIGA) to efficiently solve the DPFSP targeted at the mini-mal makespan. In MLIGA, we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search, by extending, reconstructing, and reinforcing the information from previous solutions. In addition, we design a two-layer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism. Meanwhile, to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA, a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules. At last, a discrete adaptive learning rate is employed to enhance the stability of the memory and learning mechanisms. Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism, and the results show that this mechanism is capable of improving the performance of IGA to a large extent. Furthermore, through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks, we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs. This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a Multi-Task Simultaneous Supervision Dual Resource-Constrained Scheduling (MTSSDRC) system that considers differences in skill between operators, aiming to minimize makespan and balance operator workload. Workload balance is calculated using the Workload Smoothness Index (WSI). The mathematical model developed uses three techniques: Mixed-Integer Linear Programming (MILP), Mixed-Integer Quadratic Programming (MIQP), and Mixed-Integer Quadratically Constrained Programming (MIQCP). These techniques can handle scheduling cases on a small to medium scale. Results from MILP focus on minimizing makespan, with an additional constraint for calculating the WSI. MIQP focuses on workload balance so that the WSI value becomes an objective function. It also adds a constraint for the allowable makespan value. The result from MIQP shows that the WSI value is lower than in MILP, and the makespan values are equal to the MILP makespan value. Next, MIQCP aims to minimize makespan with a constraint for the allowable WSI value. The MIQCP model produces a makespan value adjusted to a WSI value close to zero. Finally, further analysis is presented regarding the influence of differences in operator skills based on the results of the three models. Based on these models, operators with better skills will be assigned more frequently than others. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology incorporating various hardware devices and software applications to exchange, analyze, and process a huge amount of data. IoT uses cloud and fog infrastructures, comprising different hardware and software components like computing machines, networking components, storage, and virtualization elements. They can receive, process, store, and exchange data in real time. A cloud is a centralized system containing large data centres that are far from client devices. However, as IoT generates massive amounts of data, issues like latency, response time, execution of tasks within their deadline, and bandwidth arise when data is sent to the cloud for processing. Compared to the cloud, fog computing is vital as a distributed system consisting of millions of devices located at the minimum distance from the client devices. In addition, fog infrastructure reduces bandwidth and latency because it is closer to the end-user. However, maximizing utilization of resources, minimizing response time, and ensuring the completion of deadline-constrained tasks within their deadline are important research problems in fog computing. This research proposes a task scheduling technique called Resource Aware Prioritized Task Scheduling (RAPTS) in a heterogeneous fog computing environment. The aim is to execute deadline-constrained tasks within their deadlines, minimize response time and cost, as well as makespan, and maximize resource utilization of the fog layer. The RAPTS is implemented using iFogSim and its performance is evaluated regarding response time, resource utilization, task deadlines, cost, and makespan. The results have been compared with state-of-the-art fog schedulers like RACE (CFP) and RACE (FOP). The results reveal that the RAPTS have shown up to 29%, 53%, 15%, 11%, and 43% improvement in terms of resource utilization, response time, makespan, cost, and meeting task deadlines, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computing. The primary purpose of the task scheduling mechanism is to reduce the cost and makespan and determine which virtual machine (VM) needs to be selected to execute the task. It is widely acknowledged as a nondeterministic polynomial-time complete problem, necessitating the development of an efficient solution. This paper presents an innovative approach to task scheduling and allocation within cloud computing systems. Our focus lies on improving both the efficiency and cost-effectiveness of task execution, with a specific emphasis on optimizing makespan and resource utilization. This is achieved through the introduction of an Advanced Max–Min Algorithm, which builds upon traditional methodologies to significantly enhance performance metrics such as makespan, waiting time, and resource utilization. The selection of the Max–Min algorithm is rooted in its ability to strike a balance between task execution time and resource utilization, making it a suitable candidate for addressing the challenges of cloud task scheduling. Furthermore, a key contribution of this work is the integration of a cost-aware algorithm into the scheduling framework. This algorithm enables the effective management of task execution costs, ensuring alignment with user requirements while operating within the constraints of cloud service providers. The proposed method adjusts task allocation based on cost considerations dynamically. Additionally, the presented approach enhances the overall economic efficiency of cloud computing deployments. The findings demonstrate that the proposed Advanced Max–Min Algorithm outperforms the traditional Max–Min, Min–Min, and SJF algorithms with respect to makespan, waiting time, and resource utilization. [ABSTRACT FROM AUTHOR]

This article addresses the permutation flowshop scheduling problem with the objective of minimizing the total completion time subject to a makespan constraint. The makespan is related to system utilization while the total completion time is related to the waiting time, and hence to the work in process (WIP); in real contexts, focusing on both total completion time and makespan allows a good trade-off between WIP and utilization to be found. Two local search algorithms are developed in the paper and, by using an extensive computational experience on literature benchmark instances, they are proved to be able to find good solutions both for regular and no-wait flowshops. [ABSTRACT FROM AUTHOR]

Recent advances in cloud computing, large language models, and deep learning have started a race to create massive High-Performance Computing (HPC) centers worldwide. These centers increase in energy consumption proportionally to their computing capabilities; for example, according to the top 500 organization, the HPC centers Frontier, Aurora, and Super Computer Fugaku report energy consumptions of 22,786 kW, 38,698 kW, and 29,899 kW, respectively. Currently, energy-aware scheduling is a topic of interest to many researchers. However, as far as we know, this work is the first approach considering the idle energy consumption by the HPC units and the possibility of turning off unused units entirely, driven by a quantitative objective function. We found that even when turning off unused machines, the objectives of makespan and energy consumption still conflict and, therefore, their multi-objective optimization nature. This work presents empirical results for AGEMOEA, AGEMOEA2, GWASFGA, MOCell, MOMBI, MOMBI2, NSGA2, and SMS-EMOA. The best-performing algorithm is MOCell for the 400 real scheduling problem tests. In contrast, the best-performing algorithm is GWASFGA for a small-instance synthetic testbed. [ABSTRACT FROM AUTHOR]

The electrical sector forces manufacturing companies of electrical solutions to continually innovate and implement new processes for greater efficiency. The growing demand for electrical energy, as well as the need to adapt to hybrid operations that combine multi-project operation models with continuous production models, requires efficient workflow management. Accordingly, this article proposes a Genetic Algorithm (GA) approach for solving the scheduling problem in a Flexible Hybrid Flow Shop (FHFS) environment considering a transfer batch approach to minimize makespan and total tardiness. The approach is inspired by a real-world application in the electrical industry and also accounts for unrelated parallel machines, precedence, release times, and due dates for jobs at each production center as key constraints. Three real-data scenarios were generated and evaluated. In the first scenario, a 7 % improvement in makespan was observed compared to real execution times. In Scenario 2, the makespan improved significantly by 33 %, and only 17.4 % of jobs were delayed, compared to 96 % in the real data. Likewise, GA showed a lightly better performance over Tabu Search (TS) in 3.01 % for makespan while the delayed jobs found by GA were 25 % below those obtained by TS. These results highlight the potential of the proposed method to improve overall production efficiency, not only in the electrical sector but also in similar industries. [ABSTRACT FROM AUTHOR]

Flexible job shop scheduling problem (FJSSP) is a complex and challenging problem that plays a crucial role in industrial and manufacturing production. FJSSP is an expansion of the standard job shop scheduling problem (JSSP). One of FJSSP's objectives that the manufacturing system competing for is minimizing the makespan. This paper uses a new natureinspired metaheuristic optimization algorithm called the Apiary Organizational-Based Optimization algorithm (AOOA) to solve the FJSSP. This Algorithm simulates the organizational behavior of honeybees inside the apiary and translates their activities and vital processes during their lifecycle into phases that can solve such NP-hard problems. Two benchmark datasets, Brandimarte and Hurink, with 10 MK instances and 24 (edata, rdata, and vdata) instances respectively, were used to demonstrate the ability of AOOA to solve FJSSP. Moreover, the results of AOOA were compared with a set of state-of-the-art algorithms and statistically measured using the paired samples t-test and p-value, RPD, and group-based superiority statistical analysis to test its performance. AOOA outperformed Elitism GA, Enhanced GA, Improved GA, and MOGWO in solving all 10 MK instances and HICSA in solving 9 MK instances out of 10. Moreover, AOOA overcame CS, CS-BNG, CS-ILF, CHA, and MCA in solving 24, 12, 12, 23, and 24 instances of edata, rdata, and vdata, respectively. AOOA proved its robustness, showing promising outcomes. [ABSTRACT FROM AUTHOR]

In this paper, we will study the permutation flow shop scheduling problem (PFSSP) with sequence independent setup time (SIST). This constraint is the most common encountered in industrial production. In this case, the SIST constraint depends on the technology nature of the machine, as well as the means used to prepare it for the execution of a new job. The purpose of this paper is to develop an artificial intelligence system and to train a neural network model for solving the flow shop scheduling problem. The objective function is to minimize the total completion time, which is known as makespan. The latter is an important task in manufacturing systems. The paper begins by suggesting an exact and four approximate methods: a mixed integer linear programming (MILP), an artificial neural network (ANN), and three e cient heuristics. The first heuristic is based on Johnson's rule algorithm (ABJR), the second on the Nawaz-Enscore and Ham algorithm (NEH), and the last on the greedy randomized adaptive search procedure algorithm (GRASP). We aim to verify the e ectiveness of our resolution algorithms by considering randomly generated instances with n jobs and m machines in the flow shop factory. Our goal is to determine the optimal sequence of n jobs to be scheduled on m machines. The paper moves to the comparison between the studied heuristics. The numerical results demonstrate that the NEH algorithm outperforms the other approximate methods for our considered problem. Indeed, the NEH heuristic performs a success rate of 82.81% and achieves a minimum relative percentage deviation value of 0.0139%. It was observed that ANN method outperforms GRASP and gives sometimes best results than ABJR. The numerical simulations align with our theoretical postulations given by RPD values. [ABSTRACT FROM AUTHOR]

In healthcare, real-time decision making is crucial for ensuring timely and accurate patient care. However, traditional computing infrastructures, with their wide ranging capabilities, suffer from inherent latency, which compromises the efficiency of time-sensitive medical applications. This paper explores the potential of fog computing to better address this challenge, proposing a new framework that uses deep reinforcement learning (DRL) to advance task scheduling in crucial healthcare. The paper addresses the limitations of cloud computing systems. It proposes and replaces a fog computing architecture in supporting low latency for healthcare applications. This architecture reduces transmission latency by placing processing nodes close to the source of data generation, namely IoT-enabled healthcare devices. The foundation of this approach is the DRL model, which is designed to dynamically optimize the partition of computational tasks across fog nodes to improve both data throughput and operational response times. The effectiveness of the proposed DRL based fog computing model is validated with a series of simulations performed with the SimPy simulation environment. In such simulations, diverse healthcare scenarios, ranging from continuous patient monitoring systems to crucial emergency response applications, are recreated, providing a rich framework for testing the real-time processing capabilities of the model. This algorithm, DRL, has been fine-tuned and extensively implemented in these scenarios to show how the algorithm controls and optimizes tasks and their urgency in accordance with resource demand. By dynamically learning from real-time system states and optimizing task allocation to minimize delays, the DRL model reduces the makespan by up to 30% compared to traditional scheduling approaches. Comparative performance analysis indicated a 30% reduction in task completion times, a 40% reduction in operational latency, and a 25% improvement in fault tolerance relative to traditional scheduling approaches. The flexibility of the DRL model is further considered through its application to diverse real-time data processing contexts in industrial automation and smart traffic systems.

The rapid expansion of cloud data centers, driven by the increasing demand for diverse user services, has escalated energy consumption and greenhouse gas emissions, posed severe environmental risks, and increased operational costs. Addressing these challenges requires innovative solutions for optimizing resource allocation without compromising service quality. This paper presents the Enhanced Multi-Objective Optimization Algorithm for Task Scheduling (EMO-TS). This novel approach integrates Deep Reinforcement Learning (DRL) and Enhanced Electric Fish Optimization (EEFO) to create an adaptive, dynamic, and energy-efficient scheduling framework. The primary objective of EMO-TS is to significantly reduce the energy consumption of cloud data centers while maintaining high levels of resource utilization, time efficiency, and service quality. Through the hybrid methodology of DRL and EEFO, EMO-TS dynamically adjusts task scheduling based on real-time workloads and operational conditions, effectively minimizing power consumption without sacrificing system performance. Additionally, EMO-TS introduces improvements in makespan and task execution, ensuring timely completion and optimal resource use. A comprehensive set of experiments and simulations demonstrates the practical implications of EMO-TS’s results. EMO-TS outperforms traditional scheduling approaches, reducing energy consumption by up to 25% and decreasing makespan by 15%. These results underscore the algorithm’s potential to address cloud service providers’ economic and environmental concerns, offering a practical solution for green cloud computing initiatives. Furthermore, the integration of renewable energy sources within the EMO-TS framework shows potential for further reducing the carbon footprint of cloud operations, aligning with global sustainability goals.

Since the increasing demand for surgeries in hospitals, the surgery scheduling problems have attracted extensive attention. This study focuses on solving a surgery scheduling problem with setup time. First, a mathematical model is created to minimize the maximum completion time (makespan) of all surgeries and patient waiting time, simultaneously. The time by the fatigue effect is included in the surgery time, which is caused by doctors’ long working time. Second, four mate-heuristics are optimized to address the relevant problems. Three novel strategies are designed to improve the quality of the initial solutions. To improve the convergence of the algorithms, seven local search operators are proposed based on the characteristics of the surgery scheduling problems. Third, Q-learning is used to dynamically choose the optimal local search operator for the current state in each iteration. Finally, by comparing the experimental results of 30 instances, the Q-learning based local search strategy’s effectiveness is verified. Among all the compared algorithms, the improved artificial bee colony (ABC) with Q-learning based local search has the best competitiveness.

A set of jobs has to be processed on identical machines. Every job may be processed on any available machine without preemptions. The criterion is to minimise the makespan (i.e. the completion time of the last job in a schedule). During the realisation of a schedule, durations of some jobs may deviate from the initial values estimated before scheduling. Other jobs have fixed durations that are known before scheduling. We conduct a stability analysis of the optimal semi-active schedule. First, we derive necessary and sufficient conditions for an optimal schedule to be unstable with respect to infinitely small variations of the non-fixed durations (the stability radius of an unstable schedule is equal to zero). Second, we show that the stability radius of an optimal schedule could be infinitely large. Furthermore, several lower and upper bounds on the stability radius have been established. Third, we derive a formula and develop an algorithm for calculating stability radii. [ABSTRACT FROM AUTHOR]

The cloud provides a valuable environment for users to run their applications, which are used in various fields such as bioinformatics, astronomy, biodiversity, and image analysis. Executing tasks across various virtual machines while considering multiple objectives simultaneously is highly challenging and is classified as a Non-Polynomial (NP)-Complete problem. Another significant challenge is offloading workflow applications (WAs) with valid execution sequences while ensuring compliance with dependency constraints among all tasks. This paper introduces a novel Energy-Cost Aware Workflow Offloading using a quantum-inspired differential evolution (EC-QIDE) algorithm EC-QIDE considers several conflicting factors, including Makespan, resource utilization, energy consumption, cost, and load balance. Quantum Vectors (QVs) are created using quantum bits and updated using a quantum angle. These QVs are then decoded using a novel hashing technique. The fitness function is designed to incorporate the diverse objectives mentioned. Extensive simulations were conducted and compared against state-of-the-art techniques. Both variance analysis and a Friedman test were performed to evaluate the results. Additionally, Taguchi’s parametric statistical technique was applied for further analysis. The simulation results indicate that EC-QIDE surpassed existing approaches in terms of Makespan by 6.44%, resource utilization by 4.43%, energy consumption by 1.03%, load balance by 42.28%, and cost by 0.30%. [ABSTRACT FROM AUTHOR]

Abstract Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm. Many authors developed Task Scheduling algorithms by using metaheuristic techniques, hybrid approaches but all these algorithms give near optimal solutions but still TSP is a highly challenging and dynamic scenario as it resembles NP hard problem. Therefore, to tackle the TSP in cloud computing paradigm and schedule the tasks in an effective way in cloud paradigm, we formulated Adaptive Task scheduler which segments all the tasks comes to cloud console as sub tasks and fed these to the scheduler which is modeled by Improved Asynchronous Advantage Actor Critic Algorithm(IA3C) to generate schedules. This scheduling process is carried out in two stages. In first stage, all incoming tasks are segmented as sub tasks. After segmentation, all these sub tasks according to their size, execution time, communication time are grouped together and fed to the (ATSIA3C) scheduler. In the second stage, it checks for the above said constraints and disperse them onto the corresponding suitable processing capacity VMs resided in datacenters. Proposed ATSIA3C is simulated on Cloudsim. Extensive simulations are conducted using both fabricated worklogs and as well as realtime supercomputing worklogs. Our proposed mechanism evaluated over baseline algorithms i.e. RATS-HM, AINN-BPSO, MOABCQ. From results it is evident that our proposed ATSIA3C outperforms existing task schedulers by improving makespan by 70.49%. Resource cost is improved by 77.42%. Energy Consumption is improved over compared algorithms 74.24% in multi cloud environment by proposed ATSIA3C.

Task scheduling is the act of allocating tasks in a certain way to make the best use of the resources at hand. Users of the service must make their demands online since cloud computing is the method used to offer services through the internet. In this paper, a new hybrid optimization model is introduced for secure task scheduling in cloud which includes six fold objective functions such as makespan, execution time, Quality of Service (QoS), utilization cost and security. In security constraint, trust evaluation and risk probability was determined. Black Widow Combined Seagull Optimization (BWCSO) algorithm was proposed for obtaining the best optimization result by combining Black Widow Optimization (BWO) and Seagull Optimization Algorithm (SOA). Cycle crossover (CX) was introduced to produce an offspring from its parents in which each slot is filled by an element from a different parent. Finally, the suggested algorithm's performance was assessed, and the best outcome was found with respect to makespan. [ABSTRACT FROM AUTHOR]

This article aims to design a new secured data transmission, VM migration, and placement. VM is constructed for hosting the applications by taking the essential resources, and OS is given by the user after receiving a request from the customer by the data center of the cloud. Here, the best fitness index-based Squirrel Search Algorithm (BFI-SSA) to minimize the number of active servers in the cloud. Further, the BFI-SSA is used for implementing the optimal VM migration with the help of a multiobjective function using the makespan and energy consumption. For migrated VM, the secured data transmission is performed by the same improved SSA with "data sanitization and restoration" with adaptive key management. This BFI-SSA algorithm is intended to generate the key and to increase the privacy of the migrated data. The energy consumption of the designed method attains 8% regarding the best rate value. Moreover, the designed method attains a minimum cost function to get a higher convergence rate in the 40th iteration. The given offered method attains a better makespan rate regarding best rate values. Thus, the simulation result has outperformed the suggested model and has achieved low energy and power consumption when compared to other-state-of-art methods. [ABSTRACT FROM AUTHOR]

One of the most challenging aspects of cloud computing is task scheduling. User needs are changing rapidly in a dynamic environment, and the resources can fluctuate depending on demand because they are virtual. This study presents a hybrid task scheduling model that combines Particle Swarm Optimization and Whale Optimization techniques to address the challenges of task scheduling and achieve the best performance. The method is analyzed and scored based on its "makespan," "resource utilization," and "convergence." Test results indicate that the proposed method reduces the makespan in all cases. Additionally, it increases resource utilization compared to the existing state-of-the-art methods. Furthermore, resource utilization increases across the board with the number of tasks performed. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a crucial technology widely utilized in various sectors in recent years. One significant application is in the healthcare system, particularly in mobile health and remote patient monitoring for individuals with different medical conditions such as kidney disease, heart disease, cancer, hypertension, diabetes, respiratory issues, and stroke. Integrating IoT with cloud computing can enhance the efficiency of healthcare systems and facilitate the creation of novel applications in the future. Load balancing is a significant issue in cloud computing systems that must be addressed. Solving the problem will decrease response time, power usage, and cost and improve server availability. This work consists of developing and executing a healthcare system utilizing IoT and addressing the load-balancing issue in cloud computing using an Improved version of the Grey Wolf Optimization (GWO) algorithm. The suggested method is named Improved GWO Virtual Machine Selector (IGWO-VMS). The proposed method chooses the Optimal Virtual Machine (VM) from a set of VMs based on its fitness value. Various tasks are prioritized and allocated to the most suitable VMs according to their Instruction Millions (IM), with tasks with high IM being assigned to VMs with high fitness values. The results showed that the suggested technique decreases latency and packet loss while maximizing throughput in healthcare systems. The efficiency and success of this technology surpass other state-of-the-art methods in decreasing makespan time and total processing time and ensuring load balancing across virtual machines. The GoCJ dataset used contain a number of jobs in terms of Million Instructions (MI) obtained from the workload behaviour witnessed in google cluster trace. The values of makespan time, throughput time and Standard Deviation (STD) where (23.05), (899.8979) and (177.7675), respectively, in case of applying 500, 600 and 900 tasks. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) devices, IoT applications require real-time and low-latency responses. Fog computing is a suitable platform for processing Internet of Things applications. However, fog computing devices are distributed, dynamic, and have limited resources, so the allocation of fog computing resources to execute heterogeneous and delay-sensitive tasks in the Internet of Things is a significant challenge. In this paper, we mathematically formulate the resource allocation problem to minimize the makespan while meeting the quality of service (QoS) requirements of IoT tasks. Next, we propose two learning automata, namely an automaton for select tasks (ATF) and an automaton for select virtual machine (Avf) to efficiently map IoT tasks to FNs. In this approach, a task is selected from the set of ATF actions and then, a Fog node is selected from the set of Avf actions. If the requirements for executing the tasks on the fog nodes are met, then the resource is allocated to the task. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different fog configurations. The proposed algorithms are evaluated in a simulation environment by extending the iFogsim to simulate a realistic fog environment. The experimental results indicate that the performance of the proposed algorithm is better compared with existing algorithms in terms of MK, response time, delay, Processing time, and cost with the increasing number of task submissions. [ABSTRACT FROM AUTHOR]

The recent focus on cloud computing is due to its evolving platform and features like multiplexing users on shared infrastructure and on-demand resource computation. Efficient use of computer resources is crucial in cloud computing. Effective task-scheduling methods are essential to optimize cloud system performance. Scheduling virtual machines in dynamic cloud environments, marked by uncertainty and constant change, is challenging. Despite many efforts to improve cloud task scheduling, it remains an unresolved issue. Various scheduling approaches have been proposed, but researchers continue to refine performance by incorporating diverse quality-of-service characteristics, enhancing overall cloud performance. This study introduces an innovative task-scheduling algorithm that improves upon existing methods, particularly in quality-of-service criteria like makespan and energy efficiency. The proposed technique enhances the Snake Optimization Algorithm (SO) by incorporating sine chaos mapping, a spiral search strategy, and dynamic adaptive weights. These enhancements increase the algorithm's ability to escape local optima and improve global search. Compared to other models, the proposed method shows improvements in cloud scheduling performance by 6%, 4.6%, and 3.27%. Additionally, the approach quickly converges to the optimal scheduling solution. [ABSTRACT FROM AUTHOR]

In order to successfully address the multiprocessor work scheduling issues, a novel hybrid approach is presented in this paper. Executing a group of tasks on a small number of processors is the main goal of a task scheduling method. The job scheduling issue is viewed as a multi-objective optimisation issue in this study. The goals of this optimisation challenge are to determine the system's least energy consumption and minimum makespan (the sum of the schedule lengths for each task's execution). In order to lower the processors' energy usage, the Dynamic Voltage Frequency Scaling (DVFS) level has the task scheduling issue enabled. To determine the goals, a hybrid algorithm called Hybrid Grey Wolf Crow Search Optimisation (HGWCSO) is suggested. The Grey Wolf Optimisation (GWO) and Crow Search Optimisation (CSO) algorithms are combined to create the HGWCSO algorithm. The social structure and hunting strategy of wolves serve as the inspiration for the GWO metaheuristic algorithm. The CSO, on the other hand, is a metaheuristic algorithm that was motivated by crows' clever conduct. The proposed hybrid approach is put into practise using tasks that are produced at random, and the results of this comparison are classic methods. The proposed method's simulation results are contrasted with those from five existing widely used methods. The proposed technique has also been contrasted with some newly created metaheuristic algorithms using various real-world data sets. According to the simulation results, HGWCSO outperforms other algorithms in terms of lowering makespan and energy usage. The proposed algorithm has successfully improved the makespan and energy consumption with a maximum of 31.07% and 58.36%, respectively, compared to other algorithms. [ABSTRACT FROM AUTHOR]