Multi-objective task scheduling in cloud-fog computing using reptile search algorithm

In this new age, with the maturation of Internet of things (IoT), a multitude of advanced and innovative services are foreseen. Cloud, an accustomed computing technology, being circumscribed by the huge traffic cannot assuage the real-time services demanded by the cross-vertical IoT applications. Over and above this, though the fog computing paradigm, a hopeful alternative providing real-time services, was introduced, still, fog and cloud collaboratively may not be able to bear the tremendous amount of requests that arises from the numerous vertical IoT applications, because of the resource-bounded nature of fog. An indecent resource management and load balancing strategy in the fog infrastructure may further lead to a deterioration in the quality of service (QoS) and failure in providing services in real time. Without disregarding the unignorable issues and challenges in fog, this paper A Resource-Aware Load Balancing Strategy for Real-Time, Cross-vertical IoT Applications has been envisioned. The designed architecture and the proposed model are presented comprehensively with an emphasis on elucidating the resource-aware load balancing strategy. The equations and algorithms for resource management mechanism and load balancing are presented meticulously. Following the end, the efficacy of the proposed methodology is validated using CloudSim and the performance is evaluated in terms of load balance, resource utilization, and power consumption based on employed fog nodes.KeywordsIoTLoad balancingResource utilizationFog computingCross-vertical applications

Since the rapid development of deep learning (DL) technology, large-scale GPU clusters receive a large number of DL workloads daily. To speed up the completion time, the workloads usually occupy several GPUs on a server. However, workload scheduling inevitably generates resource fragmentation, which results in many scattered GPU resources being unavailable. Existing works address improving resource utilization by reducing GPU resource fragmentation, while they focus on resource scheduling for a single cluster and ignore multiple clusters. Multi-cluster scenarios, such as virtual clusters and geo-distributed clusters, require load balancing to avoid some clusters exhausting resources while some clusters are idle while improving resource utilization, which is not well addressed by existing works. In this paper, we propose MCG-Sched, a scheduling strategy to reduce resource fragmentation in multiple GPU clusters while maintaining load balancing among clusters. MCG-Sched measures the fragmented resources with the distribution of workload demands and uses a scheme that minimizes fragmentation in workload scheduling. Meanwhile, MCG-Sched achieves balanced load scheduling across clusters through the load balancing index. MCG-Sched senses the workload requests in the waiting queue, and prioritizes the workloads by combining fragmentation measurement and load balancing index to maximize resource utilization and load balancing during load peak. Our experiments show that MCG-Sched reduces unallocated GPUs up to 1.45× and workload waiting time by more than 40% compared to existing fragmentation-aware methods and achieves effective load balancing.

Cloud computing is a key enabler of modern computing services, offering scalability and flexibility. However, efficient management of cloud resources remains challenging due to limited capacity and the increasing number of tasks requiring timely execution. An effective task scheduling strategy is therefore essential to improve resource allocation and utilization, reduce operational costs and energy consumption, and support high availability-especially for long-term jobs. In this paper, we propose a new scheduling approach that combines a Social-Based Algorithm (SBA) with Deep Reinforcement Learning (DRL), referred to as SBA-DRL. This method allocates tasks to resources by learning from workload patterns and adapting to workload characteristics in a batch scheduling context. We evaluate SBA-DRL using both a synthetic dataset and the real-world Google Cloud Jobs (GoCJ) under workloads ranging from 200 to 1,000 tasks. On the synthetic dataset, our method reduces cost by 20.21% and energy consumption by 25.31%, while improving resource utilization by 9.36%. On the GoCJ dataset, it achieves up to 28.94% lower cost, 8.16% less energy use, and a 14.04% increase in resource utilization. In both cases, SBA-DRL also demonstrates better performance in resource allocation and high-availability management compared to existing heuristics, meta-heuristics, hybrid, and machine learning-based schedulers. These results indicate that the proposed SBA-DRL approach effectively addresses key challenges in cloud task scheduling, offering a practical solution to enhance the efficiency and sustainability of cloud systems.

Cloud computing has become a critical component of modern IT infrastructure, offering businesses scalability, flexibility, and cost efficiency. Unoptimized cloud migration strategies can lead to significant financial waste due to inefficient resource allocation, redundant workloads, and unpredictable cloud expenses. Traditional methods often rely on static provisioning and manual decision-making, leading to suboptimal cloud resource utilization. This research introduces an AI-driven framework for intelligent cloud planning and migration aimed at reducing cloud costs while maintaining high performance and compliance standards. The proposed framework leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques to automate workload distribution, real-time scaling, and dynamic cost optimization. It integrates Predictive Analytics Engine: Uses AI models (Long Short-Term Memory LSTMs, CNNs, and Transformers) to analyze historical workload data and forecast future resource demands. Optimization Algorithm: Implements AI-driven cost minimization functions, optimizing resource allocation while maintaining Quality of Service (QoS). Automated Migration Engine: Reduces manual intervention by executing AI-based cloud workload transfers efficiently. Security and Compliance Module: Uses explainable AI (XAI) and federated learning to maintain cloud security, privacy, and regulatory compliance. A proof of concept (PoC) is developed and evaluated across multiple cloud platforms (AWS, Azure, Google Cloud) with real-world datasets. Experimental results indicate that the AI-driven framework achieves: Cost savings of up to 42% compared to traditional cloud migration strategies. Resource utilization improvement by 53%, ensuring minimal wastage. Reduction in system downtime by 75%, leading to higher reliability. Reduction in manual intervention by 85%, automating resource scaling and load balancing. The research paper also presents real-world case studies across finance, healthcare, e-commerce, and manufacturing sectors, demonstrating the tangible impact of AI-based cloud optimization. This research explores future advancements in cloud computing, including Quantum AI for cloud workload acceleration, Blockchain for transparent cloud cost auditing, and Decentralized AI governance for multi-cloud management. This study contributes to the growing field of AI-driven cloud cost optimization, providing a roadmap for enterprises, cloud architects, and AI researchers to achieve cost-efficient, high-performance, and automated cloud management.

Resource management is the process of task scheduling and resource provisioning to provide requirements of cloud users. Since cloud resources are often heterogeneous, task scheduling and resource provisioning are major challenges in this area. Various methods have been introduced to improve resource utilization and thus increase the efficiency of cloud computing. Existing methods can be divided into several categories, including mathematical and statistical methods, heuristic- and meta-heuristic-based methods, and machine-learning-based methods. Since the resource management problem is NP-complete, several optimization methods have been also exploited in this area. Coral reefs algorithm is an evolutionary method that has showed appropriate convergence and response time for some problems, and thus is used in this paper to combine with reinforcement learning to improve efficiency of resource management in cloud environments. The proposed method of this paper consists of two phases. The initial allocation of resources to ready-to-perform tasks is done using the coral reefs algorithm in the first phase. The tasks are considered as corals and the resources are considered reefs in this method. The second phase utilizes reinforcement learning to avoid falling into the local optima and to make optimal use of resources using a long-term approach. The proposed model of this paper, called MO-CRAML, introduces a new hybrid algorithm for improving utilization and load balancing of cloud resources using the combination of coral reefs optimization algorithm and reinforcement learning. The results of the experiments show that the proposed algorithm has better performance in cloud resource utilization and load balancing in comparison with some other important methods of the literature.

In the evolving landscape of cloud computing, efficient task scheduling plays a pivotal role in optimizing resource utilization and enhancing performance. This paper presents a novel hybrid algorithm that combines the strengths of Estimation of Distribution Algorithms (EDA) and Genetic Algorithms (GA) to address multi-objective task scheduling challenges. The proposed approach aims to balance conflicting objectives such as minimizing execution time, energy consumption, and cost while maximizing resource utilization. By leveraging the probabilistic modeling capabilities of EDA and the evolutionary search efficiency of GA, the hybrid algorithm achieves a more diverse solution set and faster convergence compared to traditional methods. Extensive simulations demonstrate the effectiveness of the proposed algorithm across various cloud environments and task complexities. The results highlight significant improvements in achieving Pareto-optimal solutions, offering a robust framework for cloud service providers to enhance scheduling efficiency. This study underscores the potential of hybrid metaheuristic techniques in addressing the dynamic and complex nature of cloud computing task scheduling, paving the way for more resilient and adaptive cloud infrastructures. Keywords: Cloud Computing, Task Scheduling, Estimation of Distribution Algorithm, Genetic Algorithm, Multi-Objective Optimization, Load Balancing, Resource Utilization.

The basic idea behind the Internet of Things (IoT) is to connect every physical Thing to the Internet. IoT adds ability in Things to sense and communicate with other Things. One of the main concerns in IoT is forming an optimal service composition to fulfil the user requirements while balancing the quality of service (QoS) parameters. So, in this paper, the service composition problem in IoT has been addressed using multi-objective optimisation. An optimal solution to this problem has been provided through proposed novel hybrid and proposed algorithm Multi-Objective Hybrid Hyper-Heuristic Flower Pollination Algorithm (MOHHFPA). The superiority of this algorithm is proved by empirically and statistically comparing it with existing multi-objective algorithms, namely the Non-dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Flower Pollination Algorithm (MOFPA), and the Multi-Objective Hyper-Heuristic Search Algorithm (MOHypEA). The proposed algorithm is empirically evaluated using a real-world case study.

Multi-objective discrete Cuckoo search algorithm for optimization of bag-of-tasks scheduling in fog computing environment

The Internet of Things (IoT) recent widespread popularity has sparked the research to implement communication networks that achieve better performance, flexibility, scalability and provide efficient resource management. 5G cellular networks provide a reliable framework for IoT devices to communicate effectively by relying on cloud technology known as Cloud Radio Access Network (C-RAN). However, the tremendous increase in wireless IoT network traffic leads to an imbalance in IoT network traffic, large number of blocked connections, and degradation in Quality of Service (QoS) of the IoT network. This paper aims to propose a solution for improving the QoS based on predicted load and exploiting the network re-mapping capability of C-RANs. It proposes to re-configure logical connections of Base Band Units (BBU) and Radio Resource Heads (RRH) based on predicting IoT network traffic and results in balanced load among the IoT network. The traffic load is predicted by employing Markov Model based on current users location for each cell. Ant Colony Optimization (ACO) technique is employed to search optimized mapping between BBU and RRH. The balanced traffic load leads to the minimization of blockage connections in the IoT network and hence improves the QoS. The obtained results indicate that the proposed technique ensures the maximization of QoS by minimizing the number of blockage connections as well as handovers which leads to more balanced IoT network traffic.

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users' requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

Multi-objective optimisation is an unavoidable requirement in different steps of embedded systems design, including task mapping and scheduling. In this paper, a new multi-objective genetic algorithm-based task mapping and scheduling (abbreviated as MOGATS) is presented for heterogeneous embedded system design. In MOGATS, the architecture of the hardware platform and the set of tasks in the form of a task graph are assumed to be given as the inputs. Task mapping and scheduling problems are modelled as a genetic algorithm-based optimisation approach. In summary, our task scheduling tool is the first multi-objective task scheduling in the design stage of embedded systems to help the designer to figure out which set of scheduling would provide their desired outcome. Additionally, in comparison to EGA-TS, the state-of-art task scheduling algorithm, in terms of speedup and SLR, we have gain 27.8 and 28.6 immediate improvements respectfully.

Traditional wireless sensor network (WSN) is deployed based on specific task lacking of flexibility and ability to process and compute multi-task in parallel, which usually leads to deploy redundant WSNs for diversified Internet of things (IoT) requirements. In order to solve the problem, a software defined wireless sensor network (SDWSN) resource scheduling and mapping mechanism is proposed for multi-task scenarios in WSNs. In particular, SDWSN is introduced to support diversified IoT applications on a same WSN by abstracting the physical resources into logical ones. In order to fully explore the resources of WSN, a network virtual layer is set between data layer and control layer of the SDWSN through FlowVisor, which can enable a sensor node in SDWSN to process and compute multi-task in parallel. In addition, a dynamic alliance is establish for each task through the non-linear weight discrete particle swarm (NWDPSO) algorithm to complete resource mapping from logical network to physical network. The results show that the proposed multi-task resource scheduling and mapping mechanism can not only improve resource utilization and load balancing, but also reduces network energy consumption and task completion time.

Grid computing actively changed the sphere of large-scale distributed processing, allowing the subsequent employment of geographically distributed and heterogeneous resources. However, efficient scheduling of tasks in such environments is not a simple feat as it has to address issues such as dynamic workloads, heterogeneity of resources as well as scalability. This research work adopts a heuristic-based task scheduling framework which combines GA, ACO, and PSO to achieve maximum performance in terms of less makespan, improved resource utilization, load balance, and energy consumption. The framework was implemented and evaluated using the GridSim simulation tool under different tasks of load varying from 100 up to 500. A comparative analysis showed that, in general, heuristic schedulers, especially PSO, were more effective than FCFS in all the tested criteria. Other methods showed higher makespan while still producing an evenly distributed load, PSO provided the shortest make span, the fastest resource utilization and a very low average wait and response time. Therefore, the research adds to the existing literature about intelligent adaptive scheduling technologies in grids through proposing a scalable solution fitting both demands for execution efficiency and economy. These results provide insights on the impacts of heuristic optimization on the developments of the grid computing to be more responsive, energy efficient and high throughput.

Cloud computing provide services dynamically according to the contract between service providers and users. However, Inappropriateness of scheduling task on VMs can lead huge resource waste and load unbalance, which becomes a seriously challenging problem. Current Swarm intelligence algorithms like genetic algorithm (GA), particle swarm optimization (PSO) are combination of random initialization and local search algorithm. It avoids inconsistent results for different problem instances. However, existing Swarm intelligence works sometimes search the optima without analysing task scheduling situations comprehensively, global search efficiency is low and convergence is too early. In this paper, we propose SNSK‐IPSO algorithm, which develops as a two‐phases algorithm: enumerating all distributed solutions between VMs and tasks, finding the optimal solution through IPSO. It not only minimizes the execution time, but also improves resource utilization and load balance. Several experiments demonstrate that our novel algorithm outperforms others in terms of achieving load balance, higher resource utilization and lower execution times.

Cloud computing suffers from various challenging issues in Internet of Things (IoT) networks like real-time response, energy-efficient execution, and cost of computation. Fog is an emerging distributed computing paradigm which is useful for delay-sensitive tasks in IoT network. An offloading strategy decides where to offload the task and a task scheduling strategy chooses an appropriate fog node based on the requirements of the task while meeting the quality of services (QoS) criteria. Although the computational offloading and task scheduling problem has been widely studied, there is very limited research on delay-energy tradeoff. We propose a fog network that follows an M/M/c queue for computational offloading and a differential scale-based Best Worst Method (BWM) for computation of optimal weights in multi-objective task scheduling. The optimization problem minimizes the execution delay while meeting QoS criteria. The numerical experiments show the efficacy for the different QoS criteria.