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The increasing dependence on cloud computing as a cornerstone of modern technological infrastructures has introduced significant challenges in resource management. Traditional load-balancing techniques often prove inadequate in addressing cloud environments’ dynamic and complex nature, resulting in suboptimal resource utilization and heightened operational costs. This paper presents a novel smart load-balancing strategy incorporating advanced techniques to mitigate these limitations. Specifically, it addresses the critical need for a more adaptive and efficient approach to workload management in cloud environments, where conventional methods fall short in handling dynamic and fluctuating workloads. To bridge this gap, the paper proposes a hybrid load-balancing methodology that integrates feature selection and deep learning models for optimizing resource allocation. The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, SLADRO , combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. Extensive simulations conducted on a real-world dataset called Google Cluster Trace dataset reveal that the SLADRO model significantly outperforms traditional load-balancing approaches, yielding notable improvements in throughput, makespan, resource utilization, and energy efficiency. This integration of advanced techniques offers a scalable and adaptive solution, providing a comprehensive framework for efficient load balancing in cloud computing environments.

Fog computing minimizes latency and bandwidth consumption by processing data near the source, but it has the challenge of workload balancing across the dynamic and resource limited fog nodes. Uneven task assignment can result in bottlenecks, idle resources, and lowered Quality of Service (QoS) standards. In this work, we introduce a hybrid metaheuristic load balancing algorithm for fog computing by combining the Bacterial Colony Optimization (BCO) and the Particle Swarm Optimization (PSO) techniques to enhance load balancing. BCO has good solution space exploration capabilities, whereas PSO has the advantage of quick convergence; the hybrid takes advantage from both to achieve reduced makespan with better VM usage. The proposed algorithm has been developed in the Python language, with original BCO and hybrid modules, along with a standard PSO executable. The program has been tested on a synthetic offline task–VM dataset generated by CloudSim 6.0 with size ranges from 100 to 10,000 tasks and poison distribution of variety of tasks arriving, with identical experiment settings. The results indicate the hybrid BCO–PSO to exhibit significant makespan reduction with increased VM utilization compared to the individual BCO, PSO, as well as the Adaptive Inertia Weight Particle Swarm Optimization (AIW–PSO) algorithms for most test scenarios, with faster convergence for high workload scenarios. In the high-load cases, the hybrid reduced makespan by 32.76% compared to AIW–PSO for 5000 tasks and by 35.79% compared to AIW–PSO for 10000 tasks. These experimental results indicate that the proposed hybrid algorithm can be an effective, adaptive solution for task allocation in fog-inspired computational scheduling scenarios. This evaluation focuses on computation-side task scheduling using a synthetic task–VM model and keeping network-level factors such as latency or bandwidth idle.

Major problems and issues in Internet of Things (IoT) systems include load balancing, lowering operational expenses, and power usage. IoT devices typically run on batteries because they lack direct access to a power source. Geographical conditions that make it difficult to access the electrical network are a common cause. Finding ways to ensure that IoT devices consume the least amount of energy possible is essential. When the network is experiencing high traffic, locating and interacting with the next hop is critical. Finding the best route to load balance by switching to a less crowded channel is hence crucial in network congestion. Due to the restrictions indicated above, this study analyzes three significant issues—load balancing, energy utilization, and computation cost—and offers a solution. To address these resource allocation issues in the IoT, we suggest a reliable method in this study termed Dynamic Energy-Efficient Load Balancing (DEELB). We conducted several experiments, such as bandwidth analysis, in which the DEELB method used 990.65 kbps of bandwidth for 50 operations, while other existing techniques, such as EEFO (Energy-Efficient Opportunistic), DEERA (Dynamic Energy-Efficient Resource Allocation), ELBS (Efficient Load-Balancing Security), and DEBTS (Delay Energy Balanced Task Scheduling), used 1700.91 kbps, 1500.82 kbps, 1300.65 kbps, and 1200.15 kbps of bandwidth, respectively. The experiment’s numerical analysis showed that our method was superior to other ways in terms of effectiveness and efficiency.

Lightweight container technology has emerged as a fundamental component of cloud-native computing, with the deployment of containers and the balancing of loads on virtual machines representing significant challenges. This paper presents an optimization strategy for container deployment that consists of two stages: coarse-grained and fine-grained load balancing. In the initial stage, a greedy algorithm is employed for coarse-grained deployment, facilitating the distribution of container services across virtual machines in a balanced manner based on resource requests. The subsequent stage utilizes a genetic algorithm for fine-grained resource allocation, ensuring an equitable distribution of resources to each container service on a single virtual machine. This two-stage optimization enhances load balancing and resource utilization throughout the system. Empirical results indicate that this approach is more efficient and adaptable in comparison to the Grey Wolf Optimization (GWO) Algorithm, the Simulated Annealing (SA) Algorithm, and the GWO-SA Algorithm, significantly improving both resource utilization and load balancing performance on virtual machines.

Cloud-edge-end (CEE) computing is a hybrid computing paradigm that converges the principles of edge and cloud computing. In the design of CEE systems, a crucial challenge is to develop efficient offloading strategies to achieve the collaboration of edge and cloud offloading. Although CEE offloading problems have been widely studied under various backgrounds and methodologies, load balance, which is an indispensable scheme in CEE systems to ensure the full utilization of edge resources, is still a factor that has not yet been accounted for. To fill this research gap, we are devoted to developing a dynamic load balance -aware CEE offloading strategy. First, we propose a load evolution model to characterize the influences of offloading strategies on the system load dynamics and, on this basis, establish a latency model as a performance metric of different offloading strategies. Then, we formulate an optimal control model to seek the optimal offloading strategy that minimizes the latency. Second, we analyze the feasibility of typical optimal control numerical methods in solving our proposed model, and develop a numerical method based on the framework of genetic algorithm. Third, through a series of numerical experiments, we verify our proposed method. Results show that our method is effective.

Cloud environment handles heterogeneous services, data, and users collaborating on different technologies and resource scheduling strategies. Despite its heterogeneity, the optimality in load scheduling and data distribution is paused due to unattended requests for a prolonged time. This article addresses the aforementioned issue using a Two-level Scheduling and Distribution Framework (TSDF) using Fuzzy Logic (FL). This framework houses different fuzzification processes for load balancing and data distribution across different resource providers. First, the fuzzification between regular and paused requests is performed that prevents prolonged delays. In this process, a temporary resource allocation for such requests is performed at the end of fuzzification resulting in maximum waiting time. This is the first level optimality determining feature from which the second level’s scheduling occurs. In this level, the maximum low and high delay exhibiting distributions are combined for joint resource allocations. The scheduling is completely time-based for which the cumulative response delay is the optimal factor. Therefore, the minimum time-varying requests observed in the second level are fuzzified for further resource allocations. Such allocations follow the distribution completed intervals improving its distribution (13.07%) and reducing the wait time (7.8%).

In multi-end-to-end path request planning, the control plane may not be able to meet all path request requirements under limited bandwidth resources. Moreover, suboptimal path planning can lead to localized network congestion, which in turn causes an overall imbalance in network load. Therefore, the multi-domain control plane needs to consider more network resource states during the path selection, such as link weights, load saturation, and resource occupancy rates, in order to select the optimal paths to maximize the satisfaction of data plane requirements while maintaining network load balance. To address such issues, we first derive a cross-domain communication load balancing objective function based on network modeling. Through collaborative processing among multi-domain controllers, the coordinated planning of cross-domain paths and the collaborative installation of flow tables are achieved. Then, we transform the cross-domain path planning problem into a clique-finding problem under a set of backup paths. Finally, we provide a heuristic approximate solution method for this problem. In terms of cross-domain communication, we adopt a collaborative approach among multiple controllers to achieve coordinated planning of cross-domain paths and collaborative installation of flow tables. Simulation results show that our proposed scheme outperforms the traditional method in terms of path allocation success rate, network load balancing degree, and data transmission delay, especially in cross-domain communication under high-density path requests in SDN networks.

In many computing and networking applications, arriving tasks have to be routed to one of many servers, with the goal of minimizing queueing delays. When the number of processors is very large, a popular routing algorithm works as follows: select two servers at random and route an arriving task to the least loaded of the two. It is well known that this algorithm dramatically reduces queueing delays compared to an algorithm, which routes to a single randomly selected server. In recent cloud computing applications, it has been observed that even sampling two queues per arriving task can be expensive and can even increase delays due to messaging overhead. So there is an interest in reducing the number of sampled queues per arriving task. In this paper, we show that the number of sampled queues can be dramatically reduced by using the fact that tasks arrive in batches (called jobs). In particular, we sample a subset of the queues such that the size of the subset is slightly larger than the batch size (thus, on average, we only sample slightly more than one queue per task). Once a random subset of the queues is sampled, we propose a new load-balancing method called batch-filling to attempt to equalize the load among the sampled servers. We show that, asymptotically, our algorithm dramatically reduces the sample complexity compared to previously proposed algorithms.

The Internet of Things (IoT) and cloud computing have revolutionized the technological era unabatedly. These technologies have impacted our lives to a great extent. The traditional cloud model faces a variety of complications with the colossal growth of IoT and cloud applications, such as network instability, reduced bandwidth, and high latency. Fog computing is utilized to get around these problems, which brings IoT devices and cloud computing closer. Hence, to enhance system, process, and data performance, fog nodes are planted to disperse the load on cloud servers using fog computing, which helps reduce delay time and network traffic. Firstly, in this article, we highlight the various IoT–fog–cloud models for distributing the load uniformly. Secondly, an efficient solution is provided using fog computing for balancing load among fog devices. A performance evaluation of the proposed mechanism with existing techniques shows that the proposed strategy improves performance, energy consumption, throughput, and resource utilization while reducing response time.

The introduction of digital technologies into the activities of companies is based on software and hardware systems, which must function reliably and without interruption. The forecasting of the completion of storage area networks (SAN) is an essential tool for ensuring the smooth operation of such systems. The aim of this study is to develop a system of the modelling and simulation of the further loading of SAN on previously observed load measurements. The system is based on machine learning applied to the load prediction problem. Its novelty relates to the method used for forming input attributes to solve the machine learning problem. The proposed method is based on the aggregation of data on observed loading measurements and the formalization of the problem in the form of a regression analysis problem. The artificial dataset, synthesized stochastically according to the given parameter intervals and simulating SAN behavior, allowed for more extensive experimentation. The most effective algorithm is CatBoost (gradient boosting on decision trees), which surpasses other regression analysis algorithms in terms of R2 scores and MAE. The selection of the most significant features allows for the simplification of the prediction model with virtually no loss of accuracy, thereby reducing the number of confessions used. The experiments show that the proposed prediction model is adequate to the situation under consideration and allows for the prediction of the SAN load for the planning period under review with an R2 value greater than 0.9. The model has been validated on a series of real data on SAN.