Scalable and Adaptable Hybrid LSTM Model with Multi-Algorithm Optimisation for Load Balancing and Task Scheduling in Dynamic Cloud Computing Environments

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

Packet switching remains one of the bottlenecks in building fast Internet routers. Load balancing and switch scheduling are two important algorithms in the effort to maximize the throughput and minimize the latency of these packet switches. A load balancing algorithm regulates the traffic to conform to the service rates while a switch scheduling algorithm allocates the service rates adaptive to the arrival patterns. Many existing load balancing and switch scheduling algorithms are very similar. We show that load balancing and switch scheduling systems are dual systems based on the linear queue dynamics approximation. This allows us to cast a load balancing problem as a scheduling problem, and vice versa. We further show an example of designing a new algorithm for load balancing using an existing scheduling algorithm based on the duality. The duality perspective also allows us to solve unknown problems. We find the entropy rate of the randomized bandwidth allocation system with linear queue dynamics based on the knowledge of the entropy rate of the randomized load balancing system. For the general case, we find both an upper and a lower bound on the entropy rate. The joint use of dual load balancing and switch scheduling algorithms leads to performance gains as we show using mean field analysis.

When provided as a service, Cloud Computing (CC) makes it possible to access dynamically scalable and regularly virtualized resources through the Internet. It is hoped that this research will contribute to the disciplines of load balancing and task scheduling by understanding the concept of load balancing in cloud computing through the use of a simulator known as "CloudSim." In this research, it was found that, one of the most significant challenges in cloud computing is load balancing. This prevents scenarios in which some nodes are heavily loaded while others are idle or performing limited work. Load balancing (LB) spreads the dynamic local workload evenly among all the nodes in the cloud. The most important decision to make is the sort of program to run on the available system in order to maximise resource utilisation. Scheduling is the process of allocating a job to the resources that can complete it in the Cloud Computing environment.A heuristic is a strategy that may be used to solve issues rapidly. It is vital to have efficient and effective scheduling procedures in place in order to make full use of the large array of cloud computing possibilities. As a consequence of this investigation, a novel scheduling algorithm is introduced that is derived from two old approaches, Min-min and Max-min, and is designed to capitalise on their advantages while eliminating their disadvantages. The parameters are then evaluated by the CloudSim simulator. There are high-level techniques that function as the driving force behind a problem-specific heuristic known as Meta-Heuristics. A New Meta-Heuristic methodology has been developed by investigating and analysing previously developed processes. The primary purpose is to avoid the drawbacks of iterative improvement, particularly descents, by allowing the local search to escape local optima while doing local searches. One technique to accomplish this is to provide more "intelligent" possibilities to the local search engine rather than random initial responses as a way to assist it. The proposed meta-heuristic model is evaluated using CloudSim and the advantages of two existing techniques, Particle Swarm Optimisation and Genetic Algorithm with the results of several simulations.

Over the past few years, there has been keen research interest in load balancing and task scheduling in the cloud as the extensive amount of data that is stored in the server leads to significantly increased load. This can be resolved by using a hybrid algorithm in which the honeybee behavior algorithm’s advantages are integrated with fuzzy logic to conduct task scheduling and as well as balancing in the cloud. The design of this hybrid algorithm aims to enhance prior approaches. It is developed as per ABC and merges the important QoS factors along with power consumption so that the power that virtual machines (VMs) consume on the host can be precisely assessed, thereby ensuring efficient load balancing algorithm. The present study aims to evaluate the VMs’ power consumption by taking into account crucial QoS factors for selecting which host and virtual machine will be best suited for receiving the task. CloudSim was used to simulate the ILBA_HB algorithm. In terms of makespan, average response time, and degree of imbalance, the performance of the ILBA HB algorithm is compared to that of the LBA HB and HBB-LB algorithms. According to the results, the proposed algorithm outperformed LBA_HB and HBB-LB.

This book has addressed several issues in multiprocessor architectures and parallel algorithms for Integrated Vision Systems. The approach has been to consider computational requirements for vision applications in an integrated environment in designing a multiprocessor architecture rather than to propose architecture solutions to perform individual algorithms efficiently. An IVS involves algorithms from several levels of processing and the characteristics of algorithms in each level differ tremendously from algorithms in other levels. However, these algorithms need to exist in a system simultaneously and interact with each other. Therefore, a multiprocessor architecture suitable for IVS applications must be partitionable and reconfigurable. It must have the capability to allocate resources dynamically, provide for dynamic load balancing and task scheduling, provide fast and flexible communication, provide efficient I/O, and be fault-tolerant, in addition to providing raw processing power.

Metaheuristic-Based Frameworks for Energy-Efficient Load Balancing and Task Scheduling in Cloud Computing Systems

An Adaptive Swarm Optimization Technique for Load Balancing and Task Scheduling in Cloud Computing

Load balancing and task partitioning are important components of distributed computing. The optimum performance from the distributed computing system is achieved by using effective scheduling and load balancing strategy. Researchers have well explored CPU, memory, and I/O-intensive tasks scheduling, and load balancing techniques. But one of the main obstacles of the load balancing technique leads to the ignorance of applications having a mixed nature of tasks. This is because load balancing strategies developed for one kind of job nature are not effective for the other kind of job nature. We have proposed a load balancing scheme in this paper, which is known as Mixed Task Load Balancing (MTLB) for Cluster of Workstation (CW) systems. In our proposed MTLB strategy, pre-tasks are assigned to each worker by the master to eliminate the worker's idle time. A main feature of MTLB strategy is to eradicate the inevitable selection of workers. Furthermore, the proposed MTLB strategy employs Three Resources Consideration (TRC) for load balancing (CPU, Memory, and I/O). The proposed MTLB strategy has removed the overheads of previously proposed strategies. The measured results show that MTLB strategy has a significant improvement in performance.

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Optimizing data pipeline performance in modern GPU architectures is critical for achieving high computational throughput and efficient resource utilization in data-intensive applications. With the rise of deep learning, scientific simulations, and real-time analytics, GPUs have become integral in accelerating data processing tasks. However, ensuring optimal performance involves addressing several challenges, such as memory bandwidth limitations, data transfer bottlenecks between CPU and GPU, and efficient parallel execution of workloads. This research explores techniques for improving data pipeline performance by focusing on memory management, load balancing, and task scheduling. One key strategy is optimizing data movement through techniques like memory coalescing, which minimizes access latency, and overlapping data transfers with computation. Furthermore, leveraging the architectural advances in modern GPUs, such as unified memory and NVLink, can significantly reduce data transfer overhead. Task parallelism and efficient workload distribution across multiple GPU cores also play a crucial role in enhancing pipeline throughput. Additionally, the study highlights the importance of tuning GPU kernels and optimizing data preprocessing steps to ensure minimal latency and maximum throughput. By adopting advanced profiling tools and performance monitoring techniques, bottlenecks can be identified, and pipeline optimization strategies can be fine-tuned. The findings presented provide a comprehensive approach for designing and optimizing data pipelines, leading to significant performance improvements in GPU-based systems, ultimately driving the next generation of high-performance computing applications.

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance. This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. These adaptive strategies are: i) Master Initiate Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). The measured results show that the adaptive (MIS & WIS) strategies dramatically improve the performance of HDC raytracing system and remedy the defects of static and RTS strategies. Performances of the all investigated strategies are evaluated on manager/master and workers model of HDC system. In performance comparisons of MIS and WIS strategies, the WIS strategy shows a slightly better speedup and scalability over MIS strategy.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

We consider the problem of maximizing the weighted sum data rate in multi-cell and multi-carrier wireless data systems in the presence of interference. We present a scheme that jointly considers load balancing, user scheduling, and interference mitigation to improve the system performance. Our proposed scheme iteratively applies two processes. The first process solves the sub-problem of load balancing and user scheduling while fixing the power allocation of each BS (and thus fixing the interference). We prove that this sub-problem is NP-hard, and devise a 1/2-approximation algorithm to solve the problem. We also consider an extended model capturing finite queue size and propose a 1/2-approximation algorithm under this model. The second process solves the problem of interference mitigation assuming fixed load balancing and user scheduling. We develop a local-improvement based algorithm to solve this problem. Via simulations, we demonstrate that our joint scheme improves both average system throughput and fairness significantly. Compared to the scheme with fixed user-BS association and 1/3 frequency reuse, the lowest 10% cell-edge users obtain more than 60% performance improvement and 90%of users enjoy more than 30%throughput improvement.

In the proposed research work, novel heuristic energy efficiency architecture model is designed to minimize the response time and environmental impact, by considering task scheduling and load balancing operations. Scalable framework is used to enhance the QoS with auto scaling techniques. In this technique cloud resources are allocated and booted quickly to meet response time requirements, which depends upon incoming load. To improve the utilization of resources for load balancing, resource consolidation considers with two energy aware algorithm which includes VM placement and Live VM migration. Then modified Best Fit Decreasing (BFD) algorithm is used for VMs placement by considering CPU utilization and VMs resource consumption rate. Live migration is processed with significant moving constraints such as CPU state, memory content, storage content and network connections. The main aim of energy efficient model is to keep the CPU utilization under specified threshold limit for improving resource efficiency and ensure QoS.

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.