Optimized load balancing mechanism in parallel computing for workflow in cloud computing environment

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well.

In the last few decades, a novel branch of intelligent computation algorithms has been inspired by a swarm intelligence theory which imitates the behavior of animals. These algorithms are successfully applied to solve many kinds of simple and complex problems in several fields such as optimization problems, pattern recognition, image processing, features section, and task scheduling in cloud and parallel computing. Cloud computing has not only become the preferred environment for several companies, but also helps others to overcome many server-related issues by utilizing characteristics such as reliability, flexibility, high scalability, and security. Therefore, many intelligent computation algorithms are used to improve this environment. In this chapter, an overview of swarm intelligence for solving the scheduling problems of tasks in cloud computing is presented, including particle swarm optimization, cat optimization algorithm, artificial bee colony, lion optimization algorithm, whale optimization algorithm, bat algorithm, gray wolf optimizer, cuckoo search algorithm, hybrid swarm algorithms, and multi-objective swarm optimization. All these algorithms are described and presented with their achievements in solving task scheduling issues in cloud computing.

Many intelligent applications of Cloud and Edge can be modeled to be stochastically arrived workflows. Elastically provisioning resources such as containers to these applications is crucial to minimize resource costs while fulfilling deadlines. Most of existing workflow scheduling algorithms are either not for randomly arrived workflows from users of Edge Computing or only consider workflows in pure Cloud Computing. In this article, an elastic resource scheduling method, which integrates loosely coupled workflow scheduling with resource auto-scaling, is developed for stochastically arrived workflows in Cloud and Edge to minimize long-term operating costs while meeting deadline constraints. Experimental results illustrate that our approach achieves a lower overall cost and a higher success ratio compared with the state-of-art algorithm.KeywordsEdge computingCloud computingMicroserviceContainersWorkflow scheduling

Cloud computing and big data: Technologies and applications

Users may access virtual, scalable, and dynamic resources using cloud computing, which is a novel technology that charges them only for the resources they use. This technology is reliant on a network to function. Several services, like as e-commerce apps, use the whole network as a result of the sheer scope of the network. Cloud computing has progressed through three phases in terms of practical development: distributed computing, parallel computing, and grid computing. Cloud computing is a cutting-edge technology that installs networks of computers, many of which are situated in faraway locations, to conduct operations on massive amounts of data in real time, main challenge in cloud workflow Scheduling in edge context. In proposed approach we used hybrid approach using Tabu and Grey Wolf Optimization. In experiment improved results of approx. 5-10% average in all performance metrics are obtained.

Cloud computing has emerged as a dominant platform for hosting complex applications, offering scalable and flexible resources on demand. However, the dynamic and heterogeneous nature of cloud environments poses significant challenges for efficient workflow scheduling, particularly when aiming to minimize total execution time, energy consumption, and operational cost. In this research, we propose a novel hybrid approach that integrates the Heterogeneous Earliest Finish Time (HEFT) algorithm with an Improved Grey Wolf Optimizer (IGWO) enhanced by differential evolution strategies and survival-of-the-fittest mechanisms. These enhancements strengthen exploration and exploitation by adaptively mutating and refining task allocations while eliminating weaker solutions. The use of HEFT-based initialization provides a strong starting population, and the DE-driven IGWO refinement accelerates convergence and avoids premature stagnation. Together, these two-level optimization strategy ensures faster convergence and higher energy-efficient workflow scheduling compared to earlier HEFT metaheuristic approaches. To evaluate the effectiveness of the proposed hybrid method, extensive experiments were conducted on randomly generated workflows with varying task and dependency complexities. The performance analysis demonstrates that the hybrid HEFT-IGWO approach consistently outperforms standard HEFT, traditional GWO, and standalone metaheuristic techniques in terms of minimizing makespan, reducing energy consumption, and lowering cloud infrastructure costs. This study highlights the potential of combining heuristic initialization with evolutionary optimization to achieve energy-efficient, cost-effective workflow scheduling in cloud computing environments.

One of the issues in cloud computing is workflow scheduling. A workflow models the process of executing an application comprising a set of steps and its objective is to simplify the complexity of application management. Workflow scheduling maps each task to a proper resource and sorts tasks on each resource to meet some efficiency measures such as processing and transmission costs, load balancing, quality of service, and etc. Task scheduling is an NP-Complete problem. In this study, meta-heuristic firefly algorithm (FA) is used to present a workflow scheduling algorithm. The purpose of the proposed scheduling algorithm is to explore optimal schedules such that the cost of processing and transmission of the whole workflow are minimized while there will be load balancing among the processing stations. The proposed algorithm is implemented in MATLAB and its efficiency is compared with cat swarm optimization (CSO) algorithm. The evaluations show that the proposed algorithm outperforms CSO in finding better solutions.

Internet, since the beginning of its work, has undergone many changes one of the latest changes is how the internet cloud computing. New technologies cloud computing offers because of features all kinds of facilities to the users as a service. Each evolution, change and novel concept in the world of technologies has its own problems and complications. Accordingly, benefiting from cloud computing is no exception to this rule and it has challenged researchers and proponents in this research domain. Indeed, some major challenges in cloud computing are: load balancing, safety, reliability, ownership, data backup, data portability and supporting several platforms. One challenge for such matters in the field of cloud computing is load balancing optimization in the cloud. The so-called cloud computing, including virtualization, distributed computing, networking, software and Web services. With respect to the ever increasing significance of load balancing in cloud computing the researchers in this paper intended to improve load balancing by using a novel method. The related studies were reviewed, evaluated and compared with each other. The efficiency of the proposed method was analyzed and compared with those of other studies. The results of the present study revealed that the proposed method is better than other dynamic virtual machine (VM) consolidation algorithms in terms of reducing SLA (service levels agreement) violation and the amount of transmitted data volume transmission has to present a better performance than other methods.

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Efficient and secure task scheduling in cloud communication using hybrid convolutional neural network and enhanced encryption techniques

Cloud computing is a solution that can provide remote computing today. It provides users with a high-performance computing resource that can be used at any time. When users are dealing with large tasks, if the task scheduling algorithm is not scientific and reasonable, it will cause unnecessary energy waste in cloud computing. This paper proposes a new scheduling algorithm based on the currently widely used scheduling algorithm HEFT, which aims to save scheduling time in task scheduling. Comparing the optimized algorithm with the benchmark algorithm, the results show that it can also be completed within the deadline, but it can save up to 24% of energy consumption.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

This paper presents a scalable parallel computing framework for simulating track buckling under dynamic train loads, enabling large‐scale railway track stability analysis. A three‐dimensional (3D) track model is developed using finite element‐based Euler–Bernoulli beam formulations for rails, dynamic force inputs, and nonlinear interactions at the sleeper–ballast interface to capture dynamic buckling behavior. To address computational challenges in simulating extended track sections, the framework employs message passing interface–based parallelization, optimizing load balancing, and minimizing interprocess communication overhead. Unlike approaches that simulate long tracks virtually by recycling a small domain, the proposed method maintains complete dynamic and structural detail across the entire track length. It dynamically adjusts lateral rail stiffness and incorporates thermal compression effects to enable simulation of buckling behavior, while efficiently scaling across high‐performance computing clusters. Case studies demonstrate the framework's ability to simulate large‐scale tracks under combined thermal gradients and dynamic train loads, achieving near‐linear speedup and reducing runtime by up to 90% compared to serial approaches. Additionally, a machine learning–based buckling risk assessment is presented as a use case, where a model trained on long‐track simulation results predicts buckling risk across extended sections. By integrating 3D track dynamics, parallel computing, and data‐driven risk assessment, this work provides a powerful tool for evaluating railway infrastructure resilience under extreme operational conditions.

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022