Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach

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.

Cloud computing is a new development in the world of Information Technology (IT) infrastructure and it has brought could of challenges. Task scheduling is one of the main features that allows to be efficient in cloud-computing to guarantee the effectiveness of work with the resources and make the completion time as short as possible. It should, however, be pointed out that the task scheduling in cloud computing belongs to the NP-complete optimization problems. In order to eliminate the difficulties related to task scheduling in cloud computing, a number of algorithms have been presented. One of them is also an original version of the list scheduling scheduling technique, but its implementation is specifically aimed at efficient schedules of tasks execution and load balancing in a cloud environment. This method is based on the Heterogeneous Earliest Finish Time (HEFT) strategy but with some modifications that enhance its efficiency as compared to keeping a similar level of algorithm complexity. I carried out experiments on randomly created Directed Acyclic Graphs (DAGs) with a view to testing the usefulness of the algorithm. The test done on the WorkFlowSim simulator involves testing the real and synthetic workflows. The experiments point out the difference in the effectiveness of the offered mechanism compared to the existing algorithms. As demonstrated by the experiments, the suggested would outperform the current scheduling algorithms by efficiency and use of resources. This algorithm has potential of providing improved results than any prior solutions to the task scheduling problem in computer computing although the task problem is a complex one.

Interest in cloud computing has grown considerably over recent years, primarily due to scalable virtualized resources. So, cloud computing has contributed to the advancement of real-time applications such as signal processing, environment surveillance and weather forecast where time and energy considerations to perform the tasks are critical. In real-time applications, missing the deadlines for the tasks will cause catastrophic consequences; thus, real-time task scheduling in cloud computing environment is an important and essential issue. Furthermore, energy-saving in cloud data center, regarding the benefits such as reduction of system operating costs and environmental protection is an important concern that is considered during recent years and is reducible with appropriate task scheduling. In this paper, we present an energy-aware task scheduling approach, namely EaRTs for real-time applications. We employ the virtualization and consolidation technique subject to minimizing the energy consumptions, improve resource utilization and meeting the deadlines of tasks. In the consolidation technique, scale up and scale down of virtualized resources could improve the performance of task execution. The proposed approach comprises four algorithms, namely Energy-aware Task Scheduling in Cloud Computing(ETC), Vertical VM Scale Up(V2S), Horizontal VM Scale up(HVS) and Physical Machine Scale Down(PSD). We present the formal model of the proposed approach using Timed Automata to prove precisely the schedulability feature and correctness of EaRTs. We show that our proposed approach is more efficient in terms of deadline hit ratio, resource utilization and energy consumption compared to other energy-aware real-time tasks scheduling algorithms.

Task scheduling in cloud computing based on grey wolf optimization with a new encoding mechanism

In the research of cloud computing, the advantages and disadvantages of cloud task scheduling algorithm will affect the operation efficiency and service quality of the whole cloud computing system. Evolutionary algorithm is the sum of a series of specific algorithms inspired by the phenomenon of biological evolution in nature. One of the common points of these algorithms is that individuals must be mutated according to certain rules in the running process, so as to avoid falling into local optimum. In order to improve the efficiency of cloud task scheduling in cloud computing, this paper proposes a new mutation strategy which changes the genetic algorithm in evolutionary algorithm. It uses cloudsim platform to simulate cloud task scheduling in cloud computing, and uses particle swarm optimization algorithm to optimize its parameters. The experimental results show that the proposed evolutionary algorithm with improved mutation strategy has the function of cloud task scheduling, and its performance is also improved after the parameters are optimized by particle swarm optimization algorithm. The proposed algorithm improves the mutation step and explores the essence of mutation in evolutionary algorithm, which provides a reference for other research.

Cloud computing provides computer resources such as hardware and software as a service to consumers over a network. The primary idea of cloud computing is to distribute massive amounts of storage, processing, and information to scientific applications. In cloud computing, user tasks are organized and executed with appropriate resources to supply services successfully. In order to complete task scheduling, a variety of task allocation approaches are employed. This work proposes an efficient task scheduling method to improve the task scheduling approach. Optimization techniques are often used to solve NP-hard situations. This work presents an effective scheduling task method for cloud computing systems based on the RAO-3 algorithm. We evaluate the performance of our approach by applying it to three instances. According to the data, the proposed technique provided the optimal solution in terms of schedule length, speedup, efficiency, and throughput.KeywordsHeterogeneous resourcesRAO-3 algorithmTask schedulingCloud computing

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization’s profit as well as users’ satisfaction. A customer’s request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

Cloud computing has spread fast because of its high performance distributed computing. It offers services and access to shared resources to internet users through service providers. Efficient performance of task scheduling in clouds is one of the most important research issues which needs to be focused on. Various task scheduling algorithms for cloud based on metaheuristic techniques have been examined and showed high performance in reasonable time such as scheduling algorithms based on Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). In this paper, we propose a new task-scheduling algorithm based on Lion Optimization Algorithm (LOA), for cloud computing. LOA is a nature-inspired population-based algorithm for obtaining global optimization over a search space. It was proposed by Maziar Yazdani and Fariborz Jolai in 2015. It is a metaheuristic algorithm inspired by the special lifestyle of lions and their cooperative characteristics. The proposed task scheduling algorithm is compared with scheduling algorithms based on Genetic Algorithm and Particle Swarm Optimization. The results demonstrate the high performance of the proposed algorithm, when compared with the other algorithms.

Task scheduling method of cloud computing is the key step to achieve its high performance computing. In order to effectively solve the problem of task scheduling in cloud computing, this paper proposes a novel dynamic task scheduling algorithm based on improved genetic algorithm. On the basis of the genetic algorithm, the proposed algorithm gives full consideration to the dynamic characteristics of the cloud computing environment. The CloudSim simulation platform is selected for simulation; experimental results show that the proposed algorithm can effectively improve the throughput of cloud computing systems, and can significantly reduce the execution time of task scheduling.

Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost. Index Terms— Task Scheduling, Multiobjective, Cloud Computing, Cost, Makespan.

This Cloud computing (CC) infrastructure has many issues including scheduling, budgeting & load balancing (LB). Among them, the biggest challenge for load balancing is a cloud platform. In task scheduling environment generally, the occurrence of load imbalance tends to uncertainty and complexity. Cloud computing is growing Internet-based computing platform & innovative that is emerging & one of its biggest tasks. The goal is to use resources efficiently & decrease resource consumption in the cloud environment. This can be achieved by increasing the LB rate when selecting the best resources for low work failure rates with low lead times. This paper discusses load balancing based on advanced genetic algorithms in the cloud computing platform.

This paper proposes to solve the task scheduling problem in cloud computing by using a load balance aware genetic algorithm LAGA with Min-min and Max-min methods. Task scheduling problems are of great importance in cloud computing, and become especially challenging when taking load balance into account. Our proposed LAGA algorithm has several advantages when solving this kind of problems. Firstly, by introducing the time load balance TLB model to help establish the fitness function with makespan, the algorithm benefits from the ability to find the solution that performs best on load balance among a set of solutions with the same makespan. More importantly, the interaction between makespan and TLB helps the algorithm to minimize makespan in the same time. Secondly, Min-min and Max-min methods are used to produce promising individuals at the beginning of evolution, leading to noticeable improvement of evolution efficiency. We evaluated LAGA on several task scheduling problems and compared with a Min-min, Max-min improved version of genetic algorithm MMGA, which does not use the TLB strategy. The results show that LAGA can obtain very competitive results with good load balancing properties, and outperform MMGA in both makespan and TLB objectives.

Task scheduling, which plays a crucial role in cloud computing and is the critical factor influencing the performance of cloud computing, is an NP-hard problem that can be solved with a heuristic algorithm. In this paper, we propose a novel heuristic algorithm, called biogeography-based optimization (BBO), and a new hybrid migrating BBO (HMBBO) algorithm, which integrates the migration strategy with particle swarm optimization (PSO). Both methods are proposed to solve the problem of scheduling-directed acyclic graph tasks in a cloud computing environment. The basic idea of our approach is to exploit the advantages of the PSO and BBO algorithms while avoiding their drawbacks. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. Both simulation and real-life experiments are conducted to verify the effectiveness of HMBBO. The experiment shows that compared with several classic heuristic algorithms, HMBBO has advantages in terms of global search ability, fast convergence rate and a high-quality solution, and it provides a new method for task scheduling in cloud computing.