An Improved Ant Colony Optimization Algorithm for Scheduling in Cloud Computing Environment

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.

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.

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.

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.

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

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.

Particle Swarm Optimization (PSO) is a new metaheuristic algorithm based on the social behavior of animals. PSO is widely growing and accepted among researchers to find the optimum solution in very large space with low computational complexity. There are various controlled parameters in PSO,and inertia weight (IW) is one of them. An appropriate strategy for varying inertia weight improves the PSOperformance. Recent research works related to varying inertia weight strategy considered small values of w, generally between 0 and 1.The aim of this research paper is to investigate existing varying inertia weight strategy, their effect on PSO performance and to design the new variant of IW for better performance. The same strategy have been implemented in two different proposed inertia weight variants of PSO, namely Modified Simple Random Inertia Weight (MSRIW), and Modified Oscillating Inertia Weight (MOIW). We compared the performance of proposed variants with different existing inertia weight variants of PSO. The proposed strategy is better than existing in term of convergence speed. Further this strategy can be implemented for task scheduling in cloud computing for better performance.

In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR - Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments

Cloud computing is a platform that hosts applications and services for businesses and users to accesses computing as a service. In this paper, we identify two scheduling and resource allocation problems in cloud computing. We describe Hadoop MapReduce and its schedulers, and present recent research efforts in this area including alternative schedulers and enhancements to existing schedulers. The second scheduling problem is the provisioning of virtual machines to resources in the cloud. We present a survey of the different approaches to solve this resource allocation problem. We also include recent research and standards for inter-connecting clouds and discuss the suitability of running scientific applications in the cloud.

WITHDRAWN: Efficient Secure Aware Scheduling Model for Enhancing Security and Workflow Model in Cloud Computing

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.

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

To maximize resource utilization and system throughput in cloud platforms, hardware resources are often shared across multiple virtualized services or applications. In such a consolidated scenario, performance of applications running concurrently in the same physical host can be negatively affected due to interference caused by resource contention. This should be taken into account for efficient scheduling of such applications and performance prediction at user level. Nevertheless, resource scheduling in cloud computing is usually based solely on resource capacity, implemented by heuristics such as bin-packing. Our previous work has introduced an interference-aware scheduling model for web-applications considering their resource utilization profile, and to classify applications we applied fixed interference intervals based on common utilization patters. Although this resulted in placements with better overall results, we observed that some applications with more dynamic workload patterns were wrongly classified with intervals. In this paper, we propose an alternative to the use of intervals and present an interference-aware application classifier for cloud-based applications that deals better with dynamic workloads. Our classifier defines automatically interference levels ranges combining two well-known machine learning techniques: Support Vector Machines and K-Means. Preliminary experiments evaluated the applied machine learning techniques in three quality metrics: Accuracy, F1-Score and Rand Index, observing rates over 80%. The proposed solution creates a workload-aware fine-grained classification that was compared with previous work over different workload scenarios. The results demonstrate that our classification approach improves the placement efficiency by 23% on average.

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.