Metaheuristic Algorithms Based Server Consolidation for Tasks Scheduling in Cloud Computing Environment

Cloud computing is a very well known technology for all business people, software developers, end-users, and so on. Significant researches are going on to balance the cloud load. The migration of heavily loaded Virtual Machines (VMs) into lightly loaded Physical Machines (PMs) balances the Cloud load. In Resource Intensity Aware Load Balancing (RIAL) method, based on the weight of resources under utilization, it selected the VMs from heavily loaded PMs for migration and chosen the lightly loaded PMs as destination. An Improved RIAL was proposed to consider both lightly and heavily loaded PMs as destination. Later it was enhanced in the proposed Power Consumption Aware- Traffic Aware- IRIAL (PT-IRIAL) method with the consideration of power consumption, temperature and traffic measures to select the VMs for migration and select PMs for destination. From all these, in this current paper, the crossover and mutation process of GA is utilized to optimally select the migration VMs and choose the destination PMs. Thus this GA based load optimization algorithm optimally maps the migration VMs with the destination PMs efficiently.

Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments

Cloud computing turns to be a big shift from the conventional perception of the IT resources. It is a transpiring computing technology that is increasingly stabling itself as the promising future of distributed on-demand computing. The processes comprised in it are the ones that act as a vital backbone and which strengthen the entire stream of cloud computing as a whole. In specific, Task scheduling is the one such phenomena that enhances the cloud computing in terms of performance. Hence task scheduling that is considered as a predominant one amidst others is what this paper comprises all about. Maximizing the profit via assigning the whole task to the virtual machine is what the problem of scheduling deals with. Although there prevails many more ways to resolve this problem, this paper explores one such solution that consumes lesser number of resources, having lower cost and much importantly consuming lesser energy. By making a profound research regarding this approach of scheduling so as to represent the multi-objective function, both lion optimization algorithm and gravitational search algorithm are hybridized. In spite of having certain drawbacks which could be avoided although, the brighter side relies the merits of making use of both lion search and gravitational search algorithm. There could be many means of measurement for computing the performance of the algorithm. The different algorithms that aid to depict the comparable study encompasses gravitational search algorithm, genetic algorithm and lion, particle swarm optimization. The experimental results serve as the evident for depicting the bitterness of our proposed algorithm compared to the prevailing approaches. As an unexplored path may seem trivial but is effective so does the betterment of our lion approach.

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

In Internet, cloud computing plays an important role to share information and data. Virtualization is an important technique in the cloud environment to share data and information. It is also important computing environment to enables academic IT resources or industry through on-demand dynamically allocation. The main aim of this research paper is to explore the basic knowledge terms of the virtualization and how virtualization works in cloud system. We will explain about how to maintain the virtualization with optimized resources such as storage, network, application, server, and client in cloud computing. We will compare different open-source-based hypervisors or virtual monitor machines (VMM) that are in use today, and we will discuss several issues of virtualization which will be very helpful to the researchers for further study.

Virtual machine placement (VMP) technology is widely used in cloud computing systems. The existing VMP methods mainly aimed at improving the cloud resource utilization, such as load balancing among physical machines (PMs), but they may result in virtual machine (VM) performance degradation because of the great resource contention among VMs running on top of the same PM. In contract to existing VMP algorithms, this paper proposes a virtual machine (VM) Performance maximization and physical machine (PM) Load balancing Virtual Machine Placement method (PLVMP) in cloud, which tries to maximize VM performance and balance PM workload from both users’ and cloud providers’ perspectives. First, we study the relationship between PM workload and VM performance to train a new and improved VM performance model, which can predict VM performance more accurately and offer help to the following VMP. Second, we take VM performance maximization and PM workload balancing into account to formulate the VMP as an optimization problem, which tries to maximize VM performance for users and make load balancing among PMs for cloud providers. Third, we propose a greedy-based algorithm to solve the VMP problem efficiently. We then evaluate PLVMP with other VMP methods on CloudSim platform and a real OpenStack platform. The results show PLVMP can maximize the VM performance significantly and make a good load balancing among PMs.

HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment

The Cloud computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand. The Scheduling problem for cloud computing environment has a lot of awareness as the applications tasks could be mapped to the available resources to achieve better results. One of the main existed algorithms of task scheduling on the available resources on the cloud environment is based on the Particle Swarm Optimization (PSO). According to this PSO algorithm, the applications tasks are allocated to the available resources to minimize the computation cost only. In this paper, a modified PSO algorithm has been introduced and implemented for solving task scheduling problem in the cloud. The main idea of the modified PSO is that the tasks are allocated on the available resources to minimize the execution time in addition to the computation cost. This modified PSO algorithm is called Modified Particle Swarm Optimization (MPOS).The MPOS evaluations have been illustrated using different time, and cost parameters and their effects in the performance measures such as utilization, speedup, and efficiency. According to the implementation results, it is found that the modified MPOS algorithm outperforms the existed PSO.

Influence of initialization on the performance of metaheuristic optimizers

In this paper, a comparative study of different wavelet filters using improved sub-band adaptive thresholding function for denoising of satellite images, based on evolutionary algorithms, has been performed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC) and particle swarm optimisation (PSO) are used for obtaining the parameters of adaptive thresholding function required for optimum performance. The visual and quantitative results clearly show the increased efficiency and flexibility of the proposed CS algorithm based on Meyer wavelet filter over various other wavelet filters for image denoising. From the comparative study of different wavelet filters, it is found that the proposed Meyer wavelet-based CS algorithm denoising approach gives better performance in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and mean as compared to ABC- and PSO-based denoising approach. The proposed technique has been tested on several satellite images. The quantitative (EKI or EPI, mean, MSE, SNR and PSNR) and visual (denoised images) results show the superiority of the proposed technique over conventional and state-of-art image denoising techniques.

Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High-speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get few migrations and this paper introduces novel behavior of VM selection from overutilized PMs using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with another state-of-the-art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters, such as Service Level Agreement (SLA) violation, and energy consumption against various load variation scenarios to support elasticity. The proposed algorithm has outperformed other techniques by considerable margin in terms of QoS, and the details are presented in the results section. The simulation results demonstrate that the proposed technique exhibits 6.3% and 6.7% enhancement in terms of reduced energy consumption compared to both Cuckoo Search (CS) and general Dragonfly (DF) techniques, and 3% decrease in SLA violations in comparison to current methods. Additionally, the results reveal an 11% enhancement in VM migration compared to existing approaches.

Virtual machine (VM) migration in the cloud computing (CC) environment is an important issue to solve many problems, such as: Load Balancing, accomplished by migrating VMs out of overloaded/overheated servers, and Server Consolidation, where servers can be selectively brought down for maintenance after migrating their workload to other servers. In this paper, we propose an efficient VM migration algorithm based on minimization of migrations in CC to improve efficiency and response the requirements for user and restrict of service level agreements (SLA) violation. Experimental results showed the effectiveness of the proposed algorithm compared with existing algorithm.

Task scheduling is considered as one of the most critical problems in cloud computing environment. The main target of task scheduling includes scheduling jobs on virtual machines as well as improves performance. This study employed Grey Wolf Optimization (GWO) algorithm with modifications on the fitness function by making it handles multi-objectives insingle fitness; the makespan and cost are the objectives included in the fitness in order to solve task scheduling problem. The main target of this technique is to reduce both cost and makespan. CloudSim tool is used to evaluate the objectives of the proposed method. The simulation results showed that the proposed method (Modified Grey Wolf Optimizer - MGWO) has better performance than both the traditional Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) with makespan based fitness in terms of makespan, cost and degree of imbalance.

Server consolidation through virtualization improves resource utilization significantly in Cloud Data Centres (CDCs). We study the case of a CDC hosting heterogeneous Physical Machines (PMs) as a variable size vector bin-packing problem. The PMs have different configurations of multiple resources like CPU, RAM, Disk Storage and Network Bandwidth. In this paper, we propose PMNeAR-vector heuristic for PM selection in PM-heterogeneity aware Virtual Machine (VM) initial placement. The proposed heuristic is compared with well-known heterogeneity aware FFD-DRR and BFD bin centric heuristics using a dataset with random instances of both VMs and PMs of heterogeneous configurations. Fifty rounds of VM initial placement simulation experiments were conducted to validate the average resource wastage. The results show that on average FFD-DRR and BFD bin centric heuristics are wasting 22.62% and 37.27% more resource units compared to the proposed PMNeAR-vector heuristic.

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system