Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Navigating the Cloud: An In-Depth Exploration of HISA Load Balancing for Dynamic Task Appropriation

In a cloud computing (CC) environs,job exhibit variations in durations, start times, and execution times when assigned to virtual machines (VMs). Therefore, achieving load balancing (LB) across these VMs becomes crucial to optimize system proficiency and presentation. The present research introduces a novel LB method leveraging two optimization algorithms to address VM load balancing challenges. The initiated Dynamic Improved HISA Load Balancing proposal integrates an augment harmony-inspired algorithm with a simulated annealing algorithm for dynamic task allocation.In the harmony-inspired algorithm, an improved strategy for calculating Harmony Memory Consideration Rate (HMCR) is employed through a linear decreasing approach, updating HMCR and Pitch Adjustment Rate (PAR) values dynamically. A threshold probability is then evaluated to determine the finest suitability of the current Harmony, choosing eachof the make better harmony-inspired algorithm or simulated annealing for task allocation across available cloud resources.Simulations are conducted using the CloudSim simulator, considering scenarios with 3 or 5 VMs and 10 to 50 cloudlets. Each scenario is tested five times under operational conditions, and only the best performance outcomes are reported. Experimental results specify such a initiated Dynamic Enhanced HISA-LB proposal outperforms the prevail LBMPSO approach, demonstrating either minimized makespan or enhanced resource utilization with increased performance

A Modified Black Hole-Based Task Scheduling Technique for Cloud Computing Environment

The issue of scheduling is one of the most important ones to be considered by providers of the cloud computing in the data center. Using a suitable solution lets the providers of cloud computing use the available resources more. Additionally, the satisfaction of clients is met through provision of service quality parameters. Most of the solutions for this problem aim at one of the service quality factors and in order to achieve this goal, variety of methods are used. Using the algorithm of modified black hole in this paper, a proper solution is presented to tackle the problem of scheduling the affairs in cloud environment. The proposed method reduces makespan, increases degree of load balancing, and improves the resource`s utilization by considering the capability of each virtual machine. We have compared the proposed algorithm with existing task scheduling algorithms. Simulation results indicate that the proposed algorithm makes a good improvement regarding the makespan and amount of resource utilization compared to schedulers based on Random assignment and particle swarm optimization Algorithms

Resource allocation optimization in large scale distributed systems

We studied the problem of resource allocation in large scale distributed applications such as Online Social Networks (OSN) and Cloud Computing. In such settings, resource allocation schemes need to efficient as well as adaptive to the time-varying environments. The abstract resource allocation problem concerns with how to optimally use resources for different tasks. In the context of this dissertation, the resources are servers and the tasks are (a) the virtual machines in the cloud computing setting, and or users for on-line social network applications. It is well-known that the general resource allocation problem is NP-hard. Therefore, in this dissertation, we study a number of heuristic algorithms designed for two primary objectives: 1) achieve reliability via load balancing among resource providers and 2) minimizing the energy consumption by reducing unnecessary intercommunication loads among the servers. Specifically, the dissertation has three main components. The first component deals with optimal assignment of user data to servers to maximize load balance and minimize power consumption. In this component, we propose a novel Distributed Perturbed Greedy Search (DPGS) algorithm which combine both deterministic search and random search to speed the convergence while avoiding local optimum. The empirical shows that the DPGS has a fast convergence rate to the near optimal solution even when the environment changes. The second component deals with the analysis on the convergence rates of a general simulated annealing algorithm via the notion of adiabatic time. We then apply the results to characterize the convergence rates for simulated annealing algorithm when applied to the optimal assignment in the component one. Finally, the third component of the dissertation is concerned with optimal assignment of virtual machines to servers in the context of cloud computing, in order to minimize the energy subject to a given performance requirement. We show that the problem can be approximated well as a convex problem, and propose convex relaxation technique to find the optimal solution

The Contemporary Affirmation of Taxonomy and Recent Literature on Workflow Scheduling and Management in Cloud Computing

The Cloud computing systemspreferred over the traditional forms of computing such as grid computing, utility computing, autonomic computing is attributed forits ease of access to computing, for its QoS preferences, SLA2019;s conformity, security and performance offered with minimal supervision. A cloud workflow schedule when designed efficiently achieves optimalre source sage, balance of workloads, deadline specific execution, cost control according to budget specifications, efficient consumption of energy etc. to meet the performance requirements of today2019; svast scientific and business requirements. The businesses requirements under recent technologies like pervasive computing are motivating the technology of cloud computing for further advancements. In this paper we discuss some of the important literature published on cloud workflow scheduling

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical hos

A Survey on Meta-Heuristic Scheduling Optimization Techniques in Cloud Computing Environment

As cloud computing is turning out to be evident that the eventual fate of the cloud industry relies on interconnected cloud systems where the resources are probably going to be provided by various cloud service suppliers. Clouds are also seen as being multifaceted; if the user requires only computing capacity and wishes to personalize it as per his requirements, the infrastructure cloud suppliers are able to provide this convenience as virtual machines.Many optimized meta-heuristic scheduling techniques are introduced for scheduling of bag-of-tasks applications in heterogeneous framework of clouds.The overall analysis demonstrates that, utilizing different meta-heuristic techniques can offer noteworthy benefits in the terms of speed and performance