Solving Task Scheduling Problem in Cloud Computing Environment Using Orthogonal Taguchi-Cat Algorithm

In cloud computing datacenter, task execution delay is no longer accidental. In recent times, a number of artificial intelligence scheduling techniques are proposed and applied to reduce task execution delay. In this study, we proposed an algorithm called Orthogonal Taguchi Based-Cat Swarm Optimization (OTB-CSO) to minimize total task execution time. In our proposed algorithm Taguchi Orthogonal approach was incorporated at CSO tracing mode for best task mapping on VMs with minimum execution time. The proposed algorithm was implemented on CloudSim tool and evaluated based on makespan metric. Experimental results showed for 20VMs used, proposed OTB-CSO was able to minimize makespan of total tasks scheduled across VMs with 42.86%, 34.57% and 2.58% improvement over Minimum and Maximum Job First (Min-Max), Particle Swarm Optimization with Linear Descending Inertia Weight (PSO-LDIW) and Hybrid Particle Swarm Optimization with Simulated Annealing (HPSO-SA) algorithms. Results obtained showed OTB-CSO is effective to optimize task scheduling and improve overall cloud computing performance with better system utilization

Priority Scheduling Implementation for Exam Schedule

Scheduling is a common problem that has been raised for a long time. Many algorithms have been created for this problem. Some algorithms offer flexibility in terms of constraints and complex operations. Because of that complexity, many algorithms will need huge computation resources and execution time. A platform like a web application has many restrictions such as execution time and computation resources. A complex algorithm is not suited for the web application platform. Priority scheduling is a scheduling algorithm based on a priority queue. Every schedule slot will produce a queue based on the constraints. Each constraint will have a different weight. Weight in queue represents their priority. This algorithm provides a light algorithm that only needs a few computations and execution times. The exam schedule is one of many problems in educational institutions. A web application is a popular platform that can be accessed from everywhere. Many educational institutions use web platforms as their main system platform. Web platforms have some restrictions such as execution time. Due to web platform restrictions, priority scheduling is a suitable algorithm for this platform. In this study, the author tries to implement a priority scheduling algorithm in scheduling cases with a website platform and shows that this algorithm solution can be an alternative for solving scheduling cases with low computational resources

A Budget-constrained Time and Reliability Optimization BAT Algorithm for Scheduling Workflow Applications in Clouds

AbstractEffective scheduling is one of the key concerns while executing workflows in the cloud environment. Workflow scheduling in clouds refers to the mapping of workflow tasks to the cloud resources to optimize some objective function. In this paper, we apply a recently developed meta-heuristic method called the BAT algorithm to solve the multi-objective problem of workflow scheduling in clouds that minimizes the execution time and maximizes the reliability by keeping the budget within user specified limit. Comparison of the results is made with basic, randomized, evolutionary algorithm (BREA) that uses greedy approach to allocate resources to the workflow tasks on the basis of low cost, high reliability and improved execution time machines. It is clear from the experimental results that the BAT algorithm performs better than the basic randomized evolutionary algorithm

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures

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

An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

As user service demands change constantly, task scheduling becomes an extremely significant study area within the cloud environment. The goal of scheduling is distributing the tasks on available processors in order to achieve the shortest possible makespan while adhering to priority constraints. In heterogeneous cloud computing resources, task scheduling has a large influence on system performances. The various processes in the heuristic-based algorithm of scheduling will result in varied makespans when heterogeneous resources are utilized. As a result, a smart method of scheduling must be capable of establishing precedence efficacy for each task to decrease makespan time. In our study, we develop a novel efficient method of scheduling tasks according to the firefly algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. We evaluate the performance of our algorithm by putting it through three situations with changing amounts of processors and numbers of tasks. The findings of the experiment reveal that our suggested technique found optimal solutions substantially more frequently in terms of makespan time when compared with other methods