OLOA: Based Task Scheduling in Heterogeneous Clouds

Pradeep, K.; Jacob, T. Prem

doi:10.22266/ijies2019.0228.12

Cited by 26 publications

(5 citation statements)

References 18 publications

(19 reference statements)

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“…Previous task-planning techniques, such as GA and PSO techniques, are contrasted to the lion optimization. With WTA, researchers and scientists have access to a massive database of workflow traces that accurately reflect the functioning of actual scientific processes/workflows [24]. These traces are utilized to build DRL models that can learn to optimize resource allocation and job scheduling for comparable processes in cloud computing.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

Alhazmi

2023

IJRITCC

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The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

Alhazmi

2023

IJRITCC

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“…In [10] Services offered by the cloud computing are features that try to increase the scalability and performance at the same time, these features are efficient in terms of cost and minimum in terms of maintenance of account which makes the cloud a favoured option when allocation is done dynamic for the allocation of resources. Among the various benefits the cloud provides, the scheduling of task is an important attribute which helps to decrease the cost for operation at the same time increases the performance.…”

Section: Related Workmentioning

confidence: 99%

RCOA Scheduler: Rider Cuckoo Optimization Algorithm for Task Scheduling in Cloud Computing

2022

IJIES

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Cloud computing, a term which has become very popular on the internet platform which has evolved over time and gained more attention due to various features that are being introduced like multiplex numerous users on the identical physical structure, computing of resource on request and many more. Cloud computing is considered to be an enormous pool having essential resources which the users can make use of to complete their task through the internet. Therefore, in order to complete the tasks requested by the users, a scheduling algorithm is required making scheduling a vital part of cloud computing. Though numerous solutions with respect to scheduling techniques have been proposed in in recent literature, researcher's continuously carryout performance upgradation by considering a mixture of QoS parameters thereby enhancing the cloud performance. In this regard, we had introduced an optimal and ideal task scheduling algorithm and challenge the other prevailing algorithms in terms of QoS parameters like makespan and energy. The proposed algorithm increases the performance of the scheduling in cloud by making use of an enhanced meta-heuristic hybrid algorithm called Rider Cuckoo Optimization Algorithm (RCOA), which is an advanced version of the already existing Rider Optimization Algorithm. The proposed RCOA technique had produced an improvement of 5.6%, 4.36% and 2.27% for makespan and had reduced the energy to the tune of 16.8%, 19.18%, and 16.15% when compared with Particle Swarm Optimization (PSO), Cuckoo Search (CS), Rider Optimization Algorithm (ROA) algorithms respectively when used with 25 VMs. Also the proposed approach attains the objective of optimal scheduling swiftly and achieves convergence in a small duration of time.

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“…Various techniques have been developed to address this issue, for example, min-min, first come first serve (FCFS), max-min and minimum completion time techniques. Despite their adoption in many CC systems, some research has shown that these algorithms are unsuitable for scheduling large amounts of data [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm

Ravinder,

Vurukonda

2023

IJEECS

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<p>The cloud computing trend on the internet is vital as it allows data and applications to be managed over the internet instead of requiring personal devices. The job of users is scheduled in the resources of the cloud in order to improve performance. Schedu ling tasks is an non - deterministic polynomial (NP) - hard problem, as it may have multiple solutions. Various researchers have proposed different load balancing and job scheduling algorithms to optimize the scheduling process in cloud environments, each with disadvantages. Therefore, this research proposes a novel hybrid load balancing and scheduling of tasks by the whale optimization algo rithm (WOA) and seagull optimization algorithm (SOA) in the cloud. This hybrid proposed whale - seagull optimization algorithm (WSOA) optimizes task scheduling in the cloud b y reducing processing time, response, and execution time, maximizing central processing unit (CPU) utilization, memory utilization, throughput, reliability, and balancing the load. The algorithm is simulated using the CloudSim toolkit package. As compared with existing approaches, simulation results showed better performance in terms of response time, processing time, execution time, CPU utilization, memory utilization, throughput, and reliability and is analyzed by comparing with the harries hawks optimiza tion (HHO), hybrid dragonfly and firefly algorithm (ADA), spider monkey algorithm (SMA) and bird swarm optimization (BSO).</p>

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OLOA: Based Task Scheduling in Heterogeneous Clouds

Cited by 26 publications

References 18 publications

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

RCOA Scheduler: Rider Cuckoo Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm

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