Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization

Lakra, Atul Vikas; Yadav, Dharmendra Kumar

doi:10.1016/j.procs.2015.04.158

Cited by 137 publications

(67 citation statements)

References 9 publications

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“…They also tried to use crossover and mutation functions of the genetic algorithm along with the PSO model. Lakro et al [18] investigated various variables and their optimization in cloud computing environments. They tried to present a multi-variable optimization algorithm for scheduling and improving performance of data centers.…”

Section: Review Of Literaturementioning

confidence: 99%

A New Task Scheduling Algorithm using Firefly and Simulated Annealing Algorithms in Cloud Computing

Fanian¹,

Bardsiri²,

Shokouhifar³

2018

ijacsa

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Abstract-Task scheduling is a challenging and important issue, which considering increases in data sizes and large volumes of data, has turned into an NP-hard problem. This has attracted the attention of many researchers throughout the world since cloud environments are in fact homogenous systems for maintaining and processing practical applications needed by users. Thus, task scheduling has become extremely important in order to provide better services to users. In this regard, the present study aims at providing a new task-scheduling algorithm using both firefly and simulated annealing algorithms. This algorithm takes advantage of the merits of both firefly and simulated annealing algorithms. Moreover, efforts have been made in regards to changing the primary population or primary solutions for the firefly algorithm. The presented algorithm uses a better primary solution. Local search was another aspect considered for the new algorithm. The presented algorithm was compared and evaluated against common algorithms. As indicated by the results, compared to other algorithms, the presented method performs effectively better in reducing to make span using different number of tasks and virtual machines.

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Section: Review Of Literaturementioning

confidence: 99%

A New Task Scheduling Algorithm using Firefly and Simulated Annealing Algorithms in Cloud Computing

Fanian¹,

Bardsiri²,

Shokouhifar³

2018

ijacsa

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show abstract

“…Hence, one of the main problems of cloud computing is the interference between the virtual machines and their load imbalance of the SLA, and the effective placement of virtual machines greatly reduces or increases the profitability of the cloud computing of virtual machines. In [10], researchers have only considered the strategy of placing virtual machines but have not considered the quality requirements of user applications in load balancing of virtual machines [11]. They have provided a framework for load balancing strategies in the cloud computing environment and have proposed a method to evaluate the resource allocation strategies in the cloud computing environment, seeking to focus on optimizing the awareness and compatibility of network load balancing strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Sadeghi¹

2018

ijacsa

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Abstract-Load balancing, reliability, and traffic are among the service-oriented issues in software engineering, and cloud computing is no exception to this rule and has put many challenges ahead of experts in this field. Considering the importance of the load balancing process in cloud computing, the purpose of this paper is to provide an appropriate solution for load balancing load in complex cloud systems using an adaptive fuzzy neural system. This system consists of four layers, and a particular operation is performed on each layer. The results of the experiments show that the system has better performance in the criteria mentioned above (balancing, traffic and reliability).

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“…In [22], Lakra and Yadav, introduced a multi-objective task scheduling algorithm for mapping tasks to VMs via non-dominated sorting after quantifying the Quality of Service values of tasks and VMs. The proposed algorithm mainly considered improving the throughput of the datacenter and reducing the cost without violating the Service Level Agreement (SLA) for an application in cloud SaaS environment.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

2017

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Cloud computing is a ubiquitous network access model to a shared pool of configurable computing resources where available resources must be checked and scheduled using an efficient task scheduler to be assigned to clients. Most of the existing task schedulers, did not achieve the required standards and requirements as some of them only concentrated on waiting time or response time reduction or even both neglecting the starved processes at all. In this paper, we propose a novel hybrid task scheduling algorithm named (SRDQ) combining Shortest-JobFirst (SJF) and Round Robin (RR) schedulers considering a dynamic variable task quantum. The proposed algorithms mainly relies on two basic keys the first having a dynamic task quantum to balance waiting time between short and long tasks while the second involves splitting the ready queue into two sub-queues, Q1 for the short tasks and the other for the long ones. Assigning tasks to resources from Q 1 or Q 2 are done mutually two tasks from Q 1 and one task from Q 2 . For evaluation purpose, three different datasets were utilized during the algorithm simulation conducted using CloudSim environment toolkit 3.0.3 against three different scheduling algorithms SJF, RR and Time Slice Priority Based RR (TSPBRR) Experimentations results and tests indicated the superiority of the proposed algorithm over the state of art in reducing waiting time, response time and partially the starvation of long tasks.

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Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization

Cited by 137 publications

References 9 publications

A New Task Scheduling Algorithm using Firefly and Simulated Annealing Algorithms in Cloud Computing

A New Task Scheduling Algorithm using Firefly and Simulated Annealing Algorithms in Cloud Computing

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling technique

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