A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling

Gutiérrez-García, J. Octavio; Sim, Kwang Mong

doi:10.1016/j.future.2012.01.005

Cited by 93 publications

(51 citation statements)

References 28 publications

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“…Goal of the scheduling algorithms are achieving better performance. The concept of scheduling the bag-of-task (BoT) application was proposed [23] in agent based scheduling concept. In this paper 14 scheduling concepts are executed concurrently.…”

Section: Related Workmentioning

confidence: 99%

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Muthu¹,

Enoch²

2017

IJIES

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Cloud computing is a powerful computing technology, which render a flexible services at anywhere to the user. One of the major issue of cloud computing was scheduling. In this work, a bacterial foraging optimization algorithm with genetic algorithm (GABFO) was combined to find out trustworthy scheduling problems in cloud workflow. Generally job scheduling and resource allocation in cloud is a tedious optimization problem at the time of considering QoS requirements. Lot of existing works under scheduling only concentrates on cost optimization and deadline problems, and it ignores the importance of reliability, availability and robustness. The main subscription of my work is to state a new optimized approach to schedule the jobs efficiently and allocate the resources in a efficient manner by introducing GABFO algorithm. Experiments were done in PSO, Genetic, BFO and then Genetic and BFO was combined to generate a hybrid optimized result, and the work was compared with above mentioned algorithms. The algorithms were executed for 52 iterations and totally 10 runs are calculated. The size of the job as well as virtual machines was varied for each iteration to calculate performance variation. We considered the optimization parameter as time and cost, and throughput. The work is implemented in cloudsim to create a simulated cloud environment. Final result shows better performance and maximum resource utilization in GABFO when compared to PSO, GA, BFO.

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Section: Related Workmentioning

confidence: 99%

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Muthu¹,

Enoch²

2017

IJIES

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“…Other efforts made in literature in these areas of resource scheduling include: Greedy Particle Swarm Optimization (GPSO) [39], Task Length and User Priority (ie. Credit Based Scheduling Algorithm) [40], Cost based scheduling [41], Energy efficient optimization methods [42], Activity based costing [43], [44], Reliability Factor Based [45], Context aware scheduling [46],Dynamic slot based scheduling [47], [48], Multi-Objective Tasks Scheduling Algorithm [49], Public Cloud Scheduling Algorithm with Load Balancing [50], Agent-based elastic Cloud bag-of-tasks concurrent scheduling [51], Analytic hierarchy process (task scheduling and resource allocation) [52], Swarm scheduling [53], Profitdriven scheduling [54], Dynamic trusted scheduling [55], Community-aware scheduling algorithm [56], Adaptive energy-efficient scheduling [57], grid, cloud and workflow scheduling [58]. In these algorithms, job/task length and priority are mostly the parameters analyzed.…”

Section: B Related Research Effortsmentioning

confidence: 99%

Analysis of Cloud Network Management Using Resource Allocation and Task Scheduling Services

Okafor¹,

Ugwoke²,

Obayi³

et al. 2016

ijacsa

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Abstract-Network failure in cloud datacenter could result from inefficient resource allocation; scheduling and logical segmentation of physical machines (network constraints). This is highly undesirable in Distributed Cloud Computing Networks (DCCNs) running mission critical services. Such failure has been identified in the University of Nigeria datacenter network situated in the south eastern part of Nigeria. In this paper, the architectural decomposition of a proposed DCCN was carried out while exploring its functionalities for grid performance. Virtualization services such as resource allocation and task scheduling were employed in heterogeneous server clusters. The validation of the DCCN performance was carried out using trace files from Riverbed Modeller 17.5 in order to ascertain the influence of virtualization on server resource pool. The QoS metrics considered in the analysis are: the service delay time, resource availability, throughput and utilization. From the validation analysis of the DCCN, the following results were obtained: average throughput (bytes/Sec) for DCCN = 40.00%, DCell = 33.33% and BCube = 26.67%. Average resource availability response for DCCN = 38.46%, DCell = 33.33%, and BCube = 28.21%. DCCN density on resource utilization = 40% (when logically isolated) and 60% (when not logically isolated). From the results, it was concluded that using virtualization in a cloud DataCenter servers will result in enhanced server performance offering lower average wait time even with a higher request rate and longer duration of resource use (service availability). By evaluating these recursive architectural designs for network operations, enterprises ready for Spine and leaf model could further develop their network resource management schemes for optimal performance.

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“…The problem is even more complex and challenging when the virtualized clusters are used to execute a large number of tasks in a Cloud computing platform [21,22]. For this reason, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks in Grid and Cloud environments [23]. Recently many papers are published which used evolutionary algorithms like genetic, ant colony, bee colony and PSO (Particle Swarm Optimization) for optimization problems [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

Milani¹,

Navin²

2015

IJITCS

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Abstract-Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).

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A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 93 publications

References 28 publications

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Analysis of Cloud Network Management Using Resource Allocation and Task Scheduling Services

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

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