Comparative Analysis of Latest Task Scheduling Techniques in Cloud Computing environment

Anushree, B; Xavier, V. M. Arul

doi:10.1109/iccmc.2018.8487908

Cited by 16 publications

(14 citation statements)

References 6 publications

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“…Disadvantages: The primary drawback of the proposed method is that its lack of understanding in the virtual machine-based task scheduling. Anushree B. et al (2018) [7] exhibited that Task planning is an indispensable part in the field of the cloud condition. Client demands i.e assignments were booked to specific assets at a particular occasion of time.…”

Section: Related Workmentioning

confidence: 99%

“…A cloud supplier initially builds up a processing framework called cloud, where a couple of virtual machines are interconnected through this; the provider shapes the undertaking of the customers. Distributed computing is certifiably not a respectful model to offer the customer to a typical pool of configurable processing assets that can be promptly given and discharged low care effort or administration will consider the particular errand planning [7] of better execution registering approaches. Cloud load adjusting server allocates the heap at the period of growing the couple of CPUs or memories for their assets to scale up with the extended solicitations.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Task Scheduling in Cloud Computing: A Survey

Hamid¹

2019

IJRASET

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The increase of cloud computing is so exponential that it offers facts connection between special structures and devices. Due to this boom in connectivity and rapid utilization cloud network desires a statistics grid or computing grid comprising of different type of processing gadgets to perform the query this is despatched to the cloud network. This work provides a review on optimized undertaking scheduling in cloud computing environment. The main element of cloud computing is offering desirable response time for end users, that affords a primary impediment in achievement of cloud computing. All components should coordinate to deal with this mission. This can be handled through a suitable Task scheduling algorithm. So, there's a need of efficient mission scheduling method in implementation of cloud computing surroundings. Due to boom in era and increase in range of statistics facilities the venture dealing with ability of each information centres is foremost concern.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Task Scheduling in Cloud Computing: A Survey

Hamid¹

2019

IJRASET

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“…Hence detailed review enables the readers to decide a suitable approach for suggesting better schemes for scheduling the user's application. Rawat P. et al [45] studied the different policies used for efficient resource utilization. There are three primary resources in the computing paradigm, i.e., Storage, computing, and network resources.…”

Section: Existing Meta-heuristic Approachesmentioning

confidence: 99%

Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique

2019

IJITEE

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In the present era of technology and innovation, computing provides as a service. The service-oriented computing paradigm is a subset of utility and distributed computing paradigm. Hardware specification, platform, and application provide a service to the users in a different time zone. Resource utilization and request completion time is the prime focus of the service providers and consumers. In this work, we proposed a nature-inspired evolution and astrology science-based approach. The Big-Bang Big-Crunch optimization method based task allocation technique focuses on efficient resource utilization of IaaS (Infrastructure as a Service) cloud. We focused on three performance metrics, which may be user-oriented and service provider oriented. Performance metrics measure in terms of average resource utilization cost. Average Finish time, an average start time, and operational cost (customer-oriented) performance metrics are consumer-centric. The simulation performs using five cloud resources, six cloud configurations, and user-defined population size, and number iterations. The selection operator includes a fitness function that depends on estimated resource cost in the duration of execution. BB-BC cost-aware model provides optimal resource utilization, and minimum average finish time, and minimum average start time (ms) of cloudlets. The results exhibit that the proposed astrology, evolution based meta-heuristic approach outperforms the static (first come first serve (FCFS)), dynamic, and meta-heuristic (Genetic cost-aware, Genetic execution time aware and Particle Swarm optimization) approaches.

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“…Task scheduling is supported by optimization algorithm in which the key constraints that are taken in account are deadline and budget [6]. Tasks are scheduled for the purpose if reducing makespan and increasing the resource utilization [7]. Broker acts as intimidator who manages information of datacenter (DC) and The common advantages of using federated cloud are: economical, availability at diverse location, satisfied SLA and others [8].…”

Section: Introductionmentioning

confidence: 99%

Entropy Based Monotonic Task Scheduling and Dynamic Resource Mapping in Federated Cloud Environment

2022

IJIES

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Multiple cloud computing services are collectively managed as federated cloud. The growth of users into this federated cloud for accessing a variety of services has introduced challenges on resource utilization and load imbalance that consumes larger waiting time of users. To address these two issues, this paper proposes data center (DC) clustering, virtual machine (VM) clustering, resource mapping and task scheduling. Initially the DCs are clustered using region based fuzzy possibilistic C-means clustering (R-FPCM) algorithm. DC clustering is performed by gathering the information of data dependency, million instructions per second (MIPS), latency, storage, bandwidth and counts of VM. Depending on DC clustering, the VMs are clustered by multi-objective density-based spatial clustering based on the estimated capacity and bandwidth. In order to balance load, Markov chain is applied to predict future load and balance accordingly. An intermediate broker is employed to monitor the VM resources and map based on the user's task requirement. Service level agreement (SLA) is satisfied for every user by the broker and then fast 1 to N resource mapping algorithm is involved for mapping resources. This is followed by entropy-based monotonic scheduling algorithm that arranges user tasks in an order that is determined from task type, task size, task arrival time and deadline. The dynamic computation of entropy enables to improve scheduling with the current status of the system. The extended simulations of this proposed system are simulated in Cloudsim and the results are evaluated in terms of execution time, latency, resource utilization and response time and compared with the performance of Capacity based VM clustering algorithm. These simulation results show that the performance of the proposed system is much better than the existing approach.

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Comparative Analysis of Latest Task Scheduling Techniques in Cloud Computing environment

Cited by 16 publications

References 6 publications

Optimized Task Scheduling in Cloud Computing: A Survey

Optimized Task Scheduling in Cloud Computing: A Survey

Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique

Entropy Based Monotonic Task Scheduling and Dynamic Resource Mapping in Federated Cloud Environment

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