Effective Allocation of Resources and Task Scheduling in Cloud Environment using Social Group Optimization

Praveen, S. Phani; Rao, K. Ram Mohan; Janakiramaiah, B.

doi:10.1007/s13369-017-2926-z

Cited by 52 publications

(23 citation statements)

References 19 publications

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“…By applying the delayed scheduling by enhancing the data locality in MapReduce, Radha and Rao [28] have shown the performance improvement in slot Utilization and Hadoop cluster. Praveen et al [29] proposed an effective resource allocation using a social group optimization algorithm in conjunction with the scheduling of tasks by application of shortest-job-first scheduling technique for minimizing the makespan time and maximizing throughput.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Deshmukh

Rao

Shabaz

2021

Security and Communication Networks

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Modern big data applications tend to prefer a cluster computing approach as they are linked to the distributed computing framework that serves users jobs as per demand. It performs rapid processing of tasks by subdividing them into tasks that execute in parallel. Because of the complex environment, hardware and software issues, tasks might run slowly leading to delayed job completion, and such phenomena are also known as stragglers. The performance improvement of distributed computing framework is a bottleneck by straggling nodes due to various factors like shared resources, heavy system load, or hardware issues leading to the prolonged job execution time. Many state-of-the-art approaches use independent models per node and workload. With increased nodes and workloads, the number of models would increase, and even with large numbers of nodes. Not every node would be able to capture the stragglers as there might not be sufficient training data available of straggler patterns, yielding suboptimal straggler prediction. To alleviate such problems, we propose a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating stragglers without moving data to a centralized location. The proposed framework shares information among the various models, allowing us to use larger training data and bring training time down by avoiding data transfer. We rigorously evaluate the proposed method on various datasets with high accuracy results.

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Section: Related Workmentioning

confidence: 99%

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Deshmukh

Rao

Shabaz

2021

Security and Communication Networks

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“…The computers' advancement rather than its improved computing ability in providing services as a utility like water usage and electricity usage bill was started long back in the year 1970s [28]. High technological advancements both at hardware and software level shifted the computing paradigm from single PC usage to considerable servers used for computing like utility, grid, and cloud with improved performance, reliability, scalability, and autonomy in varied services deployment, storage, processing.…”

Section: Related Workmentioning

confidence: 99%

Optimize the Cost of Resources in Federated Cloud by Collaborated Resource Provisioning and Most Cost-effective Collated Providers Resource First Algorithm

Kumar¹,

Prakash²

2021

IJACSA

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Cloud Computing works as the best solution for providing many of its services for cloud consumer agents with different requests for huge computational VM's with large storage capacity. The instance requests of cloud consumers will dynamically change as per their usage of application requirements with the demand for business growth, and singlevendor cloud becomes a constraint to satisfy these needs of the cloud consumers. Federated Cloud can contribute its solution approaches to meet these dynamic needs of cloud consumer requests of resource instances. The interoperability of clouds was made realistic with cloud federation. This paper provides an optimized solution approach where a set of collaborated cloud providers will provide services to satisfy consumer agents' multiple requests. It presents the two-phase collaborated resource provisioning (CCRP) approach and Most Cost-Effective Collated Providers Resources First (MCECPRF) algorithm. The algorithm's efficiency has been tested with specific data set for optimizing the cost for cloud consumer agents and analyzes the cancellation of requests, decision time for provisioning for different VM configurations within specific time slots.

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“…It is mandatory to allocate the resources and using good and suitable scheduling algorithm to complete the given task in the cloud in minimal amount of time. In [23], S.Phani Praveenet. al, proposed a method with two phases as resource allocation and tasks scheduling.…”

Section: Data Classification Properties Access Control Content Storagementioning

confidence: 99%

Enhanced Data security through Deep Data Classification in the Cloud Computing

2020

IJETER

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Data is a valued asset especially when data moving to the cloud. Cloud computing model attracts different users because of its various benefits such as scalability, simplicity, expense reduction, and high resource resistance. Cloud computing is a wide-ranging architecture based on different models for providing different software services and hardware services. There may be different types of data and the degree of protection needed for all the data is also may vary. Data privacy and data security is the major area of research and development in the cloud computing. Privacy protection and data leakage became essential thing for so many organizations moving on to cloud. Several classification methods that are expressed various parameters based on different dimensions for providing the data security can be based on the level and are required protection. But, the latest approaches introduced by the cloud, related to distributed resources, multi-tenancy idea, computation outsourcing, high dynamism of the model, data warehousing and the non-transparent style of cloud increase the security and privacy concerns and makes building and maintaining trust among cloud service providers and consumers is a serious security issue. In this paper, we proposes a new approach to improve security of data in cloud computing. It proposes a classification model to classify data before being presented into a suitable encryption system based on the category. Because data in cloud has not the same sensitivity level, encrypting these different types of data with the same algorithms can lead to a lack of security or resources. By this scheme, we can achieve high security for our data, and can optimize the computation cost and resource consumption while ensuring data confidentiality.The efficiency of the proposed classification scheme is analysed with the sample dataset collected.

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Effective Allocation of Resources and Task Scheduling in Cloud Environment using Social Group Optimization

Cited by 52 publications

References 19 publications

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Optimize the Cost of Resources in Federated Cloud by Collaborated Resource Provisioning and Most Cost-effective Collated Providers Resource First Algorithm

Enhanced Data security through Deep Data Classification in the Cloud Computing

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