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To Convalesce Task Scheduling in a Decentralized Cloud Computing Environment

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  • Arun Sangwan
  • Gaurav Kumar
  • Sorabh Gupta

Abstract

With the contempo slump and the immutable crush to deliver more services at a lower cost. Delivery model offers lower cost, and can make quick construction services. IT economics are changing rapidly, and large companies, in particular, looking for new ways to secure capital at a lower cost to maintain the viability of the company. Task scheduling problems are first class related to the overall efficiency of cloud computing facilities. Most developed algorithms for automation planning approach in one parameter of quality of service (QoS). However, if we consider more than one QoS parameter then the problem becomes more challenging. To address the problem, we need to introduce a scheduling strategy for multi-workflows with multiple QoS constrained for cloud computing. We need to introduce an optimized algorithm for task scheduling in cloud computing and its implementation. Furthermore, Load Balancing is a method to distribute workload across one or more servers, network interfaces, hard drives, or other computing resources. Use these components with the load balancing, on the one chamber, grow well in redundancy.

Suggested Citation

  • Arun Sangwan & Gaurav Kumar & Sorabh Gupta, 2016. "To Convalesce Task Scheduling in a Decentralized Cloud Computing Environment," Review of Computer Engineering Research, Conscientia Beam, vol. 3(1), pages 25-34.
    • Handle: RePEc:pkp:rocere:v:3:y:2016:i:1:p:25-34:id:1443
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    Cited by:

    1. Leila Hosseini & Shaojie Tang & Vijay Mookerjee & Chelliah Sriskandarajah, 2020. "A Switch in Time Saves the Dime: A Model to Reduce Rental Cost in Cloud Computing," Information Systems Research, INFORMS, vol. 31(3), pages 753-775, September.
    2. Muhammad Shuaib Qureshi & Muhammad Bilal Qureshi & Muhammad Fayaz & Wali Khan Mashwani & Samir Brahim Belhaouari & Saima Hassan & Asadullah Shah, 2020. "A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems," International Journal of Distributed Sensor Networks, , vol. 16(8), pages 15501477209, August.

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