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SECURE AND ENERGY AWARE TASK SCHEDULING IN CLOUD USING DEEP LEARNING AND CRYPTOGRAPHIC TECHNIQUES


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1 Tiruppur Kumaran College for Women, India
 

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Cloud Computing is one amid emerging technology greatly necessitated aiding computing on demand services by letting users for subsequent pay-per-use-on-demand scheme. The service cloud providers have non insignificant impacts on ideal resources exploitation and cost benefit in case of Energy aware task scheduling in cloud. Presently Minimum Migration Time (MMT) policy was employed for Virtual Machines migration and offering an energy proficient cloud service. Nonetheless prevailing methodologies never concentrated on any security for cloud. The confidential data protection is highly demanded now-a-days due to increasing users for cloud computing. Hence robust security system for cloud computing is greatly demanded by various cloud researchers. An enhanced approach is presented for mitigating these concerns in which Artificial Bee Colony Optimization (ABC) is deployed for queuing all incoming tasks into multi-level. Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms are checked initially. The SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms integration is done for evading job starva¬tion probability in SJF-MMBF. The over utilized host detection is achieved through Adaptive Neuro Fuzzy Inference System (ANFIS) and Virtual Machines (VMs) migration is attained via Minimum Migration Time (MMT) policy from over-utilized hosts to other hosts for energy consumption reduction. Also, security in cloud is greatly achieved by presenting a novel cryptographic technique. There are several advantages such as sharing hardware, software and losing data fear deficiency and due to which current demand for cloud computing is greatly necessitated. The significant information on cloud is maintained by business person, hence data security is vital concern as there is hacking and unauthorized access probability. Here cloud data encryption is attained through elliptic curve cryptography, hence successful and secure storage on cloud is accomplished thereby. The authorized user might access cloud data via key in the suggested system.
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Abstract Views: 461

PDF Views: 237




  • SECURE AND ENERGY AWARE TASK SCHEDULING IN CLOUD USING DEEP LEARNING AND CRYPTOGRAPHIC TECHNIQUES

Abstract Views: 461  |  PDF Views: 237

Authors

S Rekha
Tiruppur Kumaran College for Women, India
C Kalaiselvi
Tiruppur Kumaran College for Women, India

Abstract


Cloud Computing is one amid emerging technology greatly necessitated aiding computing on demand services by letting users for subsequent pay-per-use-on-demand scheme. The service cloud providers have non insignificant impacts on ideal resources exploitation and cost benefit in case of Energy aware task scheduling in cloud. Presently Minimum Migration Time (MMT) policy was employed for Virtual Machines migration and offering an energy proficient cloud service. Nonetheless prevailing methodologies never concentrated on any security for cloud. The confidential data protection is highly demanded now-a-days due to increasing users for cloud computing. Hence robust security system for cloud computing is greatly demanded by various cloud researchers. An enhanced approach is presented for mitigating these concerns in which Artificial Bee Colony Optimization (ABC) is deployed for queuing all incoming tasks into multi-level. Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms are checked initially. The SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms integration is done for evading job starva¬tion probability in SJF-MMBF. The over utilized host detection is achieved through Adaptive Neuro Fuzzy Inference System (ANFIS) and Virtual Machines (VMs) migration is attained via Minimum Migration Time (MMT) policy from over-utilized hosts to other hosts for energy consumption reduction. Also, security in cloud is greatly achieved by presenting a novel cryptographic technique. There are several advantages such as sharing hardware, software and losing data fear deficiency and due to which current demand for cloud computing is greatly necessitated. The significant information on cloud is maintained by business person, hence data security is vital concern as there is hacking and unauthorized access probability. Here cloud data encryption is attained through elliptic curve cryptography, hence successful and secure storage on cloud is accomplished thereby. The authorized user might access cloud data via key in the suggested system.

References