6,873 research outputs found

    ERAM2 - ENERGY BASED RESOURCE ALLOCATION WITH MINIMUM RECKON AND MAXIMUM RECKON

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    The emerging field of cloud computing has flexibility and dominant computational architecture that offers ubiquitous services to users. It is different from traditional architecture because it accommodates resources in a unified way. Due to rapid growth in demands for providing the resources and computation in cloud environments, Resource allocation is considered as primary issues in performance, efficiency, and cost.  For the provisioning of resource, Virtual Machine (VMs) is employed to reduce the response time and executing the tasks according to the available resources.  The users utilize the VMs based on the characteristics of the tasks for effective usage of resources. This helps in load balancing and avoids VMs being in an idle state. Several resource allocation techniques are proposed to maximize the utility of physical resource and minimize the consuming cost of Virtual Machines (VMs). This paper proposes an Energy-Based Resource Allocation with Minimum Reckon and Maximum Reckon (ERAM2); which achieves an efficient scheduling by matching the user tasks on Resource parameters like Accessibility, Availability, Cost, Reliability, Reputation, Response time, Scalability and Throughput in the terms of Maximum Reckon and Minimum Reckon. This paper proposes an Ant Colony - Maximum Reckon and Minimum Reckon (AC-MRMR) method to consolidate all the available resource based on the pheromone value; the score is calculated for each pheromone value. When the score value exceeds Threshold limit then task migration process is carried out for optimized resource allocation of tasks

    Towards efficient resource provisioning in MapReduce

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    The paper presents a novel approach and algorithm with mathematical formula for obtaining the exact optimal number of task resources for any workload running on HadoopMapReduce. In the era of Big Data, energy efficiency has become an important issue for the ubiquitous Hadoop MapReduce framework. However, the question of what is the optimal number of tasks required for a job to get the most efficient performance from MapReduce still has no definite answer. Our algorithm for optimal resource provisioning allows users to identify the best trade-off point between performance and energy efficiency on the runtime elbow curve fitted from sampled executions on the target cluster for subsequent behavioral replication. Our verification and comparison show that the currently well-known rules of thumb for calculating the required number of reduce tasks are inaccurate and could lead to significant waste of computing resources and energy with no further improvement in execution time

    Centralized Cloud Service Providers in Improving Resource Allocation and Data Integrity by 4G IoT Paradigm

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    Due to the expansion of Internet of Things (IoT), the extensive wireless, and 4G networks, the rising demands for computing calls and data communication for the emergent EC (EC) model. By stirring the functions and services positioned in the cloud to the user proximity, EC could offer robust transmission, networking, storage, and transmission capability. The resource scheduling in EC, which is crucial to the accomplishment of EC system, has gained considerable attention. This manuscript introduces a new lighting attachment algorithm based resource scheduling scheme and data integrity (LAARSS-DI) for 4G IoT environment. In this work, we introduce the LAARSS-DI technique to proficiently handle and allot resources in the 4G IoT environment. In addition, the LAARSS-DI technique mainly relies on the standard LAA where the lightning can be caused using the overall amount of charges saved in the cloud that leads to a rise in electrical intensity. Followed by, the LAARSS-DI technique designs an objective function for the reduction of cost involved in the scheduling process, particularly for 4G IoT environment. A series of experimentation analyses is made and the outcomes are inspected under several aspects. The comparison study shown the improved performance of the LAARSS-DI technology to existing approaches

    Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality

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    The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can decrypt and reconstruct data into its original format. The implementation results depicts that the proposed scheme outperforms existing systems in various aspects, making it a reliable solution for cloud service providers to enhance data security while reducing storage costs

    A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing

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    Abstract: For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the user's resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the user's budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this method's performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario
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