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The Apache Hadoop with cloud had become an emerging and popular service. Irrespective of its huge dominance in large scale data processing, it has challenges yet to be addressed. The primary challenges in yarn scheduler are the abilities to automate and control the resource allocation to different workloads in order to meet the deadline-based Service Level Agreement (SLA) in the cloud environment with optimal energy consumption. Our study with the Hadoop YARN addresses this problem in a controlled homogeneous environment. In cloud datacenters, heterogeneity had become a normal phenomenon. Hence, this paper proposes the problem of energy-aware heterogeneous Hadoop Yarn cloud with deadline based SLA. We proposed a SLA-Aware Green Scheduling (SAGS), a Dynamic Voltage/Frequency Scaling (DVFS) based approach along with SLA-Aware scheduling algorithm in the heterogeneous environment. We evaluated SAGS by using benchmark datasets and, compared its performance with previously proposed solutions. Our observation with experimental results shows that, the proposed approach outperforms existing approaches.

Aims: Protect Patient Healthcare Records. Background: The adaptability of the digital healthcare system is a major factor in its recent rise in popularity. Utilizing the digital healthcare system has resulted in an ever-increasing number of healthcare apps. The Internet of Medical Things-(IoMT) is a newly emerging digital healthcare system using various biomedical sensors and the cutting-edge capabilities of wireless systems and cloud computing. Since IoMT can exchange data between various connecting nodes thanks to the combination of other technologies, security and energy consumption provide the greatest challenge to the IoMT infrastructure Objective: Reduce the cost of communication in order to strengthen defenses against unauthorized access and increase energy efficiency Method: This study provides a protocol for protecting patients' medical records called the request-type-based energy-aware framework (Re-EAF). The primary goal is to reduce the cost of communication in order to strengthen defenses against unauthorized access and increase energy efficiency. An identifying unit called a & quot; request-type" has been proposed. The proposed method avoids treating all requests the same by instead characterizing them based on the identified criteria and characteristics. Using Constrained Application Protocol (CoAP), remote patient monitoring can increase the safety of gathered data. Result: Using Constrained Application Protocol (CoAP), remote patient monitoring can increase the safety of gathered data. Using a software-defined networking (SDN) framework, our research ensures that data and requests are sent and received as effectively and efficiently as possible while conserving energy. Conclusion: In this research, the transmitted healthcare data is encrypted via cipher Block-chaining. The experimental study demonstrates that the suggested Re-EAF consumes less energy while producing a higher throughput than conventional methods.

Cloud services are offered to consumers based on Service Level Agreements (SLAs) signed between Cloud Service Provider (CSP) and consumer. Due to on-demand provisioning of resources there is exponential growth of cloud consumers. Job scheduling is one of the areas that has attracted researchers to improve performance of cloud management system. Along with the on premise infrastructure, Small and Medium Enterprises (SMEs) also depend on public cloud infrastructure (leading to hybrid cloud) for seamless continuity of their businesses. In this context, ensuring SLAs and effective management of hybrid cloud resources are major challenging issues to be considered. Hence, there is a need for an effective scheduling algorithm which considers multiple objective functions like SLA (deadline), cost and energy while making scheduling decisions. Most of the state of the art schedulers in hybrid cloud environment considered single objective function. However, in real world, it is inadequate for scheduling effectiveness. To overcome this problem, we proposed an integrated framework which ensures SLAs (deadline), cost effectiveness and energy efficiency with an underlying scheduling algorithm known as SCE-TS. This algorithm is evaluated with different workloads and SLAs using a cloud platform. The empirical study revealed that the proposed framework improves scheduling efficiency in terms of meeting SLAs, cost and energy efficiency. It is evaluated and compared with the state of the art and found to be effective in making scheduling decisions in cloud environment.