With the exponential growth of the Industrial Internet of Things (IIoT), multiple outlets are constantly producing a vast volume of data. It is unwise to locally store all the raw data in the IIoT devices since the energy and storage spaces of the end devices are strictly constrained. self-organization and short-range Internet of Things (IoT) networking also support outsourced data and cloud computing, independent of the distinctive resource constraint properties. For the remainder of the findings, there is a sequence of unfamiliar safeguards for IoT and cloud integration problems. The delivery of cloud computing is highly efficient, storage is becoming more and more current, and some groups are now altering their data from in house records Cloud Computing Vendors' hubs. Intensive IoT applications for workloads and data are subject to challenges while utilizing cloud computing tools. In this report, we research IoT and cloud computing and address cloud-compatible problems and computing techniques to promote the stable transition of IoT programs to the cloud.

https://journal.qubahan.com/index.php/qaj/article/download/36/21

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

The distributed energy system (DES) architecture is subject to confusion about renewable energy limits, primary energy supply and energy carriers' costs. For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential. The participation of end-users could influence the management of the system and the volatility of energy prices. By delivering auxiliary services using demand side-resource to increase system reliability, robust planning, constraint control and scheduling, consumers may support grid operators. The optimized approach to managing energy resources enhances demand response to renewable energy sources integrally, controls the demand curve with load versatility as the system requires it. The opportunity to adjust/regulate the charging profile by choosing a particular device. This article discusses a literature and policy analysis that looks at the role of energy management system aggregators and the end-users participating in subsidiary systems within Smart Grid programmers and technologies. In the implementation of aggregators for energy management systems, the objective is to understand the patterns, threats, obstacles and potential obstacles.

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Energy Management for Internet of Things via Distributed Systems

The cloud is the best method used for the utilization and organization of data. The cloud provides many resources for us via the internet. There are many technologies used in cloud computing systems; each one uses a different kind of protocols and methods. Many tasks can execute on different servers per second, which cannot execute on their computer. The most popular technologies used in the cloud system are Hadoop, Dryad, and another map reducing framework. Also, there are many tools used to optimize the performance of the cloud system, such as Cap3, HEP, and Cloudburst. This paper reviews in detail the cloud computing system, its used technologies, and the best technologies used with it according to multiple factors and criteria such as the procedure cost, speed cons and pros. Moreover, A comprehensive comparison of the tools used for the utilization of cloud computing systems is presented.

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Comparison Among Cloud Technologies and Cloud Performance

Cloud computing is the requirement based on clients and provides many resources that aim to share it as a service through the internet. For optimal use, Cloud computing resources such as storage, application, and other services need managing and scheduling these services. The principal idea behind the scheduling is to minimize loss time, workload, and maximize throughput. So, the scheduling task is essential to achieve accuracy and correctness on task completion. This paper gives an idea about various task scheduling algorithms in the cloud computing environment used by researchers. Finally, many authors applied different parameters like completion time, throughput, and cost to evaluate the system.

https://turcomat.org/index.php/turkbilmat/article/download/612/413

Task Scheduling Algorithms in Cloud Computing: A Review

Cloud computing, data mining, and big online data are discussed in this paper as hybridization possibilities. The method of analyzing and visualizing vast volumes of data is known as the visualization of data mining. The effect of computing conventions and algorithms on detailed storage and data communication requirements has been studied. When researching these approaches to data storage in big data, the data analytical viewpoint is often explored. These terminology and aspects have been used to address methodological development as well as problem statements. This will assist in the investigation of computational capacity as well as new knowledge in this area. The patterns of using big data were compared in about fifteen articles. In this paper, we research Big Data Mining Approaches in Cloud Systems and address cloud-compatible problems and computing techniques to promote Big Data Mining in Cloud Systems.

https://journal.qubahan.com/index.php/qaj/article/download/46/30

Comprehensive Survey of Big Data Mining Approaches in Cloud Systems

Smart phones are becoming essential in our lives, and Android is one of the most popular operating systems. Android OS is wide-ranging in the mobile industry today because of its opensource architecture. It is a wide variety of applications and basic features. App users tend to trust Android OS to secure data, but it has been shown that Android is more vulnerable and unstable. Identification of Android OS malware has become an emerging research subject of concern. This paper aims to analyze the various characteristics involved in malware detection. It also addresses malware detection methods. The current detection mechanism utilizes algorithms such as Bayesian algorithm, Ada grad algorithm, Naïve Bayes algorithm, Hybrid algorithm, and other algorithms for machine learning to train the sets and find the malware. Review Article Omer et al.; AJRCOS, 7(4): 59-69, 2021; Article no.AJRCOS.68044 60

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Efficiency of Malware Detection in Android System: A Survey

The operating system (OS) is now being widely used in many of the various modern applications in recent years, ranging from diabetic monitoring to other strategic planning. The main function of any OS is to control and coordinate all hardware resources among the commands of the same/different user, which increases the efficiency of advanced comprehensive applications. In an autonomous computer system, the CPU is one of the important resources to manage and process all activities which require scheduling techniques on a processor. Since the early days of computing and other multi-programming OS, various studies have been assigned to CPU scheduling techniques based on processes management and performance evaluation. Thus, outlining the many issues related to scheduling methodologies and the weaknesses that need to be addressed. This review paper is organized based on two distinct perspectives: the implement strategies of CPU scheduling technique and criteria-based measures used, which assess how the strategies are analyzed and used under performance evaluation.

/pdf/cpu-scheduling-techniques-a-review-on-novel-approaches-295meyow84.pdf

CPU Scheduling Techniques: A Review on Novel Approaches Strategy and Performance Assessment

Distributed systems performance is affected significantly by cache coherence protocols due to their role in data consistency maintaining Also, cache coherent protocols have a great task for keeping the interconnection of caches in a multiprocessor environment Moreover, the overall performance of distributed shared memory multiprocessor system is influenced by the used cache coherence protocol type The major challenge of shared memory devices is to maintain the cache coherently Therefore, in past years many contributions have been presented to address the cache issues and to improve the performance of distributed systems This paper reviews in a systematic way a number of methods used for the cache-coherent protocols in a distributed system

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Cache Coherence Protocols in Distributed Systems

Through the huge growth of heavy computing applications which require a high level of performance, it is observed that the interest of monitoring operating system performance has also demanded to be grown widely. In the past several years since OS performance has become a critical issue, many research studies have been produced to investigate and evaluate the stability status of OSs performance. This paper presents a survey of the most important and state of the art approaches and models to be used for performance measurement and evaluation. Furthermore, the research marks the capabilities of the performance-improvement of different operating systems using multiple metrics. The selection of metrics which will be used for monitoring the performance depends on monitoring goals and performance requirements. Many previous works related to this subject have been addressed, explained in details, and compared to highlight the top important features that will very beneficial to be depended for the best approach selection.

https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/download/516/328

Lailan M. Haji

Papers

Comparison Among Cloud Technologies and Cloud Performance

Efficiency of Malware Detection in Android System: A Survey

CPU Scheduling Techniques: A Review on Novel Approaches Strategy and Performance Assessment

Cache Coherence Protocols in Distributed Systems

A State of Art Survey for OS Performance Improvement