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I think I had assumed that everyone would have some level of understanding of what cloud computing is. I started answering the question with that assumption in my mind. But after a few sentences, I realized I wasn't making any sense to her. I paused and in the back of my mind, I was looking for something that makes her understand it. Fortunately, something struck my mind. I told her that cloud computing is like using a laundromat versus buying your own washer-dryer. That was it. She seemed satisfied with the analogy and left.
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The event was over and I got back to my normal routine but I was still thinking of the analogy I gave to that lady. I thought of taking the analogy a bit seriously and started building it. Here is my laundromat story.
There are many other attributes associated with cloud computing such as fault tolerance, lower cost of ownership (pay-per-use), low time to market, etc. I think the analogy gives you enough idea to relate to these attributes as well.
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Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey.
The idea of Fog computing or Edge Computing is to extend the cloud nearer to the Internet of Things (IOT) devices. Through the internet of things (IOT), we can generate a numerous volume and variety of data. With the increase in the number of internet connected devices, the increased demand of real-time, low latency services, data security are the biggest challenges for the cloud computing framework. In order to overcome above challenges, Fog Computing came into picture. The computing reduces the predictable latency in the latency-sensitive of IoT applications such as healthcare services which is primary objective of this computing. The paper argues that the Fog Computing is the best platform solutions towards this goal for a number of critical IoT services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuator Networks (WSANs).
In cloud computing, load balancing is essential since it guarantees effective resource utilisation and reduces response times. It effectively distributes incoming workload across available servers. Load balancing involves dividing tasks among multiple systems or resources over the internet. By distributing traffic and workloads, load balancing ensures that no server or computer is overloaded, underutilized, or idle in a cloud computing environment. To enhance overall performance in the cloud, it optimises a number of variables, including execution time, response time, and system stability. To increase the effectiveness and reliability of cloud services, load balancing evenly distributes traffic, workloads, and computing resources across the environment. The proposed method, Enhanced Dynamic Load Balancing for Cloud Computing, adjusts load distribution and maintains a balanced system by considering variables like server capacity, workload distribution, and system load. By incorporating these factors and employing adaptive threshold adjustment, this approach optimizes resource allocation and enhances system performance. Experimental research shows that the proposed new novel approach is more effective and efficient than current load balancing techniques. In this context of cloud computing, this ground-breaking method offers a workable substitute for dynamic load balancing.
Martin Randles, David Lamb, A. Taleb-Bendiab, A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing, 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops DOI:
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European Alliance for Innovation (EAI) is a non-profit organization and a professional community empowering global research and innovation, promoting cooperation between European and International ICT communities around the globe.
Cloud computing, the Internet of Things (IoT), and big data are three significant technological trends affecting the world's largest corporations. This book discusses big data, cloud computing, and the IoT, with a focus on the benefits and implementation problems. In addition, it examines the many structures and applications pertinent to these disciplines. Also, big data, cloud computing, and the IoT are proposed as possible study avenues. 152ee80cbc
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