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Cloud computing is a promising technology for providing efficient virtualized compute and storage resources to users on a pay-per-usage model. Large-scale geographically distributed data centers have been established to support the increasing demand for cloud services. Execution of data-intensive workloads is a challenging problem especially when o...
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... Moreover, in this paper, the cost of moving the users' data to the VMs is considered as an important constraint in finding selecting the DC with the proper host to serve the cloud users' requests. Specifically, our LECC model made the following contributions beyond previous works: (1) it models data placement at DCs, while previous work considers replications of the data; (2) it proposes algorithms for different cloud users' requests taking into account the location of the data used by users' applications running in VMs, whereas prior works ignore data locality; (3) we recommend different advanced placement methods to enhance the power plan of the DCs such that carbon emission cost is minimized, while the previous works concentrated on the initial VM placement methods; (4) We design and apply a novel ML model to weight the different terms in the multi-objective optimization problem so that the whole cloud provider cost is minimized, while prior works manually weights among their sub-objectives. ...
The wide adoption of the cloud computing model in the business environment has led to a rapid increase in the development of geo-distributed data centers (DCs) to support customers’ needs. On the other hand, cloud DCs that contain thousands of computing and storage nodes consumes a large amount of energy that leads to a high carbon footprint. Therefore, minimizing the geo-distributed DCs’ energy consumption is a must which will decrease the cloud providers’ operational cost and minimize the high non-environment carbon emission. In addition, minimizing the cloud users’ network latency, in such a distributed environment, is one of the important challenges faced by cloud providers. Thus, to address these challenges, this paper proposed a novel location, energy, carbon, and cost-aware (LECC) virtual machine (VM) placement model for geo-distributed cloud DCs. Both online and offline placement problems are tackled. The migration technology is employed in consolidating the VMs to less number of active servers for significant energy reductions. The VM placement problem is formulated as a multi-objective optimization problem and solved by greedy policies. Also, an intelligent machine-learning model is constructed and implemented to leverage the performance of the LECC model. To validate the usefulness of the LECC model, extensive simulations using synthetic and real data are conducted on CloudSim toolkit. The experimental results showed the merits of our proposed LECC model in solving the important VM placement problem.
The technical advancements and the availability of massive amounts of data on the Internet draw huge attention from researchers in the areas of decision-making, data sciences, business applications, and government. These massive quantities of data, known as big data, have many benefits and applications for researchers. However, the use of big data consumes a lot of time and imposes enormous computational complexity. This survey describes the significance of big data and its taxonomy and details the basic terminologies used in big data. It also discusses the technologies used in big data applications as well as their various complexities and challenges. The survey focuses on the various techniques presented in the literature to restrain the issues associated with big data. In particular, the review concentrates on big data techniques in accordance with processing, security, and storage. It also discusses the various parameters associated with big data, such as availability and velocity. The study analyses big data terminologies and techniques in accordance with several factors, such as year of publication, performance metrics, achievement of the existing models, and methods utilized. Finally, this review article describes the future direction of research and highlights big data possibilities and solicitations with a detailed sketch of the big data processing frameworks.
