The Experts below are selected from a list of 17256 Experts worldwide ranked by ideXlab platform
Tatarnikova T. - One of the best experts on this subject based on the ideXlab platform.
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Recording and Storage Traffic management in Storage systems
2021Co-Authors: Tatarnikova T.Abstract:The article discusses a complex solution for managing Traffic recording and Storage in data Storage systems. In the conditions of modern legislation, the issue of storing a large amount of data becomes acute. Physical Storage management avoids the unnecessary costs of scaling Storage systems. The article proposes the structure of a hardware and software complex for managing physical data Storage for Storage systems that can be used by owners of technological communication networks to store Traffic. Control mechanisms are considered, such as the distribution of data over various media using Kohonen neural networks and forecasting capacity extension using a statistical model and machine learning methods. © 2020 Copyright for this paper by its authors
Li Yan - One of the best experts on this subject based on the ideXlab platform.
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ASCAR: Fully Automatic Storage Contention Management System
eScholarship University of California, 2017Co-Authors: Li YanAbstract:High-performance parallel Storage systems, such as those used for high-performance computing and data centers, can suffer from performance degradation when a large number of clients are contending for limited resources, like bandwidth. This kind of contention is common among any Storage systems that have to serve a large number of users or applications, and can lower the performance of the system and cause unpredictable speed variances. The performance degradation can cause significant resource waste for large Storage systems.This thesis describes the Automatic Storage Contention Alleviation and Reduction system (ASCAR), a Storage Traffic management system for improving the bandwidth utilization and fairness of resource allocation. ASCAR is a fully autonomous software system. It requires no change to the hardware or system design, and integrates well with existing systems. On the high level, ASCAR measures the system's and workload's running states and tunes one or more parameters in order to push a user designated performance metric higher. The metric can be any measurable properties of the system or the workloads, such as I/O throughput, latency, or application runtime.ASCAR includes two sets of algorithms for different tuning requirements. The first method is rule-based. Each client's control agent regulates the Traffic independently according to a preloaded rule set. Rule-based client controllers are fast responding to burst I/O because no runtime coordination between clients or with a central coordinator is needed; they are also autonomous so the system has no scale-out bottleneck. Finding optimal rules can be a challenging task that requires expertise and numerous experiments. ASCAR includes the SHAred-nothing Rule Producer (SHARP) that produces and refines control rules iteratively without the need of human supervision. SHARP systematically explores the solution space of possible rule designs and evaluating the target workload under the candidate rule sets.The second method uses a neural network-based reinforcement learning method called Q-learning to perform continual analyzing of the states of the system and workload, and to tune the values of the Traffic control parameters. This method is named CAPES,Computer Automated Performance Enhancement System. Deep Q-Learning (DQL) is an unsupervised machine learning method that requires no prior knowledge of the system or workloads, does not need existing dataset for training, and performs well on diverse input data featuring long delays between action and reward. Most complex Storage systems show such a property: there is usually a long delay between setting a Traffic control parameter and the change in Traffic metrics. A multilayered deep neural network is chosen as DQL's value function, and experience replay is used to mitigate overfitting.SHARP and CAPES are synergistic and cover different tuning requirements. SHARP is best for relatively stable workloads, requires no runtime communication between agents, and therefore can easily scale to support very large Storage systems. CAPES is best for tuning unpredictable workloads and requires communication between monitoring and control agents.Evaluation of SHARP and CAPES are done on the Lustre parallel file system. Lustre distributes I/O requests to many servers in parallel in order to reach high performance, and can multiply the number of application I/O requests, causing contention throughout the system. SHARP and CAPES are both effective at improving the throughput of the test workloads during the evaluation, some by as much as 45%
S R Balasundaram - One of the best experts on this subject based on the ideXlab platform.
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cost effective approaches for content placement in cloud cdn using dynamic content delivery model
International Journal of Cloud Applications and Computing archive, 2018Co-Authors: Sajitha S Banu, S R BalasundaramAbstract:Cloud providers give Storage access and efficient content placement and delivery services to content providers by optimizing cloud-based content delivery. The cost-efficient model should not only consider the content delivery cost but also the Storage cost associated with the cloud network. In this article, a novel cloud-based content delivery model is proposed that uses shared Storage models for cost optimization in content delivery. Shared Storages are placed in different areas of the content delivery network and an efficient replica placement strategy is employed using optimization techniques. Different content delivery schemes are used in proposed model for different situations and overall content delivery cost is optimized. Experimental results show better performance and lesser cost in terms of Storage, Traffic and latency and also satisfy Quality-of-Service (QoS) and Quality-of-Experience (QoE) in content delivery using PSO when compared to ACO and GA.
Agarwal, Shubhi Lall - One of the best experts on this subject based on the ideXlab platform.
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Cloud Computing Technology: Architecture, Uses & Disadvantages
Recent Trends in Data Mining and Business Forecasting, 2021Co-Authors: Agarwal, Shubhi LallAbstract:In this era of Cloud Computing Technology which has penetrated in almost every sector, the basic knowledge of Cloud is essential nourishment for everyone. Cloud computing which is reliant on internet has the most complex architecture of computation. It is a combination of compiled integrated and networked hardware, software and internet infrastructure. It has numerous benefits over grid computing and this paper evaluates cloud computing by reviewing more than 30 articles on cloud computing. The outcome of this review gives a full insight on cloud computing. This review paper describes in short the introduction, evolution, types and components of cloud computing. It also sheds light on different approaches of cloud computing and its advantages. The application area of cloud computing will be growing day by day and almost all small and giant industries are using cloud computing to accomplish Storage, Traffic, hardware requirements and services. Therefore, there is major impact of cloud computing on society and business
Sajitha S Banu - One of the best experts on this subject based on the ideXlab platform.
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cost effective approaches for content placement in cloud cdn using dynamic content delivery model
International Journal of Cloud Applications and Computing archive, 2018Co-Authors: Sajitha S Banu, S R BalasundaramAbstract:Cloud providers give Storage access and efficient content placement and delivery services to content providers by optimizing cloud-based content delivery. The cost-efficient model should not only consider the content delivery cost but also the Storage cost associated with the cloud network. In this article, a novel cloud-based content delivery model is proposed that uses shared Storage models for cost optimization in content delivery. Shared Storages are placed in different areas of the content delivery network and an efficient replica placement strategy is employed using optimization techniques. Different content delivery schemes are used in proposed model for different situations and overall content delivery cost is optimized. Experimental results show better performance and lesser cost in terms of Storage, Traffic and latency and also satisfy Quality-of-Service (QoS) and Quality-of-Experience (QoE) in content delivery using PSO when compared to ACO and GA.