Topic
Cloud computing
About: Cloud computing is a research topic. Over the lifetime, 156433 publications have been published within this topic receiving 1963602 citations. The topic is also known as: cloud platform & cloud.
Papers published on a yearly basis
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Papers
TL;DR: An ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection that can effectively reduce the number of features and has a high detection rate and classification accuracy when compared to other classification techniques.
Abstract: Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.
328 citations
Proceedings Article•
1 Jan 2011TL;DR: It is argued that these three challenges must be overcome before outsourcing database software and management becomes attractive to many users, and cost-effective for service providers.
Abstract: This paper introduces a new transactional “database-as-a-service” (DBaaS) called Relational Cloud. A DBaaS promises to move much of the operational burden of provisioning, configuration, scaling, performance tuning, backup, privacy, and access control from the database users to the service operator, offering lower overall costs to users. Early DBaaS efforts include Amazon RDS and Microsoft SQL Azure, which are promising in terms of establishing the market need for such a service, but which do not address three important challenges: efficient multi-tenancy, elastic scalability, and database privacy. We argue that these three challenges must be overcome before outsourcing database software and management becomes attractive to many users, and cost-effective for service providers. The key technical features of Relational Cloud include: (1) a workload-aware approach to multi-tenancy that identifies the workloads that can be co-located on a database server, achieving higher consolidation and better performance than existing approaches; (2) the use of a graph-based data partitioning algorithm to achieve near-linear elastic scale-out even for complex transactional workloads; and (3) an adjustable security scheme that enables SQL queries to run over encrypted data, including ordering operations, aggregates, and joins. An underlying theme in the design of the components of Relational Cloud is the notion of workload awareness: by monitoring query patterns and data accesses, the system obtains information useful for various optimization and security functions, reducing the configuration effort for users and operators.
328 citations
1 Jan 2013
Abstract: Clouds cover about 70% of Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the entire globe and across the wide range of spatial and temporal scales that compose weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climate data records must be compiled from different satellite datasets and can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors and retrieval methods. The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel (GEWEX Data and Assessment Panel since 2011), provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities), IR soun...
327 citations
TL;DR: It is shown that trust between client organization and cloud provider is a strong predictor of successful cloud deployment.
Abstract: Trust between client organization and cloud provider is a strong predictor of successful cloud deployment.
327 citations
TL;DR: A novel Hierarchical Federated Edge Learning (HFEL) framework is introduced in which model aggregation is partially migrated to edge servers from the cloud and achieves better training performance compared to conventional federated learning.
Abstract: Federated Learning (FL) has been proposed as an appealing approach to handle data privacy issue of mobile devices compared to conventional machine learning at the remote cloud with raw user data uploading By leveraging edge servers as intermediaries to perform partial model aggregation in proximity and relieve core network transmission overhead, it enables great potentials in low-latency and energy-efficient FL Hence we introduce a novel Hierarchical Federated Edge Learning (HFEL) framework in which model aggregation is partially migrated to edge servers from the cloud We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization To solve the problem, we propose an efficient resource scheduling algorithm in the HFEL framework It can be decomposed into two subproblems: resource allocation given a scheduled set of devices for each edge server and edge association of device users across all the edge servers With the optimal policy of the convex resource allocation subproblem for a set of devices under a single edge server, an efficient edge association strategy can be achieved through iterative global cost reduction adjustment process, which is shown to converge to a stable system point Extensive performance evaluations demonstrate that our HFEL framework outperforms the proposed benchmarks in global cost saving and achieves better training performance compared to conventional federated learning
327 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2026 | 91 |
| 2025 | 9,873 |
| 2024 | 13,154 |
| 2023 | 16,155 |
| 2022 | 25,943 |
| 2021 | 12,056 |