Assessing end-to-end performance and security in cloud computing

2017 ◽  
Author(s):  
Kaiqi Xiong ◽  
Mufaddal Makati
Keyword(s):  
Cloud Computing ◽  
End To End

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

scholarly journals Protected Data Using Hybrid Algorithm in Cloud

2018 ◽  
Vol 7 (3.27) ◽  
pp. 318
Author(s):  
K Kalaiselvi ◽  
N Jayashri ◽  
G Saraswathi
Keyword(s):  
Cloud Computing ◽  
Hybrid Algorithm ◽  
Low Cost ◽  
Hybrid Algorithms ◽  
Sensitive Information ◽  
Key Generation ◽  
End User ◽  
End To End ◽  
Cryptographic Techniques

Cloud computing providing confidentiality over the insensitive data was the major issue related to security. It verifies the data owned by the server through linear computations. The proposed work enables security and efficiency using the cryptographic techniques of hybrid algorithms, securing the sensitive information that is present in the cloud. In the hybrid algorithm, it is the combination of problem encryption, key generation, result decryption and proof generation. It also validates the results which are being computed and also provides end-to-end confidentiality over the data to both the end user. The uses of hybrid algorithm results in a random key generation, encrypt/decrypt, and validate the satisfied results. This will provide a low cost to both server and client.  

scholarly journals End-to-end diagnosis of cloud systems against intermittent faults

2020 ◽  
pp. 40-40
Author(s):  
Chao Wang ◽  
Zhongchuan Fu ◽  
Yanyan Huo
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Computing Environment ◽  
Bp Network ◽  
Cloud Computing Environment ◽  
Intermittent Faults ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
End To End ◽  
Detection Mechanisms

The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.

Maintaining User Control While Storing and Processing Sensor Data in the Cloud

2013 ◽  
Vol 5 (4) ◽  
pp. 97-112 ◽  
Author(s):  
Martin Henze ◽  
René Hummen ◽  
Roman Matzutt ◽  
Daniel Catrein ◽  
Klaus Wehrle
Keyword(s):  
Cloud Computing ◽  
Sensor Networks ◽  
Data Protection ◽  
Data Access ◽  
Sensor Data ◽  
Cloud Architecture ◽  
Data Owner ◽  
Integrity Protection ◽  
Sensitive Sensor ◽  
End To End

Clouds provide a platform for efficiently and flexibly aggregating, storing, and processing large amounts of data. Eventually, sensor networks will automatically collect such data. A particular challenge regarding sensor data in Clouds is the inherent sensitive nature of sensed information. For current Cloud platforms, the data owner loses control over her sensor data once it enters the Cloud. This imposes a major adoption barrier for bridging Cloud computing and sensor networks, which we address henceforth. After analyzing threats to sensor data in Clouds, the authors propose a Cloud architecture that enables end-to-end control over sensitive sensor data by the data owner. The authors introduce a well-defined entry point from the sensor network into the Cloud, which enforces end-to-end data protection, applies encryption and integrity protection, and grants data access. Additionally, the authors enforce strict isolation of services. The authors show the feasibility and scalability of their Cloud architecture using a prototype and measurements.

Workload Migration to Cloud

2017 ◽  
pp. 279-298
Author(s):  
Choong Thio ◽  
Jim Cook
Keyword(s):  
Cloud Computing ◽  
Critical Area ◽  
Efficient Manner ◽  
Cloud Migration ◽  
End To End

Workload migration to cloud is a critical area in increasing the adoption of cloud. In order to fully leverage the power of cloud computing, clients need to determine what workloads and applications are good candidates in the cloud and migrate them quickly and in an efficient manner into the cloud. The main goal of this chapter is to explore and study how workloads can be migrated into cloud. In addition, this chapter will also describe the overall end-to-end process for cloud migration and its resulting benefits.

Sign in / Sign up

Export Citation Format

Share Document