EasyCache: a transparent in-memory data caching approach for internetware

2014 ◽  
Author(s):  
Wei Wang ◽  
Zhaohui Liu ◽  
Yong Jiang ◽  
Xinchen Yuan ◽  
Jun Wei
Keyword(s):  
Data Caching

scholarly journals EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

2019 ◽  
Vol 9 (12) ◽  
pp. 2560 ◽  
Author(s):  
Yunkon Kim ◽  
Eui-Nam Huh
Keyword(s):  
Intelligent Transportation System ◽  
Streaming Data ◽  
Data Traffic ◽  
High Quality ◽  
Data Caching ◽  
Resource Limited ◽  
Quality Video ◽  
Key Factor ◽  
Efficient Data ◽  
Art Research

This paper explores data caching as a key factor of edge computing. State-of-the-art research of data caching on edge nodes mainly considers reactive and proactive caching, and machine learning based caching, which could be a heavy task for edge nodes. However, edge nodes usually have relatively lower computing resources than cloud datacenters as those are geo-distributed from the administrator. Therefore, a caching algorithm should be lightweight for saving computing resources on edge nodes. In addition, the data caching should be agile because it has to support high-quality services on edge nodes. Accordingly, this paper proposes a lightweight, agile caching algorithm, EDCrammer (Efficient Data Crammer), which performs agile operations to control caching rate for streaming data by using the enhanced PID (Proportional-Integral-Differential) controller. Experimental results using this lightweight, agile caching algorithm show its significant value in each scenario. In four common scenarios, the desired cache utilization was reached in 1.1 s on average and then maintained within a 4–7% deviation. The cache hit ratio is about 96%, and the optimal cache capacity is around 1.5 MB. Thus, EDCrammer can help distribute the streaming data traffic to the edge nodes, mitigate the uplink load on the central cloud, and ultimately provide users with high-quality video services. We also hope that EDCrammer can improve overall service quality in 5G environment, Augmented Reality/Virtual Reality (AR/VR), Intelligent Transportation System (ITS), Internet of Things (IoT), etc.

scholarly journals DCSS Protocol for Data Caching and Sharing Security in a 5G Network

Network ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 75-94
Author(s):  
Ed Kamya Kiyemba Edris ◽  
Mahdi Aiash ◽  
Jonathan Loo
Keyword(s):  
Mobile Networks ◽  
Service Providers ◽  
Security Analysis ◽  
Mobile Network ◽  
End Users ◽  
Third Party ◽  
Control Mechanisms ◽  
Security Measures ◽  
Data Caching ◽  
Pi Calculus

Fifth Generation mobile networks (5G) promise to make network services provided by various Service Providers (SP) such as Mobile Network Operators (MNOs) and third-party SPs accessible from anywhere by the end-users through their User Equipment (UE). These services will be pushed closer to the edge for quick, seamless, and secure access. After being granted access to a service, the end-user will be able to cache and share data with other users. However, security measures should be in place for SP not only to secure the provisioning and access of those services but also, should be able to restrict what the end-users can do with the accessed data in or out of coverage. This can be facilitated by federated service authorization and access control mechanisms that restrict the caching and sharing of data accessed by the UE in different security domains. In this paper, we propose a Data Caching and Sharing Security (DCSS) protocol that leverages federated authorization to provide secure caching and sharing of data from multiple SPs in multiple security domains. We formally verify the proposed DCSS protocol using ProVerif and applied pi-calculus. Furthermore, a comprehensive security analysis of the security properties of the proposed DCSS protocol is conducted.

A 32b CMOS microprocessor with on-chip instruction and data caching and memory management

2005 ◽  
Author(s):  
D. Archer ◽  
D. Deverell ◽  
F. Fox ◽  
P. Gronowski ◽  
A. Jain ◽  
...  
Keyword(s):  
Memory Management ◽  
Data Caching ◽  
On Chip

Data Caching in Next Generation Mobile Cloud Services, Online vs. Off-Line

2017 ◽  
Author(s):  
Yang Wang ◽  
Shuibing He ◽  
Xiaopeng Fan ◽  
Chengzhong Xu ◽  
Joseph Culberson ◽  
...  
Keyword(s):  
Cloud Services ◽  
Mobile Cloud ◽  
Next Generation ◽  
Data Caching
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