The rapid growth in technology and wide use of internet has increased smart applications such as intelligent transportation control system, and Internet of Things, which heavily rely on an efficient and reliable connectivity network. To overcome high bandwidth work load on the network, as well as minimize latency for real-time applications, the computation can be moved from the central cloud to a distributed edge cloud. The edge computing benefits various smart applications that uses distributed network for data analytics and services. Different from the existing cloud management solutions, edge computing needs to move cloud management services towards distributed heterogeneous edge nodes for multi-tenant user applications. However, existing cloud management services do not offer remote deployment of multi-tenant user applications on the cloud of edge nodes. In this paper, we propose a practical edge cloud software framework for deploying multi-tenant distributed smart applications. Having multiple distributed end nodes, auto discovery of all active end nodes is required for deploying multi-tenant user applications. However, existing cloud solutions require either private network or fixed IP address, which is not achievable for the distributed edge nodes. Most of the edge nodes connected through the public internet without fixed IP, and some of them even connect through IEEE 802.15 based sensor networks. We propose to build a software platform to manage the distributed edge nodes as well as support services to deploy and launch isolated, multi-tenant user applications through a lightweight container. We propose an architectural solution to remotely access edge cloud management services through intermittent internet connections. We open sourced our whole set of software solutions, and analyzed the major performance metrics of the edge cloud platform.
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Hardening the Security of Multi-Access Edge Computing through Bio-Inspired VM Introspection
The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system.
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- NSF-PAR ID:
- 10301146
- Date Published:
- Journal Name:
- Big data and cognitive computing
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2504-2289
- Page Range / eLocation ID:
- 50
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/bdcc5040052
