The serverless and functions as a service (FaaS) paradigms are currently trending among cloud providers and are now increasingly being applied to the network edge, and to the Internet of Things (IoT) devices. The benefits include reduced latency for communication, less network traffic and increased privacy for data processing. However, there are challenges as IoT devices have limited resources for running multiple simultaneous containerized functions, and also FaaS does not typically support long-running functions. Our implementation utilizes Docker and CRIU for checkpointing and suspending long-running blocking functions. The results show that checkpointing is slightly slower than regular Docker pause, but it saves memory and allows for more long-running functions to be run on an IoT device. Furthermore, the resulting checkpoint files are small, hence they are suitable for live migration and backing up stateful functions, therefore improving availability and reliability of the system.

Checkpointing and Migration of IoT Edge Functions

The Internet of Things (IoT) has rapidly transitioned from a novelty to a common, and often critical, part of residential, business, and industrial environments. Security vulnerabilities and exploits in the IoT realm have been well documented. In many cases, improving the security of an IoT device by hardening its software is not a realistic option, especially in the cost-sensitive consumer market or in legacy-bound industrial settings. As part of a multifaceted defense against botnet activity on the IoT, this paper explores a method based on monitoring the network activity of IoT devices. A notable benefit of this approach is that it does not require any special access to the devices and adapts well to the addition of new devices. The method is evaluated on a publicly available dataset drawn from a real IoT network.

Monitoring IoT Networks for Botnet Activity

The Internet of Things (IoT) continues to expand in terms of the number of connected devices. To handle the data produced by those devices, gateways are deployed to collect data, possibly to analyze it, and finally to send it to the cloud or to the end-user to support new services. This process involves complex software that is deployed on those gateways. Moreover, the dynamicity due to new services, mobility, etc., could be corrupted by new events that then require the deployment of software components on additional equipment. Those new events arise in at least two fundamental ways: devices that may change their geographical location; and limitations due to hardware resources and energy consumption. We propose to use autonomic monitoring and control in response to a changing environment in order to manage deployed software with little or no human intervention. A new generic approach is described, based on a semantic model of the system being monitored. Much of the power of the proposed approach is accomplished through a novel use of checkpointing in order to control the software deployed on the gateway

/pdf/intelligent-checkpointing-strategies-for-iot-system-3xrran7wl9.pdf

Intelligent Checkpointing Strategies for IoT System Management

Currently, cloud service providers have used a variety of operational mechanisms to support the company's business processes. Therefore, the services are stored on the company's server, which presents in the form of infrastructure, platform, software, and function. There are several vulnerabilities that have been faced in the implementation such as system failure, natural disasters, human errors, or attacks from unauthorized parties. In addition, the time of unavailability of services can be minimized by doing an LM, in which many servers have been used the containers to behave like a service provider. Actually, its existence replaces the virtual machine that requires more resources although the process only can be done through docker with checkpoint and restore in userspace (CRIU). In this research, LM processes are done to the docker container using CRIU by analyzing the quality of service (QoS), memory, and CPU usage. Thus, the simulation is carried out by establishing the LM using 2 (two) different platforms through scenarios with one and three containers respectively. The performance analysis results aim to examine several indicators in comparison with the achievable result to reduce problems that occurred in the cloud service.

https://www.beei.org/index.php/EEI/article/download/2742/2126

Live migration using checkpoint and restore in userspace (CRIU): Usage analysis of network, memory and CPU

Typical devices of the Internet of Things are usually under-powered, and have limited RAM. This is due to energy and cost concerns. Yet, IoT applications require increasingly complex programs with increasingly large amounts of data. In principle, an application could manage the increasing data within the limited RAM by saving and loading data from the file system as needed. But managing the use of RAM in this way is both time-consuming and error-prone for the code developer. We propose instead a novel architecture in which different semantic scenes are implemented as independent operating system processes. As the need arises to switch from one scene to another, the currently running process, which represents the current scene, is checkpointed and a process representing the new scene is restarted from a checkpoint image. This solution employs checkpointing to provide a simpler framework for the end programmer, while at the same time resulting in higher performance. For example, experiments show that restarting an old process from a checkpoint image is about 25 times faster than starting a new process. When using an mmap-based optimization (deferring the paging in of virtual memory pages until runtime), restarting an old process is about 500 times faster. Overall, checkpoint and restart each execute in less than 0.2 seconds on a Raspberry Pi B.

/pdf/smart-scene-management-for-iot-based-constrained-devices-3598hqhyoa.pdf

Francois Aissaoui

Papers

Smart scene management for IoT-based constrained devices using checkpointing