Resource Management for Latency-Sensitive IoT Applications With Satisfiability

Abstract

Satisfying the software requirements of emerging service-based Internet of Things (IoT) applications has become challenging for cloud-centric architectures, as applications demand fast response times and availability of computational resources closer to end-users. Meeting application demands must occur at runtime, facing uncertainty and in a decentralized manner, something that must be reflected in system deployment. We propose a decentralized resource management technique and accompanying technical framework for the deployment of service-based IoT applications at the edge. Faithful to services engineering, applications we consider are composed of interdependent tasks, which in the IoT setting may be concretized as containerized microservices or serverless functions. A deployment for an arbitrary application is found at runtime through satisfiability; the mapping produced is compliant with tasks' individual resource requirements and latency constraints by construction. Our approach ensures seamless deployment at runtime, assuming no design-time knowledge of device resources or the current network topology. We evaluate the applicability and realizability of our technique over single-board computers as edge devices, particularly in the absence of cloud resources.

Privacy-Aware Task Assignment for IoT Audit Applications on Collaborative Edge Devices

“…e proposed method first decomposes an IoT application into a set of simple tasks, then automatically discovers qualified edge devices, and finally assigns tasks to appropriate edge devices. Similarly, Avasalcai et al [2] proposed a decentralized resource management framework for deploying delay-sensitive IoT applications at the edge of the network and found deployment solutions that meet the requirements through satisfiability modulo theory (SMT) technology.…”

“…With the development of the Internet of ings (IoT), various IoT applications emerge as the times require, such as disaster relief, public safety, and face recognition [1][2][3]. According to Garner [4], the global IoT-enabled applications and infrastructure market will represent a 33 billion US dollar opportunity in 2025.…”

“…Edge computing has recently moved beyond the initial principle of utilizing IoT devices to collect and preprocess sensory data and is now able to combine and coordinate multiple IoT devices to provide processing for IoTapplications [1,14,15]. erefore, the advantages of edge computing such as low latency and local data processing are further highlighted [2,16]. In this paper, we refer to these resource-constrained IoT devices with data collection and task processing capabilities as edge devices.…”

Distributed System Resource Management and Deployment Platform Considering Big Data

“…(2) High system reliability The distributed nature of a power distribution system makes it imply a kind of fault tolerance, if your work session makes the entire system at least partially available and working to a certain extent; while on one machine, when a computer fails, The entire system will not continue to work [11][12].…”

A Two-tier Multi-objective Service Placement in Container-based Fog-Cloud Computing Platforms

“…Hence, cloud computing is impractical for Internet of Things applications that require real-time services [16]. To address this limitation, fog computing serves as an intermediary layer between the cloud and IoT, reducing network traffic and guaranteeing rapid response times for several latency-sensitive IoT applications [17][18][19]. While inheriting most of cloud computing's features, fog computing primarily provides processing and storage resources at the network's edge [20].…”