1,721,053 research outputs found
A Practical way to Handle Service Migration of ML-based Applications in Industrial Analytics
No full textNowadays, Machine learning (ML) plays a significant role in Industrial Analytics. It enables predictive analytics, and helps uncovering essential insights to transform industries. As a result, real-time data analytics has become an essential requirement for industrial engineering jobs. Edge computing enables local intelligence and real-time analytics that are key for industry processes to take autonomous decisions locally at the edge of the network. However, outages in edge datacenters can jeopardize the whole plant security. In this paper, we proposed a practical approach to effectively handling service and data migration of ML-based applications in Industrial Analytics scenarios in the presence of a lack of computing resources at the edge. We argue that in this context the value of data is inversely proportional to their age and is very important to work with fresher data. In this paper, we describe our architectural approach for service and data handoff and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. We evaluate our proposed approach in terms of drop of accuracy in a well-known edge computing emulator, i.e., openLEON. The experimental results show the benefit of our solution with respect to standard techniques
Handling Data Handoff of AI-based Applications in Edge Computing Systems
Get PDFEdge computing aims at better supporting low-latency applications. One of its key techniques is computation offloading, the process that outsources computing tasks from resourced-constrained mobile devices and moves them to edge data centers. In this paper, we tackle an emerging problem within the umbrella of computation offloading, i.e., migration of offloaded inference tasks of Artificial Intelligence (AI) trained models. Such context tailors migration aspects of data-sensitive services where i) the value of the updates is inversely proportional to the data age and ii) outage is highly detrimental to accuracy. To tackle this challenge, we propose Mobile Edge Data-handoff (MED) a framework able to relocate inference or online training tasks from one edge datacenter to another by moving only the necessary data to minimize any accuracy drop during the process. We implemented MED in a well-known edge computing emulator, openLEON, and experimentally verified its performance with an AI-based Industry 4.0 application that forecasts the gas flow in a chemical plant. For our experiments, we use a real, open-source dataset that contains sensors readings. Collected results show that MED, employing proactive data handoff algorithms, is able to minimize the packet loss during the handoff thereby providing guarantees on the inference accuracy
Monitoring 5G Core Networks Vulnerabilities With eBPF
No full textThe current design of 5G Core Network (5G CN) adopts a cloud-native service-based architecture, where Network Functions (NFs) are exposed as services that can be dynamically composed and managed to achieve high flexibility. These NFs are interconnected via interfaces that Standardization Development Organizations (SDOs) like 3GPP have standardized. The complexity of the interconnections and data sensitivity make these interfaces vulnerable. In this letter, we advocate the use of extended Berkeley Packet Filter (eBPF) to monitor the 5G CN interfaces activities. eBPF programs run in kernel space of the host machine, thereby providing visibility of all programs and this is especially convenient for observability of 5G CN NFs. With a specific use case implemented in Open Air Interface (OAI), we demonstrate the benefits of the eBPF framework to identify session deletion attacks and mitigate associated risks
Performance evaluation of hybrid crowdsensing systems with stateful CrowdSenSim 2.0 simulator
No full textOn the Efficiency of Service and Data Handoff Protocols in Edge Computing Systems
No full textThe Multi-access Edge Computing (MEC) enables a new layer of edge middleboxes, acting as local proxies with virtualized resources deployed at edge localities. To support scalable, low-latency, and locally managed service provisioning, MEC relies on computation offloading, the process that outsources computing tasks from resourced constrained mobile devices and moves it to edge data centers. In this paper, we tackle a specific sub-problem within the umbrella of computation offloading. We argue that it is convenient to migrate a service because of the lack of computing resources in the anchor edge data center even if a device, such as industrial IoT devices, is not moving. In this paper, we extensively evaluate the efficiency of data and service handoff protocols. Specifically, we thoroughly assess protocols, that we designed in our past work, in a well-known edge computing emulator, i.e., openLEON. These protocols migrate data and service either in a reactive fashion, i.e., upon realizing of resource exhaustion, or proactively, i.e., beforehand to swiftly minimize the downtime. We experimentally verify their performance for a typical MEC use case, i.e., video. Our results show that by being proactive, the service interruption downtime reduces by a factor of 4 times
Enriching Remote Control Applications with Fog Computing
Get PDFFog computing has emerged in the recent years as a paradigm tailored to serve geo-distributed applications requiring low latency. Remote Control (RC) applications allow a mobile device to control another device from remote. To enrich Quality of Experience (QoE) of RC applications, in this paper we investigate the use of fog computing as a viable platform to offload computation of tasks that would be expensive if performed locally on a mobile device. The proposed approach, supported with next 5G communication systems, will enable a Tactile Internet experience. In this paper we study and compare offload policies to accommodate tasks in the fog platform and analyze the requirements to minimize outages
Power Comparison of Cloud Data Center Architectures
Get PDFpeer reviewedPower consumption is a primary concern for cloud computing data centers. Being the network one of the non- negligible contributors to energy consumption in data centers, several architectures have been designed with the goal of improv- ing network performance and energy-efficiency. In this paper, we provide a comparison study of data center architectures, covering both classical two- and three-tier design and state-of-art ones as Jupiter, recently disclosed by Google. Specifically, we analyze the combined effect on the overall system performance of different power consumption profiles for the IT equipment and of different resource allocation policies. Our experiments, performed in small and large scale scenarios, unveil the ability of network-aware allocation policies in loading the the data center in a energy-proportional manner and the robustness of classical two- and three-tier design under network-oblivious allocation strategies
Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing
Get PDFWearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications
A Cost-Effective Distributed Framework for Data Collection in Cloud-based Mobile Crowd Sensing Architectures
Get PDFpeer reviewedMobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns
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