Universal Mobile Service Execution Framework for Device-To-Device Collaborations

There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or significantly overburdened, such as on a battlefield, disaster zones, isolated rural areas, or crowded public venues. To enable collaboration among the devices in opportunistic networks, code offloading and Remote Method Invocation are the two major mechanisms to ensure code portions of applications are successfully transmitted to and executed on the remote platforms. Although these domains are highly enjoyed in research for a decade, the limitations of multi-device connectivity, system error handling or cross platform compatibility prohibit these technologies from being broadly applied in the mobile industry. To address the above problems, we designed and developed UMSEF - an Universal Mobile Service Execution Framework, which is an innovative and radical approach for mobile computing in opportunistic networks. Our solution is built as a component-based mobile middleware architecture that is flexible and adaptive with multiple network topologies, tolerant for network errors and compatible for multiple platforms. We provided an effective algorithm to estimate the resource availability of a device for higher performance and energy consumption and a novel platform for mobile remote method invocation based on declarative annotations over multi-group device networks. The experiments in reality exposes our approach not only achieve the better performance and energy consumption, but can be extended to large-scaled ubiquitous or IoT systems

Monitoring in fog computing: state-of-the-art and research challenges

Fog computing has rapidly become a widely accepted computing paradigm to mitigate cloud computing-based infrastructure limitations such as scarcity of bandwidth, large latency, security, and privacy issues. Fog computing resources and applications dynamically vary at run-time, and they are highly distributed, mobile, and appear-disappear rapidly at any time over the internet. Therefore, to ensure the quality of service and experience for end-users, it is necessary to comply with a comprehensive monitoring approach. However, the volatility and dynamism characteristics of fog resources make the monitoring design complex and cumbersome. The aim of this article is therefore three-fold: 1) to analyse fog computing-based infrastructures and existing monitoring solutions; 2) to highlight the main requirements and challenges based on a taxonomy; 3) to identify open issues and potential future research directions.This work has been (partially) funded by H2020 EU/TW 5G-DIVE (Grant 859881) and H2020 5Growth (Grant 856709). It has been also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52 PID2019-108713RB-C52 / AEI / 10.13039/501100011033)

CRDTs in highly volatile environments

Publisher Copyright: © 2022 The Author(s)The implementation of collaborative applications in highly volatile environments, such as the ones composed of mobile devices, requires low coordination mechanisms. The replication without coordination semantics of Conflict-Free Replicated Data Types (CRDTs) makes them a natural solution for these execution contexts. However, the current CRDT models require each replica to know all other replicas beforehand or to discover them on-the-fly. Such solutions are not compatible with the dynamic ingress and egress of nodes in volatile environments. To cope with this limitation, we propose the Publish/Subscribe Conflict-Free Replicated Data Type (PS-CRDT) model that combines CRDTs with the publish/subscribe interaction model, and, with that, enable the spatial and temporal decoupling of update propagation. We implemented PS-CRDTs in Thyme, a reactive storage system for mobile edge computing. Our experimental results show that PS-CRDTs require less communication than other CRDT-based solutions in volatile environments.publishersversionpublishe

Conflict-Free Replicated Data Types in Dynamic Environments

Over the years, mobile devices have become increasingly popular and gained improved computation capabilities allowing them to perform more complex tasks such as collaborative applications. Given the weak characteristic properties of mobile networks, which represent highly dynamic environments where users may experience regular involuntary disconnection periods, the big question arises of how to maintain data consistency. This issue is most pronounced in collaborative environments where multiple users interact with each other, sharing a replicated state that may diverge due to concurrency conflicts and loss of updates. To maintain consistency, one of today’s best solutions is Conflict-Free Replicated Data Types (CRDTs), which ensure low latency values and automatic conflict resolution, guaranteeing eventual consistency of the shared data. However, a limitation often found on CRDTs and the systems that employ them is the need for the knowledge of the replicas whom the state changes must be disseminated to. This constitutes a problem since it is inconceivable to maintain said knowledge in an environment where clients may leave and join at any given time and consequently get disconnected due to mobile network communications unreliability. In this thesis, we present the study and extension of the CRDT concept to dynamic environments by introducing the developed P/S-CRDTs model, where CRDTs are coupled with the publisher/subscriber interaction scheme and additional mechanisms to ensure users are able to cooperate and maintain consistency whilst accounting for the consequent volatile behaviors of mobile networks. The experimental results show that in volatile scenarios of disconnection, mobile users in collaborative activity maintain consistency among themselves and when compared to other available CRDT models, the P/S-CRDTs model is able to decouple the required knowledge of whom the updates must be disseminated to, while ensuring appropriate network traffic values

Dense Moving Fog for Intelligent IoT: Key Challenges and Opportunities

As the ratification of 5G New Radio technology is being completed, enabling network architectures are expected to undertake a matching effort. Conventional cloud and edge computing paradigms may thus become insufficient in supporting the increasingly stringent operating requirements of \emph{intelligent~Internet-of-Things (IoT) devices} that can move unpredictably and at high speeds. Complementing these, the concept of fog emerges to deploy cooperative cloud-like functions in the immediate vicinity of various moving devices, such as connected and autonomous vehicles, on the road and in the air. Envisioning gradual evolution of these infrastructures toward the increasingly denser geographical distribution of fog functionality, we in this work put forward the vision of dense moving fog for intelligent IoT applications. To this aim, we review the recent powerful enablers, outline the main challenges and opportunities, and corroborate the performance benefits of collaborative dense fog operation in a characteristic use case featuring a connected fleet of autonomous vehicles.Comment: 7 pages, 5 figures, 1 table. The work has been accepted for publication in IEEE Communications Magazine, 2019. Copyright may be transferred without notice, after which this version may no longer be accessibl

DeepFT: Fault-tolerant edge computing using a self-supervised deep surrogate model

The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm. However, edge solutions are typically resource-constrained, posing reliability challenges due to heightened contention for compute capacities and faulty application behavior in the presence of overload conditions. Although a large amount of generated log data can be mined for fault prediction, labeling this data for training is a manual process and thus a limiting factor for automation. Due to this, many companies resort to unsupervised fault-tolerance models. Yet, failure models of this kind can incur a loss of accuracy when they need to adapt to non-stationary workloads and diverse host characteristics. Thus, we propose a novel modeling approach, DeepFT, to proactively avoid system overloads and their adverse effects by optimizing the task scheduling decisions. DeepFT uses a deep-surrogate model to accurately predict and diagnose faults in the system and co-simulation based self-supervised learning to dynamically adapt the model in volatile settings. Experimentation on an edge cluster shows that DeepFT can outperform state-of-the-art methods in fault-detection and QoS metrics. Specifically, DeepFT gives the highest F1 scores for fault-detection, reducing service deadline violations by up to 37% while also improving response time by up to 9%