Near-optimal Cloud-Network Integrated Resource Allocation for Latency-Sensitive B5G
Masoud Shokrnezhad and Tarik Taleb
IEEE Global Communications Conference (GLOBECOM) ’22
Nowadays, while the demand for capacity continues
to expand, the blossoming of Internet of Everything is bringing in
a paradigm shift to new perceptions of communication networks,
ushering in a plethora of totally unique services. To provide these
services, Virtual Network Functions (VNFs) must be established
and reachable by end-users, which will generate and consume
massive volumes of data that must be processed locally for
service responsiveness and scalability. For this to be realized, a
solid cloud-network Integrated infrastructure is a necessity, and
since cloud and network domains would be diverse in terms of
characteristics but limited in terms of capability, communication
and computing resources should be jointly controlled to unleash
its full potential. Although several innovative methods have
been proposed to allocate the resources, most of them either
ignored network resources or relaxed the network as a simple
graph, which are not applicable to Beyond 5G because of its
dynamism and stringent QoS requirements. This paper fills
in the gap by studying the joint problem of communication
and computing resource allocation, dubbed CCRA, including
VNF placement and assignment, traffic prioritization, and path
selection considering capacity constraints as well as link and
queuing delays, with the goal of minimizing overall cost. We
formulate the problem as a non-linear programming model, and
propose two approaches, dubbed B&B-CCRA and WF-CCRA
respectively, based on the Branch & Bound and Water-Filling
algorithms. Numerical simulations show that B&B-CCRA can
solve the problem optimally, whereas WF-CCRA can provide
near-optimal solutions in significantly less time.
Deep Reinforcement Learning for Dependency-aware Microservice Deployment in Edge Computing
Chenyang Wang, Bosen Jia, Hao Yu, Xiuhua Li, Xiaofei Wang and Tarik Taleb
IEEE Global Communications Conference (GLOBECOM) ’22
Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyses an attention-based microservice representation approach for extracting system context. The attention modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm’s priorities in terms of average system cost and system reward.
Performance Analysis of Storage Systems in Edge Computing Infrastructures
Antonios Makris, Ioannis Kontopoulos, Evangelos Psomakelis, Stylianos Nektarios Xyalis, Theodoros Theodoropoulos and Konstantinos Tserpes
MDPI Applied Sciences, Special Issue: Cloud, Fog and Edge Computing in the IoT and Industry Systems
Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature of the edge as well as its limited resource capabilities pose several challenges in implementing or choosing an efficient edge-enabled storage system. Therefore, it is imperative for the research community to contribute to the clarification of the purposes and highlight the advantages and disadvantages of various edge-enabled storage systems. This work aspires to contribute toward this direction by presenting a performance analysis of three different storage systems, namely MinIO, BigchainDB, and the IPFS. We selected these three systems as they have been proven to be valid candidates for edge computing infrastructures. In addition, as the three evaluated systems belong to different types of storage, we evaluated a wide range of storage systems, increasing the variability of the results. The performance evaluation is performed using a set of resource utilization and Quality of Service (QoS) metrics. Each storage system is deployed and installed on a Raspberry Pi (small single-board computers), which serves as an edge device, able to optimize the overall efficiency with minimum power and minimum cost. The experimental results revealed that MinIO has the best overall performance regarding query response times, RAM consumption, disk IO time, and transaction rate. The results presented in this paper are intended for researchers in the field of edge computing and database systems.
AR-based Remote Command and Control Service: Self-driving Vehicles Use Case
Oussama El Marai, Tarik Taleb, JaeSeung Song
IEEE Network Magazine
The recent technological advances in many fields have significantly contributed to the development of the Advanced Driver Assistance System (ADAS), which in turn will greatly contribute to the flourishing of self-driving vehicles that can operate autonomously in all road scenarios. Until then, keeping the human input in the loop remains vital to either make decisions in unseen situations or approve vehicles’ proposed decisions. In this paper, we leverage VR technology to provide remote assistance for self-driving in critical situations. Specifically, we study the delivery of a 360° live stream at high resolution (4K) to a remote operation center for supporting self-driving vehicles’ decisions when, for example, merging onto the highway. The 360° video stream will be consumed by a human operator wearing a head-mounted display for increased flexibility, faster control, and an immersive experience. In addition, the 360° stream is augmented with relevant context data, such as the vehicle’s speed and distance to other road objects, in order to increase the human operator’s awareness of the vehicle and its surroundings. Depending on the human operator’s proximity to the source, the video stream can either be viewed through the cloud or the edge, which further reduces the glass-to-glass latency. Experimental results demonstrate the effectiveness of employing VR technology to remotely command and control self-driving vehicles in critical situations. The results show that a 360° stream at 4K resolution can be delivered in sub-second glass-to-glass latency, which allows the operator to make timely decisions.
Manos Kamarianakis, Antonis Protopsaltis, Michail Tamiolakis, George Papagiannakis
We present a novel integration of a real-time continuous tearing algorithm for 3D meshes in VR, suitable for devices of low CPU/GPU specifications, along with a suitable particle decomposition that allows soft-body deformations on both the original and the torn model.
CHARITY Project presents the highlights of the project developments
Second workshop on the future of XR: Current ecosystem and upcoming opportunities
Fermin Calvo presented the highlights of the CHARITY project during the "2nd Workshop on the future of XR: Current ecosystem and upcoming opportunities" organised by H2020 ARETE and H2020 iv4xr projects.
Cyango Presentation and what we are doing with CHARITY
7th International XR Conference - ISCTE
João Rodrigues from Dotesfera presented their ongoing work on CHARITY and Cyango, the application behind the VR Tour creator use case.
Uwe Herzog as coordinator of the CHARITY Project presented at “‘Success Stories and Use Cases from the European Cloud Community” session of H-Cloud Summit 2021, the CHARITY’s holographic assistant use case.
Luis Cordeiro from OneSource presented at the International Symposium on 6G Networking in Lisbon the future of XR Services, their upcoming network and computing challenges and the role of 6G in addressing them.