Zhiqi Chen

Bio: Zhiqi Chen is an academic researcher from Nanjing University. The author has contributed to research in topics: Edge computing & Enhanced Data Rates for GSM Evolution. The author has an hindex of 1, co-authored 5 publications receiving 15 citations.

A Novel Algorithm for NFV Chain Placement in Edge Computing Environments

Abstract: Edge computing is gaining popularity these years, more service providers are shifting their services from clouds to the edge for better QoS provision. Recent studies in NFV also tend to deploy Network Function Virtualization (NFV) services in the edge network. However, the NFV deployment in the edge network is a challenging problem and differs from the similar problem in data centers. We mainly focus on a new NFV Chain Placement (NCP) problem in the paper. It is well known that the edge of the network is dynamic, and edge computing aims to utilize the physical edge resources efficiently and quickly. We first prove that the NCP problem is NP-complete. Then we propose a novel metric that can better measure the balance condition of the physical resources. We also analyze its advantages. Finally, we design an efficient algorithm, MINI, based on this metric. We evaluate MINI using extensive simulations. The results show that MINI has great advantages over a genetic algorithm (GA) in terms of physical resource utilization, acceptance rate, and running time.

Towards Revenue-Driven Multi-User Online Task Offloading in Edge Computing

Abstract: Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of $2(1+\xi)\ln (d+1)$ 2 ( 1 + ξ ) ln ( d + 1 ) with a considerable probability, where $d$ d is the maximum number of process slots of an edge node and $\xi$ ξ is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms.

Sum of squares: a new metric for NFV service chain placement in edge computing environments and efficient heuristic algorithms

Abstract: Both network function virtualization (NFV) and edge computing (EC), especially the latter, are attracting more and more attention in recent years. A growing number of network service providers are migrating their services from the cloud to the edge for better QoS services, while the recent researches on NFV also concentrate on deploying NFV services in edge computing networks. However, NFV deployment in edge networks is a troublesome challenge and is fairly alien from conventional NFV deployment problems in data centres. Edge network differs from the data center network in the following two aspects: firstly, edge nodes are constrained in computing capacity, and secondly, the network connections between edge nodes are unstable and dynamic, which may show large variance over time. This means edge computing should be designed for high-efficient use of physical edge nodes’ resources. To address the challenges above, we investigate a new NFV Service Chain Placement problem in edge computing environments (NSCP-EC) in this paper. We first prove that the NSCP-EC problem is NP-complete. Then we propose a new metric which can better measure the capacity utilization rate of physical resources, and analyze its advantages with details. Based on the new metric, we propose two heuristic but efficient algorithms called MINI and MINI-tree. To confirm the performance of the two algorithms, we conduct simulations. The result demonstrates that MINI gains an advantage over genetic algorithm (GA) and MINI-tree orevails over MINI in tree topology conditions in the aspects of physical resource utilization, acceptance rate and running time. Both theoretical analysis and simulation results confirm the feasibility of the algorithms.

Service deployment and resource allocation method in consideration of multi-user mobility

Abstract: The invention provides a service deployment and resource allocation method in consideration of multi-user mobility. The service deployment and resource allocation method is applied to an edge computing network scene. According to the method, modeling is carried out according to an edge computing scene, the edge computing scene is regarded as an optimization problem integrating service computing time delay overhead, transmission time delay overhead and service migration overhead, and in combination with decision constraints, the optimization problem is solved, so that a multi-user service deployment and computing resource allocation scheme is obtained. The method fills in the field blank, supports multi-user service deployment, considers the mobility of users, has wide applicability, and improves the task allocation and execution efficiency in an edge computing scene, thereby improving the overall processing performance of the network.

Research on the Technology of the Multi-operating System Co-development Based on the Unity Platform

Abstract: Currently, the technology of the multi-operating system co-development has been widely used, which shows great advantages in simplifying technical requirements and reducing costs. A Unity platform is the platform that is able to support 28 platform games and be published on the multiple platforms during compiling at one time. Aiming at the duplication problem of the workloads in the multiple operating system, by considering the traditional research method of the MVC model-based software and the research technology of the Unity3D platform software, this paper proposes system detection and system adaptation algorithms, for researching on the technology of the multiple operating systems. By means of detecting the current system, the description field of this system can be obtained by the system detection algorithm. Based on the system description field, the specific module at compile time is adopted by the system adaptation algorithm, which can solve the differences in the implementation of the different operating system functions. This paper combines with a specific development project case, describes the algorithm implementation method, explains the technical difficulties of our algorithm in practice paper, enabled it to be successfully released on both Windows and Android platforms, and finally verifies the feasibility of the technology.

Multi-Access Edge Computing: A Survey

Abstract: Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks’ architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

Trusted Cloud-Edge Network Resource Management: DRL-Driven Service Function Chain Orchestration for IoT

Abstract: Private and public networks sharing resources for Internet of Things (IoT) network through network function virtualization (NFV) and software-defined networking (SDN) forms a heterogeneous cloud-edge environment. However, the heterogeneous cloud-edge network faces trust and adaptation issues in resource allocation. To address these two problems, we introduce consortium blockchain and deep reinforcement learning (DRL) to construct the trusted and auto-adjust service function chain (SFC) orchestration architecture. In the architecture, this article integrates the consortium blockchain into the distributed SFC orchestration model to realize trusted resource sharing. In addition, for realizing auto-adjusted service provision, this article designs a dynamic hierarchical SFC orchestration algorithm (DHSOA) based on DRL to minimize the orchestration cost and improve the quality of service. Moreover, considering the dynamics of network entities, this article proposes a time-slotted model to support dynamic service migration which adapts to the high-mobility IoT network. The simulation results show that DHSOA has better performance than the link-state routing algorithm and deep $Q$ -network placement algorithm not only in cost saving of 15.8% and 10.1% but also in time saving of 22.0% and 10.0%.

Survey on Placement Methods in the Edge and Beyond

Abstract: Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

A Survey on Mobile Edge Computing Infrastructure: Design, Resource Management, and Optimization Approaches

Abstract: Emerging 5G cellular networks are expected to face a dramatic increase in the volume of mobile traffic and IoT user requests due to the massive growth in mobile devices and the emergence of new compute-intensive applications. Running high-intensive compute applications on resource-constrained mobile devices has recently become a major concern, given the constraints of finite computation and limited storage capacities. Mobile Edge Computing (MEC) has recently become the key technology to overcome these issues by providing cloud computing capabilities and placing IT infrastructures at the mobile network edge. In this survey, we present a list of relevant research papers for the MEC infrastructure implementation phases, including (1) MEC infrastructure designing and dimensioning, (2) MEC infrastructure virtualization using Network Function Virtualization (NFV) concept, and the use of virtualized service placement and auto-scaling methods to deploy an agile system framework, (3) MEC resource management frameworks, and (4) approaches used to optimize the MEC resources on the physical infrastructure. The main focus of this survey is to determine the required aspects to implement an auto-scaled and proactive MEC-NFV infrastructure to support a dynamic and heterogenous mobile users’ demand at mobile network operators.