In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users’ retrieval of app data. However, an edge server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative edge data caching problem (CEDC) as a constrained optimization problem and prove that it is $\mathcal {NP}$ NP -complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.

Online Collaborative Data Caching in Edge Computing

Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers to serve nearby users. Caching data on edge servers can minimize users’ data retrieval latency. However, such cache data are subject to both intentional and accidental corruption in the highly distributed, dynamic, and volatile edge computing environment. Given a large number of edge servers and their limited computing resources, how to effectively and efficiently audit the integrity of app vendors’ cache data is a critical and challenging problem. This article makes the first attempt to tackle this Edge Data Integrity (EDI) problem. We first analyze the threat model and the audit objectives, then propose a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers. We propose a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit. VMHT can ensure the audit accuracy of EDI-V by maintaining sampling uniformity. EDI-V allows app vendors to inspect their cache data and locate the corrupted ones efficiently and effectively. Both theoretical analysis and comprehensively experimental evaluation demonstrate the efficiency and effectiveness of EDI-V.

Auditing Cache Data Integrity in the Edge Computing Environment

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

Edge Intelligence: Empowering Intelligence to the Edge of Network

Edge Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying their services on edge servers attached to base stations or access points in close proximity to mobile users. From the edge infrastructure provider’s perspective, a cost-effective <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge server placement ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP) aims to place <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge servers within a particular geographic area to maximize the number of covered mobile users, i.e., to maximize the <i>user coverage</i> . However, in the distributed and volatile ECC environment, edge servers are subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. Mobile users connected to a failed edge server have to access services in the remote cloud if they are not covered by any other edge servers. This significantly impacts mobile users quality of experience. Thus, the robustness of the edge server network (referred to as network robustness hereafter) in a specific area must be considered in edge server placement. In this article, we formally model this joint user coverage and network robustness oriented <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge server placement ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP-CR) problem, and prove that finding the optimal solution to this problem is <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hard. To tackle this ESP-CR, we first propose an integer programming based optimal approach (namely ESP-O) for finding optimal solutions to small-scale <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP-CR problems. Then, we propose an approximation approach, namely ESP-A, for solving large-scale <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP-CR problems efficiently and theoretically prove its approximation ratio. Finally, the performance of these two approaches are experimentally evaluated against three representative approaches on a widely-used real-world dataset. 

Trading off Between User Coverage and Network Robustness for Edge Server Placement

Mobile edge computing has emerged as a new distributed computing paradigm that overcomes the limitations of traditional cloud computing. In an edge computing environment, an app vendor can hire computing and storage resources on edge servers for deploying their applications to deliver lower-latency services to their app users. Under a budget constraint, an optimal edge application deployment strategy allows an app vendor to deploy application instances on edge servers in a specific area and provide services to the most app users in the area. In this paper, we make the first attempt to tackle this edge application deployment (EAD) problem. Specifically, we formulate the EAD problem as a constrained optimization problem and prove its $\mathcal{NP}$ -hardness. Then, we propose an optimal approach named EAD- $opt$ to find the optimal solution of EAD based on integer programming, and an approximation approach named EAD- $apx$ to find approximate solutions in large-scale EAD scenarios efficiently. We evaluate our approaches by conducting experiments on a widely used real-world data set and a synthetic data set with comparison against two baseline approaches. The experimental results demonstrate that our approaches can solve the EAD problem effectively and efficiently.

Optimal Application Deployment in Mobile Edge Computing Environment

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.

Data Caching Optimization in the Edge Computing Environment

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed in close proximity to mobile users, caching popular data on edge servers can ensure mobile users' low-latency access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data caching problem on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we tackle this data caching problem in the edge computing environment from a service provider's perspective to maximize its data caching revenue. This problem is challenging because there is a trade-off between the benefit produced and the cost incurred by caching data on edge servers. In the meantime, the constraint for data access latency must also be fulfilled. We formulate the data caching problem in the edge computing environment as an integer programming (IP) problem and prove its NP-completeness. To solve this problem effectively and efficiently in large-scale scenarios, we propose an approximation approach to find near-optimal solutions. Experiments are conducted on a real-world dataset to evaluate our approaches.

Dequan Zheng

Papers

Data Caching Optimization in the Edge Computing Environment

Data Caching Optimization in the Edge Computing Environment