Edge computing allows app vendor to deploy its applications and relevant data on distributed edge servers to serve its users in close proximity. Caching data on edge servers can minimize users' data retrieval latency. However, such data are subject to both intentional and accidental corruption in the highly distributed and dynamic 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 paper 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 data replicas during the audit. VMHT can ensure audit accuracy 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.

Edge computing (EC) is an emerging paradigm that extends cloud computing by pushing computing resources onto edge servers that are attached to base stations or access points at the edge of the cloud in close proximity with end-users. Due to edge servers’ geographic distribution, the EC paradigm is challenged by many new security threats, including the notorious distributed Denial-of-Service (DDoS) attack. In the EC environment, edge servers usually have constrained processing capacities due to their limited sizes. Thus, they are particularly vulnerable to DDoS attacks. DDoS attacks in the EC environment render existing DDoS mitigation approaches obsolete with its new characteristics. In this article, we make the first attempt to tackle the edge DDoS mitigation (EDM) problem. We model it as a constraint optimization problem and prove its $\mathcal {NP}$ -hardness. To solve this problem, we propose an optimal approach named EDMOpti and a novel game-theoretical approach named EDMGame for mitigating edge DDoS attacks. EDMGame formulates the EDM problem as a potential EDM Game that admits a Nash equilibrium and employs a decentralized algorithm to find the Nash equilibrium as the solution to the EDM problem. Through theoretical analysis and experimental evaluation, we demonstrate that our approaches can solve the EDM problem effectively and efficiently.

Edge computing (EC) can overcome several limitations of cloud computing. In the EC environment, a service provider can deploy its application instances on edge servers to serve users with low latency. Given a limited budget K for deploying applications in a particular geographical area, some approaches have been proposed to achieves various optimization objectives, e.g., to maximize the servers' coverage, to minimize the average network latency, etc. However, the robustness of the services collectively delivered by the service provider's applications deployed on the edge servers has not been considered at all. This is a critical issue, especially in the highly distributed, dynamic and volatile EC environment. We make the first attempt to tackle this challenge. Specifically, we formulate this Robustness-oriented Edge Application Deployment(READ) problem as a constrained optimization problem and prove its NP-hardness. Then, we provide an integer programming based approach READ-O for solving it precisely, and an approximation algorithm READ-A for efficiently finding near-optimal solutions to large-scale problems. READ-A's approximation ratio is not worse than K/2, which is constant regardless of the total number of edge servers. Evaluation of the widely-used real-world dataset against five representative approaches demonstrates that our approaches can solve the READ problem effectively and efficiently.

Edge computing, as a new computing paradigm that extends cloud computing, allows a service vendor to deploy its services on distributed edge servers to serve its service users in close geographic proximity to those edge servers. Caching edge data on edge servers can minimize service users data retrieval latency. However, such edge data are subject to intentional and accidental corruption in the highly distributed and dynamic edge computing environment. Verifying the integrity of service vendors edge data accurately and efficiently is a critical security problem. Given a potentially large number of edge servers and their limited computing capacities, verifying the integrity of the edge data on each edge server individually is computation expensive. In this paper, we make the first attempt to tackle this Edge Data Integrity (EDI) problem from the service vendors perspective by proposing an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows a service vendor to inspect its edge data and localize corrupted edge data on multiple edge servers accurately and efficiently. To evaluate its performance, we implement ICL-EDI and conduct extensive experiments to demonstrate its effectiveness and efficiency.

The new mobile edge computing (MEC) paradigm fundamentally changes the data caching technique by allowing data to be cached on edge servers attached to base stations within hundreds of meters from users. It provides a bounded latency guarantee for latency-sensitive applications, e.g., interactive AR/VR applications, online gaming, etc. However, in the highly distributed MEC environment, cache data is subject to corruption and their integrity must be ensured. Existing centralized data integrity assurance schemes are rendered obsolete by the unique characteristics of MEC, i.e., unlike cloud servers, edge servers have only limited computing and storage resources and they are deployed massively and distributed geographically. Thus, it is a new and significant challenge to ensure cache data integrity over tremendous geographically-distributed resource-constrained edge servers. This paper proposes the CooperEDI scheme to guarantee the edge data integrity in a distributed manner. CooperEDI employs a distributed consensus mechanism to form a self-management edge caching system. In the system, edge servers cooperatively ensure the integrity of cached replicas and repair corrupted ones. We experimentally evaluate its performance against three representative schemes. The results demonstrate that CooperEDI can effectively and efficiently ensure cache data integrity in the MEC environment.

The rapid growth of mobile apps has significantly promoted the use of third-party libraries in mobile app development. However, mobile app developers are now facing the challenge of finding useful third-party libraries for improving their apps, e.g., to enhance user interfaces, to add social features, etc. An effective approach is to leverage collaborative filtering (CF) to predict useful third-party libraries for developers. We employed Matrix Factorization (MF) approaches - the classic CF-based prediction approaches - to make the predictions based on a total of 31,432 Android apps from Google Play. However, our investigation shows that there is a significant lack of diversity in the prediction results - a small fraction of popular third-party libraries dominate the prediction results while most other libraries are ill-served. The low diversity in the prediction results limits the usefulness of the prediction because it lacks novelty and serendipity which are much appreciated by mobile app developers. In order to increase the diversity in the prediction results, we designed an innovative MF-based approach, namely LibSeek, specifically for predicting useful third-party libraries for mobile apps. It employs an adaptive weighting mechanism to neutralize the bias caused by the popularity of third-party libraries. In addition, it introduces neighborhood information, i.e., information about similar apps and similar third-party libraries, to personalize the predictions for individual apps. The experimental results show that LibSeek can significantly diversify the prediction results, and in the meantime, increase the prediction accuracy.