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 Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying their services on edge servers in close proximity to mobile users. From the edge infrastructure provider's perspective, a cost-effective k-edge server placement aims to place k-edge servers within a particular geographic area to maximize the number of covered mobile users (i.e., user coverage). 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. 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 users quality of experience. Thus, the robustness of the edge server network (i.e., network robustness) must be considered. In this paper, we formally model this joint user coverage and network robustness oriented k-edge server placement (kESP-CR) problem, and prove that finding the optimal solution to this problem is NP-hard. To tackle this kESP-CR problem, we propose an integer programming-based optimal approach for the small-scale kESP-CR problems and approximation approach for the large-scale kESP-CR problem. Finally, extensive experiments are conducted to evaluate their performance.

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.

As enterprises around the globe embrace globalization, strategic alliances among enterprises have become an important means to gain competitive advantages. Enterprises cooperate to improve the quality or lower the prices of their services, which introduce quality correlations, i.e., the quality of a service is associated with other services. Existing approaches for service composition have not fully and systematically considered the quality correlations between services. In this paper, we propose a novel approach named Q2C (Query of Quality Correlation) to systematically model quality correlations and enable efficient queries of quality correlations for service compositions. Given a service composition and a set of candidate services, Q2C first preprocesses the quality correlations among the candidate services and then constructs a quality correlation index graph to enable efficient queries for quality correlations. Extensive experiments are conducted on a real-world web service dataset to demonstrate the effectiveness and efficiency of Q2C.