Mobile Edge Computing (MEC) is a pivotal technology that provides agile-response services by deploying computation and storage resources in proximity to end-users. However, resource-constrained edge servers fall victim to Denial-of-Service (DoS) attacks easily. Failures to mitigate DoS attacks effectively hinder the delivery of reliable and sustainable edge services. Conventional DoS mitigation solutions in cloud computing environments are not directly applicable in MEC environments because their design did not factor in the unique characteristics of MEC environments, e.g., constrained resources on edge servers and requirements for low service latency. Existing solutions mitigate edge DoS attacks by transferring user requests from edge servers under attacks to others for processing. Furthermore, the heterogeneity in end-users' resource demands can cause resource fragmentation on edge servers and undermine the ability of these solutions to mitigate DoS attacks effectively. User requests often have to be transferred far away for processing, which increases the service latency. To tackle this challenge, this paper presents a fragmentation-aware gaming approach called HEDMGame that attempts to minimize service latency by matching user requests to edge servers' remaining resources while making request-transferring decisions. Through theoretical analysis and experimental evaluation, we validate the effectiveness and efficiency of HEDMGame, and demonstrate its superiority over the state-of-the-art solution.

Mobile edge computing (MEC) offers a new computing paradigm that turns computing and storage resources to the network edge to provide minimal service latency compared to cloud computing. Many research works have attempted to help app vendors allocate users to appropriate edge servers for high-performance service provisioning. However, existing edge user allocation (EUA) approaches have ignored fairness in users' data rates caused by interference, which is crucial in service provisioning in the MEC environment. To pursue fairness in EUA, edge users need to be assigned to edge servers so their quality of experience can be ensured at minimum costs without significant service performance differences among them. In this paper, we make the first attempt to address this fair edge user allocation (FEUA) problem. Specifically, we formulate the FEUA problem, prove its N P-hardness, and propose an optimal approach to solve small-scale FEUA problems. To accommodate large-scale FEUA scenarios, we propose a game-theoretic approach called FEUAGame that transforms the FEUA problem into a potential game that admits a Nash equilibrium. FEUA employs a decentralized algorithm to find the Nash equilibrium in the potential game as the solution to the FEUA problem. A widely-used real-world data set is utilised to experimentally compare the performance of FEUAGame to four representative approaches. The numerical outcomes show the effectiveness and efficiency of the proposed approaches in solving the FEUA problem

In the past several years, we have witnessed a variety of mechanisms for protecting mobile users’ location privacy, e.g., k-anonymity, cloaking, encryption, etc. Unfortunately, existing techniques suffer from a common limitation - mobile users’ locations must be sent to remote cloud servers. In this article, a novel architecture named LBS@E is proposed for building delocalized location-based services (LBSs) in the 5G mobile edge computing (MEC) environment that do not require users’ locations. Mobile users can retrieve local information from LBSs deployed on nearby edge servers based on LBS@E. In this way, LBS@E tackles the location privacy problem innovatively by resolving the root cause of the conventional location privacy problem. However, LBS@E raises new challenges to location privacy. A mobile user can still be localized to a particular privacy area co-covered by the edge servers accessed by the mobile user. A small privacy area puts the mobile user's location at the risk of being approximated. In the meantime, the size of the utility area, which determines the amount of local information retrievable for the mobile user, is positively correlated with the number of edge servers accessed by the mobile user. Thus, given a set of accessible edge servers, the mobile user needs to determine which ones to access so that the retrievable local information is maximized and the risk of being localized is minimized. In this article, we model this new location privacy protecting problem formally, analyze its problem hardness and propose an integer programming based approach for finding the optimal solution. Extensive experiments are conducted on a widely-used real-world dataset to evaluate the proposed approach.

Mobile edge computing (MEC), as a key technology that facilitates 5G networks, provides a new and prospective mobile computing paradigm that allows the deployment of edge servers at base stations geographically close to mobile users to reduce their end-to-end network latency. Similar to cloud servers, edge servers running 24/7 in an MEC system consume a large amount of energy, contribute a significant proportion of global carbon emissions, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint - mobile users can be served by neighbor edge servers only; 2) latency constraint - mobile users’ workloads should be processed by their neighbor edge servers to ensure low latency; and 3) capacity constraint - edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme - the emerging radio access scheme for 5G. Communication resources like channels and transmit power in the NOMA-based MEC system must be systematically considered with computing resources like CPU, memory and storage to fulfill mobile users’ resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its $\mathcal {NP}$NP-hardness. Then, we propose a two-phase game-theoretical approach, named EDRGame, to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against three baseline approaches and two state-of-the-art approaches on a widely-used real-world dataset. The results show that it solves the EDR problem effectively and efficiently.

With the rollout of the 5G network around the globe, a massive number of edge servers have been deployed to host online applications demanding low service latency for users. These edge servers constitute multi-access edge computing (MEC) systems. Running 24/7, edge servers consume tremendous energy and take up a great part of global carbon emissions. The edge energy-saving (EES) problem is needed to facilitate energy-efficient edge resource provisions. Unfortunately, existing energy-saving approaches designed for data centers are becoming impractical. First, edge servers are used to provide services to a specific geographical area. Its energy utilization is impacted by the temporal distribution of users within its coverage. Second, a user could be accommodated by any of its neighbor edge servers. Third, it is possible to activate and deactivate individual physical machines that facilitate an edge server as needed. Thus, EES is designed to save the system energy of physical machines in a long term by serving the users over time. EES problem has been formulated systematically and its problem hardness has been analyzed theoretically, then we propose EESaver (Edge Energy Saver) for formulating EES strategies dynamically over time to facilitate green MEC. EESaver's superior performance is tested comprehensively.

Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users’ geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its $\mathcal {NP}$NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41% on average.

Mobile edge computing (MEC) raises a variety of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users’ data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an OnLine approach for solving dynamic EUA problems in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated on a public dataset.

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 $k$ edge server placement ( $k$ ESP) aims to place $k$ edge servers within a particular geographic area to maximize the number of covered mobile users, i.e., to maximize the 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. 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 $k$ edge server placement ( $k$ ESP-CR) problem, and prove that finding the optimal solution to this problem is $\mathcal {NP}$ -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 $k$ ESP-CR problems. Then, we propose an approximation approach, namely ESP-A, for solving large-scale $k$ 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.

Mobile edge computing (MEC), as an emerging and prospective mobile computing paradigm, allows a content provider to serve its users by allocating their mobile devices to nearby edge servers to lower the latency in the delivery of its content to those mobile services. From the content provider's perspective, a cost-effective mobile device allocation (MDA) aims to allocate maximum mobile devices to minimum edge servers. However, the allocation of excessive mobile devices to an edge server may result in severe communication interference and consequently, impact mobile devices' data rates. Sometimes, not all the mobile devices can be allocated to edge servers and thus have to retrieve content from the remote cloud through base stations with high latency. The connection between these mobile devices and base stations also incur communication interference. In this paper, we formally model this Interference-aware mobile edge device allocation (I-MEDA) problem, and propose a game-theoretic based approach named I-MEDAGame to formulate the I-MEDA problem as an I-MEDA game. In the I-MEDA game, allocation decisions are made for individual mobile devices in parallel to alleviate the need for centralized optimization. Our theoretical analysis of I-MEDAGame shows that it admits at least one Nash equilibrium. To solve the I-MEDA problem, I-MEDAGame employs a novel decentralized algorithm to find the Nash equilibrium of the IMEDA game. The performance of I-MEDAGame is theoretically analyzed and experimentally evaluated. The results show that I-MEDAGame can solve the I-MEDA problem effectively and efficiently, outperforming four representative approaches significantly.

Edge computing, as an emerging and prospective distributed computing paradigm, allows a service provider to serve its users by allocating them to nearby edge servers delivering services with low latency. From the service provider’s perspective, a cost-effective service user allocation aims to allocate maximum service users to minimum edge servers. Such an allocation leverages multi-tenancy to reduce the resources hired by the service provider for serving the service users. However, the allocation of excessive service users to an edge server may result in severe interference and consequently impact their data rates. There is a trade-off between multi-tenancy and interference in the pursuit of a cost-effective service user allocation. In this article, we formally model this service user allocation (SUA) problem, and prove that it is NP-hard to find the optimal solution to an SUA problem. To solve the SUA problem effectively and efficiently, we propose a game-theoretic approach, namely MI-SUAGame, to formulate the SUA problem as a potential game. We analyze the game and prove its admission to a Nash equilibrium. Then, a novel decentralized algorithm is designed for finding a Nash equilibrium in the game as the solution to the SUA problem. The performance of MI-SUAGame is theoretically analyzed and experimentally evaluated against the state-of-the-art approach. The results show that it can solve the SUA problem effectively and efficiently.

Edge Computing, extending cloud computing, has emerged as a prospective computing paradigm. It allows a SaaS (Software-as-a-Service) vendor to allocate its users to nearby edge servers to minimize network latency and energy consumption on their devices. From the SaaS vendor’s perspective, a cost-effective SaaS user allocation (SUA) aims to allocate maximum SaaS users on minimum edge servers. However, the allocation of excessive SaaS users to an edge server may result in severe interference and consequently impact SaaS users’ data rates. In this article, we formally model this problem and prove that finding the optimal solution to this problem is NP-hard. Thus, we propose ISUAGame, a game-theoretic approach that formulates the interference-aware SUA (ISUA) problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the ISUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the ISUA problem can be solved effectively and efficiently.

The new edge computing paradigm extends cloud computing by allowing service vendors to deploy their service instances and data on distributed edge servers to serve their service users in close geographic proximity to those edge servers. Caching edge data on edge servers profoundly reduces the retrieval latency perceived by users. However, these edge data are subject to corruption due to intentional and/or accidental exceptions. This is a major challenge for service vendors but has been overlooked. Thus, verifying the integrity of edge data accurately and efficiently is a critical security problem in the edge computing environment. A unique characteristic of the edge computing environment is that edge servers suffer from constrained computing capacities. Thus, verifying data integrity on massive edge servers individually is computationally expensive and impractical. In this paper, we tackle this Edge Data Integrity (EDI) problem with an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows service vendors to inspect data integrity and localize corrupted edge data cached 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.

In the multi-access edge computing (MEC) environment, app vendors’ data can be cached on edge servers to ensure low-latency data retrieval. Massive users can simultaneously access edge servers with high data rates through flexible allocations of transmit power. The ability to manage networking resources offers unique opportunities to app vendors but also raises unprecedented challenges. To ensure fast data retrieval for users in the MEC environment, edge data caching must take into account the allocations of data, users, and transmit power jointly. We make the first attempt to study the Data, User, and Power Allocation (DUPA$^3$3) problem, aiming to serve the most users and maximize their overall data rate. First, we formulate the DUPA$^3$3 problem and prove its $\mathcal {NP}$NP-completeness. Then, we model the DUPA$^3$3 problem as a potential DUPA$^3$3 game admitting at least one Nash equilibrium and propose a two-phase game-theoretic decentralized algorithm named DUPA$^3$3Game to achieve the Nash equilibrium as the solution to the DUPA$^3$3 problem. To evaluate DUPA$^3$3Game, we analyze its theoretical performance and conduct extensive experiments to demonstrate its effectiveness and efficiency.

Networked edge servers constitute an edge storage system in edge computing (EC). Upon users’ requests, data must be delivered from edge servers in the system or from the cloud to users. Existing studies of edge storage systems have unfortunately neglected the fact that an excessive number of users accessing the same edge server for data may impact users’ data rates seriously due to the wireless interference. Thus, users must first be allocated to edge servers properly for ensuring their data rates. After that, requested data can be delivered to users to minimize their average data delivery latency. In this paper, we formulate this Interference-aware Data Delivery at the network Edge (IDDE) problem, and demonstrate its NP-hardness. To tackle it effectively and efficiently, we propose IDDE-G, a novel approach that first finds a Nash equilibrium as the strategy for allocating users. Then, it finds an approximate strategy for delivering requested data to allocated users. We analyze the performance of IDDE-G theoretically and evaluate its performance experimentally to demonstrate the effectiveness and efficiency of IDDE-G on solving the IDDE problem.

Mobile edge computing (MEC), as an emerging technology, allows application vendors to deploy application instances on edge servers to deliver low-latency services to nearby end-users. However, due to hardware faults, software exceptions, or cyberattacks, edge servers are prone to failures in the highly distributed and dynamic MEC environment. Hence service reliability must be ensured when failures occur. This raises a critical and open problem - improving service reliability when deploying application instances in the MEC environment. In this article, we jointly consider both user coverage and service reliability when deploying application instances on edge servers with a given application deployment budget $\mathcal {K}$ . We formally define this joint C overage- R eliability for $\mathcal {K}$ - B udgeted E dge A pplication D eployment ( CR-BEAD ) problem and model it as a constrained optimization problem. Next, we propose an optimal approach (named BEAD-O ) based on integer programming to find optimal solutions to small-scale CR-BEAD problems. We also propose a greedy approach named BEAD-G with a constant approximation ratio of $1 - 1/e$ to solve large-scale CR-BEAD problems efficiently. Extensive experimental evaluation against three representative approaches illustrates the effectiveness and efficiency of our approaches.

Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

Similar to cloud servers which are well-known energy consumers, edge servers running 24/7 jointly consume a tremendous amount of energy and thus require energy-saving management. However, the unique characteristics of edge computing make it a new and challenging problem to manage edge servers in an energy-efficient manner. First, an individual edge server is usually used to serve a specific region. The temporal distribution of end-users in the area impacts the edge server's energy utilization. Second, multiple base stations may cover an end-user simultaneously and the end-user can be served by the physical machines attached to any of the base stations. Serving the end-users in an area with minimum physical machines can minimize the edge servers' overall energy consumption. Third, physical machines facilitating an edge server can be powered off individually when not needed to minimize the edge server's energy consumption. We formulate this Energy-efficient Edge Server Management (EESM) problem and analyze its problem hardness. Next, a game-theoretical approach, i.e., EESM-G, is proposed to address EESM problems efficiently. The superior performance of EESM-G is tested on a public real-world dataset.

In recent years, edge computing has emerged as a prospective distributed computing paradigmthat overcomes several limitations of cloud computing. In the edge computing environment, a service provider can deploy its application instances on edge servers at the edge of the network to serve its own userswith low latency. Given a limited budgetK for deploying applications on the edge servers in a particular geographical area, a number of approaches have been proposed very recently to determine the optimal deployment strategy that achieves various optimization objectives, e.g., tomaximize the servers' coverage, tominimize 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 edge computing environment. In this article, wemake the first attempt to tackle this challenge. Specifically, we formulate this Robustness-oriented Edge Application Deployment (READ) problemas a constrained optimization problemand prove its NP-hardness. Then, we provide an integer programming based approach named READ-O for solving this problemprecisely. We also provide an approximation algorithm, namely READ-A, for finding near-optimal solutions to largescaleREADproblems efficiently. We prove its approximation ratio is not worse than K/2, which is a constant regardless of the total number of edge servers. We evaluate our approaches experimentally on a widely-used real-world dataset against five representative approaches. The experiment results demonstrate that our approaches can solve the READproblemeffectively and efficiently.

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.