Edge computing has become a key technology to enable various Internet of Things (IoT) applications. We envision that in the future, edge computing systems will be platforms like crowdsourcing websites where IoT application providers can offload their tasks to the platform and pay it via the edge computing services, and the platform needs to seek appropriate edge servers (ESs) for executing tasks. To ensure ESs participate and provide good performance, i.e., to meet the stringent QoS requirement of IoT applications, the platform needs to properly reward them for computing services. In this paper, we consider the scenario where both the platform and ESs are self-interested entities, and focus on the setting that the payment made by application providers is ex-ante. We formulate a joint revenue maximization, resource allocation and task offloading optimization problem, and tackle it with a Stackelberg game formulation. A centralized algorithm based on Bayesian Optimization is proposed to solve the game. Moreover, we consider a practical setting where the platform is unable to collect full information when application providers and ESs may refuse to provide their sensitive information. To this end, we develop a decentralized solution based on neural network optimization together with a privacy-preserving information exchange protocol. We evaluate both mechanisms through numerical studies, and results indicate they are effective as compared to representative baselines.

To provide reliable and elastic Multi-access edge computing services, one feasible solution is to federate geographically proximate edge servers to form a logically centralized resource pool. Optimization of such systems, however, becomes challenging. In this paper, we study the problem of maximizing users’ QoE in a MEC-based network, through jointly optimizing service caching, resource allocation and task offloading decisions. We formulate a mixed-integer nonlinear programming (MINLP) problem for the task and establish its NP-hardness. To tackle it efficiently, we propose a novel two-stage algorithmic solution based on approximation and decomposition theory. The proposed algorithm achieves high system performance while at the same time, ensures all constraints from different layers are satisfied. Meanwhile, the structure of the algorithm also fits the multi-layer optimizing feature, making it suitable to be implemented at different layers. In addition, we propose a distributed and online version of our mechanism with very limited information exchange between MEC servers, and further demonstrate how the cost of service switches from real MEC systems can be incorporated into our framework. We evaluate our mechanisms through simulations with both synthetic and real-world traces, and results indicate they are effective as compared to representative baseline algorithms.

Multi-access edge computing promises satisfactory user experience by offloading tasks to the MEC server deployed at the network edge. However, since the MEC server is often resource-limited as compared to the cloud infrastructure, how to efficiently utilize its resources for system performance optimization becomes a challenge. In this paper, we study this problem with the aim at maximizing user's QoE through jointly optimizing service selection, computation resource allocation and task offloading decision, which is less studied in existing literature. We formulate a mixed-integer nonlinear programming problem (MINLP) for the task and propose a utility-based approach together with a low-complexity resource-efficiency based heuristic to address the problem. We consider realistic settings, where centralized solutions may not apply and an optimal mechanism needs to adapt as system operates. A distributed algorithm based on the Lagrangian-dual based decomposition theory is proposed, and we prove all sub-problems derived can be efficiently solved. In line with the current VM technology, we develop a cost-aware online algorithm that explicitly incorporates the cost of service switches into service selection and resource allocation. We evaluate our mechanism through both synthetic and trace-driven simulations, and results indicate they are effective as compared to representative baseline algorithms.

By placing popular contents on the network edges, edge caching becomes a promising technique to improve the quality of experience (QoE) of the end users and reduce backhaul link congestion. In this paper, we examine an open market of crowdsourced video edge caching, where within each time slot, the newcome private edge devices strategically declare their own bids to the Video Content Provider (VCP) operator for contributions; and the operator optimally recruits caching devices among the newcome and existing served devices to maximize the expected QoE, under a budget constraint. From the perspective of newcome edge devices, we propose and study a novel pricing problem, namely Pri-CVEC, to determine the bid prices for profit maximization. The problem is challenging due to the importing of strategic interactions between the newcome devices and the VCP operator, and competition between the newcome and the existing served devices.We formulate it as a stackelberg knapsack problem. By leveraging the dynamic programming and linear programming-relaxation method, we propose Pri-DP and Pri-LPR algorithm, respectively. We extensively conduct simulation experiments to verify the advantages of our approaches.

Cache optimization, i.e., determining the optimal content placement and routing paths, is essential for obtaining high performance of cache-enabled networks. This paper studies the problem of optimizing system throughput and content delivery cost over cache networks with lossy links (i.e., ICN-based wireless IoT systems), where content is divided into packet-level chunks, and packets may be lost in transmission. We first propose a new performance metric - the expected overall content routing cost for satisfied requests (RCS), for better characterizing content delivery cost under packet losses. RCS at the same time possesses the attractive mathematical property of super-modularity. We then formulate an optimization problem for the task through jointly optimizing content caching and request routing, and analyze it under fixed-routing scenario. The formulated problem is NP-hard and we prove it is reducible to the one of minimizing content routing cost without packet losses. We establish rules for the reduction, and leverage existing efficient algorithm to solve the problem. We also propose a potential-based online algorithm that is simple and adaptive to traffic changes and packet losses. The effectiveness of our mechanism is validated through extensive simulations over a wide array of network topologies.

In this paper, we consider the problem of allocating cache resources among multiple content providers. The cache can be partitioned into slices and each partition can be dedicated to a particular content provider or shared among a number of them. It is assumed that each partition employs the least recently used policy for managing content. We propose utility-driven partitioning, where we associate with each content provide a utility that is a function of the hit rate observed by the content provider. We consider two scenarios: (1) content providers serve disjoint sets of files and (2) there is some overlap in the content served by multiple content providers. In the first case, we prove that cache partitioning outperforms cache sharing as cache size and a number of contents served by providers go to infinity. In the second case, it can be beneficial to have separate partitions for overlapped content. In the case of two providers, it is usually always beneficial to allocate a cache partition to serve all overlapped content and separate partitions to serve the non-overlapped contents of both providers. We establish conditions when this is true asymptotically but also present an example where it is not true asymptotically. We develop online algorithms that dynamically adjust partition sizes in order to maximize the overall utility and prove that they converge to optimal solutions, and through numerical evaluations we show they are effective.

Phishing is the third cyber-security threat globally and the first cyber-security threat in China. There were 61.69 million phishing victims in China alone from June 2011 to June 2012, with the total annual monetary loss more than 4.64 billion US dollars. These phishing attacks were highly concentrated in targeting at a few major Websites. Many phishing Webpages had a very short life span. In this paper, we assume the Websites to protect against phishing attacks are known, and study the effectiveness of machine learning based phishing detection using only lexical and domain features, which are available even when the phishing Webpages are inaccessible. We propose several novel highly effective features, and use the real phishing attack data against Taobao and Tencent, two main phishing targets in China, in studying the effectiveness of each feature, and each group of features. We then select an optimal set of features in our phishing detector, which has achieved a detection rate better than 98%, with a false positive rate of 0.64% or less. The detector is still effective when the distribution of phishing URLs changes.

Traffic volume control is one of the fundamental requirements in traffic generation and transformation. However, due to the complex interactions between the generated traffic and replay environment (delay, packet loss, connection blocking, etc), controlling traffic volume in interactive network traffic replay becomes a challenging problem. In this paper, we present a novel model-based analytical method to address this problem where the generated traffic volume is regulated through adjustment of input traffic volume. By analyzing the replay mechanism in terms of how packets are processed, and properly choosing buffered packets amount and to-be-received packets amount as system states, we present a novel model-based analytical method to obtain the desired input volume. The traffic volume control problem is then converted to a state prediction problem where we employ Recursive Least Square (RLS) filter to predict system states. As compared to other adaptive control techniques, our method does not involve any learning scheme and hence completely requires no convergence time. Experimental studies further indicate that our method is efficient in tracking target traffic volume (both static and time-varying) and works under a wide range of network conditions.

Interactive network traffic replay plays an important role in testing and evaluating in-line network security devices such as Firewalls, IPSs, etc. In this paper we present a balance-based method for improving the performance of interactive TCP traffic replay. The new method is based on the inherent feature of the TCP protocol, that is, two communicating peers keep synchronized with each other using data acknowledgment. This feature is converted as a balance mechanism in interactive TCP traffic replay and incorporated into the current state-based method. In this way, the cost of state-checking can be significantly reduced and the replay performance is thus enhanced. To validate the effectiveness of the method we implement it by building an interactive replay system. The experimental results indicate that: 1) balance-checking reduces the overhead of state-checking by 40%; 2) the balance-based method enhances the overall replay performance by an average of 5% when the actual TCP traffic traces are replayed.