This paper studies an online service caching problem, where an edge server, equipped with a prediction window of future service request arrivals, needs to decide which services to host locally subject to limited storage capacity. The edge server aims to minimize the sum of a request forwarding cost (i.e., the cost of forwarding requests to remote data centers to process) and a service instantiating cost (i.e., that of retrieving and setting up a service). Considering request patterns are usually non-stationary in practice, the performance of the edge server is measured by dynamic regret, which compares the total cost with that of the dynamic optimal offline solution. To solve the problem, we propose a randomized online algorithm with low complexity and theoretically derive an upper bound on its expected dynamic regret. Simulation results show that our algorithm significantly outperforms other state-of-the-art policies in terms of the runtime and expected total cost.

Real-time traffic management and control have become necessary in today's networks due to their complexity and cybersecurity risks. With the increase in Internet use, threats are more prevalent and require real-time detection and analysis to prevent network intrusions. As the number of data flow increases, the number and the types of attacks increase, which makes detecting intrusions challenging. Therefore, over the last years, many researchers have focused on different ways to detect and more importantly prevent these intrusions. In this work, we describe the design and evaluation of a network security traffic analysis platform (NSTAP) that collects, searches, and analyzes traffic data in real time in order to filter out malicious flows. Through charts, tables, histograms, and other visualization methods, we demonstrate that the platform can produce powerful and useful insights with simple time-domain analytics of large data volumes. This work is intended to be the foundation for more automation tools based on machine learning.

Providing differentiated services to meet the unique requirements of different use cases is a major goal of the fifth generation (5G) telecommunication networks and will be even more critical for future 6G systems. Fulfilling this goal requires the ability to assure quality of service (QoS) end to end (E2E), which remains a challenge. A key factor that makes E2E QoS assurance difficult in a telecommunication system is that access networks (ANs) and core networks (CNs) manage their resources autonomously. So far, few results have been available that can ensure E2E QoS over autonomously managed ANs and CNs. Existing techniques rely predominately on each subsystem to meet static local QoS budgets with no recourse in case any subsystem fails to meet its local budgets and, hence will have difficulty delivering E2E assurance. Moreover, most existing distributed optimization techniques that can be applied to assure E2E QoS over autonomous subsystems require the subsystems to exchange sensitive information such as their local decision variables. This paper presents a novel framework and a distributed algorithm that can enable ANs and CNs to autonomously "cooperate" with each other to dynamically negotiate their local QoS budgets and to collectively meet E2E QoS goals by sharing only their estimates of the global constraint functions, without disclosing their local decision variables. We prove that this new distributed algorithm converges to an optimal solution almost surely, and also present numerical results to demonstrate that the convergence occurs quickly even with measurement noise.

We study the problem of minimizing the (time) average security costs in large systems comprising many interdependent subsystems, where the state evolution is captured by a susceptible-infected-susceptible (SIS) model. The security costs reflect security investments, economic losses and recovery costs from infections and failures following successful attacks. However, unlike in existing studies, we assume that the underlying dependence graph is only weakly connected, but not necessarily strongly connected. When the dependence graph is not strongly connected, existing approaches to computing optimal security investments cannot be applied. Instead, we show that it is still possible to find a good solution by perturbing the problem and establishing necessary continuity results that then allow us to leverage the existing algorithms.

This paper studies a problem of jointly optimizing two important operations in mobile edge computing without knowing future requests, namely service caching, which deter-mines which services to be hosted at the edge, and service routing, which determines which requests to be processed locally at the edge. We aim to address several practical challenges, including limited storage and computation capacities of edge servers and unknown future request arrival patterns. To this end, we formulate the problem as an online optimization problem, in which the objective function includes costs of forwarding requests, processing requests, and reconfiguring edge servers. By leveraging a natural timescale separation between service routing and service caching, namely, the former happens faster than the latter, we propose an online two-stage algorithm and its randomized variant. Both algorithms have low complexity, and our fractional solution achieves sublinear regret. Simulation results show that our algorithms significantly outperform other state-of-the-art online policies.

We study the problem of minimizing the (time) average security costs in large networks/systems comprising many interdependent subsystems, where the state evolution is captured by a susceptible-infected-susceptible (SIS) model. The security costs reflect security investments, economic losses and recovery costs from infections and failures following successful attacks. We show that the resulting optimization problem is nonconvex and propose a suite of algorithms – two based on convex relaxations, and the other two for finding a local minimizer, based on a reduced gradient method and sequential convex programming. Also, we provide a sufficient condition under which the convex relaxations are exact and, hence, an optimal solution of the original problem can be recovered. Numerical results are provided to validate our analytical results and to demonstrate the effectiveness of the proposed algorithms.

We study the problem of minimizing the (time) average security costs in large systems comprising many interdependent subsystems, where the state evolution is captured by a susceptible-infected-susceptible (SIS) model. The security costs reflect security investments, economic losses and recovery costs from infections and failures following successful attacks. We show that the resulting optimization problem is non-convex and propose two algorithms - one for solving a convex relaxation, and the other for finding a local minimizer, based on a reduced gradient method. Also, we provide a sufficient condition under which the convex relaxation is exact and its solution coincides with that of the original problem. Numerical results are provided to validate our analytical results and to demonstrate the effectiveness of the proposed algorithms.