Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursuessocial cost minimizationover time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting thespatial-temporal diversitiesinworkload and resource costamong federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges:user deadline preferenceandECs’ strategic behaviors. We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved$\mathcal {NP}$-hard. By leveraging user deadline tolerance, we develop a Lyapunov-baseddeadline-drivenjoint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. Theservice priorityimposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs. Given selfishness of individual ECs, we further design an auction-based cooperative mechanism to elicittruthful bidson workload and resource cost. Rigorous theoretical analysis and extensive simulations are performed, validating the efficiency of JCPS in realizing cost reduction and user satisfaction.

Combined with many different attack forms, advanced persistent threats (APTs) are becoming a major threat to cyber security. Existing security protection works typically either focus on one-shot case, or separate detection from response decisions. Such practices lead to tractable analysis, but miss key inherent APTs persistence and risk heterogeneity. To this end, we propose a Lyapunov-based security-aware defense mechanism backed by threat intelligence, where robust defense strategy-making is based on acquired heterogeneity knowledge. By exploring temporal evolution of risk level, we introduce priority-aware virtual queues, which together with attack queues, enable security-aware response among hosts. Specifically, a long-term time average profit maximization problem is formulated. We first develop risk admission control policy to accommodate hosts' risk tolerance and response capacity. Under multiple attacker resources, defense control policy is implemented on two-stage decisions, involving proportional fair resource allocation and host-attack assignment. In particular, distributed auction-based assignment algorithm is designed to capture uncertainty in the number of resolved attacks, where high-risk host-attack pairs are prioritized over others. We theoretically prove our mechanism can guarantee bounded queue backlogs, profit optimality, no underflow condition, and robustness to detection errors. Simulations on real-world data set corroborate theoretical analysis and reveal the importance of security awareness.

The scarcity of efficient charging infrastructures has suppressed penetration rate of Electric Vehicles (EVs). This paper mainly focuses on the Fast Charging Station (FCS) placement problem, especially with the elastic demand. We first propose the distance preference and waiting time preference to capture the elastic EV charging demand. Moreover, a fixed-point equation is proposed to illustrate the relationship between serving demand rate and waiting time at station. We further formulate the problem of FCS placement with elastic demand as a bi-level optimization problem. In the upper level, we propose a heuristic algorithm to determine the optimal location of charging stations, with the goal of maximizing overall system profit. In the lower level, queueing theory is utilized to analyze the optimal capacity of each charging station. The sufficient condition for the existence of optimal station capacity is given. Simulation results demonstrate the effectiveness of our approach in improving system profit and reducing demand loss rate.