In real-time applications under wide-area networks, providing a demanded quality of service for end users is an issue. Recent studies adopt distributed processing for server selection problems to reduce data synchronization delay and total interaction delay, assuming that link delays over the distributed system are exactly known. No study has addressed the problem of such a distributed server selection in properly handling the delay uncertainty. This paper proposes a robust optimization model for the distributed server selection problem against the delay uncertainty. We handle the delay uncertainty of user-server and server-server links by defining two -ellipsoidal uncertainty sets. The proposed model determines allocated servers for multiple users to minimize the weighted sum of data synchronization delay and total interaction delay over the distributed system. We formulate the proposed model as a mixed integer second-order cone programming problem. We prove that the distributed server selection problem with uncertain delays is NP-complete. We compare the proposed model with baseline models, focusing on delay uncertainty and distributed processing. The numerical results show that the proposed model can achieve a lower objective value than the baseline models, indicating the benefit of utilizing -ellipsoidal uncertainty sets to handle delay uncertainty.

In network function virtualization, a network service is provided by a service function chain (SFC), which consists of a chain of virtual network functions (VNFs) within a specific order. SFC parallelism allows parallel processing among VNFs to reduce the end-to-end service delay. Existing works handle the service delay without considering traffic uncertainty, which leads to degraded performance on parallel structure balancing and deployment cost saving in the parallelized SFC deployment problem. This paper proposes a robust deployment model for parallelized SFCs against traffic uncertainty that satisfies the requirement of balanced parallel structures and minimizes the deployment cost. We define a traffic uncertainty set that handles both the variation of service traffic arrival rates and the fluctuation of parallel structures. We apply VNF sharing to improve the efficiency of resource allocation. We formulate the proposed model as a mixed integer second-order cone programming (MISOCP) problem. We introduce a heuristic algorithm to handle larger-size problems, where the MISOCP approach is intractable to obtain a solution in a practical time. Numerical results show the advantages of the proposed model in terms of deployment cost over the baseline models.

In network function virtualization, a network service is provided by a service function chain (SFC), which consists of a chain of virtual network functions (VNFs) within a specific order. SFC parallelism allows parallel processing among VNFs to reduce the end-to-end service delay. Existing works handle the service delay without considering traffic uncertainty, which leads to degraded performance on parallel structure balancing and deployment cost saving in the parallelized SFC deployment problem. This paper proposes a robust deployment model for parallelized SFCs against traffic uncertainty that satisfies the requirement of balanced parallel structures and minimizes the deployment cost. We define a traffic uncertainty set that handles both the variation of service traffic arrival rates and the fluctuation of parallel structures. We apply VNF sharing to improve the efficiency of resource allocation. We formulate the proposed model as a mixed integer second-order cone problem. Numerical results show the advantages of the proposed model in terms of deployment cost over the baseline models.

In network function virtualization, virtual network functions (VNFs) are usually chained in specific orders to generate service function chains (SFCs). Recently, SFC parallelism has been presented to enable VNFs to run in parallel to reduce the end-to-end service delay. Existing works handle the issue of unbalanced parallel branches by assuming predefined linear delay models, which have limitations in efficient resource allocation and deployment cost savings. This paper proposes a deployment model for parallelized SFC that handles the imbalance issue with considering that the delay of each VNF depends on both arriving traffic and allocated computing resources, to improve the flexibility of computing resource allocation. We consider a nonlinear relationship between delay, allocated computing resources, and arriving traffic. We apply VNF sharing to improve the efficiency of resource allocation. We formulate the proposed model as a mixed integer second-order cone programming problem (MISOCP) to minimize the total deployment cost, with satisfying the end-to-end delay requirement. We also introduce a heuristic algorithm to solve the original problem, because the MISOCP approach is intractable to handle larger-size problems in practical time. Numerical results show that the proposed model achieves lower deployment cost than the baseline models.

In network function virtualization, virtual network functions (VNFs) are usually chained in specific orders to generate service function chains (SFCs). Recently, SFC parallelism has been presented to enable VNFs to run in parallel to reduce the end-to-end service delay. Existing works handle the issue of unbalanced parallel branches by assuming predefined linear delay models, which have limitations in efficient resource allocation and deployment cost savings. This paper proposes a deployment model for parallelized SFC that handles the imbalance issue with considering that the delay of each VNF depends on both the arriving traffic and the allocated computing resources, to improve the flexibility of computing resource allocation. We consider a non-linear relationship between delay, allocated computing resources, and arriving traffic. We apply VNF sharing to improve the efficiency of resource allocation. We formulate the proposed model as a mixed integer second-order cone problem to minimize the total deployment cost, with satisfying the end-to-end delay requirement. Numerical results show that the proposed model achieves lower deployment cost than the baseline models.

Wireless Internet of Things (IoT) devices densely populate our daily life, but also attract many attackers to attack them. In this paper, we propose a new Heterogeneous Susceptible-Exposed-Infected-Recovered (HSEIR) epidemic model to characterize the effect of heterogeneity of infected wireless IoT devices on malware spreading. Based on the proposed model, we obtain the basic reproduction number, which represents the threshold value of diffusion and governs that the malware is diffusion or not. Also, we derive the malware propagation scale under different cases. These analyses provide theoretical guidance for the application of defense techniques. Numerical simulations validated the correctness and effectiveness of theoretical results. Then, by using Pontryagin’s Minimum Principle, optimal control strategy is proposed to seek time-varying cost-effective solutions against malware outbreaks. More numerical results also showed that some control strategies, such as quarantine and vaccination, should be taken at the beginning of the malware outbreak immediately and become less necessary after a certain period. However, the repairing and fixing strategy, for example applying antivirus patches, would be keep on going constantly.

Whereas eco-feedback has been widely studied in HCI and environmental psychology, online manual control and automated control have been rarely studied with a focus on their long-term quantitative impact and usability. To address this, an intervention was tested with eighty office workers for twenty-seven weeks. Through the long-term field test, it was found that the addition of online controls in the feedback intervention led to more energy savings than feedback only and worked better for light and phone usage than computer and monitor usage. The addition of automated control led to the greatest savings but was less effective for efficient users than inefficient ones.