Information Spread across Social Network Services with Non-Responsiveness of Individual Users

This paper investigates the dynamics of information spread across social network services (SNSs) such as Twitter using the susceptible-infected-recovered (SIR) model. In the analysis, the non-responsiveness of individual users is taken into account; a user probabilistically spreads the received information, where not spreading (not responding) is equivalent to that the received information is not noticed. In most practical applications, an exact analytic solution is not available for the SIR model, so previous studies have largely been based on the assumption that the probability of an SNS user having the target information is independent of whether or not its neighbors have that information. In contrast, we propose a different approach based on a “strong correlation assumption”, in which the probability of an SNS user having the target information is strongly correlated with whether its neighboring users have that information. To account for the non-responsiveness of individual users, we also propose the “representative-response-based analysis”, in which some information spreading patterns are first obtained assuming representative response patterns of each user and then the results are averaged. Through simulation experiments, we show that the combination of this strong correlation assumption and the representative-response-based analysis makes it possible to analyze the spread of information with far greater accuracy than the traditional approach

CELL-LOSS-RATIO ANALYSIS WITH INSUFFICIENT KNOWLEDGE OF TRAFFIC CHARACTERISTICS

Abstract This paper evaluates cell-loss ratio in an output buffer of an ATM node based on the observed relative frequency of the number of cell arrivals during a fixed interval. The central issue of this evaluation problem is that the unique evaluation result of cell-loss ratio cannot be derived because the relative frequency of the number of cell arrivals does not completely describe the traffic characteristics of cell streams. Thus! this paper focuses on the derivation of the worst-case performance, that is, the cell-loss-ratio upper bound. Two cell-loss-ratio upper bounds are derived- one for when the cell streams are stationary, and one for when cell streams are stationary and ergodic. Numerical results show that cell streams with the same relative frequency of the number of cell arrivals can have quite different cell-loss ratios, and that the derived formula gives the actual upper bounds. 1

Optimal Resource Provisioning and Task Offloading for Network-Aware and Federated Edge Computing

Compared to cloud computing, mobile edge computing (MEC) is a promising solution for delay-sensitive applications due to its proximity to end users. Because of its ability to offload resource-intensive tasks to nearby edge servers, MEC allows a diverse range of compute- and storage-intensive applications to operate on resource-constrained devices. The optimal utilization of MEC can lead to enhanced responsiveness and quality of service, but it requires careful design from the perspective of user-base station association, virtualized resource provisioning, and task distribution. Also, considering the limited exploration of the federation concept in the existing literature, its impacts on the allocation and management of resources still remain not widely recognized. In this paper, we study the network and MEC resource scheduling problem, where some edge servers are federated, limiting resource expansion within the same federations. The integration of network and MEC is crucial, emphasizing the necessity of a joint approach. In this work, we present NAFEOS, a proposed solution formulated as a two-stage algorithm that can effectively integrate association optimization with vertical and horizontal scaling. The Stage-1 problem optimizes the user-base station association and federation assignment so that the edge servers can be utilized in a balanced manner. The following Stage-2 dynamically schedules both vertical and horizontal scaling so that the fluctuating task-offloading demands from users are fulfilled. The extensive evaluations and comparison results show that the proposed approach can effectively achieve optimal resource utilization