The robustness of human social networks against epidemic propagation relies on the propensity for physical contact adaptation. During the early phase of infection, asymptomatic carriers exhibit the same activity level as susceptible individuals, which presents challenges for incorporating control measures in epidemic projection models. This article focuses on modeling and cost-efficient activity control of susceptible and carrier individuals in the context of the susceptible-carrier-infected-removed (SCIR) epidemic model over a two-layer contact network. In this model, individuals switch from a static contact layer to create new links in a temporal layer based on state-dependent activation rates. We derive conditions for the infection to die out or persist in a homogeneous network. Considering the significant costs associated with reducing the activity of susceptible and carrier individuals, we formulate an optimization problem to minimize the disease decay rate while constrained by a limited budget. We propose the use of successive geometric programming (SGP) approximation for this optimization task. Through simulation experiments on Poisson random graphs, we assess the impact of different parameters on disease prevalence. The results demonstrate that our SGP framework achieves a cost reduction of nearly 33% compared to conventional methods based on degree and closeness centrality.

The temporal nature of data in Internet of Things (IoT) networks necessitates periodic updates of cached content at edge devices, while multicasting dynamic content can enhance network efficiency. This paper addresses the challenge of joint cache updating and multicast scheduling in a cache-enabled, queue-equipped small base station (SBS) with limited cache capacity, which accesses a macro base station (MBS) to download (update) uncached (cached) content and serves requests through multicasting. We formulate a two-stage optimization problem to minimize the average age of information (AAoI) per request, subject to constrained average queueing delay and access rate. The first stage employs the Lyapunov drift-plus-penalty method at the SBS to schedule multicasting and downloading (updating) uncached (cached) content. The second stage, implemented at the MBS, leverages deep reinforcement learning (DRL) to determine the content replacement policy. Simulation results show that the DRL-based cache replacement policy yields up to 50%, 59%, and 60% improvements in AAoI compared to the maximum age, least-recently-used, and least-frequently-used baseline policies, respectively.

Edge computing (EC) has emerged as a paradigm aimed at reducing data transmission latency by bringing computing resources closer to users. However, the limited scale and constrained processing power of EC pose challenges in matching the resource availability of larger cloud networks. Load balancing (LB) algorithms play a crucial role in distributing workload among edge servers and minimizing user latency. This paper presents a novel set of distributed LB algorithms that leverage machine learning techniques to overcome the three limitations of our previous LB algorithm, EVBLB : (i) its reliance on static time intervals for execution, (ii) the need for comprehensive information about all server resources and queued requests for neighbor selection, and (iii) the use of a central coordinator to dispatch incoming user requests over edge servers. To offer increased control, custom configuration, and scalability for LB on edge servers, we propose three efficient algorithms: Q-learning (QL), multi-armed bandit (MAB), and gradient bandit (GB) algorithms. The QL algorithm predicts the subsequent execution time of the EVBLB algorithm by incorporating rewards obtained from previous executions, thereby improving performance across various metrics. The MAB and GB algorithms prioritize near-optimal neighbor node servers while considering dynamic changes in request rate, request size, and edge server resources. Through simulations, we evaluate and compare the algorithms in terms of network throughput, average user response time , and a novel LB metric for workload distribution across edge servers.

Information aging has gained prominence in characterizing communication protocols for timely remote estimation and control applications. This work proposes an Age of Information (AoI)-aware threshold-based dynamic frame slotted ALOHA (T-DFSA) for contention resolution in random access machine-type communication networks. Unlike conventional DFSA that maximizes the throughput in each frame, the frame length and age-gain threshold in T-DFSA are determined to minimize the normalized average AoI reduction of the network in each frame. At the start of each frame in the proposed protocol, the common Access Point (AP) stores an estimate of the age-gain distribution of a typical node. Depending on the observedstatus of the slots, age-gains of successful nodes, and maximum available AoI, the AP adjusts its estimation in each frame. The maximum available AoI is exploited to derive the maximum possible age-gain at each frame and thus, to avoid overestimating the age-gain threshold, which may render T-DFSA unstable. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the proposed T-DFSA compared to the existing optimal frame slotted ALOHA, threshold-ALOHA, and age-based thinning protocols in a considerable range of update generation rates.

Power management in multi-server data centers especially at scale is a vital issue of increasing importance in cloud computing paradigm. Existing studies mostly consider thresholds on the number of idle servers to switch the servers on or off and suffer from scalability issues. As a natural approach in view of the Markovian assumption, we present a multi-level continuous-time Markov decision process (CTMDP) model based on state aggregation of multi-server data centers with setup times that interestingly overcomes the inherent intractability of traditional MDP approaches due to their colossal state-action space. The beauty of the presented model is that, while it keeps loyalty to the Markovian behavior, it approximates the calculation of the transition probabilities in a way that keeps the accuracy of the results at a desirable level. Moreover, near-optimal performance is attained at the expense of the increased state-space dimensionality by tuning the number of levels in the multi-level approach. The simulation results were promising and confirm that in many scenarios of interest, the proposed approach attains noticeable improvements, namely a near 50% reduction in the size of CTMDP while yielding better rewards as compared to existing fixed threshold-based policies and aggregation methods.

Reconfigurability and innovative design approaches to radio frequency components and network infrastructure are critical for the development of future communication networks, particularly beyond 5G (B5G), which aim to support the proliferation of Internet of Things (IoT) devices. Leveraging its favorable performance characteristics and potentially low cost, the fluid antenna system (FAS) has emerged as a compelling solution, garnering significant interest due to its reconfigurability, small form factor, flexibility, and transparency. This paper presents a comprehensive analysis of FAS in the context of B5G networks, with a focus on its theoretical performance and practical implementations. By deriving formulas for the semi-infinite outage probability and ergodic capacity of FAS receivers in equally correlated Nakagami-m channels, we showcase the remarkable diversity performance exhibited by FAS receivers, even with a half-wavelength antenna size. Monte Carlo simulations are employed to validate our theoretical findings in terms of the number of antenna ports and transmission power.

Concurrent with the rise of real-time wireless systems enabled by the Internet of Things, the age of information (AoI) has been widely perceived as a crucial destination-centric performance metric to quantify the timeliness of data delivery. This paper deals with the analysis of information freshness in a multi-source M/G/1 queueing system with utilization of idle server time, referred to as server vacation, in an effective manner. In particular, the status update packets in our model are generated independently by a finite set of source nodes and according to a Poisson process, while their service time follows a general random variable. Using stochastic decomposition and the Laplace-Stieltjes transform, we derive the average AoI (AAoI) expression for the multi-source M/G/1 queueing model with generally-distributed vacation time in closed form. Our numerical simulations validate the accuracy of the derived AAoI expression and assess the impact of different parameters on the system performance.

Prediction and control of spreading processes in social networks (SNs) are closely tied to the underlying connectivity patterns. Contrary to most existing efforts that exclusively focus on positive social user interactions, the impact of contagion processes on the temporal evolution of signed SNs (SSNs) with distinctive friendly (positive) and hostile (negative) relationships yet, remains largely unexplored. In this paper, we study the interplay between social link polarity and propagation of viral phenomena coupled with user alertness. In particular, we propose a novel energy model built on Heider's balance theory that relates the stochastic susceptible-alert-infected-susceptible epidemic dynamical model with the structural balance of SSNs to substantiate the trade-off between social tension and epidemic spread. Moreover, the role of hostile social links in the formation of disjoint friendly clusters of alerted and infected users is analyzed. Using three real-world SSN datasets, we further present a time-efficient algorithm to expedite the energy computation in our Monte-Carlo simulation method and show compelling insights on the effectiveness and rationality of user awareness and initial network settings in reaching structurally balanced local and global network energy states.

Content caching is perceived as a promising approach to offload traffic from shared medium networks by pre-fetching and placing contents in caches during off-peak hours, and taking advantage of multicasting in the delivery times. While most existing efforts on caching are directed towards reducing the traffic rate over the shared medium, little attention is given to user preferences. Though placement based on the heterogeneity of content popularity improves the caching performance, it is understood to be a non-trivial combinatorial optimization problem. In this paper, we investigate the content caching problem under non-uniform and user-dependent demands and propose several heuristic methods by leveraging hybrid coded-uncoded caching, clustering, and the trade-off between local and global popularity of contents. Simulation results validate our analysis and show the out-performance of the proposed schemes with respect to existing methods.

In serverless architectures, the scheduler component in the controller acts as a load-balancing entity and distributes the arriving tasks based on the availability of free resources on the machines. We argue that offering a multi-step decision-making process makes it feasible to allocate tasks to machines prudently, thus reducing the average response time to users. Aiming to achieve this, we propose to equip the controller with a limited cache for diverse purposes, including preserving the hash of task outcomes and storing the history of task deployment, in this paper. The response time is then further optimized by employing a batch-based technique and grouping the tasks. Evaluation results reveal that the proposed cache-aided serverless architecture can increase the average response time speed by nearly 21 %, on average, in the decision-making process.

In this paper, we aim to derive the average age of information (AAoI) associated with the static link scheduling policies in a buffer-aided two-way relay network, where two sources exchange their updates through an intermediate relay equipped with data buffers. With regard to buffer-aided relaying, we consider the two-mode relaying scheme, where each time slot is dedicated to either broadcast or multiple access mode. Moreover, the link scheduling policy is considered to be static, which randomizes between broadcast and multiple access modes when the relay is backlogged and chooses the multiple access mode, otherwise. We establish AAoI corresponding to the sources in terms of steady-state probabilities of buffers at the relay for different instants of the network. Through numerical results, we show that the joint status of the queues at the relay has a significant effect on determining the optimal static link scheduling policy and further validate our analytical approach via simulations.

Edge computing (EC)is a promising solution to enable the next-generation delay-critical network services which are not conceivable in the traditional cloud-based architecture. EC takes the computing and storage resources closer to the end-users at the edge of the networks to eliminate the propagation delays caused by geographical distances. However, due to the lack of facilities such as cooling systems, the capacity of available resources in the edge is far less than that in the remote clouds. So, efficient utilization of the edge resources has a profound impact on the effectiveness of the edge computing paradigm. Load balancing is a key factor in achieving resource efficiency and high utilization. In this paper, we present the design of EVBLB, an efficient load balancing algorithm based on Voronoi tessellation (VT) that assigns the users' service requests to the edge servers while considering the density of edge resources in the area and the distance of the users from the assigned servers. Building on the notion of VT not only allows us to achieve these goals, but is also computable in linear time, which significantly improves the scalability and responsiveness of our proposed method as compared to existing studies. Our simulation results show that EVBLB outperforms two conventional baselines in terms of throughput, response time, task completion time, and request blocking rate.

In this paper, we use the concept of Markovian jump linear systems in order to analyze the age of information (AoI) in discrete-time Markovian systems. This approach is in fact, the discrete-time counterpart of the stochastic hybrid system (SHS) model reported for analyzing AoI in continuous=time Markovian systems and thus, is referred to as the discrete-time SHS (DT-SHS) model. We then apply the DT-SHS model in two wireless relay network settings to analyze and optimize the AoI associated with the static link scheduling policies. The first relay network comprises of one relay and a direct link between source and destination, whereas the second setting has two relays and no direct link. Moreover, a static link scheduling policy schedules the links without any knowledge about the state of the network. Using results obtained through numerical simulations, we validate our analytical approach and show the effect of relays in AoI improvement.

Content caching is a technique aimed to reduce the network load imposed by data transmission during peak time while ensuring users' quality of experience. Studies have shown that content delivery via coded caching can significantly improve beyond the performance limits of conventional caching schemes when caches and the server share a common link. Finding the optimal cache content placement however, becomes challenging under arbitrary distributions of content popularity. While existing works show that partitioning contents into three popularity levels performs better when multiple requests are received at each time slot, they neither delve into the problem analysis nor derive closed-form expressions for the optimum partitioning problem. In this paper, we analyze the coded caching scheme for a system with arbitrary content popularity, where we derive explicit closed-forms for the server load in the delivery phase and formulate the near-optimum partitioning problem. Simulation results are presented to corroborate our mathematical analysis.

Network operators are more than ever overwhelmed by increased capital investments and operational costs incurred due to the explosive mobile data traffic growth. An efficient strategy in managing operational expenditure within limits without sacrificing end-user satisfaction is to offload the traffic of content dissemination from the network backbone to local road-side units (RSUs). Allocating suitable subsets of contents to each RSU cache so as to maximize the hit ratio of requests made by vehicular entities is an optimization problem of paramount value. In this paper, we address the issue of content dissemination in a socially-aware hybrid environment with mobile end-users, wherein an overlay network comprising of a social network over a cellular vehicular network is considered. In this setting, vehicles represent the mobile nodes that obtain requested contents either from base station or from local RSUs, if available. By employing a mobile RSU that accounts for the social characteristics of the underlying network, we introduce a novel approach for content distribution in an urban environment. Results from simulation experiments reveal an average improvement of 6% in network throughput for the proposed method as compared to conventional content dissemination counterparts.

Content caching has emerged as a promising technique to reduce the backhaul multimedia traffic rising due to the proliferation of mobile devices. To address the bottleneck issue arising as a result of sparse wireless resources, the current literature is mainly focused on designing centralized or decentralized coded caching schemes. In this paper, we present a hybrid coded caching approach in a cellular network considering mobile users, where both downlink transmission from the base-station (BS) and device-to-device (D2D) communications are permitted. The proposed method comprises of two phases in content delivery. In the first phase, coded packets are delivered using decentralized coded caching which provides concurrent transmissions through spatial reuse. The BS broadcasts the remaining files using the centralized coded caching paradigm in the second phase in order to compensate for the diminishing returns in D2D communications as time progresses. We analytically derive and analyze the optimal switching point for which the network performance improves in terms of throughput and response time delay under two random user mobility models. Validated by simulation results, our hybrid strategy significantly reduces the finishing time as compared to existing schemes.

In this letter, the theory of random matrices of increasing dimension is used to construct a form of regularized linear discriminant analysis (RLDA) that asymptotically yields the lowest overall risk with respect to the bias of the discriminant in cost-sensitive classification of two multivariate Gaussian distributions. Numerical experiments using both synthetic and real data show that even in finite-sample settings, the proposed classifier can uniformly outperform RLDA in terms of achieving a lower risk as a function of regularization parameter and misclassification costs.

Distribution of limited smart grid resources among electric vehicles (EVs) with diverse service demands in an unfavorable manner can potentially degrade the overall profit achievable by the operating charging station (CS). In fact, inefficient resource management can lead to customer dissatisfaction arising due to prolonged queueing and blockage of EVs arriving at the CS for service. In this paper, a dynamic electric power allocation scheme for a charging facility is proposed and modeled as a bi-variate continuous-time Markovian process, with exclusive charging outlets being allotted to EVs of different classes in real-time. The presented mechanism enables the CS to guarantee the quality-of-service expected by customers in terms of blocking probability, while also maximizing its own overall revenue. By adopting a practical congestion pricing model within the defined profit function, the revenue optimization framework for a single CS is further extended to a load-balanced network of CSs. Simulation results for the single CS and networked models reveal considerably higher satisfaction levels for congested fast charging EV customers and improved attainable system revenue as compared to a baseline scenario which assumes no classification based on EV service preferences.

Recent studies in modern telecommunication networks have convincingly revealed that IP traffic exhibits a perceptible self-similar behavior over a wide range of time scales. Adapting the traditional Poisson model can therefore lead to erroneous conclusions regarding network performance dynamics. On the other hand, with growing demand for greater bandwidth, several optical paradigms have been proposed as substitutes for the next-generation Internet backbone. Among all these approaches, optical burst switching (OBS) has been widely recognized as a suitable alternative to optical packet switching (OPS) due to its support for bursty traffic and high bandwidth granularity. Thus, devising suitable buffers so as to accurately capture the fractal behavior of multimedia traffic in such optical core switches has become a major scientific endeavor. For the first time, in this paper, we propose an analytical model with quality of service (QoS) provision at a complete OBS network level. We then study the performance of the presented model in terms of blocking probability. Using this model, we also study the impact of burst aggregation time on the total loss probability and validate its correctness through simulation results.