This paper investigates the reliability assessment and availability prediction techniques used in modeling of advanced (next generation) wireless communication networks. The 5G, 5G+, beyond 5G (B5G) and 6G communication technologies are leading to emerging applications of wireless communication that use cloud computing, edge computing, and fog computing. In the last decade, various user-centric and service-oriented networks such as internet of things (IoT), smart cities, smart homes, smart grids, drones, and unmanned aerial vehicles (UAV) have been deployed that use technologies such as network function virtualization (NVF), software defined networking (SDN), and 5G. This has led to proliferation of IoT devices and IoT applications in various critical usage systems such as intelligent transportation systems, smart healthcare, and e-commerce. The availability and reliability of wireless connectivity in IoT devices and nodes is of significant importance as unavailability of nodes or end-user devices even for a millisecond could cause failure of healthcare systems or lead to malfunction of connected and autonomous vehicles, or compromise the smart grids power generation and distribution, leading to fatal outcomes.

When an application deployed in the cloud faces changing workload, the services of the application need scaling up or down in response. The services run on Virtual Machines (VM) or container instances. Application Providers (APs) decide on how the applications are scaled through VM provisioning and through the placement of the services on those VMs. Various drivers guide this decision making. Application performance and cost are two such drivers. In this paper, we answer the question of how APs can meet the performance constraints of their applications while minimizing the cost of the running VMs. A VM provisioning problem is formulated which expects to meet mean response time constraints and minimize the cost, where VM-types having different cost rates are used. The proposed solution is based on genetic algorithm and bottleneck strength value. For the case study, a decision maker is implemented for a web application. The proposed solution is compared against an exhaustive search, a simple genetic algorithm and a random search. It is shown that our solution is able meet response time constraints with near optimal minimization of cost. The solution also results in better cost than random search and the plain genetic algorithm solution at the expense of slightly longer runtime.

End users of cloud applications range from novice to expert depending on how experienced they are in using them. With repeated usage of an application, a user's think time gradually decreases, known as human learning phenomenon. This decrease impacts the system workload thereby affecting the application's transient performance. However, such impact of human learning on system performance has never been accounted for. In this work we propose a closed queueing network model that accounts for human learning in analyzing the transient performance of a tiered cloud application. We solve the model using discrete event simulation. The model results show that the lack of accountability of human learning may lead to incorrectly selecting a virtual machine configuration that fails to meet the system response time requirement.

Cloud-radio access network (C-RAN) is regarded as a promising solution to manage heterogeneity and scalability of future wireless networks. The centralized cooperative resource allocation and interference cancellation methods in C-RAN significantly reduce the interference levels to provide high data rates. However, the centralized solution will not be scalable due to the dense deployment of small cells with fractional frequency reuse by small cells, causing severe inter-tier and inter-cell interference turning the resource allocation and user association into a more challenging problem. In this paper, we propose an auction based distributed resource allocation method (ADRA) for a two-tier OFDM based C-RAN system. We investigate a joint user association, radio resource and power allocation problem for small cells underlying a macro C-RAN system. First, we establish a queueing model in C- RAN. We then formulate an optimization problem for joint user association and resource allocation with the aim to minimize mean response time. Resource allocation, interference and queueing stability constraints are considered in the optimization problem. To solve this problem, we propose a distributed method where small cell users and small cell base stations jointly participate using the concept of auction theory. The ADRA method is evaluated via simulations by considering the different ratio of bandwidth utilization.

We introduce an online scheduling algorithm to optimally assign a set of arriving heterogeneous tasks to heterogeneous speed-scalable processors. The goal of our algorithm is to minimize the total cost of response time and energy consumption (TCRTEC) of the tasks. We have three contributions that constitute the algorithm. First, we propose a novel task dispatching strategy for assigning the tasks to the processors. Second, we propose a novel preemptive service discipline called Smallest remaining Computation Volume Per unit Price of response Time (SCVPPT) to schedule the tasks on the assigned processor. Third, we propose a dynamic speed-scaling function that explicitly determines the optimum processing rate of each task. In our work, the processors are heterogeneous in that they may differ in their hardware specifications with respect to maximum processing rate and power functions. Tasks are heterogeneous in terms of computation volume and processing requirements. We also consider that the unit price of response time for each task is heterogeneous. Each task's unit price of response time is allowed to differ because the user may be willing to pay higher/lower unit prices for certain tasks, thereby increasing/decreasing their optimum processing rates. In our SCVPPT discipline, a task's scheduling priority is influenced by its remaining computation volume as well as its unit price of response time. Our simulation results show that SCVPPT outperforms the two known service disciplines, Shortest Remaining Processing Time (SRPT) and the First Come First Serve (FCFS), in terms of minimizing the TCRTEC performance metric. The results also show that the algorithm's dispatcher outperforms the well known Round Robin dispatcher when the processors are heterogeneous. We focus on multi-buffer, single-threading where a set of tasks is allocated to a given processor, but only one task is processed at a time until completion unless preemption is dictated by the service discipline.

We introduce an online speed-scaling algorithm that is used to determine the optimum processing rate of executing a set of N jobs by a single processor of a mobile computing device under the single-threading (multi-buffered) computing architecture. We consider heterogeneous tasks that could differ in computation volume, memory and processing requirements. By using speed-scaling, where the processor's speed is able to dynamically change within hardware and software processing constraints, the algorithm explicitly determines the optimum processing rate of executing each task. This optimum processing rate was found to be a function of the number of 'alive' tasks (N), the remaining battery energy percentage, the processor's energy inefficiency coefficient, the unit price of response time and lastly, the unit price of energy. The algorithm allows the user or OS to specify the unit cost of energy and response time for executing all tasks. The algorithm has an operation mode where all tasks' unit cost of energy is also heuristically affected by the device' remaining battery energy percentage in accordance with the micro-economic laws of demand and supply. We synthesize the algorithm by analytically minimizing the total cost of both response time and energy consumption of tasks. We also consider other conventional performance metrics to evaluate the algorithm. Using numerical simulations, we show that when the remaining battery energy percentage is factored, the algorithm performs slightly slower (mildly more slower when the battery is almost drained out), but consumes far less energy, can complete significantly more jobs and ultimately allows the mobile computing device to last longer on the go.

In cognitive radio networks, it is important to effectively use the under-utilized spectrum resources without affecting the primary users. In an underlay system, secondary users are allowed to share the channel simultaneously with primary users (with the restriction on interference level) but not in an overlay system. In this article, we consider a system where a secondary user can switch between overlay and underlay modes of operation in order to improve its throughput with limited sensing capability (i.e. sensing only one channel at a time). The results based on Markov chain analysis are satisfactorily verified using Monte-Carlo simulation. It is found that proper selection of transmission mode can provide greater improvement in throughput for a secondary user. The mode selection depends on the transition characteristics of primary users and the throughput ratio between the two modes of operation.

In cognitive radio networks, it is important to effectively use the under-utilized spectrum resources without affecting the primary users. In an underlay system, cognitive users are allowed to share the channel simultaneously with primary users with the restriction on interference level but not in an overlay system. In this paper, we consider a system where cognitive users can switch between overlay and underlay modes of operation in order to improve their throughput. The results, based on Markov chain analysis, are satisfactorily verified using Monte-Carlo simulation. It is shown that proper selection of transmission mode can provide greater improvement in throughput for cognitive users. If a primary user occupies the channel for a longer (shorter) period, then the system should allow the cognitive users to choose underlay (overlay) mode for throughput advantage. The exact strategy (or mode) switching thresholds can be found from our analysis and it depends primarily on the transition probabilities of the primary users and throughput ratio during underlay/overlay transmission.

In this paper, different power allocation schemes between the source and the cooperating relay nodes are analyzed and numerically evaluated for a two-hop decode-and-forward OFCDM based relay network. A cooperative power allocation ratio ¿ (=source power/total power) is defined and BER performance is evaluated for different values of ¿ in the relay network. It is shown that there exists an optimal power allocation ratio for different operating environment such as source-to-relay channel gains and time-frequency spreading factors. It is reported that (a) When all three channels (source-to-relay, source-to-destination and relay-to-destination) have equal gains, power ratio is found to be ¿ ¿ 0.8 (i.e., 80% and 20% of the total power is distributed among source and relay node respectively). The BER performance degrades at a faster rate when ¿ increases above the optimal value than the decrement at higher Eb/N¿. (b) For a network with stronger source-to-relay link, the optimal ¿ remains invariant at higher Eb/N¿for equal channel gain case; however, the optimal power ratio moves toward lower value of ¿ at lower Eb/N¿. (c) The optimal ¿ remains almost the same with different time-frequency spreading factors.

We propose a novel opportunistic access scheme for cognitive radios that considers the channel gain as well as the predicted idle channel probability. In contrast to previous works where a cognitive user vacates a channel only when that channel becomes busy, our scheme requires the cognitive user to switch to the channel with the next highest idle probability if the current channel's gain is below a certain threshold. We derive the threshold values that maximize the long term throughput for various transition probabilities and compare our throughput results to those obtained using a non-channel adaptive scheme. We also provide simulation results that validate our analytical results.

In this paper, we first propose a modified adaptive subcarrier allocation algorithm for a two-hop decode-and-forward OFCDM relay system and then its BER performance analysis is formulated and numerically evaluated via Monte-Carlo simulation. The effect of considering the CSI of source-base station and source-relay links are evaluated in a cooperative diversity system. Results show that the allocation of subcarriers based on source-relay link CSI provides better BER performance at higher Eb/Noand at lower Eb/No, both the source-relay and source-base station links need to be considered. We also introduce a parameter called cooperative channel state information, C-CSI-(0 ¿ v ¿ 1), that weighs in end-to-end SINR and hence impacts the subcarrier allocation in OFCDM systems. From our numerical simulation, we notice that the cross-over Eb/Nopoint (around which frequency spreading gives better performance than time spreading) moves towards the lower Eb/Nowhen v approaches 1; that is, when the subcarrier allocation is done giving more weight to source-base station link rather than the source-relay link. We also observe a relationship between the cross-over point (Eb/Noin dB) and the C-CSI, which provides additional flexibility in operating environment for OFCDM systems.

Layered queues are a canonical form of extended queueing network for systems with nested multiple resource possession, in which successive depths of nesting define the layers. The model has been applied to most modern distributed systems, which use different kinds of client-server and master-slave relationships, and scales up well. The layered queueing network (LQN) model is described here in a unified fashion, including its many more extensions to match the semantics of sophisticated practical distributed and parallel systems. These include efficient representation of replicated services, parallel and quorum execution, and dependability analysis under failure and reconfiguration. The full LQN model is defined here and its solver is described. A substantial case study to an air traffic control system shows errors (compared to simulation) of a few percent. The LQN model is compared to other models and solutions, and is shown to cover all their features.

In this article, subcarrier and power allocation schemes are proposed and analyzed for different scenarios for a two-hop decode-and-forward OFCDM based relay network. In subcarrier allocation, the effect of considering the channel state information (CSI) of source-base station and source-relay link are evaluated in a cooperative diversity system. Results show that allocation of subcarriers based on source-relay node CSI provides better BER performance at higher Eb/No, and at lower Eb/No, both the source-relay and source-base station links need to be considered. From our numerical simulation, we also noticed that the cross-over Eb/No, point (around which frequency spreading gives better performance than time spreading) moves towards the lower Eb/No, when the subcarrier allocation is done giving more weight to source-base station link rather than the source-relay link which provides additional flexibility in operating environment for OFCDM systems. In power allocation, a cooperative power allocation ratio lambda (=source node power/total power) is defined and BER performance is evaluated for different values of lambda in the relay network. It is found that there exists an optimal power allocation ratio for different operating environment such as source-to-relay channel gains and time-frequency spreading factors. It is reported that: (a) when all three channels (source-to-relay, source-to-destination and relay-to-destination) have equal gains, power ratio is found to be lambda ap 0.8 (i.e., 80% and 20% of the total power is distributed among source and relay node respectively). The performance degrades at much faster rate when lambda increases above the optimal value at higher Eb/No. On the other hand, the performance remains almost the same when the decrement in lambda is less than the optimal value. (b) For a network with stronger source-to-relay link, the optimal lambda remains almost the same as the case with equal channel gains at higher Eb/No; however, the optimal power ratio moves toward lower value of lambda of 0.65 at lowerb/No. (c) The optimal lambda remains almost the same with different time-frequency spreading factors.

Dependable-LQNS is a software tool for modeling and evaluating performability of fault-tolerant layered distributed applications that use a separate architecture for failure detection and reconfiguration. It takes into account the effects of management architecture, application software architecture, failure of management and application components in the dependability computation. It uses a combination of minpath algorithms, AND-OR graphs, non-coherent fault trees and Layered Queueing modeling in the analysis.

Increasingly, fault-tolerant distributed software applications use a separate architecture for failure detection instead of coding the mechanisms inside the application itself. Such a structure removes the intricacies of the failure detection mechanisms from the application, and avoids repeating them in every program. However, successful system reconfiguration now depends on the management architecture (which does both fault detection and reconfiguration), and on management subsystem failures, as well as on the application. This paper presents an approach which computes the architecture-based system reconfiguration coverage simultaneously with its performability.