Stochastic Reward Nets Research Articles

Mobility‐aware modeling and evaluation of IoT systems using stochastic reward nets

SummaryThe frequent geographical changes of mobile nodes in Internet of Things (IoT) systems affect communication, activities, and behaviors. In such scenarios, it is crucial to establish a system model capable of evaluating quality of service (QoS) measures. However, the existing formal modeling techniques pose complexities in modeling mobility. To deal with these challenges, this study aims to propose a model that simplifies the process of modeling mobility within IoT systems. This paper presents a method for modeling mobility within IoT systems by leveraging a widely recognized extension of stochastic Petri nets known as stochastic reward nets (SRNs). The proposed method enhances the SRN model by incorporating the location concept, resulting in a novel extension called mobile SRN (MSRN). In this work, a case study utilizes the MSRN to evaluate the suggested features, examining various scenarios and investigating the impact of factors such as environmental conditions, sensor sampling rate, and the permissible distance of the node from the sink.

An SRN-based model for quantitative evaluation of IoT quality attributes

Today, the Internet of things (IoT) is widely used in various fields, including health control, smart cities, intelligent buildings, and so on. One of the severe concerns in IoT systems is the issue of energy consumption and its management. IoT systems have limited energy resources, and in this regard, these limited resources must be managed appropriately. To design and build IoT systems, various aspects such as usable chips, types of communication protocols, timing of sending and receiving data, and so on, directly affect the system's energy consumption. Therefore, it is necessary to model and evaluate the energy consumption of IoT systems before building and implementing the system. Using an appropriate model makes it possible to investigate and understand how much the system consumes energy and how it is in conformity with the system's demands. This paper presents a stochastic reward net (SRN)-based model for modeling and quantitative evaluation of system energy consumption. To solve and evaluate the model, the proposed model is converted into an SRN model based on a series of automatic transformations. The proposed model is used in a case study to show how the model works and the results are given in the paper.

Performability analysis of adaptive drone computation offloading with fog computing

Performance and availability are mutually-dependent quality aspects of drone application systems. Computations on the drone, like image processing, need good performance but may harm the availability of the service. The availability concern can be mitigated by offloading computation tasks to a computing infrastructure like fog computing. However, the latency and the reliability of the communication network have non-negligible impacts on the performance when offloading. This paper focuses on analyzing the tradeoffs between performance and availability in a drone system with various computing modes. We also present a novel adaptive computation offloading scheme called Performability-Aware offloading (PA-Offload). PA-Offload adaptively determines a condition to start or stop offloading by evaluating the expected performance and availability under given environmental status. We introduce a performability measure as an indicator to determine the condition and present comprehensive Stochastic Reward Nets (SRNs) to estimate the expected performabilities for a drone processing system. The numerical studies considering multiple network channels, drones, and fog-computing resources show that PA-Offload can effectively choose better computation modes under different workloads and network conditions. The evaluation results show that PA-Offload consistently achieves the highest performability over the drone processing mode, the fog offloading mode, and other baseline algorithms for offloading decisions.

Evaluation of the functionality of mobile wireless sensor networks using stochastic reward nets

Evaluation of the functionality of mobile wireless sensor networks using stochastic reward nets

SPM: A Novel Hierarchical Model for Evaluating the Effectiveness of Combined ACDs in a Blockchain-Based Cloud Environment

Cloud computing provides blockchain a flexible and cost-effective service by on-demand resource sharing, which also introduces additional security risks. Adaptive Cyber Defense (ACD) provides a solution that continuously changes the attack surface according to the cloud environments. The dynamic characteristics of ACDs give defenders a tactical advantage against threats. However, when assessing the effectiveness of ACDs, the structure of traditional security evaluation methods becomes unstable, especially when combining multiple ACD techniques. Therefore, there is still a lack of standard methods to quantitatively evaluate the effectiveness of ACDs. In this paper, we conducted a thorough evaluation with a hierarchical model named SPM. The proposed model is made up of three layers integrating Stochastic Reward net (SRN), Poisson process, and Martingale theory incorporated in the Markov chain. SPM provides two main advantages: (1) it allows explicit quantification of the security with a straightforward computation; (2) it helps obtain the effectiveness metrics of interest. Moreover, the hierarchical architecture of SPM allows each layer to be used independently to evaluate the effectiveness of each adopted ACD method. The simulation results show that SPM is efficient in evaluating various ACDs and the synergy effect of their combination, which thus helps improve the system configuration accordingly.

Analyzing stochastic reward nets by model checking and parallel simulation

This paper deals with Stochastic Reward Nets (SRN), which are a powerful extension of Generalized Stochastic Petri Nets (GSPN). SRN have proved their usefulness in modelling and analysis of performance, availability and reliability of complex timed systems. SRN are supported by special-case tools like the Stochastic Petri Net Package (SPNP) which enables both analytic (based on Markov Reward Models) and simulative studies. The work described in this paper argues that there is still a gap in SRN analysis concerning functional correctness and non-deterministic property checking. Toward this a novel approach is proposed, which is based on two developed tools. First a formal reduction of SRN onto the Timed Automata (TA) of the popular Uppaal toolbox was defined and implemented. The Uppaal reduction enables a more complete investigation of SRN models, not allowed by existing SRN tools. However, the practical use of Uppaal forbids to study the performance of large models. Then, an SRN kernel, inspired by the carried formal Uppaal modelling and reasoning, was achieved in Java using the Theatre actor system. The realization supports the parallel simulation of scalable models. The paper applies the developed tools to a realistic grid-computing model and reports some experimental results, together with good execution performance (speedup) when using a scalable version of the grid model on a shared memory multi-core machine.

Performability evaluation of switch-over Moving Target Defence mechanisms in a Software Defined Networking using stochastic reward nets

It is essential to comprehend different aspects of performability of a system adopting Moving Target Defence (MTD) techniques. A number of previous works showed significant progress on security effectiveness evaluation for MTD mechanisms. While, a lesser amount of studies considered the impact of running MTD mechanisms on system dependability metrics, exposing a critical missing on the comprehension of pros and cons of MTD mechanisms in terms of security and dependability. In this paper, we present comprehensive modelling and analysis of time-based switch-over MTD strategies complying with IP shuffling techniques deployed in a Software Defined Network (SDN) using stochastic reward net (SRN). To investigate the impact of MTD strategies along with system availability on service performance metrics, we propose performability SRN models for various switch-over MTD strategies. The modelled behaviours of the switch-over MTD strategies are based on the integration of service management policies (drop/accept) with time-based switch-over policies (zero-time, fixed-time, and variable-time waiting policies) Critical performability metrics are comprehensively evaluated, including (i) system availability, downtime minutes, and Capacity-oriented Availability (COA) of a service, (ii) service throughput, (iii) response time of a job, (iv) average utilization of a server, (v) number of lost jobs, and (vi) operational cost (power consumption and business profit loss). The analysis results reveal sophisticated operational system behaviours and the impact of MTD strategies on system performability metrics. This study can help design and plan the development and adoption of MTD strategies in practice regarding the trade-offs between security and performability assurance.

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets.

The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics.

Modeling Epidemic Routing: Capturing Frequently Visited Locations While Preserving Scalability

This paper investigates the performance of epidemic routing in mobile social networks considering several communities which are frequently visited by nodes. To this end, a monolithic Stochastic Reward Net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting preferences. This model is not scalable enough, in terms of the number of nodes and frequently visited locations. In order to achieve higher scalability, the folding technique is applied to the monolithic model, and an approximate folded SRN is proposed to evaluate performance of epidemic routing. Discrete-event simulation is used to validate the proposed models. Both SRN models for predicting the performance of epidemic routing exhibit high accuracy. We also propose an Ordinary Differential Equation (ODE) model for epidemic routing and compare it with the folded model. The obtained results show that the folded model is more accurate than the ODE model. Moreover, it is proved that the number of transmissions by the time of delivery follows a uniform distribution, for a general class of networks, where positions of nodes are always independent and identically distributed.

An Stochastic Reward net Model for Performance Analysis of Network layer in Mobile Ad Hoc Network Under the Workload of Misbehvaior Nodes

The most usage of Mobile Ad-hoc Network in emergency and critical cases, needs a precise evaluation of its performance toward security challenges. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing steady-state performance metrics. In addition to a lack of the required visual tools to understand and assess network operations, there is no theoretical background for mentioned simulators, too. In this research, we propose a performance evaluation framework for mobile ad hoc networks under the workload of misbehavior nodes, using Stochastic Reward Net (SRN). The presented framework encompasses two separate SRN models for modeling routing process and data flowing process which are equipped with a set of mathematical equations for deriving network parameters. Each of the models modified in ordered to simulate a network with three different attack strategies, Black-hole, Packet dropping and Flooding. The evaluation conducted in terms of nodes density, nodes velocity and number of misbehavior nodes using two performance metrics, Packet Delivery Ratio and End_to_End Delay. The correctness of the model investigated with the additional use of NS-2 network simulator. For all performance metrics, the gained value from SRN model well matched to the value obtained from simulation environment with a considerable lesser time for execution.

Modeling and Evaluation of Multi-Hop Wireless Networks Using SRNs

As multi-hop wireless networks are attracting more attention, the need to evaluate their performance becomes essential. In order to evaluate the performance metrics of multi-hop wireless networks, including sending and receiving rates of a node as well as the collision probability, a model based on Stochastic Reward Nets (SRNs) is proposed. The proposed SRN models a typical node in such networks, considered as a general template to be applied to any wireless node. The SRN model of a single node is designed to take transmission effects of all neighboring nodes into account, while ignoring the ones whose transmission has no effect on the node under-study. Applying the proposed SRN to each node in a multi-hop wireless network, we can model any arbitrary topology and evaluate the mean queue size, collision probability, sending and receiving rates of the nodes. Common issues related to Medium Access Control (MAC) and physical layer of wireless networks, e.g. hidden and exposed node problems, collision, transmission, interference and carrier sense ranges, are considered in the proposed SRN model. The results obtained by the proposed model, in two different scenarios, are validated with the results obtained from the discrete-event simulation conducted in ns-2 simulator to show the applicability and accuracy of the proposed SRN model.

Evaluation of memory performance in NUMA architectures using Stochastic Reward Nets

Understanding memory performance in multi-core platforms is a prerequisite to perform optimizations. To this end, this paper presents analytical models based on Stochastic Reward Nets (SRNs) to model and evaluate the memory performance of Non-Uniform Memory Access (NUMA) multi-core architectures. The approach considers the details of the architecture and first proposes a monolithic SRN model that evaluates the memory performance in terms of the mean memory response time. Since the monolithic model incurs a state space explosion with an increasing number of cores and memory controllers, two approximate models are presented that are able to evaluate large-scale NUMA architectures. The SRNs are validated through measurements on two NUMA multi-core platforms, a 64-core AMD Opteron server and a 72-core Intel system. The results demonstrate the ability of the proposed models to accurately compute the mean memory response time on NUMA architectures. The results also provide valuable information that runtime systems and application designers can use to optimize execution of parallel applications on such architectures.

Job Completion Time Under Migration-Based Dynamic Platform Technique

Migration-based Dynamic Platform (MDP) technique, a type of Moving Target Defense (MTD) techniques, defends against sophisticated cyber-attacks by randomly and dynamically selecting a platform for executing service/job. Security defense mechanisms protect service/job usually at the cost of degrading its performance. Therefore, it is valuable to make a trade-off between service/job security and its performance. However, previous researches on MTD techniques either focused on analyzing MTD effectiveness of protecting service/job or studied service/job performance with the assumption that attacks on service/job make no influence on its execution. This article aims to apply analytical modeling techniques to investigate the impact of MDP technique on job completion time in a system under attack. We use Stochastic Reward Nets (SRNs) to develop a Markov chain-based model for capturing typical behaviors of the adversary, the vulnerable system and a job. The formulas are derived for calculating the metrics of interest. Numerical analysis is conducted to study the impact of key parameters on job completion time and job security loss.

A phase expansion for non-Markovian availability models with time-based aperiodic rejuvenation and checkpointing

This paper presents a stochastic framework, consisting of stochastic reward net (SRN) for capturing the transient behaviors of the system and its related non-Markovian state transition diagram, to model an operational software system that undergoes aperiodic time-based rejuvenation and checkpointing schemes, and further to investigate whether there exists the optimal rejuvenation schedule that maximizes the system steady-state availability. A phase expansion approach is adopted to solve the non-Markovian availability models, which are actually neither the semi-Markov processes nor the Markov regenerative processes. Our numerical results show an appropriate rejuvenation trigger timing range, resulting in the positive improvement effect on the system availability of a database system, and that there exists the optimal rejuvenation trigger timing maximizing the system availability.

Numerical Evaluation of Job Finish Time Under MTD Environment

Moving target defense (MTD) has recently emerged as a game-changer in the confrontation between cyberattack and defense. MTD mechanism constantly and randomly changes the system configurations to create uncertainty of the attack surface against cyber-adversaries. To date, researches on the evaluation of MTD techniques either focused on analyzing the effectiveness of MTD or studying the system performance loss due to the use of MTD. The impact on job/service running on the protected system is always ignored. In this paper, we propose an SRN (Stochastic reward net) based analytical modeling approach to investigate how MTD techniques influence the job running on protected system from the perspective of job finish time. The SRN model developed in this paper captures the behaviors of both the adversary and the job execution process. Furthermore, we carry out numerical analysis to study the impact of different system parameters on job finish time and other evaluation metrics. The results in this paper can help defenders choose a better MTD configuration to complete the job execution as soon as possible.

Analytical evaluation of resource allocation algorithms and process migration methods in virtualized systems

In this paper, analytical models, based on the stochastic reward nets (SRNs), are proposed to analyze the impact of resource allocation algorithms and process migration methods on the power consumption and performance of virtualized systems. In the proposed models, each computer offers a certain capacity of computational, data, and communication resources and resource requesters, namely processes, are categorized into three main categories: compute-intensive, data-intensive and communication-intensive, according to their required resources. Since the processing rate of a computer reduces when the number of the processes running on the computer increases, we apply the migration methods to keep the performance of the system at a high level and reduce the power consumption as much as it is possible. The proposed SRNs appropriately model the migration of processes among computers by applying two types of migration methods: power-aware and performance-aware. Furthermore, by applying the proposed models, different resource allocation algorithms, e.g. First-fit, Best-fit, Worst-fit, and Random, can be compared in terms of the power consumption and performance. The numerical results, cross-validated with the CloudSim framework, show that the proposed models can be appropriately used to analyze different resource allocation algorithms and migration methods, and thereby, help system providers to make treatment decisions with confidence.

Hierarchical Stochastic Models for Performance, Availability, and Power Consumption Analysis of IaaS Clouds

Infrastructure as a Service (IaaS) is one of the most significant and fastest growing fields in cloud computing. To efficiently use the resources of an IaaS cloud, several important factors such as performance, availability, and power consumption need to be considered and evaluated carefully. Evaluation of these metrics is essential for cost-benefit prediction and quantification of different strategies which can be applied to cloud management. In this paper, analytical models based on Stochastic Reward Nets (SRNs) are proposed to model and evaluate an IaaS cloud system at different levels. To achieve this, an SRN is initially presented to model a group of physical machines which are controlled by a management layer. Afterwards, the SRN models presented for the groups of physical machines in the first stage are combined to capture a monolithic model representing an entire IaaS cloud. Since the monolithic model does not scale well for large cloud systems, two approximate SRN models using folding and fixed-point iteration techniques are proposed to evaluate the performance, availability, and power consumption of the IaaS cloud. The existence of a solution for the fixed-point approximate model is proved using Brouwer's fixed-point theorem. A validation of the proposed monolithic and approximate models against both an ad-hoc discrete-event simulator developed in Java and the CloudSim framework is presented. The analytic-numeric results obtained from applying the proposed models to sample cloud systems show that the errors introduced by approximate models are insignificant while an improvement of several orders of magnitude in the state space reduction of the monolithic model is obtained.

Performance Evaluation of Epidemic Content Retrieval in DTNs With Restricted Mobility

In some applicable scenarios, such as community patrolling, mobile nodes are restricted to move only in their own communities. Exploiting the meetings of the nodes within the same community and the nodes within the neighboring communities, a delay tolerant network (DTN) can provide communication between any two nodes. In this paper, two analytical models based on stochastic reward nets (SRNs) are proposed to evaluate the performance of the epidemic content retrieval in such multi-community DTNs. Performance measures computed by the proposed models are the average retrieval delay and the average number of transmissions. The monolithic SRN model proposed in the first step is not scalable, in terms of the number of communities and nodes, due to the state space explosion in the underlying Markov chain. In order to solve the scalability problem of the monolithic model, an approximate model based on the folding technique is presented which allows us to evaluate the performance of large-scale DTNs. In order to cross-validate the results obtained from the proposed models, we extend the ONE simulator to support our network model. The analytic-numeric results indicate that both models have good accuracy, and the folded model reduces the state space highly, achieving good scalability without any significant loss of accuracy.

Unified power and performance analysis of cloud computing infrastructure using stochastic reward nets

In this paper, power and performance of cloud infrastructures are analytically modeled and numerically evaluated using stochastic reward nets (SRNs). The main objective of the proposed model is to analyze the impact of the temperature and network traffic on power and performance of cloud computing systems, which has been ignored in most of previously presented approaches. In the proposed model, incoming tasks are divided into two categories, cold and hot, demonstrating different power consumption patterns, that can fail due to bandwidth constraints and congestion. By applying the proposed model, different resource allocation scenarios can be evaluated and compared, in terms of power and performance, when hot and cold tasks are assigned to virtual machines and the network congestion is taken into consideration. The results obtained from the proposed model, cross-validated with the CloudSim framework, show that the scenario which uses the minimum number of racks and the maximum number of servers is optimal in terms of both the power and performance measures.

Analytical modeling of RLC protocol of LTE using stochastic reward nets

SummaryRLC (radio link control) is a layer 2 protocol of LTE used for reliable transmission of data packets in communication networks. This protocol operates in three modes, namely, transparent mode (TM), unacknowledged mode (UM), and acknowledged mode (AM). In this paper, analytical models are developed to study the flow of data streams in all the three modes of the RLC protocol. Stochastic reward net (SRN) is adopted as a modeling technique as it easily captures various aspects of this protocol such as protocol data unit (PDU) transmission, assigning header with each PDU, requesting for status report, retransmission of PDU(s), management of PDU losses due to expiration of various timers (quick repeat timer, poll timer, status timer). Throughput, average delay and probability of PDU loss are evaluated as the performance measures using the SRN models. Based on these performance measures, a comparative study between the UM and AM is presented which depicts that the modes are performing their key functions decently. Analytical results are also validated via simulation.