Traditionally, train operating costs, including track access charges(TAC) and delay penalties, are paid to third-party operators and railway infrastructure providers based on the actual status of the train. However, due to the lack of price approval and transparency in charges, the payment process can be lengthy and difficult to ensure fairness. In contrast, parametric settlement based on blockchain allows for predetermined amounts to be paid using real-time event data. This decentralized model can be implemented in environments without regulatory frameworks. In blockchain architectures, the execution of smart contracts depends significantly on the parameters supplied by oracles. However, existing solutions often struggle to handle dynamic train environments and various conflicts of interest, which can compromise the accuracy of these parameters. This paper examines how to establish a parametric settlement of train operating expenses using a blockchain built on a distributed oracle network, focusing on the aggregation of parameters in this scenario. We propose a set of distributed intelligent transportation data dissemination models that enable the European Train Control System (ETCS) to facilitate the payment of train operating fees while resisting data tampering attacks. At the sidechain layer, we introduce a distributed detector that combines the Unscented Kalman Filter(UKF) and residual analysis for accurate train state estimation and detection. Additionally, homomorphic encryption is utilized during data transfer to safeguard the privacy of source data and to defend against Threshold Bypassing Attacks (TBA). At the main chain layer, a smart contract establishes an automated payment and approval mechanism between the participating parties. This process relies on data provided by a distributed oracles network (DON). Extensive simulation results show that this parametric settlement scheme can effectively complete the payment process while minimizing the risks associated with data tampering attacks.

Modern applications show an increasing demand for continuously processing massive data streams in real time. Mini-batching technique is commonly used for transporting streaming data across these applications. However, although a larger mini-batch size increases throughput, it also raises the end-to-end latency and easily violate the latency constraint required by real-time applications. While existing work mostly studies this problem at the computation stage, this work explores it in the streaming data transportation stage. We show that selecting a proper mini-batch size is essential for efficient streaming data delivery. We further identify the shortcomings of existing latency measurements and introduce new Quality of Service (QoS) metrics: latency violation rate, timely throughput, message loss, and duplicate rate. To address the challenge of mini-batching under varying network conditions, we first develop prediction models for the proposed QoS metrics and then adaptively adjust the mini-batch size based on these predictions. In our experiments, the random forest regressor achieves an$R^{2}$of 0.99 for performance metrics, and the multilayer perceptron achieves an MAE below 0.02 for reliability metrics. Using these predictions, the proposed adaptive strategy continuously updates the mini-batch size according to the observed network state. In a Kafka testbed with network packet loss rate approaching 25%, our strategy improves timely throughput by up to 30% compared with empirical mini-batch size selection. The results confirm the effectiveness of the adaptive mini-batching approach.

HPC systems are shared between many users.Managing their resources and scheduling compute jobs is a central task of these clusters.Scheduling also allows to control the workload and energy consumption of an HPC system.A Digital Twin of an HPC cluster can aid in the scheduling process by providing energy measurements about the system and predict scheduling decisions with a simulation.For a real-world use case, an integration of the Digital Twin with the scheduler is necessary.A possible use case are energy limitations as part of a demand response process between the HPC operator and energy supplier.Therefore, this paper introduces a plugin for Slurm, an opensource scheduler, that implements a scheduling algorithm for time-dependent cluster-wide power capping.It uses a node energy model to predict the energy consumption of jobs and can start jobs at different frequencies to stay below the configured power limit.The plugin interfaces with the Digital Twin that provides energy measurements for the compute nodes to track the system power consumption in real time and update the power limitations if necessary.The plugin is tested on a cluster and compared against a scheduling simulation of the algorithm.The analysis compares the power profile of the simulation and the real system and the allocation of the jobs over time.Differences in the execution and the power trace are analysed and discussed.

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training in the Industrial Internet of Things (IIoT). By leveraging the decentralization, immutability, and transparency of blockchain technology, Blockchain-enabled FL (BFL) has gained significant attention for enhancing FL’s security and reliability. However, BFL still faces challenges in motivating client participation. While several incentive mechanisms have been proposed, most primarily focus on short-term rewards and overlook the long-term influence of individual contributions on global model performance. To address these challenges, we propose a novel BFL framework that integrates model training with blockchain mining on the client side. Specifically, we design a long-term incentive mechanism based on repeated game theory, where the interactions between participants and the task publisher (TP) are modeled as an infinitely repeated game. We formally prove the existence of a Subgame Perfect Nash Equilibrium, providing theoretical guarantees for stable long-term cooperation. Furthermore, we introduce a hybrid reward scheme that jointly considers contributions to both training and mining tasks, encouraging sustained engagement and attracting new participants. Extensive experiments on MNIST and CIFAR-10 validate that the proposed mechanism enhances the robustness of FL and effectively promotes long-term client participation.

HPC systems have increased in size and power consumption.This has lead to a shift from a pure performance centric standpoint to power and energy aware scheduling and management considerations for HPC.This trend was further accelerated by rising energy prices and the energy crisis that began in 2022.Digital Twins have become valuable tools that enable energy and power aware scheduling of HPC clusters.This paper uses an existing Digital Twin and extends it with a node energy model that allows the prediction of the cluster power consumption.The Digital Twin is then used to simulate system-wide power capping for different energy shortages functions of varying degree.Different policies are proposed and tested towards their effectiveness in improving the job wait times and overall throughput under limiting conditions.Based on a real world HPC cluster, these policies are implemented.Depending on the pattern of the energy limitation and workload, improvements of up to 40 percent are possible compared to scheduling without policies for these conditions.

Demand for compute resources and thus energy demand for HPC is steadily increasing while the energy market transforms to renewable energy and is facing significant price increases.Optimizing energy efficiency of HPC clusters is therefore a major concern.Different possible optimization dimensions are discussed in this paper.This paper presents a digital twin design for analyzing and reducing energy consumption of a real-world HPC system.The digital twin is based on the HPC cluster at PTB.The digital twin receives information from multiple internal and external data sources to cover the different optimization opportunities.The digital twin also consists of a scheduling simulation framework that uses the data from the digital twin and real-world job traces to test the influence of the different parameters on the HPC cluster.

Nowadays blockchain systems are widely used in many different fields, not only as a kind of payment method, i.e. cryptocurrency, but also as the infrastructure of decentralized applications. A smart contract, which is a program running on the blockchain, e.g. Ethereum, enables decentralized applications without the need for any trusted third party. To deploy a smart contract every miner of the blockchain needs to perform the same function of the smart contract such that they reach a consensus on its final state. Therefore, current blockchain systems are deterministic and non-probabilistic, disallowing any randomness in smart contracts, which is a significant limitation for the applicability of blockchains. A wide range of real-world applications depend on random functions, most obvious examples are games and lottery applications. Various methods have been proposed To address the random number generation problem, such as using a trusted oracle or the block hash. All of those have different disadvantages and advantages. Noticing the lack of concrete guidance for the inclusion of randomness in smart contracts on blockchains, we investigate the state-of-the-art random number generation methods and compare them in several critical aspects including availability, unpredictability, unbiasability, verifiability, scalability, execution time and cost.

Abstract Laser beam welding has become widely applied in many industrial fields in recent years. Solidification cracks remain one of the most common welding faults that can prevent a safe welded joint. In civil engineering, convolutional neural networks (CNNs) have been successfully used to detect cracks in roads and buildings by analysing images of the constructed objects. These cracks are found in static objects, whereas the generation of a welding crack is a dynamic process. Detecting the formation of cracks as early as possible is greatly important to ensure high welding quality. In this study, two end-to-end models based on long short-term memory and three-dimensional convolutional networks (3D-CNN) are proposed for automatic crack formation detection. To achieve maximum accuracy with minimal computational complexity, we progressively modify the model to find the optimal structure. The controlled tensile weldability test is conducted to generate long videos used for training and testing. The performance of the proposed models is compared with the classical neural network ResNet-18, which has been proven to be a good transfer learning model for crack detection. The results show that our models can detect the start time of crack formation earlier, while ResNet-18 only detects cracks during the propagation stage.

To avoid the inconvenience of the deployment of large-scale blockchains, blockchain simulators are used to facilitate blockchain design and implementation. We evaluate state-of-the-art simulators and find that they suffer from low performance and scalability. To build a more general and faster blockchain simulator, we extend an existing blockchain simulator. We add a network module integrated with a network topology generation algorithm and a block propagation algorithm to simulate the block propagation efficiently. We design a binary transaction pool structure and adopt bitwise operations to accelerate the simulation and reduce memory usage. Moreover, we modularize the simulator based on five primary blockchain processes. Significant blockchain elements are implemented in individual modules and can be combined flexibly to simulate different types of blockchains. Experiments demonstrate that the new simulator reduces the simulation time by an order of magnitude and improves scalability, enabling us to simulate more than ten thousand nodes.

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

With the rapid development of the Internet of Things (IoT) and communication technology, Deep Neural Network (DNN) applications like computer vision, can now be widely used in IoT devices. However, due to the insufficient memory, low computing capacity, and low battery capacity of IoT devices, it is difficult to support the high-efficiency DNN inference and meet users’ requirements for Quality of Service (QoS). Worse still, offloading failures may occur during the massive DNN data transmission due to the intermittent wireless connectivity between IoT devices and the cloud. In order to fill this gap, we consider the partitioning and offloading of the DNN model, and design a novel optimization method for parallel offloading of large-scale DNN models in a local-edge-cloud collaborative environment with limited resources. Combined with the coupling coordination degree and node balance degree, an improved Double Dueling Prioritized deep Q-Network (DDPQN) algorithm is proposed to obtain the DNN offloading strategy. Compared with existing algorithms, the DDPQN algorithm can obtain an efficient DNN offloading strategy with low delay, low energy consumption, and low cost under the premise of ensuring “delay-energy-cost” coordination and reasonable allocation of computing resources in a local-edge-cloud collaborative environment.

Internet-of-Things (IoT) applications are becoming more resource-hungry and latency-sensitive, which are severely constrained by limited resources of current mobile hardware. Mobile cloud computing (MCC) can provide abundant computation resources, while mobile-edge computing (MEC) aims to reduce the transmission latency by offloading complex tasks from IoT devices to nearby edge servers. It is still challenging to satisfy the quality of service with different constraints of IoT devices in a collaborative MCC and MEC environment. In this article, we propose three constrained multiobjective evolutionary algorithms (CMOEAs) for solving IoT-enabled computation offloading problems in collaborative edge and cloud computing networks. First of all, a constrained multiobjective computation offloading model considering time and energy consumption is established in the mobile environment. Inspired by the push and pull search framework, three CMOEAs are developed by combing the advantages of population-based search algorithms with flexible constraint handling mechanisms. On one hand, three popular and challenging constrained benchmark suites are selected to test the performance of the proposed algorithms by comparing them to the other seven state-of-the-art CMOEAs. On the other hand, a multiserver multiuser multitask computation offloading experimental scenario with a different number of IoT devices is used to evaluate the performance of three proposed algorithms and other compared algorithms as well as representative offloading schemes. The experimental results of the benchmark suites and computation offloading problems demonstrate the effectiveness and superiority of the proposed algorithms.

With the proliferation of compute-intensive and delay-sensitive mobile applications, large amounts of computational resources with stringent latency requirements are required on Internet-of-Things (IoT) devices. One promising solution is to offload complex computing tasks from IoT devices either to mobile-edge computing (MEC) or mobile cloud computing (MCC) servers. MEC servers are much closer to IoT devices and thus have lower latency, while MCC servers can provide flexible and scalable computing capability to support complicated applications. To address the tradeoff between limited computing capacity and high latency, and meanwhile, ensure the data integrity during the offloading process, we consider a blockchain scenario where edge computing and cloud computing can collaborate toward secure task offloading. We further propose a blockchain-enabled IoT-Edge-Cloud computing architecture that benefits both from MCC and MEC, where MEC servers offer lower latency computing services, while MCC servers provide stronger computation power. Moreover, we develop an energy-efficient dynamic task offloading (EEDTO) algorithm by choosing the optimal computing place in an online way, either on the IoT device, the MEC server or the MCC server with the goal of jointly minimizing the energy consumption and task response time. The Lyapunov optimization technique is applied to control computation and communication costs incurred by different types of applications and the dynamic changes of wireless environments. During the optimization, the best computing location for each task is chosen adaptively without requiring future system information as prior knowledge. Compared with previous offloading schemes with/without MEC and MCC cooperation, EEDTO can achieve energy-efficient offloading decisions with relatively lower computational complexity.

Analyzing blockchain protocols is a notoriously difficult task due to the underlying large scale distributed networks. To address this problem, stochastic model-based approaches are often utilized. However, the abstract models in prior work turn out not to be adoptable to consortium blockchains as the consensus of such a blockchain often consists of multiple processes. To address the lack of efficient analysis tools, we propose a queueing network-based method for analyzing consistency properties of consortium blockchain protocols in this article. Our method provides a way to evaluate the performance of the main stages in blockchain consensus. We apply our framework to the Hyperledger Fabric system and recover key properties of the blockchain network. Using our method, we analyze the security properties of the ordering mechanism and the impact of delaying endorsement messages in consortium blockchain protocols. Then an upper bound is derived of the damage an attacker could cause who is capable of delaying the honest players' messages. Based on the proposed method, we employ analytical derivations to investigate both the security and performance features, and corroborate close agreement with measurements on a wide-area network testbed running the Hyperledger Fabric blockchain. With the proposed method, designers of future blockchains can provide a more rigorous analysis of their consortium blockchain schemes.

This paper proposes a novel archive maintenance for adapting weight vectors to improve the performance of the decomposition-based evolutionary algorithms for multi- and many-objective optimization problems with different Pareto front shapes (called AMAWV). AMAWV adopts a novel archive maintenance strategy for avoiding the dominance resistant solutions, as well as retaining the good diversity of non-dominated solution set. In addition, guided from the information of the archive, an adaptive weight vector method is designed to solve problems with various Pareto fronts. The proposed algorithm is compared with state-of-the-art algorithms on a number of test problems with different Pareto front shapes (the simplex-like, the inverted, the disconnected, the degenerated, the scaled, the mixed, the high dimensional). The experimental results have shown the superiority and versatility of the proposed algorithm.

The rise of streaming data processing is driven by mass deployment of sensors, the increasing popularity of mobile devices, and the rapid growth of online financial trading. Apache Kafka is often used as a real-time messaging system for many stream processors. However, efficiently running Kafka as a reliable data source is challenging, especially in the case of real-time processing with unstable network connection. We find that changing configuration parameters can significantly impact the guarantee of message delivery in Kafka. Therefore the key to solving the above problem is to predict the reliability of Kafka given various configurations and network conditions. We define two reliability metrics to be predicted, the probability of message loss and the probability of message duplication. Artificial neural networks (ANN) are applied in our prediction model and we select some key parameters, as well as network metrics as the features. To collect sufficient training data for our model we build a Kafka testbed based on Docker containers. With the neural network model we can predict Kafka's reliability for different application scenarios given various network environments. Combining with other metrics that a streaming application user may care for, a weighted key performance indicator (KPI) of Kafka is proposed for selecting proper configuration parameters. In the experiments we propose a rough dynamic configuration scheme, which significantly improves the reliability while guaranteeing message timeliness.

Modern stream processing systems need to process large volumes of data in real-time. Various stream processing frameworks have been developed and messaging systems are widely applied to transfer streaming data among different applications. As a distributed messaging system with growing popularity, Apache Kafka processes streaming data in small batches for efficiency. However, the robustness of Kafka's batching method against variable operating conditions is not known. In this paper we study the impact of the batch size on the performance of Kafka. Both configuration parameters, the spatial and temporal batch size, are considered. We build a Kafka testbed using Docker containers to analyze the distribution of Kafka's end-to-end latency. The experimental results indicate that evaluating the mean latency only is unreliable in the context of real-time systems. In the experiments where network faults are injected, we find that the batch size affects the message loss rate in the presence of an unstable network connection. However, allocating resources for message processing and delivery that will violate the reliability requirements implemented as latency constraints of a real-time system is inefficient To address these challenges we propose a reactive batching strategy. We evaluate our batching strategy in both good and poor network conditions. The results show that the strategy is powerful enough to meet both latency and throughput constraints even when network conditions are variable.

City Internet-of-Things (IoT) applications are becoming increasingly complicated and thus require large amounts of computational resources and strict latency requirements. Mobile cloud computing (MCC) is an effective way to alleviate the limitation of computation capacity by offloading complex tasks from mobile devices (MDs) to central clouds. Besides, mobile-edge computing (MEC) is a promising technology to reduce latency during data transmission and save energy by providing services in a timely manner. However, it is still difficult to solve the task offloading challenges in heterogeneous cloud computing environments, where edge clouds and central clouds work collaboratively to satisfy the requirements of city IoT applications. In this article, we consider the heterogeneity of edge and central cloud servers in the offloading destination selection. To jointly optimize the system utility and the bandwidth allocation for each MD, we establish a hybrid offloading model, including the collaboration of MCC and MEC. A distributed deep learning-driven task offloading (DDTO) algorithm is proposed to generate near-optimal offloading decisions over the MDs, edge cloud server, and central cloud server. Experimental results demonstrate the accuracy of the DDTO algorithm, which can effectively and efficiently generate near-optimal offloading decisions in the edge and cloud computing environments. Furthermore, it achieves high performance and greatly reduces the computational complexity when compared with other offloading schemes that neglect the collaboration of heterogeneous clouds. More precisely, the DDTO scheme can improve computational performance by 63%, compared with the local-only scheme.

Mobile cloud offloading migrates heavy computation from mobile devices to remote cloud resources or nearby cloudlets. It is a promising method to alleviate the struggle between resource-constrained mobile devices and resource-hungry mobile applications. Caused by frequently changing location mobile users often see dynamically changing network conditions which have a great impact on the perceived application performance. Therefore, making high-quality offloading decisions at run time is difficult in mobile environments. To balance the energy-delay tradeoff based on different offloading-decision criteria (e.g., minimum response time or energy consumption), an energy-efficient offloading-decision algorithm based on Lyapunov optimization is proposed. The algorithm determines when to run the application locally, when to forward it directly for remote execution to a cloud infrastructure and when to delegate it via a nearby cloudlet to the cloud. The algorithm is able to minimize the average energy consumption on the mobile device while ensuring that the average response time satisfies a given time constraint. Moreover, compared to local and remote execution, the Lyapunov-based algorithm can significantly reduce the energy consumption while only sacrificing a small portion of response time. Furthermore, it optimizes energy better and has less computational complexity than the Lagrange Relaxation based Aggregated Cost (LARAC-based) algorithm.

An improved multi-objective discrete particle swarm optimization (IMODPSO) algorithm is proposed to solve the task scheduling and resource allocation problem for scientific workflows in cloud computing. First, we use a strategy to limit the velocity of particles and adopt a discrete position updating equation to solve the multi-objective time and cost optimization model. Second, we adopt a Gaussian mutation operation to update the personal best position and the external archive, which can retain the diversity and convergence accuracy of Pareto optimal solutions. Finally, the computational complexity of IMODPSO is compared with three other state-of-the-art algorithms. We validate the computational speed, the number of solutions found and the generational distance of IMODPSO and find that the new algorithm outperforms the three other algorithms with respect to all three metrics.

This paper presents a multiobjective artificial bee colony (ABC) algorithm using the decomposition approach for improving the performance of MOEA/D (multiobjective evolutionary algorithm based on decomposition). Using a novel reproduction operator inspired by ABC, we propose MOEA/D-ABC, a new version of MOEA/D. Then, a modified Tchebycheff approach is adopted to achieve higher diversity of the solutions. Further, an adaptive normalization operator can be incorporated into MOEA/D-ABC to solve the differently scaled problems. The proposed MOEA/D-ABC is compared to several state-of-the-art algorithms on two well-known test suites. The experimental results show that MOEA/D-ABC exhibits better convergence and diversity than other MOEA/D algorithms on most instances.

Application partitioning that splits the executions into local and remote parts, plays a critical role in high-performance mobile offloading systems. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Cloud Computing (MCC) or Mobile Edge Computing (MEC). Due to unstable resources in the wireless network (network disconnection, bandwidth fluctuation, network latency, etc.) and at the service nodes (different speeds of mobile devices and cloud/edge servers, memory, etc.), static partitioning solutions with fixed bandwidth and speed assumptions are unsuitable for offloading systems. In this paper, we study how to dynamically partition a given application effectively into local and remote parts while reducing the total cost to the degree possible. For general tasks (represented in arbitrary topological consumption graphs), we propose a Min-Cost Offloading Partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (i.e., to determine which portions of the application must run on the mobile device and which portions on cloud/edge servers) under different cost models and mobile environments. Simulation results show that the MCOP algorithm provides a stable method with low time complexity which significantly reduces execution time and energy consumption by optimally distributing tasks between mobile devices and servers, besides it adapts well to mobile environmental changes.

Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices thus providing higher performance and saving energy. Different applications usually give different relative importance to response time and energy consumption. If a delay-tolerant job is deferred up to a given deadline, or until a fast and energy-efficient network becomes available, the transmission time will be extended, which can save energy because a more energy-efficient communication channel and a less energy-restricted computation platform may become available. However, if the reduced service time fails to cover the extra waiting time, this policy may not be competitive. In this paper, we investigate two types of delayed offloading policies, the partial offloading model where jobs can leave from the slow phase of the offloading process and be executed locally on the mobile device, and the full offloading model, where jobs can abandon the WiFi Queue and be offloaded via the Cellular Queue. In both models, we minimize the Energy-Response time Weighted Product (ERWP) metric. Not surprisingly, we find that jobs abandon the queue often when the availability of the WiFi network is low. In general, for delay-sensitive applications the partial offloading model is preferred under a suitable reneging rate, while for delay-tolerant applications the full offloading model shows very good results and outperforms the other offloading model when selecting a large deadline. From the perspective of energy consumption, the full offloading model will always be best, even if the deadline must be extremely long. Only if job response time is of high importance an optimal deadline to abort offloading in the partial offloading model or the WiFi transmission in the full offloading model can be found. For reduction of the energy consumption it will always be better to wait longer rather than compute locally or use the cellular network.

A routing graph allows to find paths in buildings quickly. Raster images of floor plans are simple to obtain but display poor performance. A manually constructed graph is quite optimal if designed by an informed person, but the process is time consuming and expensive. We describe a fast method to calculate a 2D routing graph from raster images. We adapt image processing techniques and apply a conditional erosion technique. A conditional query of every pixel by means of a predefined 3×3 image matrix calculates an approximation of common walkways through corridors and rooms. We translate the image processing idea into propositional logic formulas and simplify them. Compared to our previous work, we reduce the run time by about 90 % and the amount of needed matrices from 29 to eight or even four, depending on the specific application. We also present a parallel version at the cost of redundant edges in the resulting routing graph.

Cryptocurrency mining can be said to be the modern alchemy, involving as it does the transmutation of electricity into digital gold. The goal of mining is to guess the solution to a cryptographic puzzle, the difficulty of which is determined by the network, and thence to win the block reward and transaction fees. Because the return on solo mining has a very high variance, miners band together to create so-called mining pools. These aggregate the power of several individual miners, and, by distributing the accumulated rewards according to some scheme, ensure a more predictable return for participants.In this paper we formulate a model of the dynamics of a queue-based reward distribution scheme in a popular Ethereum mining pool and develop a corresponding simulation. We show that the underlying mechanism disadvantages miners with above-average hash rates. We then consider two-miner scenarios and show how large miners may perform attacks to increase their profits at the expense of other participants of the mining pool. The outcomes of our analysis show the queue-based reward scheme is vulnerable to manipulation in its current implementation.

In this paper, we present HyperStar2, a tool for fitting Markov Arrival Processes (MAPs) to empirical data. HyperStar2 uses a two-step approach, where the first step is cluster-based fitting of phase-type distributions and the second step is the construction of a correlation matrix. In the first step, we use the cluster-based algorithm for Hyper-Erlang distribution fitting from HyperStar hyper2012. Based on the Hyper-Erlang fitting result and the clusters of samples, in the second step we construct the correlation matrix.

During the last decade, research in indoor localization and navigation has focused on techniques, protocols, and algorithms. The first International Conference on Indoor Positioning and Indoor Navigation (IPIN) was held in 2010. Since then, this annual conference showed the progress of research and technology. The variations of evaluation methods are significant in this field: they range from none, to extensive simulations, and real-world experiments under non-lab conditions. We look at the articles published in the proceedings of IPIN by IEEE Xplore from 2010 to 2014, and analyze the development of evaluation methods. We categorized 183 randomly selected papers, in respect to five different aspects. Namely: (1) the underlying system/technology in use, (2) the evaluation method for the proposed technique, (3) the method of ground truth data gathering, (4) the applied metrics, and (5) whether the authors establish a baseline for their work.

Offloading is an advanced technique to improve the performance of mobile devices. In a mobile offloading system, heavy computations are migrated from resource constrained mobile devices to powerful cloud servers through a wireless network connection. The unreliable wireless network often disturbs system operation. Task completion can be delayed or interrupted by congestion or packet loss in the network. To deal with this problem the offloaded jobs can be locally restarted and completed in the mobile device itself.

Gnetworks extend standard queueing networks as to include different types of customers or jobs. In addition to ordinary jobs also signals, or negative jobs can arrive to a queue. A signal removes a job from the queue instead of adding one. The interpretation of a signal as retry is very natural and induces semantics to the arrival of a signal. The job that is hit by the signal first leaves the queue but then immediately returns as a new job. The mathematical specification of Gnetworks with retry has become a cumbersome task. Therefore we present in this tool-demo paper a new tool that will support the specification and analysis of Gnetwork models with retries.

Offloading is a useful approach to save energy and time for mobile devices by migrating heavy computation to remote powerful servers. However, the unreliable wireless network constrains the implementation of offloading applications. The execution continuity is always interrupted by network failures. To deal with this problem, locally re-executing the pre-determined offloading task in the mobile device is a valid method. Challenges arise due to the best trade-off between costs and benefits of Local Re-execution. In this paper, using a Stochastic Activity Network model, we defined three metrics to investigate the performance of Local Re-execution, which is launched by different timeout values. Through comprehensively comparing the simulation results, we further explored the optimal timeout value for activating Local Re-execution, and reached the conclusion that the optimum is mainly controlled by the delay of network recovery.

Richer functional mobile applications are developed through offloading the heavy computation from resource constrained mobile devices to powerful cloud servers. For guaranteeing the completion of such tasks, strong connectivity with the servers is essential. However, the wireless network is not sufficiently reliable. It is often unable to satisfy the offloading condition, and thus interrupts the program execution. To deal with this problem, locally re-executing the offloaded computation in the mobile client is an efficient way to maintain the continuity of the application. In this paper, we present a synthetical method to find the optimal moment for launching the local re-execution. First, we develop a general program engine to implement offloading applications. Then we design a Stochastic Activity Network (SAN) model based on the engine structure and three metrics to evaluate the engine's performance. The model parameters are set to values obtained from experimental data of our engine. We compare the performance of different local re-execution launching intervals with the model simulation and find the optimum. Last, the optimal launching interval is returned to configure the engine and its efficiency is verified with experiments. The result shows that starting the local re-execution after an appropriate interval provides a better performance than always offloading, and our model can effectively find the optimal launching interval.

The fast growth of cloud computing has attracted more companies to migrate their in-house IT applications into cloud and it also occurs in the medical field. A mobile healthcare system with cloud offloading is considered in this paper and it can be divided into two stages: sensor network and cloud offloading. In the first stage, information collected by body sensors should be transmitted to a remote mobile device. In order to save energy, an energy-efficient transmission scheme called cooperative multi-input multi-output (MIMO) is constructed for the data transfer when allowing individual sensor nodes to cooperate with each other. In the second stage, two offloading schemes called self-reliant multi-cloud offloading system and multi-cloud offloading system are proposed and further analyzed based on serve topology and optimal graph partition. The former provides stability but with high communication cost, while the latter reduces communication cost but is less stable. Both schemes can be applied to other scenarios in which we would like to perform offloading on multiple servers.

Restart is a common technique for improving response-times in complex systems where the causes of delays can either not be discerned, or not be addressed by the user. With restart, the user aborts a running job that exceeds a deadline, and resubmits it to the system immediately. In many common scenarios, this approach can reduce the response-times that the user experiences. Restart has been well-studied for scenarios where only one user applies restart, and typically in cases where queueing effects can be neglected. In this paper we approach the question of restart in a scenario where restart is applied by many users in a system that can be modelled as an open queueing network. We apply the G-Networks formalism to this problem. We use negative customers to model the abortion and retry of a request. The open G-network uses multiple classes with phase-type distributed service times. This allows the approximation of a preemptive repeat different behaviour as it is natural for multiple restarts of a request. We compute the response time of a request and show that an optimal restart interval can be found. The results are compared with simulation.

Recently, there emerge a variety of clouds in sky and thus, several similar cloud services (from different cloud venders) can be provided to a mobile end device. The goal of cloud-path selection is to find an optimal cloud among a certain class of clouds that provide the same service, in order to carry out the offloaded computation tasks. It is easy to choose the optimal cloud to save execution time incurred by offloading to cloud when considering only one factor. However, there are many criteria such as speed, bandwidth, price, security and availability that need to be considered when making final decisions. In this paper, a multiple criteria decision analysis approach based on the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) in a fuzzy environment is proposed to decide which cloud is the most suitable one for offloading. The AHP is used to determine the weights of the criteria for cloud-path selection, while fuzzy TOPSIS is to obtain the final ranking of alternative clouds. The numerical analysis is performed to evaluate the model.

No abstract available.

Route and service discovery in wireless multi-hop networks applies flooding or gossip routing to disseminate and gather information. Since packets may get lost, retransmissions of lost packets are required. In many protocols the retransmission timeout is fixed in the protocol specification. In this paper we demonstrate that optimization of the timeout is required in order to ensure proper functioning of flooding schemes. Based on an experimental study, we apply percolation theory and derive analytical models for computing the optimal restart timeout. To the best of our knowledge, this is the first comprehensive study of gossip routing, percolation, and restart in this context.

Ensuring Quality of Service (QoS) in wireless networks poses an open problem in many application domains. We propose an automatic on-line QoS monitoring and management infrastructure that can be incorporated into existing network setups. Based on model-based assessment of current and future QoS conditions, our solution will control traffic in the network through a combination of admission control, enforced handover, traffic shaping and transmission parameter adjustments. Correctness of the model is evaluated through experimental evaluation and simulations. We implement a prototype of the proposed system using open-source components.

In this paper we present a queueing model for service availability formulated as a Petri net. We define a metric for service availability and show how it can be estimated from the model. Assuming that a failure avoidance mechanism is present, we analyze the distribution of time-to-failure. Finally, we show in experiments how service availability depends on queue length, utilization and failure prevention and how it relates to steady-state system availability.

The objective of this paper is to provide a first analysis of the effectiveness of simple server replication vs. failure prevention in non-high-availability applications. We analyze service availability for a system with N servers where each server is modeled as a finite queue subject to failures. A Petri net analysis suggests that service availability is most effectively improved by server duplication, but for further improvement the combination with failure prevention seems most effective.

Restarts or retries are a common phenomenon in computing systems, for instance, in preventive maintenance, software rejuvenation, or when a failure is suspected. Typically, one sets a time-out to trigger the restart. We analyze and optimize time-out strategies for scenarios in which the expected required remaining time of a task is not always decreasing with the time invested in it. Examples of such tasks include the download of Web pages, randomized algorithms, distributed queries, and jobs subject to network or other failures. Assuming the independence of the completion time of successive tries, we derive computationally attractive expressions for the moments of the completion time, as well as for the probability that a task is able to meet a deadline. These expressions facilitate efficient algorithms to compute optimal restart strategies and are promising candidates for pragmatic online optimization of restart timers

In this paper jump transitions for the fluid model part in second order fluid stochastic Petri nets (FSPNs) are introduced. The extended formalism is defined, and the underlying partial differential equations are derived. With a jump a certain amount of fluid is added to a fluid place or taken out at once. This amount, the jump height, is sampled from a probability distribution. The dynamics of a model are described by second order partial differential equations that include integrals. As examples the virtual waiting time (or unfinished work) in a queueing system and a performability model of a multi-processor system are modelled.