Testing-based formal verification (TBFV) is proposed to reduce test cost and guarantee software reliability by ensuring the correctness of all traversed program paths. An ideal target is to generate adequate test cases to traverse all of its execution paths. However, it is a rather ambitious criterion that can hardly be satisfied due to the potentially great amount of test cases required. To address this problem, we propose a new criterion called Condition Sequence Coverage (CSC) to maintain a good balance between program correctness and the number of test cases. In this paper, we refine the TBFV method and introduce the theoretical foundations of CSC. We also integrate CSC with functional scenario form (FSF) to automatically generate test cases for the TBFV method. In addition, we develop the tool support for Java and validate its effectiveness and accuracy through experimental comparisons.

Cyclic Queuing and Forwarding (CQF) is an increasingly adopted mechanism in Time-Sensitive Networking (TSN) for bounding end-to-end delays through fixed-length cycles with alternating transmission queues. While timing guarantees for CQF flows are well established under both time-triggered (TT) and event-triggered (ET) implementations, the worst-case interference that CQF may impose on other traffic classes in mixed-criticality TSN networks remains an open problem. This challenge is exacerbated by the structured, non-work-conserving behavior of CQF and the complexity of its interaction with heterogeneous TSN scheduling mechanisms such as TAS, CBS, and SP. This paper presents the first formal framework for quantifying the worst-case interference caused by CQF on other schedulers under both TT- and ET-based implementations. We propose a network-calculus-based CQF real-time interface abstraction that models the residual service available to coexisting traffic. We formally derive closed-form upper bounds on CQF-induced interference, explicitly capturing bidirectional interactions between CQF and both higher- and lower-priority traffic classes. These bounds can be modularly and seamlessly integrated into existing schedulability analyses, enabling scalable and compositional timing verification in hybrid TSN architectures. Extensive evaluations on synthetic benchmarks and realistic TSN configurations demonstrate the analytical effectiveness, scalability, and practical applicability of the proposed framework in certifying end-to-end guarantees in mixed-criticality TSN systems.

Semantic segmentation is crucial in practical applications, especially in autonomous driving. Despite significant advancements in existing semantic segmentation methods, the performance of real-time segmentation approaches remains suboptimal. To address the trade-off between computational efficiency and accuracy in current methods, we propose a novel lightweight real-time semantic segmentation network named MFBPNet. Specifically, this paper introduces three core modules: (1) the Depthwise Separable Convolutional Pyramid Module (DSCPM), which expands the global receptive field and enhances deep feature representation; (2) the Local Attention Refinement Module (LARM), employing channel-wise attention to refine local feature discriminability, particularly for fine-grained objects; and (3) the Boundary Perception Feature Fusion Module (BPFM), which strengthens the feature representation of boundary regions through a multi-level feature fusion mechanism, effectively enhancing the clarity of object boundaries and mitigating boundary blurring issues. Extensive experiments on Cityscapes and CamVid datasets demonstrate that MFBPNet achieves state-of-the-art performance, attaining 75.3% mIoU at 67.3 FPS and 74.3% mIoU at 68.2 FPS, respectively. Compared to existing methods, MFBPNet achieves a superior balance between segmentation accuracy and real-time performance, rendering it highly suitable for autonomous driving systems requiring both real-time processing and high segmentation quality.

Object Goal Navigation is a task that seeks to allow intelligent agents to locate and navigate to a particular object goal in an unfamiliar environment. However, the current goal-oriented semantic policy, which is based on deep reinforcement learning (DRL), has difficulty in retaining long-term object semantic information and lacks adequate goal-oriented ability. This leads to intelligent agents having to extensively explore their environment in order to locate a goal, resulting in inefficient navigation. To address these challenges, this paper proposes a goal-oriented semantic policy based on multi-headed self-attention (MHSA) to improve the efficiency of object navigation. By using the self-attention mechanism, the policy can automatically learn and extract features relevant to goal navigation without the need for manual feature extractor design. Multiple attention heads can simultaneously focus on various semantic features to extract vital information about the objective goal. We propose a novel object-goal navigation model called SemGO based on this policy. The SemGO model is proficient at managing environments with intricate semantic structures. It can detect the correlation between global and local information, which improves navigation accuracy significantly. Additionally, it has superior generalization capabilities, making it adaptable to changes in different object goals and environments. The experimental results show that the SemGO model achieves a SPL of 0.324, a success rate of 0.635, and a reduction of DTS to 1.601m in the Gibson dataset for object-goal navigation.

Abstract Urban remote sensing image semantic segmentation has a wide range of applications, such as urban planning, resource exploration, intelligent transportation, and other scenarios. Although UNetFormer performs well by introducing the self‐attention mechanism of Transformer, it still faces challenges arising from relatively low segmentation accuracy and significant edge segmentation errors. To this end, this paper proposes DEUFormer by employing a special weighted sum method to fuse the features of the encoder and the decoder, thus capturing both local details and global context information. Moreover, an Enhanced Feature Refinement Head is designed to finely re‐weight features on the channel dimension and narrow the semantic gap between shallow and deep features, thereby enhancing multi‐scale feature extraction. Additionally, an Edge‐Guided Context Module is introduced to enhance edge areas through effective edge detection, which can improve edge information extraction. Experimental results show that DEUFormer achieves an average Mean Intersection over Union (mIoU) of 53.8% on the LoveDA dataset and 69.1% on the UAVid dataset. Notably, the mIoU of buildings in the LoveDA dataset is 5.0% higher than that of UNetFormer. The proposed model outperforms methods such as UNetFormer on multiple datasets, which demonstrates its effectiveness.

Human–Machine Pair Programming (HMPP) is a promising technique in the software development process, which means that software construction can be done in the manner that humans are responsible for developing the program while computer is responsible for monitoring the program in real-time and reporting errors. The Java runtime exceptions in the current version of the software under construction can only be effectively detected by means of its execution. Traditional software testing techniques are suitable for testing completed programs but face a challenge in building a suitable testing environment for testing the partial programs produced during HMPP. In this paper, we put forward a novel technique, called Program Segment Testing (PST) for automatically identifying errors caused by runtime exceptions to support HMPP. We first introduce the relevant involved in this technique to detect index out of bounds exceptions, a representative of runtime exceptions. Then we discuss the methodology of this technique in detail and illustrate its workflow with a simple case study. Finally, we carry out an experiment to evaluate this technique and compare it with three existing fault detection techniques using several programs to demonstrate its effectiveness.

Abstract Navigation in unknown 3D environments aims to progressively find an efficient path to a given target goal in unseen scenarios. A challenge is how to explore the navigation quickly and effectively. An end-to-end learning approach has been proposed to extract geometric shapes from RGB images, but it is not suitable for large environments due to its exhaustive exploration with exponential search space. Active Neural SLAM (ANS) presents a Neural SLAM module to maximize the exploration coverage to tackle the active SLAM task. However, ANS still frequently visits the explored areas due to the inappropriate local target selection. In this paper, we propose a Neural Attention-based Visual SLAM (NAVS) model to explore unknown 3D environments. Spatial attention is provided to quickly identify obstacles (such as similarly colored tea table or floor). We also leverage the priority of unknown regions in the short-term goal decision to avoid frequent exploration with a channel attention. The experimental results show that our model can build a more accurate map than ANS and other baseline methods with less running time. In terms of relative coverage, NAVS achieves a 0.5 $$\%$$ % improvement over ANS in overall and a 1.1 $$\%$$ % improvement over ANS in large environments.

Aiming at the problems of difficulty in extracting key features and low prediction accuracy of traditional convolutional neural networks in Chinese speech recognition, we analyze the impacts of information leakage and unstandardized phoneme features on its performance, based on the deep convolutional neural network (DCNN)-connectionist temporal classification (CTC) model. In addition, a multi-core two dimensional causal convolution fusion network layer structure of SKNet is constructed, and we propose a DCNN-CTC model for fusion of attention mechanism and SKNet multi-core 2D causal convolution network (ASKCC-DCNN-CTC), which effectively improves the accuracy and training speed of Chinese speech recognition. The simulation results show that the error rate of our model on the ST-CMDS dataset is 12.201% lower than that of the DCNN-CTC model, the performance on the THCHS30 dataset is also improved, which reveals a good generalization ability.

Efficient localization plays a significant role in mobile autonomous robots’ navigation systems. Traditional visual simultaneous localization systems based on point feature matching suffer from two shortcomings. First one is that the method of tracking features is not robust for the environments with frequent changes in brightness. Another one is the large of consecutive visual keyframes consume expensive computation and storage resources in complex environments. To solve these problems, we propose an end-to-end visual localization algorithm to solve the robust and efficiency challenges via a deep learning mode. Firstly, we perform preprocessing operations such as cropping, averaging, and timestamp alignment on datasets to reduce computational cost and time. Secondly, we use CNN networks to autonomously learn the correct features for localization, which is robust to illumination changes. Finally, we utilize LSTM networks to memorize global trajectories to improve the localization precision. We performed a broad range of experiments on both indoor and outdoor datasets. The experimental results demonstrate that the translation and orientation accuracy in outdoor scenes improved by 32.9% and 31.4%, respectively. The average improvement of translation positioning accuracy in indoor scenes is 38.4%, and the orientation improvement is 13.1%. Moreover, the effectiveness of predicting the global motion trajectories of sequential images algorithm has been verified and is superior to other CNN methods.

The connected vehicles have been considered as a remedy for modern traffic issues, potentially saving hundreds of thousands of lives every year worldwide. The Dedicated Short-Range Communications (DSRC) technology is an essential building block of this promising vision. DSRC faces volatile vehicular environments, where not only wireless propagation channels but also network topologies vary rapidly. Moreover, traffic congestions during rush hours may lead to an unprecedentedly high density of broadcasting radios, resulting in compromised reliability, efficiency and fairness of DSRC. In order to optimize the performance of DSRC, we develop a novel Online Control Approach of power and Rates (OnCAR). Supported by systematic control theories, OnCAR performs stably even in the dynamic and unpredictable vehicular environments. To the best of our knowledge, OnCAR is the first solution to address the strong coupling between communication variables. It adopts a multi-variable control model to synchronously adjust transmission power and data rates, which are two major variables determining the performance of DSRC. In addition, OnCAR leverages receiver-side measurements of performance metrics to strike a balance between overall performance and fairness. Compared with the state of the art, OnCAR enhances the overall reliability and efficiency of DSRC by 23.7% and 30.1%, respectively. Meanwhile, these numbers are achieved with a 40.1% improvement in fairness.

Video streaming has been dominating the mobile bandwidth, and is still expanding drastically. Its tremendous economic benefits have driven the automobile industry to equip vehicles with video streaming capacity. As a result, the new in-cabin Wi-Fi systems have been deployed, enabling each vehicle as a streaming hotspot on the wheels. A built-in Access Point (AP) bridges the communications between Wi-Fi devices inside and cellular networks outside. Distinct advantages offered by this system include a more powerful antenna array to improve multimedia quality, a constant energy source to power the streaming, etc. However, there exist two challenging features that may jeopardize the system performance. (1) The in-cabin Wi-Fi hotspots are mostly deployed on private vehicles, and thus are completely decentralized. (2) Video packets need to be delivered before their deadlines with small delays. Due to these features, existing algorithms may fail to efficiently schedule the in-cabin Wi-Fi video streaming. To fill the gap, we propose the Delay-awaRe dIstributed Video schedulING (DRIVING) framework. Being fully distributed and delay-aware, DRIVING not only increases the streaming goodput, but also reduces the delivery latency and deadline missing ratio. %In order to optimize this new framework, we establish cross-layer analytical models, which help us tune the framework parameters for better performance. In a typical scenario, DRIVING increases the goodput by up to 27.0%, while reducing the queueing delay and the deadline missing ratio by up to 40.0% and 38.4%, respectively.

Dedicated Short Range Communication (DSRC) protocol is a key enabling technology for enhancing road safety and transportation efficiency. Wireless Access in Vehicular Environments (WAVE) 1609.4 is a new amendment that enables multi-channel operations in DSRC. Operating intervals are divided into alternating Control Channel (CCH) Intervals and Service Channel (SCH) Intervals with an identical length. This alternating feature causes high packet losses in CCH and low throughput in SCH, and thus hinders the deployment of this protocol. The goal of our work is to provision sufficient reliability for safety messages in CCH while optimising non-safety service delivery in SCH. We develop analytical models to explore the relationship among traffic density, CCH packet loss ratio, SCH throughput, and the duration of each kind of intervals. We also design a multi-channel coordination algorithm which adaptively adjusts the duration of intervals to achieve better performance and reliability based on these models. Theoretical analysis and extensive simulation results demonstrate the accuracy of our model and the efficacy of the proposed algorithm.

Vehicle-to-vehicle safety data dissemination plays an increasingly important role in ensuring the safety and efficiency of vehicle transportation. When collecting safety data, vehicles always prefer data generated at a closer location over data generated at a distant location, and prefer recent data over outdated data. However, these data preferences have been overlooked in most of existing safety data dissemination protocols, preventing vehicles getting more precise traffic information. In this paper, we explore the feasibility and benefits of incorporating the data preferences of vehicles in designing efficient safety data dissemination protocols. In particular, we propose the concept of packet-value to quantify these data preferences. We then design PVCast, a packet-value-based safety data dissemination protocol in VANET. PVCast makes the dissemination decision for each packet based on its packet-value and effective dissemination coverage in order to satisfy the data preferences of all the vehicles in the network. In addition, PVCast is lightweight and fully distributed. We evaluate the performance of PVCast on the ns-2 platform by comparing it with three representative data dissemination protocols. Simulation results in a typical highway scenario show that PVCast provides a significant improvement on per-vehicle throughput, per-packet dissemination coverage with small per-packet delay. Our findings demonstrate the importance and necessity of comprehensively considering the data preferences of vehicles when designing an efficient safety data dissemination protocol for VANET.

In this paper, we present a novel Frequency-divided Instantaneous Neighbors Estimation (FINE) system specifically designed for density estimation in Dedicated Short Range Communication (DSRC) based vehicular networks. A large amount of vehicular applications such as navigation, traffic control, and data dissemination substantially rely on the density information. Recent works pay great attention to obtain the real-time density information and reduce the occupation time of DSRC channel. The state-of-the-art approach is the Framed Slotted ALOHA (FSA) framework, which benefits from its fine-grained time division design. However, FSA considers only time resource and is unaware of the frequency resource in DSRC. For further accelerating the density acquisition, we propose a frequency-divided approach. The core idea of FINE is to resort fine-grained channel division for parallel neighbors counting. Extensive simulations are conducted to evaluate FINE. The results demonstrate that FINE significantly outperforms existing methods. In a typical dense scenario, FINE reduces the time cost from 2 ms (FSA) to 50 μs, while maintains the accuracy at the same level as FSA.

As the complexities of automotive systems increase, designing a system is a difficult task that cannot be done manually. In this paper, we focus on wire routing and wire sizing for weight minimization to deal with more and more connections between devices in automotive systems. The wire routing problem is formulated as a minimal Steiner tree problem with capacity constraints, and the location of a Steiner vertex is selected to add a splice which is used to connect more than two wires. We modify the Kou-Markowsky-Berman algorithm to efficiently construct Steiner trees and propose an integer linear programming (ILP) formulation to relocate Steiner vertices and satisfy capacity constraints. The ILP formulation is relaxed to a linear programming (LP) formulation which has the same optimal objective and can be solved more efficiently. Besides wire routing, wire sizing is also performed to satisfy resistance constraints and minimize the total wiring weight. To the best of our knowledge, this is the first work in the literature to formulate the automotive routing problem as a minimal Steiner tree problem with capacity constraints and perform wire routing and wire sizing for weight minimization. An industrial case study shows the effectiveness and efficiency of our algorithm which provides an efficient, flexible, and scalable approach for the design optimization of automotive systems.

Cloud computing is powered by an engine known as Internet data center (IDC). As cloud computing flourishes, the energy consumption and cost for IDCs are soaring. The energy cost minimization problem for IDCs in deregulated electricity markets has generated growing interest. In this paper we study how to leverage both the geographic and temporal variation of energy price to minimize energy cost for distributed IDCs. We propose a novel architecture and two algorithms for unified spatial and temporal load balancing. Rigorous analysis shows that our algorithms have a low computational complexity, require a relaxed accuracy in electricity price estimation, and guarantee a service completion time for user requests. Using real-life electricity price and workload traces, extensive evaluations demonstrate that compared to the schemes using either spatial load balancing or temporal load balancing alone, the proposed spatio-temporal load balancing method significantly reduces energy cost for distributed IDCs.

The past decade witnessed the tremendous growth of online services and applications. Together with the increase of cloud computing, more and more computation are hosted by Internet data centers (IDCs). Today's IDCs are achieving significant advances in communication and computation capabilities. However, along with the increasing demand from IDC clients, power consumption for powering up and cooling these IDCs has been skyrocketing. Most existing works optimize the power consumption of either servers or Computer Room Air Conditioners (CRACs), and overlook the correlation between the power consumption of these two types of equipment. In this paper, we propose an adaptive power control method which leverages the correlation between the power consumption of servers and CRACs. To capture the workload uncertainties and thermal dynamics, we exploit Recursive-Least Square based Model Predictive Control (MPC) to solve the power control problem. Performance evaluations shows the effective power peak reduction using our approach.

As the complexities of automotive systems increase, designing a system is a difficult task that cannot be done manually. In this paper, we propose an algorithm for weight minimization of wires used for connecting electronic devices in a system. The wire routing problem is formulated as a Steiner tree problem with capacity constraints, and the location of a Steiner vertex is selected for adding a splice connecting more than two wires. Besides wire routing, wire sizing is also done to satisfy resistance constraints and minimize the total wiring weight. Experimental results show the effectiveness and efficiency of our algorithm.

Electric vehicle (EV) is the next-generation vehicle powered by electricity. Keeping EVs charged and ready to go will require a tight integration and coordination of an infrastructure of equipment connected to the power grid for vehicle charging and a new IT management system for monitoring, analyzing and controlling the vehicle charging. Smart grid technologies have brought opportunities to solve challenges in the EV ecosystem. Both academia and industry have been seeking technologies and applications to optimize an EV driver's driving cost. However, it still remains an open problem of how to handle the driving demand uncertainty and the electricity price uncertainty for EV charging. To address this challenge, we model the driving cost minimization problem and formulate it as an optimization problem. While the driver demand and the electricity price are varying with time, we leverage the Model Predictive Control (MPC) based method to design a dynamic charging (controlling when to charge and how much to charge) and driving (controlling the switching between electric driven mode and gasoline driven mode during the driving process) control scheme. Performance evaluation results demonstrate the effectiveness and cost efficiency of our approach.

FlexRay is an automotive network communication protocol. It provides support to transmit time-sensitive messages in automobiles. FlexRay transmits periodic messages in a static segment and a periodic messages in a dynamic segment. To improve transmission reliability, FlexRay offers hybrid data management schemes for both static and dynamic segments. However, existing approaches only schedule static segment and dynamic segment separately, leading to poor bandwidth utilization and transmission delay. Moreover, due to the bandwidth limitation, existing best-effort retransmission for all segments fails to achieve high reliability. To address these two concerns, we propose a novel and efficient scheduling scheme, called Coefficient. The idea behind Coefficient is to cooperatively schedule the static and dynamic segments, while judiciously stealing the selective slacks for reliable transmission based on practical fault models. Coefficient schedules both static and dynamic segments in the dual-channel manner based on practical fault models. Extensive experiments based on real-world case studies demonstrate that Coefficient meets the needs of both real-time transmission and reliability requirements, and delivers significant performance improvements.

To reduce cost and emission, modern datacenter operators are beginning to incorporate green energy sources into datacenters' power supply. To improve service availability, they also back up datacenters using traditional (usually brown) energy sources. However, challenge arises due to distinct characteristics of energy sources used for different goals. How to select optimal energy sources and plan their capacity for datacenters to meet cost, emission and service availability requirement remains an open research problem. In this extended abstract, we briefly describe recent work in [4], which provides a holistic solution to address this problem. In [4], we present GreenPlanning, a framework to strike a judicious balance among multiple energy sources, the electrical grid and energy storage devices for a datacenter in terms of cost, emission, and service availability. GreenPlanning explores different features and operations of both green and traditional energy sources available to datacenters. The framework minimizes the lifetime total cost including both capital and operational cost for a datacenter. We conduct extensive experiments to evaluate GreenPlanning with real-life computational workload and meteorological data traces. Results demonstrate that GreenPlanning can reduce the lifetime total cost and emission by more than 50% compared to traditional configurations without integration of green energy, while still meeting service availability requirement.

Cloud computing services are becoming integral part of people's daily life. These services are supported by infrastructure known as Internet data center (IDC). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. Both academia and industry have paid great attention to energy management of IDCs. This paper studies an important energy management problem-how to minimize energy cost for IDCs in deregulated electricity markets. We propose a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit the temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed using real-life electricity price and workload traces at an enterprise production data center. The evaluation results demonstrate that the proposed approach significantly reduces energy cost for IDCs, guarantees a service delay bound, and alleviates workload drop if the service delay bound is sufficiently large.

Along with the rapid increasing energy consumption, the energy cost of Internet data centers (IDCs) has been skyrocketing. A novel scheme of geographical load balancing was proposed to reduce electricity bills for service providers. However, one important challenge faced by service providers has not been considered properly. In service systems, the service delay faced by consumers includes the queuing delay and the transmission delay. While existing work only consider the queuing delay, the transmission delay introduced by geographical load balancing has been overlooked. It is one of the most important factors affecting the quality of service for real-time service systems. In this paper, we take the transmission delay into our design consideration and formulate a mixed-integer nonlinear programming (MINLP) problem with coupled constraint to achieve the optimal load balancing and energy cost management for IDCs while meeting the service-level agreements (SLA) of consumers. A novel heuristic based branch and bound with feedback (HBBF) algorithm is proposed to decouple the MINLP problem with coupled constraint efficiently. Extensive performance evaluations based on real electricity price data and site-to-site transmission delay data demonstrate the effectiveness of our proposed algorithm.

The recent years witnessed rapid emergence and proliferation of cloud computing. To fully utilize the compute resources, some cloud operators provide spot resources. Spot resources allow customers to bid on unused capacity. However, pricing policy of spot resources should be carefully designed and the impact on both present and future should be considered. For the present, the cloud provider can set a higher price to gain extra revenue. For the future, higher price will shift more requests with lower prices to later time and reduce the revenue of future. Meanwhile, the quality of service should be considered either since bad QoS will incur loss of potential users. In this paper, we present a demand curve to model the impact of pricing on the present and future revenue. Then we formulate the revenue maximization problem as a time-average optimization problem. Next, since this basic model fails to provide information of service delay, we extend it to a more generalized one that ensures the worst-case delay of user requests. While the future knowledge of arrival requests is unknown, it is necessary to design online algorithms for the optimization problems. We apply Lyapunov optimization framework and design an efficient online algorithm which dose not require any future knowledge of requests arrival. Evaluations based on real-life datacenter workload and Amazon EC2 Spot Price illustrate efficiency of our algorithms.

Cloud computing is supported by an infrastructure known as Internet data center (IDC). As cloud computing thrives, the energy consumption and cost for IDCs are exploding. There is growing interest in energy cost minimization for IDCs in deregulated electricity markets. In this paper we study how to leverage both geographic and temporal variation of energy price to minimize energy cost for distributed IDCs. To this end, we propose a novel spatio-temporal load balancing approach. Using reallife electricity price and workload traces, extensive evaluations demonstrate that the proposed spatio-temporal load balancing approach significantly reduces energy cost for distributed IDCs.

As a key enabling technology for the next generation inter-vehicle safety communications, The IEEE 802.11p protocol is currently attracting much attention. Many inter-vehicle safety communications have stringent real-time requirements on broadcast messages to ensure drivers have enough reaction time toward emergencies. Most existing studies only focus on the average delay performance of IEEE 802.11p, which only contains very limited information of the real capacity for inter-vehicle communication. In this paper, we propose an analytical model, showing the performance of broadcast under IEEE 802.11p in terms of the mean, deviation and probability distribution of the MAC access delay. Comparison with the NS-2 simulations validates the accuracy of the proposed analytical model. In addition, we show that the exponential distribution is a good approximation to the MAC access delay distribution. Numerical analysis indicates that the QoS support in IEEE 802.11p can provide relatively good performance guarantee for higher priority messages while fails to meet the real-time requirements of the lower priority messages.

Wireless sensor networks (WSNs) can be successfully applied in a wide range of applications. Efficient data dissemination is a fundamental service which enables many useful high-level functions such as parameter reconfiguration, network reprogramming, etc. Many current data dissemination protocols employ network coding techniques to deal with packet losses. The coding overhead, however, becomes a bottleneck in terms of dissemination delay. We exploit the concurrency potential of sensor nodes and propose MT-Deluge, a multithreaded design of a coding-based data dissemination protocol. By separating the coding and radio operations into two threads and carefully scheduling their executions, MT-Deluge shortens the dissemination delay effectively. An incremental decoding algorithm is employed to further improve MT-Deluge's performance. Experiments with 24 TelosB motes on four representative topologies show that MT-Deluge shortens the dissemination delay by 25.5-48.6 percent compared to a typical data dissemination protocol while keeping the merits of loss resilience.

Cloud computing services are becoming integral part of people's daily life. These services are supported by Internet data centers (IDCs). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. This paper studies an energy management problem - how to minimize energy cost for IDCs in deregulated electricity markets. While several existing works handle this problem by leveraging spatial diversity of electricity price, little has been done to address the temporal uncertainty in electricity price and arriving workload. This paper proposes a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed to demonstrate that the proposed approach significantly reduces energy cost for IDCs, and guarantees a service delay bound for user requests.

The rapid scaling up of modern avionics is forcing its communication infrastructure to evolve from shared medium toward multi-hop switched real-time networks. This prompts the proposal of avionics full-duplex switched Ethernet (AFDX) standard. Since its publication, AFDX has been well-received, and is deployed or to-be-deployed in state-of-the-art aircrafts, such as Airbus A380/A400M/A350, Boeing 787, Bombardier CSeries etc. On the other hand, AFDX standard only specifies the behavior that an underlying switch must follow, but leaves the architecture design open. This creates an open market for switch vendors. Among the different candidate designs for this market, the TDMA crossbar real-time switch architecture stands out as it complies with and even simplifies many mainstream switch architectures, hence lays a smooth evolution path toward AFDX. In this paper, we focus on analyzing this switch design for AFDX networks. We first prove that TDMA crossbar real-time switch architecture complies with the AFDX specifications; and derive closed-form formulae on the corresponding AFDX networks' traffic characteristics and end-to-end real-time delay bound. Then we prove the resource planning problem in the corresponding AFDX networks is NP-Hard. To address this NP-Hard challenge, we re-model the problem. Based upon the re-modeling, we propose an approximation algorithm.

This paper addresses the problem of electricity cost management for Internet service providers with a collection of spatially distributed data centers. As the demand on Internet services and cloud computing has kept increasing in recent years, the power usage associated with IDC operations has been uprising significantly. The cyber and physical aspects of IDCs interact with each other, and bring unprecedented challenges in power management. While most existing research focuses on reducing power consumptions of IDCs at one specific location, the problem of reducing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the present multielectricity-market environment, where the price of electricity may exhibit temporal and spatial diversities. Further, for these service providers, guaranteeing the quality of service (QoS; i.e., service level objectives) such as service delay guarantees to the end users is of critical importance. This paper studies the problem of minimizing the total electricity cost geared to QoS constraint as well as the location diversity and time diversity of electricity price under multiregional electricity markets. We jointly consider both the cyber and physical management capabilities of IDCs, and exploit both the center-level load balancing and the server-level power control in a unified scheme. We model the problem as a constrained mixed integer programming based on generalized benders decomposition (GBD) technique. Extensive evaluations based on real-life electricity price data for multiple IDC locations demonstrate the effectiveness of our scheme.

The study of today's Cyber-Physical System (CPS) has been an important research area. Internet Data Centers (IDCs) are energy consuming CPSs that support the reliable operations of many important online services. Along with the increasing Internet services and cloud computing in recent years, the power usage associated with IDC operations had been surging significantly. Such mass power consumption has brought extremely heavy burden on IDC operators. Recently there are extensive research on power management for IDCs. While most work only consider about dynamical optimization of IDC under electricity markets, the reaction of IDC toward electricity market has been overlooked. Due to the fact that IDCs are usually large-volume users in the electricity market, they might have market power to affect the electricity price. In this paper, we study how to address the challenge of interactions between IDC operation and electricity market price. To this end, we propose a supply function to model the market power of IDC and formulate a total electricity cost minimization problem as a non-linear programming. In order to design efficient solution method, we transform the optimization problem to a quadratic programming. Extensive performance evaluations demonstrate that the proposed method can effectively minimize the total electricity cost of IDCs by adaptively handling the interaction between IDCs and smart grid.

Maximizing the aggregate network performance over constrained computing and communication resources has been an active research area. Real-time wireless sensor network (RTWSN) is an important application of real-time and networked embedded systems. Due to the severe resources constraints and associated real-time requirements, new challenges arise in RTWSN. In this paper, we study an integrated scheme to optimize the total system performance of real-time flows over an RTWSN by exploiting multi-path routing and dynamic sampling rate assignment. We formally model the problem using nonlinear optimization and design an online distributed algorithm to obtain the optimal rate assignments on multiple paths. Extensive simulation studies demonstrates the significant performance improvements over existing proposals.

Along with the surging service demands on the cloud, the energy cost of Internet Data Centers (IDCs) is dramatically increasing. Energy management for IDCs is becoming ever more important. A large portion of applications running on data centers are data-intensive applications. MapReduce (and Hadoop) has been one of the mostly deployed frameworks for data-intensive applications. Both academia and industry have been greatly concerned with the problem of how to reduce the energy consumption of IDCs. However the critical power peak problem for MapReduce clusters has been overlooked, which is a new challenge brought by the usage of MapReduce. We elaborate the power peak problem and investigate the cause of the problem in details. Then we design an adaptive approach to regulate power peaks.

The study of Cyber-Physical System (CPS) has been an active area of research. Internet Data Center (IDC) is an important emerging Cyber-Physical System. As the demand on Internet services drastically increases in recent years, the power used by IDCs has been skyrocketing. While most existing research focuses on reducing power consumptions of IDCs, the power management problem for minimizing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the current multi-electricity market, where the price of electricity may exhibit time and location diversities. Further, for these service providers, guaranteeing quality of service (i.e. service level objectives-SLO) such as service delay guarantees to the end users is of paramount importance. This paper studies the problem of minimizing the total electricity cost under multiple electricity markets environment while guaranteeing quality of service geared to the location diversity and time diversity of electricity price. We model the problem as a constrained mixed-integer programming and propose an efficient solution method. Extensive evaluations based on real-life electricity price data for multiple IDC locations illustrate the efficiency and efficacy of our approach.