Hard Deadlines Research Articles

THE CONCEPT OF "FAIR KAZAKHSTAN" AND ITS REFLECTION IN THE RHETORIC OF SENIOR CIVIL SERVANTS (BASED ON CONTENT ANALYSIS)

Paper based on evaluation the representation concept of «Fair Kazakhstan» in speeches the public servants of high level. Such analysis is interesting as part of communication component by construction the ideological landscape of Kazakhstan. The attractiveness the idea of building a «Fair Kazakhstan» based on fact that we are talking about a process, its implementation does not have a hard deadline. Risk is following, public servants as bearers this idea should to offer new initiatives for society that bringing us closer to the «ideal» future. The research methods are case studies and content analysis, that evaluating frequency and context of use the concept in official speeches. The results show the dynamics of changes from 2019 to 2024, which indicates political and social transformations in Kazakhstan. The findings of the article highlight the role of civil servants in promoting and implementing the concept of a «Fair Kazakhstan», and also indicate the need for further research in the field of political science and sociology for a deeper understanding of this phenomenon.

Joint Task Dispatching and Bandwidth Allocation with Hard Deadlines in Distributed Serverless Edge Computing Systems

Joint Task Dispatching and Bandwidth Allocation with Hard Deadlines in Distributed Serverless Edge Computing Systems

Average delay analysis of soft deadline messages scheduled in the dynamic segment of FlexRay protocol

The FlexRay communication protocol provides high bandwidth for supporting both hard deadline and soft deadline traffic in in-vehicle communication networks. In this paper, we carry out delay analysis of soft deadline traffic which is handled by the dynamic segment of FlexRay. We model the arrival of these messages as Poisson processes, and use queuing theory to evaluate the average delay that they experience. Initially, we consider three nodes competing for service, assuming that two out of three can transmit messages in any FlexRay cycle and obtain expressions for the evolution of the corresponding queues. We also determine the range of message arrival rates for which the queues are stable. These results are then extended to the general case of N queues. The analytical results are compared with those obtained by simulation for a typical system.

Online Distributed Schedule Randomization to Mitigate Timing Attacks in Industrial Control Systems

Industrial control systems (ICSs) consist of a large number of control applications that are associated with periodic real-time flows with hard deadlines. To facilitate large-scale integration, remote control, and co-ordination, wireless sensor and actuator networks form the main communication framework in most ICSs. Among the existing wireless sensor and actuator network protocols, WirelessHART is the most suitable protocol for real-time applications in ICSs. The communications in a WirelessHART network are time-division multiple access based. To satisfy the hard deadlines of the real-time flows, the schedule in a WirelessHART network is pre-computed. The same schedule is repeated over every hyperperiod (i.e., lowest common multiple of the periods of the flows). However, a malicious attacker can exploit the repetitive behavior of the flow schedules to launch timing attacks (e.g., selective jamming attacks). To mitigate timing attacks, we propose an online distributed schedule randomization strategy that randomizes the time-slots in the schedules at each network device without violating the flow deadlines, while ensuring the closed-loop control stability. To increase the extent of randomization in the schedules further, and to reduce the energy consumption of the system, we incorporate a period adaptation strategy that adjusts the transmission periods of the flows depending on the stability of the control loops at runtime. We use Kullback-Leibler divergence and prediction probability of slots as two metrics to evaluate the performance of our proposed strategy. We compare our strategy with an offline centralized schedule randomization strategy. Experimental results show that the schedules generated by our strategy are 10% to 15% more diverse and 5% to 10% less predictable on average compared to the offline strategy when the number of base schedules and keys vary between 4 and 6 and 12 and 32, respectively, under all slot utilization (number of occupied slots in a hyperperiod). On incorporating period adaptation, the divergence in the schedules reduceat each period increase with 46% less power consumption on average.

Online Schedule Randomization to Mitigate Timing Attacks in 5G Periodic URLLC Communications

Ultra-reliable low-latency communication (URLLC) in 5G networks is designed to support time-critical applications such as industrial control systems (ICSs), where user equipment (UEs) communicate with a base station (BS) with very high reliability and low latency. Most of these communications in ICSs are periodic and associated with hard deadlines. To provide a reliable service while satisfying the hard deadlines, the BS usually reserves slots and frequencies and precomputes the schedule for such UEs. The same schedule repeats over time, which makes the slots and frequencies predictable. However, an attacker can exploit this aspect and launch timing attacks disrupting specific communication, thereby, undermining the safety of the system. To mitigate such attacks, we present an online strategy that randomizes the scheduled slots and frequencies over time without violating the flow deadlines. We use Kullback-Leibler divergence to measure the randomness in the schedules generated by our strategy with reference to a hypothetical truly random strategy. We perform security analysis of our proposed strategy using Prediction Probability to measure the predictability in the slots of the generated schedules. We evaluate the performance of our strategy against a state-of-the-art baseline, and show that our strategy performs better than the baseline across all parameter settings.

Performance of Multidevice Downlink Cell-Free System Under Finite Blocklength for uRLLC With Hard Deadlines

As an important part of beyond the fifth-generation (B5G) and the sixth-generation (6G) mobile communication systems, ultra-reliable and low latency communications (uRLLC) puts forward strict requirements for delay and reliability (e.g., 99.9999% reliability and 500 μs latency). At present, the evaluation measures of delay and reliability are usually based on infinite block length and rely on long-term statistics, which cannot meet the requirement of low latency. The cell-free system, with a very large number of distributed antennas, has the characteristics of macro-diversity and spatial sparsity, which can further enhance the performance of uRLLC. In this paper, the downlink multidevice cell-free system with hard deadlines is considered and analyzed in the finite block length (FBL) regime. The communication’s delay and reliability are described based on two instantaneous evaluation measures: transmission error (TE) and time overflow (TO) probability. From the perspective of information theory, this paper analyzes the analytic expression of TE probability for a single device and the performance impact of FBL on the traditional channel capacity analysis. Considering the multidevice TO probability in a cell-free system, the closed-form expressions of upper and lower bounds are derived and compared with the gamma approximation results. This paper further provides three methods, namely, transmission rate selection, device grouping and space division multiplexing, to balance the delay and reliability of the system and analyzes the performance.

A fault-tolerant resource locking protocol for multiprocessor real-time systems

This paper presents the first fault-tolerant resource locking protocol for multiprocessor real-time systems. Resource locking protocols control access to shared resources in real-time systems. Most of the previous studies in resource locking protocols have focused on decreasing blocking time and resource conflicts. The use of such protocols in multiprocessor safety-critical real-time systems necessitates fault tolerance in the design of these systems. However, in research focused on multiprocessor real-time systems, fault tolerance has received low attention in resource allocation studies. In addition, many solutions proposed to improve the reliability in real-time scheduling ignored resource models in their task model. This paper proposes a novel fault-tolerant resource locking protocol, called FTPIA-NPCS, which considers transient faults in shared resources. FTPIA-NPCS proposes a checkpointing-based fault-tolerant mechanism to recover from errors caused by transient faults. In this mechanism, a checkpoint is taken from the tasks, before each resource allocation. In addition to providing fault tolerance, FTPIA-NPCS solves the problem of priority inversion in NPCS protocol for periodic tasks with hard deadlines. The simulation results demonstrate that FTPIA-NPCS tolerates at least one transient fault in resources for each execution of the periodic tasks with 15% blocking time overhead in comparison to its non-fault-tolerant configuration, called PIA-NPCS. Furthermore, the blocking time of higher-priority tasks is minimized in comparison to PIA-NPCS. An analytical evaluation of the protocol supports the simulation results.

Ride the Tide of Traffic Conditions: Opportunistic Driving Improves Energy Efficiency of Timely Truck Transportation

We study the problem of minimizing fuel consumption of a heavy-duty truck traveling across the national highway network subject to a hard deadline. We focus on a real-world setting that traversing a road segment is subject to variable speed ranges due to dynamic traffic conditions. The consideration of dynamic traffic conditions not only differentiates our work from existing ones but also allows us to leverage opportunistic driving to improve fuel efficiency. The idea is for the truck to strategically wait (e.g., at highway rest areas) for benign traffic conditions, so as to traverse subsequent road segments at favorable speeds for saving fuel. We observe that traffic conditions and thus speed ranges are mostly stationary within certain duration of the day, and we term them as phases. We formulate the fuel consumption minimization problem under phased speed ranges, considering path planning, speed planning, and opportunistic driving. We prove that the problem is NP-hard, and develop a dual-subgradient algorithm for large-/national-scale instances. We characterize conditions under which the algorithm generates an optimal solution. We carry out simulations based on real-world traces over the US highway system. The results show that our scheme saves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\%$</tex-math> </inline-formula> fuel than a shortest-path based alternative, of which opportunistic driving contributes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13\%$</tex-math> </inline-formula> . Meanwhile, opportunistic driving also reduces driving time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\%$</tex-math> </inline-formula> as compared to only optimizing path planning and speed planning. As such, it offers a desirable design option to simultaneously reduce fuel consumption and hours of driving. Last but not least, our results highlight a perhaps surprising observation that dynamic traffic conditions can be exploited to achieve fuel savings even larger than those under stationary traffic conditions.

RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks With Fine-Grain Utilization

Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely heavily on artificial intelligence and machine learning algorithms to perform important system operations. Since these highly parallel applications are computationally intensive, they need to be accelerated by graphics processing units (GPUs) to meet stringent timing constraints. However, despite the wide adoption of GPUs, efficiently scheduling multiple GPU applications while providing rigorous real-time guarantees remains a challenge. Each GPU application has multiple CPU execution and memory copy segments, with GPU kernels running on different hardware resources. Because of the complicated interactions between heterogeneous segments of parallel tasks, high schedulability is hard to achieve with conventional approaches. This paper proposes RTGPU, which combines fine-grain GPU partitioning on the system side with a novel scheduling algorithm on the theory side. Through system and theory co-design, RTGPU achieves superior system throughput and real-time schedulability. In this paper, we start by building a model for the CPU and memory copy segments. Leveraging persistent threads, we then implement fine-grained GPU partitioning with improved performance through interleaved execution. To reap the benefits of fine-grained GPU partitioning and schedule multiple parallel GPU applications, we propose a novel real-time scheduling algorithm based on federated scheduling and grid search with uniprocessor fixed-priority scheduling. Our approach provides real-time guarantees to meet hard deadlines, and achieves over 11% improvement in system throughput and up to 57% schedulability improvement compared with previous work. We validate and evaluate RTGPU on NVIDIA GTX1080Ti GPU systems. Our system side techniques can be applied on mainstream NVIDIA GPUs, and the proposed scheduling theory can be used in general heterogeneous computing platforms which have a similar task execution pattern.

Auction-Based Dependent Task Offloading for IoT Users in Edge Clouds

The rapid proliferation of latency-sensitive Internet of Things (IoT) applications boosts the frequency of offloading compute-intensive tasks from IoT users to mobile edge computing (MEC) due to the limitation resources of IoT devices. It is inevitably for IoT users to compete for the computing resources of the MEC, especially when the computation tasks are dependent and have hard deadline constraints. However, most existing dependent task offloading schemes may not well consider the resource competition issues among IoT users, and possibly lead to limited system performance in multiuser scenario. To address this issue, we intend to design an auction-based dependent task-offloading mechanism to improve the efficiency of task offloading for multiple IoT users. First, we formulate the dependent task offloading as a valuation maximization problem in the trade of computing resources satisfying users’ latency requirements, which has been proved to be NP-hard. Then, by jointly considering the task graph structure and the current status of the MEC, we propose a truthful auction mechanism, named greedy winner selection strategy, in which a heuristic dependent task assignment for winners is designed to improve the efficiency of the task offloading. By conducting extensive simulations, we validate that the performance of the proposed dependent task offloading strategy is superior to existing competition algorithms, in terms of total valuations, average makespans, and success rates.

ReTSN: Resilient and Efficient Time-Sensitive Network for Automotive In-Vehicle Communication

Time-sensitive networking (TSN) is being widely investigated to provide Ethernet capabilities for in-vehicle backbone communication. However, the gate control list (GCL), as a simple mechanism for achieving timing determinism for safety-critical traffic (ST) frames with hard deadlines, is too rigid to handle the intrinsic timing uncertainty of automated driving systems (ADSs). Due to the complexity and the unpredictable operating environment, there can be delayed ST frames that can disrupt the solidly fixed timing behavior. In this work, we present one novel approach to effectively use bandwidth resources to deal with delayed ST frames, which cannot be handled by a traditional fixed GCL, and the discarding of them should be reduced to improve the system’s integrity. An acceptance test is implemented to report to the application layer when an ST frame will miss its deadline and hence be rejected, i.e., prevented from entering the switch. To further improve the efficiency of bandwidth usage, we investigate how to improve the performance of the more important Class A frames in an audio-video-bridging (AVB) switch, which adopts a credit-based shaper mechanism, and we propose a constant bandwidth server to replace the credit-based shaper while taking fairness into consideration. Evaluation with extensive experiments shows that both resilience and efficiency of the TSN are significantly enhanced compared with a credit-based shaper, especially when the traffic load is relatively high. For the delayed ST frames and event-triggered traffic, our approach is able to schedule more than the solution using the AVB switch even with a high network load.

Earliest Deadline First Scheduling for Real-Time Computing in Sustainable Sensors

Energy harvesting is a green technology that authorizes small electronic devices to be supplied for perpetual operation. It enables wireless sensors to be integrated in applications that previously were not feasible with conventional battery-powered designs. Intermittent computing and scheduling are the two central aspects of designing a Real-Time Energy Harvesting (RTEH) sensor, generally used to monitor a mission critical process. Traditional scheduling algorithms fail to timely execute the hard deadline tasks because they accommodate no fluctuations in power supply and therefore no intermittent computing. A suitable energy-harvesting-aware scheduling algorithm has been proposed so as to achieve a higher schedulability rate. Unlike the classical EDF (Earliest Deadline First) scheduler, the ED-H algorithm is idling and clairvoyant, with an improved performance in terms of the deadline missing ratio. This paper reviews the main advances in dynamic priority scheduling based on EDF for energy-neutral systems.

Online Approximation Scheme for Scheduling Heterogeneous Utility Jobs in Edge Computing

Edge computing systems typically handle a wide variety of applications that exhibit diverse degrees of sensitivity to job latency. Therefore, a multitude of utility functions of the job response time need to be considered by the underlying job dispatching and scheduling mechanism. Nonetheless, previous studies in edge computing mainly focused on optimizing a single utility function across all jobs, e.g., linear, sigmoid, or the hard deadline. In this paper, we design online job dispatching and scheduling strategies in which different jobs can be categorized by different non-increasing utility functions. Our goal is to maximize the total utility of all scheduled jobs. We first prove that no online deterministic algorithm could achieve a competitive ratio better than the lower bound <inline-formula> <tex-math notation="LaTeX">$\Omega(\frac{1}{\sqrt{\epsilon}})$</tex-math> </inline-formula> under the <inline-formula> <tex-math notation="LaTeX">$(1+\epsilon)$</tex-math> </inline-formula>-speed augmentation model. We proceed to propose an online algorithm, named as, for handling jobs with heterogeneous utilities. We prove that is <inline-formula> <tex-math notation="LaTeX">$O(\frac{1}{\epsilon^2})$</tex-math> </inline-formula>-competitive. We also design its distributed version, i.e., . We implement and on an edge computing testbed running deep learning inference jobs. With the production trace from Google Cluster, our experimental and large-scale simulation results indicate that can increase the total utility by up to <inline-formula> <tex-math notation="LaTeX">$50\%$</tex-math> </inline-formula> compared with state-of-the-art methods. Besides, the performance loss of is only <inline-formula> <tex-math notation="LaTeX">$2\%$</tex-math> </inline-formula> compared with with a small communication overhead involved. Moreover, both of our algorithms are robust to estimation errors in job processing time and transmission delay.

Optimizing Two-Truck Platooning With Deadlines

We study a transportation problem where two heavy-duty trucks travel across the national highway from separate origins to destinations, subject to individual deadline constraints. Our objective is to minimize their total fuel consumption by jointly optimizing path planning, speed planning, and platooning configuration. Such a two-truck platooning problem is pervasive in practice yet challenging to solve due to hard deadline constraints and enormous platooning configurations to consider. We first leverage a unique problem structure to significantly simplify platooning optimization and present a novel formulation. We prove that the two-truck platooning problem is weakly NP-hard and admits a Fully Polynomial Time Approximation Scheme (FPTAS). The FPTAS can achieve a fuel consumption within a ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1+\epsilon)$ </tex-math></inline-formula> to the optimal (for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon &gt;0$ </tex-math></inline-formula> ) with a time complexity polynomial in the size of the transportation network and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/\epsilon $ </tex-math></inline-formula> . These results are in sharp contrast to the general multi-truck platooning problem, which is known to be APX-hard and repels any FPTAS. As the FPTAS still incurs excessive running time for large-scale cases, we design an efficient dual-subgradient algorithm for solving large-/national- scale instances. It is an iterative algorithm that always converges. We prove that each iteration only incurs polynomial-time complexity, albeit it requires solving an integer linear programming problem optimally. We characterize a condition under which the algorithm generates an optimal solution and derive a posterior performance bound when the condition is not met. Extensive simulations based on real-world traces show that our joint solution of path planning, speed planning, and platooning saves up to 24% fuel as compared to baseline alternatives.

Open Access Monographs: Making Mandates Reality

Watch the VIDEO. In early 2022 the major research funding body in the UK (UKRI), released a policy statement mandating Open Access (OA) monographs, with an implementation date of January 1st, 2024. This date will see a shift from a hypothetical future for OA books to a concrete policy with a hard deadline. Add to this the forthcoming mandates on OA books from cOAlition S/Plan S in Europe, and potential implications of the next research impact evaluation (REF) in the UK, and it is clear that there is a pressing need for libraries and academic book publishers to understand how the sector will meet the challenges of implementing these policies. In this session we will present the following questions: what is the transition to open access for scholarly books going to look like? What pitfalls and opportunities will it bring? Who is at risk of being excluded? How do we get from where publishing and libraries are today, to making these policies a workable reality in 2024? We will also pose the crucial question: how will this transition be paid for? We’ll demonstrate how Book Processing Charges (BPCs) worsen inequality by favouring the most wealthy institutions and authors. And we’ll highlight some collective library funding models that seek to spread the funding of OA books so that no single institution bears a disproportionate cost. These models range from the large and well-funded like MIT’s Direct 2 Open and Michigan’s Fund to Mission, to the COPIM project’s own Opening the Future model which is aimed at smaller mission-driven publishers like CEU Press and Liverpool University press. With the clock ticking on imminent policies, conversation is urgently needed on the practicalities of making mandates reality. We’ll discuss what libraries can do to meet the challenge, and how supporting programmes like Opening the Future will ensure that the transition to OA is a sustainable and bibliodiverse one. The transition to OA should not leave smaller and medium-sized presses behind; nor should it rely on paying BPCs, which risks excluding any author without funding; and the transition must be sustainable for libraries. We will demonstrate that the time to support collective funding programme pilots is now, in order that they can transition from pilot to implementable policy, to the benefit of all: opening up research for the public good and building an open, global ‘collection’ that is shared by libraries in common, around the world.

Characterizing the Effect of Deadline Misses on Time-Triggered Task Chains

Modern embedded software includes complex functionalities and routines, often implemented by splitting the code across different tasks. Such tasks communicate their partial computations to their successors, forming a task chain. Traditionally, this architecture relies on the assumption of hard deadlines and timely communication. However, in actual implementations, tasks may miss their deadlines, thus affecting the propagation of their data. This article analyzes a task chain in which tasks can fail to complete their jobs according to the weakly-hard task model. We explore how missing deadlines affect chains in terms of classic latency metrics and valid data paths. Our analysis, based on mixed integer linear programming, extracts the worst-case deadline miss pattern for any given performance metric.

How windows size and number can influence the schedulability of hierarchically-scheduled time-partitioned distributed real-time systems

Partitioning techniques are implemented in the development of safety-critical applications to ensure isolation among components. An adequate scheduling of the execution of such partitions is a key challenge so that applications meet the hard deadlines imposed. In this work, we study the effect of different partition window configuration parameters, with the aim of analyzing their impact in the worst-case response times of system tasks. This is the first step in the development of an algorithm for optimizing partition windows in hierarchically-scheduled time partitioned distributed systems.

EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Objectives: To propose an algorithm to balance resource utilization and revenue generation in the cloud environment. Methods: This study proposes the Entropy-Based TOPSIS algorithm for task scheduling in IaaS Clouds (EBTASIC) to balance resource utilization and revenue generation using the objective-based Entropy Weighting Method (EWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Findings/Novelty: Various performance evaluation factors are calculated using EBTASIC and compared with baseline algorithms First Come First Serve (FCFS) and Earliest Deadline First (EDF) algorithms over 12750 lease requests with hard deadlines. The actual response time of EBTASIC is 37.61 percent faster than FCFS and 47.95 percent faster than EDF. Total time spent on lease execution by EBTASIC is reduced by 3.43 percent when compared to FCFS and 3.99 percent when compared to EDF. Turnaround time of EBTASIC is lowered by 21.14 percent when compared to FCFS and 28.81 percent when compared to EDF. EBTASIC throughput is enhanced by 40.80 percent over FCFS and 54.3 percent over EDF. The average response time of EBTASIC is shortened by 9.38 percent compared to FCFS and 14.81 percent compared to EDF. Resource utilization of the proposed algorithm EBTASIC is enhanced by 25.54 percent over FCFS and 6.79 percent over the EDF algorithm. Keywords: Task and VM Scheduling; MCDM Techniques; TOPSIS; Entropy Weighting Method; EBTASIC Algorithm

Scheduling for Urban Air Mobility using Safe Learning

This work considers the scheduling problem for Urban Air Mobility (UAM) vehicles travelling between origin-destination pairs with both hard and soft trip deadlines. Each route is described by a discrete probability distribution over trip completion times (or delay) and over inter-arrival times of requests (or demand) for the route along with a fixed hard or soft deadline. Soft deadlines carry a cost that is incurred when the deadline is missed. An online, safe scheduler is developed that ensures that hard deadlines are never missed, and that average cost of missing soft deadlines is minimized. The system is modelled as a Markov Decision Process (MDP) and safe model-based learning is used to find the probabilistic distributions over route delays and demand. Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely explore the learned models in an online fashion and develop a near-optimal non-preemptive scheduling policy. These results are compared with Value Iteration (VI) and MCTS (Random) scheduling solutions.

Slardar: Scheduling information incomplete inter-datacenter deadline-aware coflows with a decentralized framework

Nowadays, geographically distributed edge clusters and datacenters generate large volumes of wide-area network (WAN) traffic. Therefore, it is critical to schedule inter-datacenter flows, especially coflows that imply application-level semantics, to improve the performance of geo-distributed applications. However, coflows often have a mixture of soft and hard deadline requirements, which cannot be efficiently guaranteed by existing methods. In this paper, a decentralized deadline-ware coflow scheduling framework, called Slardar, is proposed. It schedules inter-datacenter coflows while considering both hard and soft deadline requirements. However, it is challenging to design such a framework without complete coflow information. Hence, we first model the intra-coflow bandwidth allocation problem as a mean-field game where each datacenter aims to minimize its own cost with only local coflow information. We further prove its mean-field equilibrium is also the optimal bandwidth allocation strategy and propose a deep reinforcement learning based algorithm to find an approximate optimal numerical solution. Then, we investigate the offline inter-coflow bandwidth allocation problem by modeling it as a linear programming problem, and an online greedy heuristic algorithm is proposed to find an approximate optimal solution. Finally, we compare Slardar with state-of-art methods by large-scale simulation experiments. Experimental results show that our proposed method can greatly improve the transmission revenue and increase the soft and hard deadline guarantee ratios by 7.5% and 10% at least.