Dynamic Priority Research Articles

Discrete Event Simulation Models for CT Examination Queuing in West China Hospital.

In CT examination, the emergency patients (EPs) have highest priorities in the queuing system and thus the general patients (GPs) have to wait for a long time. This leads to a low degree of satisfaction of the whole patients. The aim of this study is to improve the patients' satisfaction by designing new queuing strategies for CT examination. We divide the EPs into urgent type and emergency type and then design two queuing strategies: one is that the urgent patients (UPs) wedge into the GPs' queue with fixed interval (fixed priority model) and the other is that the patients have dynamic priorities for queuing (dynamic priority model). Based on the data from Radiology Information Database (RID) of West China Hospital (WCH), we develop some discrete event simulation models for CT examination according to the designed strategies. We compare the performance of different strategies on the basis of the simulation results. The strategy that patients have dynamic priorities for queuing makes the waiting time of GPs decrease by 13 minutes and the degree of satisfaction increase by 40.6%. We design a more reasonable CT examination queuing strategy to decrease patients' waiting time and increase their satisfaction degrees.

Enhancement in System Schedulability by Controlling Task Releases

In real-time systems fixed priority scheduling techniques are considered superior than the dynamic priority counterparts from implementation perspectives; however the dynamic priority assignments dominate the fixed priority mechanism when it comes to system utilization. Considering this gap, a number of results are added to real-time system literature recently that achieve higher utilization at the cost of tuning task parameters. We further investigate this problem by proposing a novel fixed priority scheduling technique that keeps task parameters intact. The proposed technique favors the lower priority tasks by blocking the release of higher priority tasks without hurting their deadlines. The aforementioned strategy helps in creating some extra space that is utilized by a lower priority task to complete its execution. It is proved that the proposed technique dominates pure preemptive scheduling. Furthermore the results obtained are applied to an example task set which is not schedulable with preemption threshold scheduling and quantum based scheduling but it is schedulable with proposed technique. The analyses show the supremacy of our work over existing fixed priority alternatives from utilization perspective.

Non-Cooperation Game for Aircraft Pushback Slot Allocation Based on Dynamic Credibility Priority

Non-Cooperation Game for Aircraft Pushback Slot Allocation Based on Dynamic Credibility Priority

Medical image steganography using priority queue with Elgamal cryptosystem

This paper describes a methodology for hiding medical records and patient's identity information inside their scan medical image files using the concept of dynamic priority queue least significant bits (LSBs) substitution. Elgamal cryptosystem is used to encrypt patient's sensitive data. The outcome of the cryptosystem is organised in various priority queue blocks and sub blocks based on a secret data length of each block. The cipher text is obtained from priority queue as highest priority (weight) data will be retrieved first. Now embed the cipher text in a medical cover image at 7th bit or 8th bit position using heap-LSB technique. Authorised receiver can retrieve the correct plain text using private keys. Performance analysis is observed using various performance measure parameters between various medical stego and cover images. Results are analysed and compared with many of existing steganography algorithms.

DPTSV: A dynamic priority task scheduling strategy for TSS deadlock based on value evaluation

This paper analyzes the threat of TCG Software Stack (TSS)/TCM Service Module (TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism. In addition, this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat. The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue. In this strategy, an algorithm for real-time value computing is also designed, and it can adjust the production curves of the real time value by setting parameters in different environment, thus enhancing its adaptability, which is followed by scheduling and algorithm description. This paper also implements the algorithm and carries out its performance optimization. Due to the experiment result from Intel NUC, it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.

Congestion Control and Fairness with Dynamic Priority for Ad hoc Networks

Recently, the network structure has changed rapidly in modern applications and has imposed priority for heterogeneous applications. In some scenarios, the priorities of the flows need to change in the middle of the communication. However, the major problem is that the packets of different applications are not served at the destination by their expected priority ratio even if the priority does not change. Considering the above problem, this paper presents a fair packet scheduling policy which collects information from each individual flow according to its priority and auto-updates the priorities of flows when required. Moreover, a hybrid congestion control technique based on queue occupancy and channel utilisation is framed to control congestion. The proposed model has been simulated using NS2 for two applications. The simulation result shows the model performs better with respect to fairness among different applications. Moreover, it achieves improved throughput by reducing packet loss.

A reconfigurable framework for compositional schedulability and power analysis of hierarchical scheduling systems with frequency scaling

This paper presents a compositional framework for the modeling and analysis of hierarchical scheduling systems. We consider both schedulability and energy consumption of individual components, while analyzing a single core setting with a voltage frequency scaling CPU. According to the CPU frequency scaling, each task has a set of different execution times. Thus, the energy consumption of the whole system varies from one execution to another.We analyze each component individually by checking the feasibility of its workload against both the CPU availability and energy consumption constraints of such a component. Our periodic task model considers both static and dynamic priorities together with preemptive and non-preemptive behaviors. The models are realized using different forms of Hybrid Automata, all of which are analyzed using variants of Uppaal. The CPU frequencies, task behavior and scheduling policies used in each component are some of the reconfigurable parameters of the system. Finally, we demonstrate the applicability and scalability of our framework by analyzing the schedulability and power consumption of an avionics system.

Analysis of the Analytical and Simulations Results with the Network Model and Dynamic Priority of Limited Buffer

problem of choosing optimal parameters for dynamic priorities in the case of the various types of service requests with linearly decreasing function of priorities, The results of analysis and simulations models of distributed computer networks with dynamic priorities and a limited buffer residence time requirements of the network is limited. The network constraint violation results in a loss requirements. A comparative analysis of the results of analytical and simulation models are conducted to verify the adequacy of the analytical models. General Terms Service, limited buffer, dynamic priorities, network

Investigation of frame mode unification and virtual channel multiplexing based on the multilayered satellite network OISLs interface

In a multi-layered optical satellite network, a standardized data transmission is a reliable guarantee to efficiently process and transfer multi-service data for the space link. The transmission frame reframing unit (TFRU) is proposed to solve the problem of different service data having low transmission efficiency in the laser link. The TFRU uses a virtual channel (VC) technology to unify the format and rate of transmitted data using second encapsulation and VC scheduling for the service data. The Priority VC schedule algorithm is proposed to further improve multiplex efficiency. According to the principle of TFRU encapsulation and arrival rate of service data, the frame dynamic priority is defined by the VC priority and frame criticality. Furthermore, the Priority VC schedule specific method is provided. The simulation results show that the throughput increases to 3.0546M, and the scheduling time delay reduces to 0.9183s. Thus, the system performance has been greatly enhanced. The cache demands are satisfied because the laser terminal data transmission rate is larger than the sum of all service data rates. Using the dynamic schedule generated TFRU frames, the priority algorithm based on the TFRU ensures frame scheduling fairness in each VC.

Dynamic Analysis of Priority with Linear Decreasing Function of a Priority in the Service System with Four Types of Application

problem of choosing optimal parameters for dynamic priorities in the case of the various types of service requests with linearly decreasing function of priorities, performed experiments and obtained results. These results can be used to develop algorithms of functioning nodes for data and their software. In this paper we propose a method for solving the problem of choosing the optimal values of these coefficients that minimize the total value of the queue length in systems with four types of applications.

A novel fuzzy decision-making system for CPU scheduling algorithm

In this research article, we present a novel fuzzy decision-making system to improve CPU scheduling algorithm of a multitasking operating system. We add intelligence to the existing scheduling algorithms by incorporating fuzzy techniques in the selection of a process to be run on CPU, which result in improved waiting and turn-around times. We implement our proposed algorithm as a simulator using C language. The simulator implements our fuzzy scheduling algorithm, reads the required parameters of all the ready to run processes from a file, and finally computes a dynamic priority (dpi) value for each process. The run queue is sorted according to each process's dpi, and the process at the head of the queue is selected to run on CPU. Finally, we compare our results with some existing proposed fuzzy CPU scheduling (PFCS) algorithms as well as with some standard CPU schedulers. Our results show improvements as compared to the work of Ajmani's PFCS (Ajmani and Sethi in BVICAM's Int J Inf Technol 5:583---588, 2013), as well as from Behera's improved fuzzy-based CPU scheduling algorithm (Behera et al. in Int J Soft Comput Eng 2:326---331, 2012). Our efforts contribute to the overall efforts of the community contributing to the fuzzification of different operating system modules. These efforts finally result in an operating system that gives convenience to its users in both certain and uncertain environments and at the same time efficiently utilize the underlying hardware and software under precise as well as fuzzy conditions (Kandel et al. in Fuzzy Sets Syst 99:241---251, 1988).

Designing of Vague Logic Based Fair-Share CPU Scheduler: VFS CPU Scheduler

CPU scheduler is the primary component of an operating system which supports multitasking. Fair Share CPU scheduler uses static policy to share the CPU time among different levels of system. Static sharing of CPU time can affect the performance of system. Moreover, Fair share scheduler does not consider the impreciseness and uncertainty related to tasks. The objective is to design a vague based fair share scheduler (VFS). VFS scheduler extends the research in the field of fair sharing of CPU time where the functions of VFS scheduler are threefold: firstly, it deals with the impreciseness of task. Secondly, it dynamically shares the CPU time among users as well as among tasks. Thirdly, it supplies the dynamic priority to each task. It improves the performance of system. The VFS scheduler has three modules, VIS-DCS vague inference system for sharing CPU time; VIS-DP vague inference system for assigning dynamic priority to each task and scheduling algorithm to schedule the tasks. The novelty of this approach is to introduce the vague logic based CPU scheduler. Simulation results show that the VFS scheduler has improved performance over the fair share scheduler.

Traffic differentiation and dynamic duty cycle adaptation in IEEE 802.15.4 beacon enabled WSN for real-time applications

The wireless sensor networks (WSNs) based on the IEEE 802.15.4 beacon enabled mode provide a deterministic resource allocation offered by the guaranteed time slot (GTS) mechanism which is ideal for real-time traffic applications that requires high quality of service (QoS). In the beacon enabled mode the active and optional inactive period is governed by superframe order (SO) and beacon order (BO). Adapting the duty cycle using the SO and BO is desirable to accommodate the changing context of the WSNs traffic for network optimisation. However, existing approaches assumed a fixed SO and BO and segregate the evaluation of the contention access period and the contention free period that forms the active segment. To improve the performance of the IEEE 802.15.4 protocol in respect of the trade-off between power consumption and end-to-end delay, within a required QoS, a combination of dynamic priorities for traffic differentiation and dynamic duty cycle adaptation in the GTS allocation process are introduced. This work demonstrates that the proposed solution provides significantly better performance than the standard allocation method by simultaneously satisfying QoS and service differentiation demands with considerable power savings which is essential for WSNs. The specific novel use of this solution is in an application scenario where the deployed network is situated in an assisted living environment with a rich suite of heterogeneous wireless based communications in place.

Transformation process of RTS scheduling analysis requirements from UML/MARTE to dynamic priority time Petri Nets

The real-time system (RTS) design is a major challenge due to the complexity of the considered application and the appearance of multiprocessor architectures. Most of the research in this area is interested in high abstraction level methods to decrease the design convolution. In particular, the Unified Modeling Language (UML) profiles and the Model Driven Engineering (MDE) aim at being an adequate solution to support the whole life cycle of RTS design with its real-time constraints and performance issues. Based on MDE and the Modeling and Analysis of Real-Time and Embedded systems (MARTE) profile, the present manuscript proposes an interactive partitioning scheduling of RTS running on multiprocessor architecture. A special attention is paid to the scheduling analysis step to accelerate the exploration of HW/SW space solution. Starting from a candidate solution modeled with UML activity and annotated with MARTE stereotypes, a mapping process to dynamic priority time Petri Nets (dPTPN) was defined. The considered dPTPN is able to prove the schedulability or provide a counterexample to determine the partitions causes of temporal fault. This counterexample presents a useful feedback to the HW/SW space solution explorer in the aim to exclude all solution containing the described partitions.

An Efficient Job Scheduling for MapReduce Clusters

The job scheduling for Map Reduce clusters has received significant attention in recent years, because it plays an important role on Map Reduce clusters. Traditional job scheduling performs poorly in assigning a task to appropriate nodes, and can not predict the resource utilization of the unexecuted tasks. To address the problems, an efficient job scheduling for Map Reduce clusters is proposed in this paper. The job scheduling introduces dynamic priority scheduling and real-time prediction model. Dynamic priority scheduling introduces the minimum cost data locality algorithm with a weight to deal with different size jobs, and real-time prediction model can predict the resource utilization of unexecuted tasks by calculating the running tasks. The resource utilization contains CPU, memory, and network. Experimental results prove that the proposed job scheduling is able to perform well in Map Reduce clusters.

Deadline Constrained Adaptive Multilevel Scheduling System in Cloud Environment

In cloud, everything can be provided as a service wherein a large number of users submit their jobs and wait for their services. Thus, scheduling plays major role for providing the resources efficiently to the submitted jobs. The brainwave of the proposed work is to improve user satisfaction, to balance the load efficiently and to bolster the resource utilization. Hence, this paper proposes an Adaptive Multilevel Scheduling System (AMSS) which will process the jobs in a multileveled fashion. The first level contains Preprocessing Jobs with Multi-Criteria (PJMC) which will preprocess the jobs to elevate the user satisfaction and to mitigate the jobs violation. In the second level, a Deadline Based Dynamic Priority Scheduler (DBDPS) is proposed which will dynamically prioritize the jobs for evading starvation. At the third level, Contest Mapping Jobs with Virtual Machine (CMJVM) is proposed that will map the job to suitable Virtual Machine (VM). In the last level, VM Scheduler is introduced in the two-tier VM architecture that will efficiently schedule the jobs and increase the resource utilization. These contributions will mitigate job violations, avoid starvation, increase throughput and maximize resource utilization. Experimental results show that the performance of AMSS is better than other algorithms.

Gaudi components for concurrency: Concurrency for existing and future experiments

HEP experiments produce enormous data sets at an ever-growing rate. To cope with the challenge posed by these data sets, experiments’ software needs to embrace all capabilities modern CPUs offer. With decreasing memory/core ratio, the one-process-per-core approach of recent years becomes less feasible. Instead, multi-threading with fine-grained parallelism needs to be exploited to benefit from memory sharing among threads.Gaudi is an experiment-independent data processing framework, used for instance by the ATLAS and LHCbexperiments at CERN's Large Hadron Collider. It has originally been designed with only sequential processing in mind. In a recent effort, the frame work has been extended to allow for multi-threaded processing. This includes components for concurrent scheduling of several algorithms - either processingthe same or multiple events, thread-safe data store access and resource management.In the sequential case, the relationships between algorithms are encoded implicitly in their pre-determined execution order. For parallel processing, these relationships need to be expressed explicitly, in order for the scheduler to be able to exploit maximum parallelism while respecting dependencies between algorithms. Therefore, means to express and automatically track these dependencies need to be provided by the framework.In this paper, we present components introduced to express and track dependencies of algorithms to deduce a precedence-constrained directed acyclic graph, which serves as basis for our algorithmically sophisticated scheduling approach for tasks with dynamic priorities. We introduce an incremental migration path for existing experiments towards parallel processing and highlight the benefits of explicit dependencies even in the sequential case, such as sanity checks and sequence optimization by graph analysis.

PriDyn: Enabling Differentiated I/O Services in Cloud Using Dynamic Priorities

Virtualization is one of the key enabling technologies for Cloud computing. Although it facilitates improved utilization of resources, virtualization can lead to performance degradation due to the sharing of physical resources like CPU, memory, network interfaces, disk controllers, etc. Multi-tenancy can cause highly unpredictable performance for concurrent I/O applications running inside virtual machines that share local disk storage in Cloud. Disk I/O requests in a typical Cloud setup may have varied requirements in terms of latency and throughput as they arise from a range of heterogeneous applications having diverse performance goals. This necessitates providing differential performance services to different I/O applications. In this paper, we present PriDyn, a novel scheduling framework which is designed to consider I/O performance metrics of applications such as acceptable latency and convert them to an appropriate priority value for disk access based on the current system state. This framework aims to provide differentiatedI/O service to various applications and ensures predictable performance for critical applications in multi-tenant Cloud environment. We demonstrate through experimental validations on real world I/O traces that this framework achieves appreciable enhancements in I/O performance, indicating that this approach is a promising step towards enabling QoS guarantees on Cloud storage.

Combinatorial abstraction refinement for feasibility analysis of static priorities

Combinatorial explosion is a challenge for many analysis problems in the theory of hard real-time systems. One of these problems is static priority schedulability of workload models which are more expressive than the traditional periodic task model. Different classes of directed graphs have been proposed in recent years to model structures like frames, branching and loops. In contrast to dynamic priority schedulers with pseudo-polynomial time analysis methods, static priority schedulability has been shown to be intractable since it is strongly coNP-hard already for the relatively simple class of cyclic digraphs. The core of this problem is the necessity to combine different behaviors of the participating tasks. We introduce a novel iterative approach to efficiently cope with this combinatorial explosion, called combinatorial abstraction refinement. In combination with other techniques it significantly reduces exponential growth of run-time for most inputs. We apply the method to static task priorities and demonstrate that a prototype implementation outperforms the state-of-the art pseudo-polynomial analysis for dynamic priority feasibility. It further shows better scaling behavior for randomly generated problem instances. We extend the approach to non-preemptive schedulers as well as static job-type priorities where jobs of different types in the same task may be assigned different static priorities. Finally, we provide a general, abstract formulation of the algorithm, since we believe that this method can be applicable to a variety of combinatorial problems in the theory of real-time systems with certain abstraction structures.

RAILWAY PROJECTS EVALUATION WITH DYNAMIC PRIORITIES – SERBIA CASE STUDY

Strategic decisions, such as transport investments, depend on a number of factors of different relevance that are often changeable over time. Evaluation of transport projects is a complex and difficult task, but also crucial for a company’s success in the market. We assume that certain relations of the system elements are functions of time, and thus we apply a dynamic approach – dynamic priorities in multi-criteria decision making. The topic of this paper is time dependent multi-criteria decision making in a transport projects evaluation. The model is tested on real data from the Serbian railway network. http://dx.doi.org/10.13033/ijahp.v7i1.292