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Subgraph matching is an important method of data mining in complex networks. In recent years, the subgraph matching algorithm based on GPU (graphics processing units) has shown obvious speed advantages.However, due to the large scale of graph data and a large number of intermediate results of subgraph matching, the memory capacity of a single GPU soon becomes the main bottleneck for processing subgraph matching algorithm of large graph. Therefore, this paper proposes a multi-GPU programming model for large graph subgraph matching. Firstly, the framework of subgraph matching algorithm based on multi-GPU is proposed, and the cooperative operation of subgraph matching algorithm on multi-GPU is realized, which solves the problem of graph scale of subgraph matching on GPU. Secondly, a dynamic adjustment technique based on query graph is used to deal with cross-partition subgraph sets, which solves the cross-partition subgraph matching problem caused by graph segmentation. Finally, based on the characteristics of SIMT (single instruction multiple threads) architecture on GPU, a priority scheduling strategy is proposed to ensure the internal load balancing of GPU, and a pipeline mechanism of shared memory is designed to optimize the cache contention of multi-core concurrency. Experiments show that the proposed multi-GPU programming model can get the correct matching results on billions of datasets. Compared with the latest GPU-based solution, the proposed algorithm framework can achieve 1.2 to 2.6 times of acceleration ratio.

Cloud computing has revolutionized the management and analysis of data for organizations, offering scalability, flexibility, and cost-effectiveness. Effective task scheduling in cloud systems is crucial to optimize resource utilization and ensure timely job completion. This research presents a novel method for job scheduling in cloud computing, employing the Johnson Sequencing algorithm across three servers. Originally developed for scheduling tasks in a manufacturing context, the Johnson Sequencing method has proven successful in resolving task scheduling challenges. Here, we adapt this method to address job scheduling among three servers within a cloud computing environment. The primary objective of the algorithm is to minimize the makespan, representing the total time required to complete all tasks. This study considers a scenario where a diverse set of jobs, each with varying processing durations, needs to be distributed across three servers using the Johnson Sequencing method. The algorithm strategically determines the optimal order for task execution on each server while accounting for job interdependencies and processing times on the individual servers. To put the Johnson Sequencing algorithm into practice for cloud computing job scheduling, we propose a three-step approach. First, we construct a precedence graph by analyzing the relationships among jobs. Subsequently, the precedence graph is transformed into a two-machine Johnson Sequencing problem by allocating jobs to servers. Finally, we employ the Dynamic Heuristic Johnson Sequencing method to determine the best order of jobs on each server, effectively minimizing the makespan. Through comprehensive simulations and testing, we compare the performance of our suggested Dynamic Heuristic Johnson Sequencing technique with existing scheduling algorithms. The results demonstrate significant improvements in terms of makespan reduction and resource utilization when employing our proposed method with three servers. Furthermore, our approach exhibits remarkable scalability and effectiveness in resolving complex job scheduling challenges within cloud computing settings. The outcomes of this research contribute to the optimization of resource allocation and task management in cloud systems, offering potential benefits to a wide range of industries and applications.

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With the uncertainty of a cloud environment and the diversity of workload requirements increasing the scheduling cost of container-based cloud, especially for load spikes of application access, optimizing the utilization efficiency of cloud resources and quality of service is the focus of container cluster technology in the future. Different from traditional virtual machine-based scheduling, containerized applications of heterogeneous workloads bring higher scheduling complexity with its elastic scaling and multi-replicas operation. To tackle this problem, we propose a priority-aware workloads scheduling algorithm PA-CCWS. Firstly, we implement workload characterization and behavior identification, quantify the analysis results with TOPSIS method, generate the workloads priority and build priority scheduling buffer queue. Meanwhile, the model learning is accelerated by the experience replay mechanism that inserts and updates the priority of historical experience through the real-time feedback of actual container scheduling from DDQN. Then, we describe containerized applications oriented deep reinforcement learning scheduling algorithm which combined with the two kinds of priorities, to optimize scheduling decision. Finally, we evaluate the effectiveness of our algorithm in terms of resource utilization, resource imbalance degree and SLA compliance rate, etc. Compared with meta-heuristic algorithm PSOS, mathematical model-based algorithm KCSS and other excellent deep reinforcement learning based scheduling algorithms such as DeepRM-Plus and RLSched applying in the container-based cloud, PA-CCWS shows better resource utilization efficiency and convergence stability in containerized applications scheduling. [ABSTRACT FROM AUTHOR]

A new era is opening for the world of information and communication technologies with the 5G networks release. Indeed 5G networks appear in modern wireless systems as solutions to traditional networks inflexibility and lack of radio resources problems. Using these networks the operators can expand their services range at will and, therefore, manage daily operations by monitoring key performance indicators (KPIs) - helping meet the quality of service (QoS) requirements much easily. To meet the QoS requirements 5G networks can be implemented alongside priority scheduling algorithms. This paper considers the operation of a wireless network slicing model under two scheduling algorithms. A comparative analysis of main performance measures is provided.

After the occurrence of a maritime disaster, to save human life and search for important property equipment in the first time, it is indispensable to efficiently transmit search and rescue sea area data to the maritime search and rescue command center (MSRCC) in real-time, so that the MSRCC can make timely and accurate decisions. The key to determining the efficiency of data forwarding is the quality of the routing protocol. Due to the high dynamics of the marine environment and the limited energy of the marine node, the coverage hole and routing path failure problems occur frequently when using the existing routing algorithm for marine data forwarding. Based on the above background, in this work, we study a low-latency and energy-efficient opportunistic routing protocol for maritime search and rescue wireless sensor networks (MSR-WSNs). Considering the adverse impact of wave shadowing on signal transmission, an effective link reliability prediction method is first investigated to quantify the link connectivity among nodes. To mitigate the end-to-end time delay, an optimal expected packet advancement is then derived by combining link con-nectivity with geographic progress threshold θ. After that, based on the link connectivity between marine nodes, the optimal expected packet advancement prediction, the distance from the sensing nodes to the sink, and the remaining energy distribution of the nodes, the priority of candidate nodes is calculated and sorted in descending order. Finally, timer-based coordination algorithm is adopted to perform the marine data packet forwarding so as to avoid packet conflict. Computer simulation results demonstrate that compared with benchmark algorithms, the data packet delivery ratio, the delay performance and the average node energy consumption (the average node speed is 20 m/s) of the proposed opportunistic routing protocol are improved by more than 21.4%, 39.2% and 18.1%, respectively. [ABSTRACT FROM AUTHOR]

An operating system is software that is designed to manage computer hardware and software resources. However, this management requires applying an ample number of techniques and algorithms which are called synchronization and scheduling. The scheduling algorithms are used to arrange the way that the CPU is assigned to the processes, while synchronization is utilized to indicate how to work with multi-processes at the same time. Therefore, they are related to each other. CPU scheduling is a vital phenomenon of an operating system. At present, numerous CPU scheduling algorithms exist as First Come First Serve) FCFS(Shortest Job First (SJF), Shortest Remaining Time First (SRTF), Priority Scheduling, and Round Robin (RR). In this paper, a survey of the current synchronization and scheduling algorithms have been presented. An overview of each technique with the main algorithms have been described in detail with the advantages and the issues of each algorithm. Furthermore, this paper has dug deep into the real-time operating system scheduling issues, which is the current trend in operating system researches.

Develop a suitable method to handle parking problem in the crowded big city, as per the demand and number of parking slots available, by giving priority to the users with help of smart parking system. This study proposed Internet of Things-(IoT) based smart parking model using the Arduino UNO model in implementation. This proposed model helps users to allocate booking users vehicles parking free available slot as per time to time availability in college campuses during technical fest in different parking slots and locations. In this working model, Passive Infrared, with ultrasonic Sensors are used which helps to deducting the available parking location and allow us to find out for giving priority based First Come First Serve based-(FCFS) scheduling of parking allotments as per slot is vacant count. This model examine the way of the proposed system with utilizing scenarios of various possible ways with FCFS which show the significance of the parking model using IoT platform. The parking availability detection is capable to find solutions for an empty vehicle parking area for monitoring and reduces users search time as per an automated smart parking model using the internet of things, sensing devices and Arduino UNO(“Uno” means one in Italian languages) in giving priority to users with scheduling model is actual findings with Smart systems. This study proposed an automated parking system to function on a real-time basis to detect all empty parking places based on the internet of things platform, with help of sensors. Arduino UNO platform brings automation to this system on per priority basis to all the users with scheduling techniques. The priority given to users with an automation system is a novelty compared with an existing system. This smart parking is working on real-time basis inputs based on that user demands priority is varying to book parking slots with first come first serve preference.

A PT. Astra Components Indonesia manufactures injection plastic goods to be sent to customers, in the production process the machines used are capable of producing several types of injection plastic goods, so that products can be provided regularly and there is a process of changing products or better known as the Dandory process. Found an obstacle that is an error in determining the order of dandory for production. Consequently, the condition of goods in the warehouse is irregular. It is detrimental to the company with the time, material and labour that has been expended for production so that an Information System is needed to determine the priority schedule for the replacement of injection plastic goods in this study taking the case at machine number A36. The system development uses the Waterfall method; the system description will be described by the Unified Modeling Language Modeling (UML) method. Data collection is done by observing directly observing production activities in the company as well as interviews with relevant parties, and for solving problems, the system will be assisted by Priority Scheduling method. The results of the research get an information system that will provide recommendations for items that will first be produced appropriately.

After the occurrence of a maritime disaster, to save human life and search for important property equipment in the first time, it is indispensable to efficiently transmit search and rescue sea area data to the maritime search and rescue command center (MSRCC) in real-time, so that the MSRCC can make timely and accurate decisions. The key to determining the efficiency of data forwarding is the quality of the routing protocol. Due to the high dynamics of the marine environment and the limited energy of the marine node, the coverage hole and routing path failure problems occur frequently when using the existing routing algorithm for marine data forwarding. Based on the above background, in this work, we study a low-latency and energy-efficient opportunistic routing protocol for maritime search and rescue wireless sensor networks (MSR-WSNs). Considering the adverse impact of wave shadowing on signal transmission, an effective link reliability prediction method is first investigated to quantify the link connectivity among nodes. To mitigate the end-to-end time delay, an optimal expected packet advancement is then derived by combining link con-nectivity with geographic progress threshold θ. After that, based on the link connectivity between marine nodes, the optimal expected packet advancement prediction, the distance from the sensing nodes to the sink, and the remaining energy distribution of the nodes, the priority of candidate nodes is calculated and sorted in descending order. Finally, timer-based coordination algorithm is adopted to perform the marine data packet forwarding so as to avoid packet conflict. Computer simulation results demonstrate that compared with benchmark algorithms, the data packet delivery ratio, the delay performance and the average node energy consumption (the average node speed is 20 m/s) of the proposed opportunistic routing protocol are improved by more than 21.4%, 39.2% and 18.1%, respectively.

Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user's quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns. [ABSTRACT FROM AUTHOR]

Modern Multi-Processor System-On-Chips (MPSOC) are widely used especially in real-time embedded systems due to their high throughput and low per unit cost. However, bounded latency is vital to guarantee fast response as well as fairness for applications running on multicore processors. In this paper, a new Priority-base Memory Controller for Embedded Systems (P M S M C) that prioritizes concurrently running applications by assigning uneven quota for each requestor is proposed. Each requestor quota is accompanied by a timer to control the dispatch rate to prevent starvation. Moreover, P M S M C can monitor the real-time application memory activity to assist the request scheduling to achieve efficient utilization of the shared DRAM resource while keeping the timing bounded. Hence, P M S M C can serve both multimedia real-time applications and hard real-time applications concurrently. For 8-core processors, P M S M C is able to achieve an overall performance speedup of 24% and 16% compared to the recently proposed WCAD and TRB-SP memory controllers, respectively. For the Energy-Delay Product (EDP) metric which combines both performance and energy consumption, P M S M C achieves lower EDPs of 25% and 60% compared to the recently proposed WCAD and TRB-SP memory controllers, respectively. • Priority-based Multi-requestor Scheduling Memory Controller PMSMC is developed that support both HRT & NHRT. • PMSMC is a multi-requestor scheduler that utilizes memory bandwidth efficiently. • PMSMC handles both Mem. intensive and Mem. Non-intensive applications effectively. • PMSMC achieves an overall performance speedup of 45% for 4-core systems. • PMSMC achieves Energy-Delay Product (EDP) reduction of 25% for 8-core systems. [ABSTRACT FROM AUTHOR]

In urban vehicular ad-hoc networks (VANET), owing to the high mobility and uneven distribution of vehicles, the rapid and reliable transmission of emergency data is a challenging task. Some bio-inspired intelligent routing protocols are effective in transmitting emergency data. However, their computational complexity and convergence speed may result in large delays. Furthermore, it has been demonstrated that parked vehicles may be used as relay nodes and thus facilitate data packet transmission in VANET. In this paper, we propose a parking-area-assisted spider-web routing protocol (PASRP) for emergency data in urban VANET. In PASRP, a spider-web transmission model is established based on the parking area by using a geographic information system and a digital map. Two control messages, request-spider and confirm-spider, are sent to obtain the transmission path from the source vehicle to the destination vehicle, and the path with the least delay is selected as the transmission path. Then, the emergency data are forwarded on the selected path using a multi-mode greedy algorithm and are prioritized by a dynamic multi-priority scheduling mechanism. Finally, the simulation results demonstrate that the proposed PASRP outperforms the existing greedy perimeter stateless routing and transmission mechanism for emergency data protocols. [ABSTRACT FROM AUTHOR]

The paper presents a method of priority scheduling that is useful during the planning of multiple-structure construction projects. This approach is an extension of the concept of interactive scheduling. In priority scheduling, it is the planner that can determine how important each of the technological and organisational constraints are to them. A planner’s preferences can be defined through developing a ranking list that defines which constraints are the most important, and those whose completion can come second. The planner will be able to model the constraints that appear at a construction site more flexibly. The article presents a general linear programming model of the planning of multiple-structure construction projects, as well as various values of each of the parameters that allow us to obtain different planning effects. The proposed model has been implemented in a computer program and its effectiveness has been presented on a calculation example.

With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum time and effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, on behalf of the comparison of three algorithms like Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR). BR gives a better result with 60 hidden layers Neurons to other algorithms. BR gives 2.05 MSE and 95.8 regressions in Validation, LM gives 2.91 MSE and 94.06 regressions with this and SCG gives 3.92 MSE and 91.85 regressions. [ABSTRACT FROM AUTHOR]

Enhancing the efficiency of power allocation and the achievable sum rate of Multiple Input Multiple Output (MIMO) based multi-user systems are essential as conventional methods are very complex. The traditional methods are not only less efficient but also do not address user fairness. The existing conventional methods use simple user ordering schemes that remain unsuitable for MIMO Non-Orthogonal Multiple Access (NOMA) system. Accordingly, a new elegant propose method using Particle Swarm Optimization (PSO) algorithm based priority scheduling in power allocation (PPPA) prioritizes the users based on the maximum power and QoS constraints. The proposed method offers a capable platform that will also provide energy efficiency in power allocation. The priority-based scheduling based on PSO algorithm that prioritizes the users optimally based on objective function, and it is effective due to faster convergence and adaptive nature of the algorithm. The proposed method for power allocation compared with the existing methods using the number of admitted users in the system. We observed that the energy efficiency and achievable rate is found to be superior by 6.76% and 58.37 Mbits/sec. Thus our proposed method not only provides better energy efficiency but also has a profound improvement in data rate. [ABSTRACT FROM AUTHOR]

This paper considers a discrete-time single-server infinite-capacity queue with two classes of packet arrivals, either delay-sensitive (class 1) or delay-tolerant (class 2), and a reservation-based priority scheduling mechanism. The objective is to provide a better quality of service to delay-sensitive packets at the cost of allowing higher delays for the best-effort packets. To this end, the scheduling mechanism makes use of an in-queue reserved place intended for future class-1 packet arrivals. A class-1 arrival takes the place of the reservation in the queue, after which a new reservation is created at the tail of the queue. Class-2 arrivals always take place at the tail of the queue. We study the delay characteristics for both packet classes under the assumption of a general independent packet arrival process. The service times of the packets are independent and have a general distribution that depends on the class of the packet. Closed-form expressions are obtained for the probability generating functions of the per-class delays. From this, moments and tail probabilities of the packet delays of both classes are derived. The results are illustrated by some numerical examples. [ABSTRACT FROM AUTHOR]

A multi-class many-server priority system operating in the quality-and-efficiency-driven regime is considered. Both arrival processes and service times are general. The many-server heavy-traffic diffusion asymptotic is characterized in terms of the corresponding limiting infinite-server process. [ABSTRACT FROM AUTHOR]

This paper introduces new scheduling algorithms supporting low-latency switching. The proposed grant-aware (GA) algorithm improves the average delay performance by using the grant information of previous iteration. The simulation result shows that the average delay of GA algorithm is about one-tenth of the existing algorithm in high-load condition. We also introduce two priority-based scheduling algorithms grant-aware and priority-aware (GAPA) algorithm and cyclic scheduling with the longest-queue-first (C-LQF) algorithm. In the priority-based scheduling, the scheduling priority of VoQ is determined based on its queue size. GAPA and C-LQF consider the priority only after the first iteration to prevent the starvation problem. The simulation result shows that GAPA and C-LQF scheduling achieves better performance than GA in terms of average delay, maximum delay, and hotspot throughput. [ABSTRACT FROM AUTHOR]

In war zones and economically deprived regions, because of extreme resource restrictions, a single provider may be the sole person in charge of providing emergency care to a group of patients. An important question the provider faces under such circumstances is whether or not to perform triage and how to prioritize the patients. By choosing to triage a particular patient, the provider can determine the health condition and thus the urgency of the patient, but that will come at the expense of delaying the actual service (stabilization or initial treatment) for that patient as well as all the other patients. Motivated by this problem, which also arises in other service contexts, we consider a service system where finitely many patients, all available at time zero, belong to one of the two possible triage classes, where each class is characterized by its waiting cost and expected service time. Patients' class identities are initially unknown, but the service provider has the option to spend time on triage to determine the class of a patient. Our objective is to identify policies that balance the time spent on triage with the time spent on service by minimizing the total expected cost. We provide a complete characterization of the optimal dynamic policy and show that the optimal dynamic policy that specifies when to perform triage is determined by a switching curve, and we provide a mathematical expression for this curve. One insight that comes out of this characterization is that the server should start with performing triage when there are sufficiently many patients and never perform triage when there are few patients. Finally, we carry out a numerical study in which we demonstrate how one can use our mathematical results to develop policies that can be used in mass-casualty triage and prioritization, and we find that there are substantial benefits to using one of these policies instead of the simpler benchmarks. The online supplement is available at https://doi.org/10.1287/mnsc.2017.2855. This paper was accepted by Assaf Zeevi, stochastic models and simulation. [ABSTRACT FROM AUTHOR]

While the modern graphics processing unit (GPU) offers massive parallel compute power, the ability to influence the scheduling of these immense resources is severely limited. Therefore, the GPU is widely considered to be only suitable as an externally controlled co-processor for homogeneous workloads which greatly restricts the potential applications of GPU computing. To address this issue, we present a new method to achieve fine-grained priority scheduling on the GPU: hierarchical bucket queuing. By carefully distributing the workload among multiple queues and efficiently deciding which queue to draw work from next, we enable a variety of scheduling strategies. These strategies include fair-scheduling, earliest-deadline-first scheduling and user-defined dynamic priority scheduling. In a comparison with a sorting-based approach, we reveal the advantages of hierarchical bucket queuing over previous work. Finally, we demonstrate the benefits of using priority scheduling in real-world applications by example of path tracing and foveated micropolygon rendering. [ABSTRACT FROM AUTHOR]

Intelligent buildings require various gadgets and sensors to automatically control the underlying environments with enhanced levels of comfort and efficiency. The ever-evolving technology and increasingly complex devices operated by heterogeneous systems have created challenges for intelligent building environments. Multi-platform type of heterogeneous systems functioning in intelligent building environments can lead to system failures with regard to deadlocks and unpredictable behavior, known as conflict occurrence. In this paper, we propose a new decision-making model with a weighted-priority scheduling algorithm that solves the conflicts to achieve efficient and sustainable communication response among heterogeneous systems. The experimental results demonstrate the enhanced response time of the model with conflict resolution. Furthermore, this model minimizes the external intervention to control the setting of such buildings. [ABSTRACT FROM AUTHOR]