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Journal articles on the topic 'Multi-Level Queue Scheduling'
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1
SEMNANI, SAMANEH HOSSEINI, and KAMRAN ZAMANIFAR. "NEW APPROACH TO MULTI-LEVEL PROCESSOR SCHEDULING." International Journal on Artificial Intelligence Tools 19, no. 03 (2010): 335–46. http://dx.doi.org/10.1142/s0218213010000212.
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The problem of finding the best quantum time in multi-level processor scheduling is addressed in this paper. Processor scheduling is one of the most important issues in operating systems design. Different schedulers are introduced to solve this problem. In one scheduling approach, processes are placed in different queues according to their properties, and the processor allocates time to each queue iteratively. One of the most important parameters of a processor's efficiency in this approach is the amount of time slices associated to each processor queue. In this paper, an ant colony optimization (ACO) algorithm is presented to solve the problem of finding appropriate time slices to assign to each processor queue. In this technique, each ant tries to find an appropriate scheduling. Ant algorithm searches the problem space to find the best scheduling. The quality of each ant's solution is evaluated using a new fitness function. This fitness function is designed according to the evaluation parameters of each processor queue and also according to the queue theory's relations. Also a heuristic function is presented which prompts ant to select better solutions. Computational tests are presented and the comparisons made with genetic algorithm (GA) and particle swarm optimization (PSO) algorithms which try to solve same problem. The results show the efficiency of this algorithm.
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Hasija, Manupriya, Akhil Kaushik, and Parveen Kumar. "A-MMLQ Algorithm for Multi-level Queue Scheduling." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 07 (2013): 221–27. https://doi.org/10.5281/zenodo.14607313.
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This being the era of advancement in computing domain, the emphasis is on better resource scheduling. Scheduling is not confined to dealing multiple tasks by a single processor. It’s a dawn with multiprocessing and multitasking. Although multiprocessor systems impose several overheads but still make the concept amazingly interesting. The scheduling field has taken a whirlwind after the notion of multiprocessing. Many of the uniprocessor algorithms do fit well under the multiprocessor systems but, still necessitating a further development aiming solely on multiprocessor scheduling. This paper thus sketches a new idea to modify and extend the well - known multi-level queue scheduling, taking into account the arrival time/ arrival sequence to conceptualize an innovative scheduling algorithm.
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Li, Ting Shun, Jiao Hui Xu, and Hui Yu. "The Messaging Mechanism Based on Multi-Level Feedback Queue Scheduling Algorithm." Advanced Materials Research 756-759 (September 2013): 1763–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1763.
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With the development of wireless communication technology, SMS , as a kind of flexible communication tools, is widely used in the various units. Aimed at large quantities of SMS processing, this paper proposes a new scheduling algorithm based on multi-level feedback queue. Multi-level feedback queue scheduling algorithm can not only make the high priority jobs response, but also make the short operations (process) done quickly.
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Iqbal, Mansoor, Muhammad Umar Shafiq, Shouzab Khan, Obaidullah, Saad Alahmari, and Zahid Ullah. "Enhancing task execution: a dual-layer approach with multi-queue adaptive priority scheduling." PeerJ Computer Science 10 (December 3, 2024): e2531. https://doi.org/10.7717/peerj-cs.2531.
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Efficient task execution is critical to optimize the usage of computing resources in process scheduling. Various task scheduling algorithms ensure optimized and efficient use of computing resources. This article introduces an innovative dual-layer scheduling algorithm, Multi-Queue Adaptive Priority Scheduling (MQAPS), for task execution. MQAPS features a dual-layer hierarchy with a ready queue (RQ) and a secondary queue (SQ). New tasks enter the RQ, where they are prioritized, while the SQ contains tasks that have already used computing resources at least once, with priorities below a predefined threshold. The algorithm dynamically calculates the time slice based on process priorities to ensure efficient CPU utilization. In the RQ, the task’s priority level defines its prioritization, which ensures that important jobs are completed on time compared to other conventional methods where priority is fixed or no priority parameter is defined, resulting in starvation in low-priority jobs. The simulation results show that MQAPS better utilizes CPU resources and time than traditional round-robin (RR) and multi-level scheduling. The MQAPS showcases a promising scheduling technique ensuring a balanced framework for dynamic adjustment of time quantum and priority. The MQAPS algorithm demonstrated optimization, fairness, and efficiency in job scheduling.
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Li, Qianmu, Shunmei Meng, Xiaonan Sang, et al. "Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing." ACM Transactions on Internet Technology 21, no. 3 (2021): 1–33. http://dx.doi.org/10.1145/3408291.
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Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.
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Wang, Xiong, Zhijun Yang, and Hongwei Ding. "Application of Polling Scheduling in Mobile Edge Computing." Axioms 12, no. 7 (2023): 709. http://dx.doi.org/10.3390/axioms12070709.
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With the Internet of Things (IoT) development, there is an increasing demand for multi-service scheduling for Mobile Edge Computing (MEC). We propose using polling for scheduling in edge computing to accommodate multi-service scheduling methods better. Given the complexity of asymmetric polling systems, we have used an information-theoretic approach to analyse the model. Firstly, we propose an asymmetric two-level scheduling approach with priority based on a polling scheduling approach. Secondly, the mathematical model of the system in the continuous time state is established by using the embedded Markov chain theory and the probability-generating function. By solving for the probability-generating function’s first-order partial and second-order partial derivatives, we calculate the exact expressions of the average queue length, the average polling period, and the average delay with an approximate analysis of periodic query way. Finally, we design a simulation experiment to verify that our derived parameters are correct. Our proposed model can better differentiate priorities in MEC scheduling and meet the needs of IoT multi-service scheduling.
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Lamjav, Erdenebayar, Otgonbayar Bataa, and Chuluunbandi Naimannaran. "Optimizing User-Level Packet Scheduling Performance through Optimal CQI-Based Resource Allocation in LTE." ICT Focus 2, no. 1 (2023): 41–53. http://dx.doi.org/10.58873/sict.v2i1.41.
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Our research investigates the Dynamic Resource Allocation in the Downlink of OFDMA-based LTE-A networks. It addresses both user-level and system-level packet scheduling performance. At the user-level, a Traffic Differentiator stage segregates packet queues from active users into different service queues based on service types. Users are prioritized within each service queue based on their QoS requirements and wireless channel conditions, utilizing SPSSA. At the system-level, fairness among users is a key consideration. We propose the PITDSA in the TD Scheduler stage, which aims to allocate just enough radio resource to real-time and non-real time services and assign the remaining available resource to background service. In the FD Scheduler stage, we propose an optimal CQI selection algorithm for resource allocation to exploit frequency domain multi-user diversity. We present our simulation results and analyses of all novel algorithms, and the performance of the Optimal CQI selection algorithm is compared with other algorithms. Our proposed scheduling algorithm demonstrates improved QoS for real-time and non-real time services while maintaining a good traded user-level and system-level performance. Future work can focus on optimizing the throughput or the fairness or both, and more advanced and complex techniques can be designed with the same goal.
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Fang, Juan, Mengxuan Wang, and Zelin Wei. "A memory scheduling strategy for eliminating memory access interference in heterogeneous system." Journal of Supercomputing 76, no. 4 (2020): 3129–54. http://dx.doi.org/10.1007/s11227-019-03135-7.
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AbstractMultiple CPUs and GPUs are integrated on the same chip to share memory, and access requests between cores are interfering with each other. Memory requests from the GPU seriously interfere with the CPU memory access performance. Requests between multiple CPUs are intertwined when accessing memory, and its performance is greatly affected. The difference in access latency between GPU cores increases the average latency of memory accesses. In order to solve the problems encountered in the shared memory of heterogeneous multi-core systems, we propose a step-by-step memory scheduling strategy, which improve the system performance. The step-by-step memory scheduling strategy first creates a new memory request queue based on the request source and isolates the CPU requests from the GPU requests when the memory controller receives the memory request, thereby preventing the GPU request from interfering with the CPU request. Then, for the CPU request queue, a dynamic bank partitioning strategy is implemented, which dynamically maps it to different bank sets according to different memory characteristics of the application, and eliminates memory request interference of multiple CPU applications without affecting bank-level parallelism. Finally, for the GPU request queue, the criticality is introduced to measure the difference of the memory access latency between the cores. Based on the first ready-first come first served strategy, we implemented criticality-aware memory scheduling to balance the locality and criticality of application access.
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Dirdal, Daniel Osmundsen, Danny Vo, Yuming Feng, and Reggie Davidrajuh. "Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms." Applied Sciences 14, no. 13 (2024): 5690. http://dx.doi.org/10.3390/app14135690.
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Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux’s completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper.
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Sefidgari, Bahram Lavi, and Sahand Pourhassan Shamchi. "Multi-Processing Feed-Back Decision Making for Scheduling Gates in Manufacturing System." Applied Mechanics and Materials 548-549 (April 2014): 1040–45. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1040.
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This research paper investigates the optimal solution to increase the productivity of production line by the assistance of decision making and job scheduling algorithms. Through the help of FCFS algorithm which was utilized in Task Queue, as well as Multi-Level Feedback for decision making and job scheduling, the minimization of idle times during the process is expected. Moreover in case of any tardiness of machines, system immediately sends feedback to renovate the machine station. Basically the novel RFID technology (Radio Frequency Identification) is used for any data transaction between system structures, including: reading, storing and categorizing the information to each tag that attached to pieces. Besides, the software has been developed for managing the global database. All in all the current system which is based on optimal suggestion of algorithms is under implementation in the CIM laboratory of Eastern Mediterranean University.
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Prasetyo, Arief, Sofyan Noor Arief, and Dhebys Suryani Hormansyah. "Analisa Perbandingan Metode Penjadwalan Pada Pemrosesan Enkripsi Dan Dekripsi RSA Terdistribusi Dengan Arsitektur Klaster SBC." Jurnal Minfo Polgan 12, no. 2 (2023): 1754–63. http://dx.doi.org/10.33395/jmp.v12i2.12983.
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Implementasi enkripsi menggunakan algoritma RSA memiliki masalah utama yaitu kebutuhan akan sumber daya komputasi yang besar. Optimasi pemrosesan algoritma RSA dapat dilakukan dengan cara membagi komputasinya secara terdistribusi dalam sebuah arsitektur sistem terdistribusi. Penelitian sebelumnya telah mengimplementasikan pemrosesan algoritma RSA secara terdistribusi pada arsitektur cluster Single Board Computer (SBC). Dan juga telah menerapkan algoritma penjadwalan proses FIFO/FCFS pada pemrosesannya. Namun, penerapan algoritma tersebut dirasa masih belum maksimal karena pada penelitian sebelumnya tidak terdapat proses Analisa hasil antar algoritma pemrosesan yang umum digunakan. Pada penelitian ini, empat buah algoritma penjadwalan proses akan diimplementasikan dan dianalisa. Algoritma tersebut antara lain algoritma FCFS/FIFO, algoritma SJF, algoritma Priority Scheduling dan algoritma Multi Level Queue Scheduling. Hasil pengujian yang dilakukan menunjukkan bahwa algoritma penjadwalan proses Shortest Job First (SJF) lebih baik karena dapat menurunkan rata-rata waktu tunggu pemrosesan hingga 29%. Karena adanya penurunan rata-rata waktu tunggu tersebut, algoritma SJF mampu menurunkan rata-rata waktu total pemrosesan hingga 21%.
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Deol, G. Joel Sunny, and Dr NagaRaju O. "A Novel Priority Based Hadoop Energy Efficient Job Scheduling and Migration Technique with Multi Level Queue on YARN Scheduler." HELIX 9, no. 2 (2019): 4864–69. http://dx.doi.org/10.29042/2019-4864-4869.
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Lee, Jonghyuk, Sungjin Choi, Taeweon Suh, and Heonchang Yu. "Mobility-aware balanced scheduling algorithm in mobile Grid based on mobile agent." Knowledge Engineering Review 29, no. 4 (2014): 409–32. http://dx.doi.org/10.1017/s0269888914000149.
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AbstractThe emerging Grid is extending the scope of resources to mobile devices and sensors that are connected through loosely connected networks. Nowadays, the number of mobile device users is increasing dramatically and the mobile devices provide various capabilities such as location awareness that are not normally incorporated in fixed Grid resources. Nevertheless, mobile devices exhibit inferior characteristics such as poor performance, limited battery life, and unreliable communication, compared with fixed Grid resources. Especially, the intermittent disconnection from network owing to users’ movements adversely affects performance, and this characteristic makes it inefficient and troublesome to adopt the synchronous message delivery in mobile Grid. This paper presents a mobile Grid system architecture based on mobile agents that support the location management and the asynchronous message delivery in a multi-domain proxy environment. We propose a novel balanced scheduling algorithm that takes users’ mobility into account in scheduling. We analyzed users mobility patterns to quantitatively measure the resource availability, which is classified into three types: full availability, partial availability, and unavailability. We also propose an adaptive load-balancing technique by classifying mobile devices into nine groups depending on availability and by utilizing adaptability based on the multi-level feedback queue to handle the job type change. The experimental results show that our scheduling algorithm provides a superior performance in terms of execution times to the one without considering mobility and adaptive load-balancing.
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Oksanych, Iryna, and Igor Shevchenko. "MODELS OF A HIERARCHICAL MULTI-AGENT SYSTEM FOR PERFORMING BUSINESS PROCESSES." Transactions of Kremenchuk Mykhailo Ostrohradskyi National University, no. 6(131) (December 26, 2021): 73–78. http://dx.doi.org/10.30929/1995-0519.2021.6.73-78.
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Purpose. Developing a set of models which formally describe the operation environment of the organizational and technical system and the interaction of software agents of different roles in performing business operations. The pres-ence of such a set of models allows you to create information technology for monitoring and routing business processes and reduce costs for business operations. Methodology. The research methods are based on systems analysis methods. Findings. A set of models of hierarchical multi-agent system for business processes has been developed. The complex comprises a static description of the operation environment of the organizational and technical system, where there are models of business process, business operation and its components. Originality. Organization of total monitoring of the operation environment of the organizational and technical system, i.e. the current state of the processes of processing applications, queues and workstations requires the development of static and dynamic business process models. Based on the static description, a model of the dynamics of business processes promoting throughout many workstations has been developed. This makes possible to monitor the status of workstations, queues and applications for business opera-tions. In particular, a formal description of the software agent, its competencies and a model of interaction of three-level agents which perform the functions of business operation executors, monitors and dispatchers has been developed. Having such tools, the second important aspect is the development of a universal hierarchical structure of the multi-agent system, in which different agents perform the roles of performers, monitors and dispatchers. Such a structure should include the regulation of the agent functions, models of agents interaction at all three levels, ways of agents-people communication. Practical value. Tests of the monitoring and scheduling system in different conditions (electronic document management, manufacturing company, human resources management department) showed a decrease in time of business operations, losses on waiting and increase in rhythm of business processes. The results of the work has enabled the development of a queue management strategy, which has showed a reduction in time of operations and a more balanced workload.
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Tuan, Le Minh, Le Hoang Son, Hoang Viet Long, et al. "ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET." Sensors 20, no. 12 (2020): 3394. http://dx.doi.org/10.3390/s20123394.
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One of the crucial problems in Industry 4.0 is how to strengthen the performance of mobile communication within mobile ad-hoc networks (MANETs) and mobile computational grids (MCGs). In communication, Industry 4.0 needs dynamic network connectivity with higher amounts of speed and bandwidth. In order to support multiple users for video calling or conferencing with high-speed transmission rates and low packet loss, 4G technology was introduced by the 3G Partnership Program (3GPP). 4G LTE is a type of 4G technology in which LTE stands for Long Term Evolution, followed to achieve 4G speeds. 4G LTE supports multiple users for downlink with higher-order modulation up to 64 quadrature amplitude modulation (QAM). With wide coverage, high reliability and large capacity, LTE networks are widely used in Industry 4.0. However, there are many kinds of equipment with different quality of service (QoS) requirements. In the existing LTE scheduling methods, the scheduler in frequency domain packet scheduling exploits the spatial, frequency, and multi-user diversity to achieve larger MIMO for the required QoS level. On the contrary, time-frequency LTE scheduling pays attention to temporal and utility fairness. It is desirable to have a new solution that combines both the time and frequency domains for real-time applications with fairness among users. In this paper, we propose a channel-aware Integrated Time and Frequency-based Downlink LTE Scheduling (ITFDS) algorithm, which is suitable for both real-time and non-real-time applications. Firstly, it calculates the channel capacity and quality using the channel quality indicator (CQI). Additionally, data broadcasting is maintained by using the dynamic class-based establishment (DCE). In the time domain, we calculate the queue length before transmitting the next packets. In the frequency domain, we use the largest weight delay first (LWDF) scheduling algorithm to allocate resources to all users. All the allocations would be taken placed in the same transmission time interval (TTI). The new method is compared against the largest weighted delay first (LWDF), proportional fair (PF), maximum throughput (MT), and exponential/proportional fair (EXP/PF) methods. Experimental results show that the performance improves by around 12% compared with those other algorithms.
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Zerwas, Johannes, Csaba Györgyi, Andreas Blenk, Stefan Schmid, and Chen Avin. "Duo: A High-Throughput Reconfigurable Datacenter Network Using Local Routing and Control." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 1 (2023): 1–25. http://dx.doi.org/10.1145/3579449.
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The performance of many cloud-based applications critically depends on the capacity of the underlying datacenter network. A particularly innovative approach to improve the throughput in datacenters is enabled by emerging optical technologies, which allow to dynamically adjust the physical network topology, both in an oblivious or demand-aware manner. However, such topology engineering, i.e., the operation and control of dynamic datacenter networks, is considered complex and currently comes with restrictions and overheads. We present Duo, a novel demand-aware reconfigurable rack-to-rack datacenter network design realized with a simple and efficient control plane. Duo is based on the well-known de Bruijn topology (implemented using a small number of optical circuit switches) and the key observation that this topology can be enhanced using dynamic (''opportunistic'') links between its nodes. In contrast to previous systems, Duo has several desired features: i) It makes effective use of the network capacity by supporting integrated and multi-hop routing (paths that combine both static and dynamic links). ii) It uses a work-conserving queue scheduling which enables out-of-the-box TCP support. iii) Duo employs greedy routing that is implemented using standard IP longest prefix match with small forwarding tables. And iv) during topological reconfigurations, routing tables require only local updates, making this approach ideal for dynamic networks. We evaluate Duo in end-to-end packet-level simulations, comparing it to the state-of-the-art static and dynamic networks designs. We show that Duo provides higher throughput, shorter paths, lower flow completion times for high priority flows, and minimal packet reordering, all using existing network and transport layer protocols. We also report on a proof-of-concept implementation of Duo's control and data plane.
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Zerwas, Johannes, Csaba Györgyi, Andreas Blenk, Stefan Schmid, and Chen Avin. "Duo: A High-Throughput Reconfigurable Datacenter Network Using Local Routing and Control." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 7–8. http://dx.doi.org/10.1145/3606376.3593537.
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The performance of many cloud-based applications critically depends on the capacity of the underlying datacenter network. A particularly innovative approach to improve the throughput in datacenters is enabled by emerging optical technologies, which allow to dynamically adjust the physical network topology, both in an oblivious or demand-aware manner. However, such topology engineering, i.e., the operation and control of dynamic datacenter networks, is considered complex and currently comes with restrictions and overheads. We present Duo, a novel demand-aware reconfigurable rack-to-rack datacenter network design realized with a simple and efficient control plane. Duo is based on the well-known de Bruijn topology (implemented using a small number of optical circuit switches) and the key observation that this topology can be enhanced using dynamic ("opportunistic") links between its nodes. In contrast to previous systems, Duo has several desired features: i) It makes effective use of the network capacity by supporting integrated and multi-hop routing (paths that combine both static and dynamic links). ii) It uses a work-conserving queue scheduling which enables out-of-the-box TCP support. iii) Duo employs greedy routing that is implemented using standard IP longest prefix match with small forwarding tables. And iv) during topological reconfigurations, routing tables require only local updates, making this approach ideal for dynamic networks. We evaluate Duo in end-to-end packet-level simulations, comparing it to the state-of-the-art static and dynamic networks designs. We show that Duo provides higher throughput, shorter paths, lower flow completion times for high priority flows, and minimal packet reordering, all using existing network and transport layer protocols. We also report on a proof-of-concept implementation of \system's control and data plane.
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Minhas, Umar Ibrahim, Roger Woods, and Georgios Karakonstantis. "Evaluation of Static Mapping for Dynamic Space-Shared Multi-task Processing on FPGAs." Journal of Signal Processing Systems 93, no. 5 (2021): 587–602. http://dx.doi.org/10.1007/s11265-020-01633-z.
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AbstractWhilst FPGAs have been used in cloud ecosystems, it is still extremely challenging to achieve high compute density when mapping heterogeneous multi-tasks on shared resources at runtime. This work addresses this by treating the FPGA resource as a service and employing multi-task processing at the high level, design space exploration and static off-line partitioning in order to allow more efficient mapping of heterogeneous tasks onto the FPGA. In addition, a new, comprehensive runtime functional simulator is used to evaluate the effect of various spatial and temporal constraints on both the existing and new approaches when varying system design parameters. A comprehensive suite of real high performance computing tasks was implemented on a Nallatech 385 FPGA card and show that our approach can provide on average 2.9 × and 2.3 × higher system throughput for compute and mixed intensity tasks, while 0.2 × lower for memory intensive tasks due to external memory access latency and bandwidth limitations. The work has been extended by introducing a novel scheduling scheme to enhance temporal utilization of resources when using the proposed approach. Additional results for large queues of mixed intensity tasks (compute and memory) show that the proposed partitioning and scheduling approach can provide higher than 3 × system speedup over previous schemes.
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Kaur, Kuljeet, Sahil Garg, Georges Kaddoum, and Neeraj Kumar. "Energy and SLA-driven MapReduce Job Scheduling Framework for Cloud-based Cyber-Physical Systems." ACM Transactions on Internet Technology 21, no. 2 (2021): 1–24. http://dx.doi.org/10.1145/3409772.
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Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.
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Seifbarghy, Mehdi, and Ashtiani Ladan Hazrati. "A cooperative covering problem under disruption considering backup coverage." International Journal of Services and Operations Management 29, no. 2 (2017): 273–88. https://doi.org/10.1504/IJSOM.2018.089257.
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In this paper, we study the location of emergency centres considering cooperative and backup coverage while natural disasters occur which can result in facility disruption. In this regard, a reliable version of cooperative covering problem is presented considering two types of candidate sites, i.e., reliable and unreliable. To achieve a fortified system against disaster, reliable candidate sites are selected from areas which are far away from the disaster harms. Furthermore, backup coverage is considered to compensate unsatisfied coverage of the demand zones due to facility disruption. The performance of the model is investigated solving numerical examples with different approaches utilising commercial software. The results confirm accurate performance of the model. They also show that both facility failure and backup coverage considerations lead to a more efficient network by incurring some additional cost.
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"Multi Level Queue Scheduling with Particle Swarm Optimization (Mlqs-Pso) of Vms in Queueing Heterogeneous Cloud Computing Systems." International Journal of Recent Technology and Engineering 8, no. 5 (2020): 864–72. http://dx.doi.org/10.35940/ijrte.e6080.018520.
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This article investigates in cloud computing systems about problem of delay optimal Virtual Machine (VM) scheduling holds constant resources with full infrastructure like CPU, memory and storage in the resource pool. Cloud computing offers users with VMs as utilities. Cloud consumers randomly demand different VM types over time, and the usual length of the VM hosting differs greatly. A scheduling algorithm for a multilevel queue divides the prepared queue towards lengthy and various queues. System is allocated with single queue in to several longer queues. The systems are allocated to one queue indefinitely, usually on any basis of process property, like memory size, process priority, or process sort. Every queue will have its self-algorithm for scheduling. Likewise, a system that’s taking in a less preference queue is so lengthy, a high-priority queue can be transferred. Using Particle Swarm Optimization Algorithm (MQPSO), Multi-level queue scheduling has been done. To evaluate the solutions, it explores both Shortest-JobFirst (SJF) buffering and Min-Min Best Fit (MMBF) programming algorithms, i.e., SJF-MMBF. The scheme incorporating the SJF-ELM-specific scheduling algorithms depending SJF buffering and Extreme Learning Machine (ELM) is also being proposed to prevent work hunger in an SJF-MMBF system. Furthermore, the queues must be planned, which is usually used as a preventive fixed priority schedule. The results of the simulation show that the SJF-ELM is ideal inside strong duty as well as maximum is environment dynamically, with an efficient average employment hosting rate.
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Rekha, S., and C. Kalaiselvi. "Load Balancing Using SJF-MMBF and SJF-ELM Approach." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, January 10, 2021, 74–86. http://dx.doi.org/10.32628/cseit21714.
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This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.
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Panduranaga Rao, M. V., and K. C. Shet. "A Research in Real Time Scheduling Policy for Embedded System Domain." CLEI Electronic Journal 12, no. 2 (2018). http://dx.doi.org/10.19153/cleiej.12.2.4.
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Scheduling a sequence of jobs released over time when the processing time of a job is only known
 at its completion is a classical problem in CPU scheduling in time sharing and real time operating
 systems. Previous approaches to scheduling computer systems have focused primarily on systemlevel
 abstractions for the scheduling decision functions or for the mechanisms that are used to
 implement them. This paper introduces a new scheduling concept New Multi Level Feedback
 Queue (NMLFQ) algorithm. It’s important to get a good response time with interactive tasks
 while keeping other tasks from starvation. In this research paper, we prove that a New version of
 the Multilevel Feedback queue algorithm is competitive for single machine system, in our opinion
 providing theoretical validation of the goodness of the idea that has proven effective in practice.
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Sarode, Sambhaji, and Jagdish Bakal. "PFPS: Priority-First Packet Scheduler for IEEE 802.15.4 Heterogeneous Wireless Sensor Networks." International Journal of Communication Networks and Information Security (IJCNIS) 9, no. 2 (2022). http://dx.doi.org/10.17762/ijcnis.v9i2.2419.
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This paper presents priority-first packet scheduling approach for heterogeneous traffic flows in low data rate heterogeneous wireless sensor networks (HWSNs). A delay sensitive or emergency event occurrence demands the data delivery on the priority basis over regular monitoring sensing applications. In addition, handling sudden multi-event data and achieving their reliability requirements distinctly becomes the challenge and necessity in the critical situations. To address this problem, this paper presents distributed approach of managing data transmission for simultaneous traffic flows over multi-hop topology, which reduces the load of a sink node; and helps to make a life of the network prolong. For this reason, heterogeneous traffic flows algorithm (CHTF) algorithm classifies the each incoming packets either from source nodes or downstream hop node based on the packet priority and stores them into the respective queues. The PFPS-EDF and PFPS-FCFS algorithms present scheduling for each data packets using priority weight. Furthermore, reporting rate is timely updated based on the queue level considering their fairness index and processing rate. The reported work in this paper is validated in ns2 (ns2.32 allinone) simulator by putting the network into each distinct cases for validation of presented work and real time TestBed. The protocol evaluation presents that the distributed queue-based PFPS scheduling mechanism works efficiently using CSMA/CA MAC protocol of the IEEE 802.15.4 sensor networks.
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Ali Rezaee, Abbas, and mohammad akbari. "A multi-channel queue model to optimize service level and staff availability in bank industry." RAIRO - Operations Research, August 19, 2024. http://dx.doi.org/10.1051/ro/2024169.
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Staff scheduling in service organizations like banks, stores, call centers, and emergency centers is critical due to direct customer interaction and uncertain service demand. This research presents a multi-objective model for scheduling bank staff, focusing on uncertain customer arrival and service rates. The model aims to optimize customer service efficiency and maximize staff satisfaction through three objective functions: minimizing the customer waiting queue length (using an M/M/C system), minimizing the number of assigned employees, and maximizing employee satisfaction by considering preferred working times. By simulating Poisson distribution for client arrival and service times, we predicted the bank's queue system performance and optimized staffing levels using the proposed model. Tested with real data from Agribank in Iran, the results showed an 8% reduction in customer waiting times and a 53% increase in employee satisfaction, demonstrating significant improvements in service efficiency and workplace morale. These percentages highlight the model's ability to effectively balance operational efficiency and employee well-being, facilitated by its transparent work pattern structure. Given the NP-hard nature of the model, we employed a meta-heuristic approach (NSGA-II) and GAMS with the ε-constraint method to solve it. Comparative results indicated that NSGA-II outperformed GAMS in both solution quality and computational time
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Fang, Juan, Li’ang Zhao, Min Cai, and Huijing Yang. "WSMP: a warp scheduling strategy based on MFQ and PPF." Journal of Supercomputing, March 10, 2023. http://dx.doi.org/10.1007/s11227-023-05127-0.
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AbstractNormally, threads in a warp do not severely interfere with each other. However, the scheduler must wait until all the threads within complete before scheduling the next warp, resulting in memory divergence. The crux of the problem is scheduling the warp in a more reasonable order. Therefore, we propose a new warp scheduling strategy called WSMP, which is based on multi-level feedback queue (MFQ) and perceptron-based prefetch filtering (PPF). All the warps are sorted beforehand according to the latency tolerance of the warps and pushed into a certain queue in MFQ. We also remold PPF to enhance the modified underlying prefetcher. We are able to strike a balance between cache hit rate and prefetch coverage then. We verify its feasibility using GPGPU-Sim, along with exclusive GPGPU workload. The results show that compared to the baseline, WSMP improves IPC by 26.45% and reduces L2 cache miss rate by 9.54% on average.
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K.A., Varun Kumar, Priyadarshini R., Kathik P.C., Madhan E.S., and Sonya A. "Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue." Sensor Review, September 12, 2022. http://dx.doi.org/10.1108/sr-01-2022-0003.
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Purpose Data traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordination Design/methodology/approach Fair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE. Findings A multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2. Research limitations/implications However, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture. Practical implications The individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level. Originality/value This study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.
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"A Multi-Level Feedback Queue Optimization Method Based on Reinforcement Learning and Dynamic Time Slice Scheduling." Automation and Machine Learning 6, no. 1 (2025). https://doi.org/10.23977/autml.2025.060113.
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Liu, Yachun, Dan Feng, Jianxi Chen, Jing Hu, Zhouxuan Peng, and Jinlei Hu. "ZNSFQ: An Efficient and High-Performance Fair Queue Scheduling Scheme for ZNS SSDs." ACM Transactions on Architecture and Code Optimization, June 26, 2025. https://doi.org/10.1145/3746230.
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The Zoned Namespace (ZNS) interface transfers most storage maintenance responsibilities from the underlying Solid-State Drives (SSDs) to the host. This shift creates new opportunities to ensure fairness and high performance in multi-tenant cloud computing environments at both hardware and software levels. However, when applications with different workloads share a single ZNS SSD hardware, traditional fair queueing schedulers fail to achieve fairness due to their limited awareness of workload characteristics. Moreover, allowing multiple outstanding requests to access the device simultaneously improves resource utilization but often leads to significant I/O interference among these requests. This interference results in over-throttling, which subsequently degrades the performance of existing fair queueing schedulers. To address the above problems, this paper proposes an efficient and high-performance fair queueing scheduling scheme for ZNS SSD (ZNSFQ) on the host side. Firstly, ZNSFQ introduces a workload-aware fair scheduler that enhances fairness by accurately estimating the I/O cost for each application based on its workload characteristics. Secondly, to optimize performance while ensuring fairness, ZNSFQ designs a request dispatch parallelism adjuster. This adjuster manages the channel-level request dispatch parallelism for each application to minimize I/O interference. Finally, ZNSFQ employs a global adaptive coordinator to alleviate device-level I/O blocking, reducing tail latency and CPU consumption while satisfying fairness and performance. A comprehensive evaluation demonstrates that ZNSFQ significantly enhances fairness and performance compared to the latest fair queuing schedulers. In sequential access scenarios, ZNSFQ enhances fairness by over 38.13% and increases I/O bandwidth by more than 49.24%. Furthermore, in random access scenarios, it reduces CPU utilization by 70.22% while maintaining both fairness and high performance.
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Jenila, L., and R. Aroul Canessane. "Cross Layer based Energy Aware and Packet Scheduling Algorithm for Wireless Multimedia Sensor Network." INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 18, no. 2 (2023). http://dx.doi.org/10.15837/ijccc.2023.2.4666.
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Video transmission using sensor networks plays a most significant role in industrial and surveillance applications. Multimedia transmission is also a challenging task in case of guaranteeing quality of service in conditions like limited bandwidth, high congestion, multi-hop routing, etc. Cross layer approach is carried out to handle multimedia transmission over sensor networks for improving network adaptivity. Cross layer based energy aware and packet scheduling algorithm is proposed here to reduce congestion ratio and to improve link quality between the routing nodes. Link quality estimation among nodes is done using Semi-Markov process. Node congestion rate is determined for identifying node’s data channel rate. Packet scheduling process determines the highly prioritized packets by using queue scheduler component thereby the active nodes are selected through link quality process and the packets are transmitted to sink based on prioritize level. Simulation analysis is carried out and the efficiency of the proposed mechanism is proved to be better while comparing with the conventional schemes.
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Husam, Suleiman and Otman Basir. "SLA-DRIVEN LOAD SCHEDULING IN MULTI-TIER CLOUD COMPUTING: FINANCIAL IMPACT CONSIDERATIONS." May 6, 2020. https://doi.org/10.5281/zenodo.3792558.
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A cloud service provider strives to effectively provide a high Quality of Service (QoS) to client jobs. Such jobs vary in computational and Service-Level-Agreement (SLA) obligations, as well as differ with respect to tolerating delays and SLA violations. The job scheduling plays a critical role in servicing cloud demands by allocating appropriate resources to execute client jobs. The response to such jobs is optimized by the cloud service provider on a multi-tier cloud computing environment. Typically, the complex and dynamic nature of multi-tier environments incurs difficulties in meeting such demands, because tiers are dependent on each others which in turn makes bottlenecks of a tier shift to escalate in subsequent tiers. However, the optimization process of existing approaches produces single-tier-driven schedules that do not employ the differential impact of SLA violations in executing client jobs. Furthermore, the impact of schedules optimized at the tier level on the performance of schedules formulated in subsequent tiers tends to be ignored, resulting in a less than optimal performance when measured at the multi-tier level. Thus, failing in committing job obligations incurs SLA penalties that often take the form of either financial compensations, or losing future interests and motivations of unsatisfied clients in the service provided. Therefore, tolerating the risk of such delays on the operational performance of a cloud service provider is vital to meet SLA expectations and mitigate their associated commercial penalties. Such situations demand the cloud service provider to employ scalable service mechanisms that efficiently manage the execution of resource loads in accordance to their financial influence on the system performance, so as to ensure system reliability and cost reduction. In this paper, a scheduling and allocation approach is proposed to formulate schedules that account for differential impacts of SLA violation penalties and, thus, produce schedules that are optimal in financial performance. A queue virtualization scheme is designed to facilitate the formulation of optimal schedules at the tier and multi-tier levels of the cloud environment. Because the scheduling problem is NPhard, a biologically inspired approach is proposed to mitigate the complexity of finding optimal schedules. The reported results in this paper demonstrate the efficacy of the proposed approach in formulating costoptimal schedules that reduce SLA penalties of jobs at various architectural granularities of the multi-tier cloud environment.
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Lin, Shengle, Wangdong Yang, Yikun Hu, et al. "HPS Cholesky: Hierarchical Parallelized Supernodal Cholesky with Adaptive Parameters." ACM Transactions on Parallel Computing, October 26, 2023. http://dx.doi.org/10.1145/3630051.
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Sparse supernodal Cholesky on multi-NUMAs is challenging due to the supernode relaxation and load balancing. In this work, we propose a novel approach to improve the performance of sparse Cholesky by combining deep learning with a relaxation parameter and a hierarchical parallelization strategy with NUMA affinity. Specifically, our relaxed supernodal algorithm utilizes a well-trained GCN model to adaptively adjust relaxation parameters based on the sparse matrix’s structure, achieving a proper balance between task-level parallelism and dense computational granularity. Additionally, the hierarchical parallelization maps supernodal tasks to the local NUMA parallel queue and updates contribution blocks in pipeline mode. Furthermore, the stream scheduling with NUMA affinity can further enhance the efficiency of memory access during the numerical factorization. The experimental results show that HPS Cholesky can outperform state-of-the-art libraries, such as Eigen LL T , CHOLMOD, PaStiX and SuiteSparse on \(79.78\% \) , \(79.60\% \) , \(82.09\% \) and \(74.47\% \) of 1128 datasets. It achieves an average speedup of 1.41x over the current optimal relaxation algorithm. Moreover, \(70.83\% \) of matrices have surpassed MKL sparse Cholesky on Xeon Gold 6248.
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Sun, Hui, Bendong Lou, Chao Zhao, et al. "An Asynchronous Compaction Acceleration Scheme for Near-Data Processing-enabled LSM-Tree-based KV Stores." ACM Transactions on Embedded Computing Systems, September 29, 2023. http://dx.doi.org/10.1145/3626097.
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LSM-tree-based key-value stores (KV stores) convert random-write requests to sequence-write ones to achieve high I/O performance. Meanwhile, compaction operations in KV stores update SSTables in forms of reorganizing low-level data components to high-level ones, thereby guaranteeing an orderly data layout in each component. Repeated writes caused by compaction ( a.k.a, write amplification) impacts I/O bandwidth and overall system performance. Near-data processing (NDP) is one of effective approaches to addressing this write-amplification issue. Most NDP-based techniques adopt synchronous parallel schemes to perform a compaction task on both the host and its NDP-enabled device. In synchronous parallel compaction schemes, the execution time of compaction is determined by a subsystem that has lower compaction performance coupled by under-utilized computing resources in a NDP framework. To solve this problem, we propose an asynchronous parallel scheme named PStore to improve the compaction performance in KV stores. In PStore, we designed a multi-tasks queue and three priority-based scheduling methods. PStore elects proper compaction tasks to be offloaded in host- and device-side compaction modules. Our proposed cross-leveled compaction mechanism mitigates write amplification induced by asynchronous compaction. PStore featured with the asynchronous compaction mechanism fully utilizes computing resources in both host and device-side subsystems. Compared with the two popular synchronous compaction modes based on KV stores (TStore and LevelDB), our PStore immensely improves the throughput by up to a factor of 14 and 10.52 with an average of a factor of 2.09 and 1.73, respectively.
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Abbasi, Felor Beikzadeh, Ali Rezaee, Sahar Adabi, and Ali Movaghar. "Fault-tolerant scheduling of graph-based loads on fog/cloud environments with multi-level queues and LSTM-based workload prediction." Computer Networks, August 2023, 109964. http://dx.doi.org/10.1016/j.comnet.2023.109964.
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Zhou, Junsheng, Wangdong Yang, Fengkun Dong, Shengle Lin, Qinyun Cai, and Kenli Li. "NUMA-aware parallel sparse LU factorization for SPICE-based circuit simulators on ARM multi-core processors." International Journal of High Performance Computing Applications, October 23, 2024. http://dx.doi.org/10.1177/10943420241241491.
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In circuit simulators that resemble the Simulation Program with Integrated Circuit Emphasis (SPICE), one of the most crucial steps is the solution of numerous sparse linear equations generated by frequency domain analysis or time domain analysis. The sparse direct solvers based on lower-upper (LU) factorization are extremely time-consuming, so their performance has become a significant bottleneck. Despite the existence of some parallel sparse direct solvers for circuit simulation problems, they remain challenging to adapt in terms of performance and scalability in the face of rapidly evolving parallel computers with multiple NUMA hardware based on ARM architecture. In this paper, we introduce a parallel sparse direct solver named HLU, which re-examines the performance of the parallel algorithm from the viewpoint of parallelism in pipeline mode and the computing efficiency of each task. To maximize task-level parallelism and further minimize the thread waiting time, HLU devises a fine-grained scheduling method based on an elimination tree in pipeline mode, which employs depth-first search (DFS-like) to iteratively search for parent tasks and then place dependent tasks in the same task queue. HLU also suggests two NUMA node affinity strategies: thread affinity optimization based on NUMA nodes topology to guarantee computational load balancing and data affinity optimization to enable effective memory placement when threads access data. The rationality and effectiveness of the sparse solver HLU are validated by the SuiteSparse Matrix Collection. In comparison with KLU and NICSLU, the experimental results and analysis show that HLU attains a speedup of up to 9.14× and 1.26x (geometric mean) on a Huawei Kunpeng 920 Server, respectively.