Multilevel Feedback Queue Research Articles

Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms

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.

Instant queue occupancy used for automatic traffic scheduling in data center networks

Datacenter applications desire low latency for short messages to provide a better user experience. Therefore, one of the goals of datacenter networks is to minimize flow completion time (FCT), especially for short flows. Multiple scheduling disciplines have been proposed to achieve this goal. In this paper, we develop a Dynamic Longer Stay Less Priority (D-LSLP) which looks at the current queue occupancy to adjust the demotion threshold for the packets of the flows arriving in the strict priority queues. Initially, D-LSLP considers every flow to be a short flow, and with the passage of time, the flow is demoted to the lower priority queue, similar to Multilevel Feedback Queue (MLFQ) Scheduling. It enables short flows to be completed in a couple of higher-priority queues. In contrast, large flows are demoted to the lower priority queues after remaining active in the queues for a certain amount of time (demotion threshold). However, for the different traffic patterns with different distribution of flow lengths, the demotion threshold for the flows should be adapted automatically. In this paper, we leverage the programmable nature of the P4 switches to measure the instant queue occupancy in the P4, which can be used to adjust the demotion threshold accordingly for the highest priority queue. This enables D-LSLP to increase or decrease its highest priority demotion threshold based on the instant queue status. D-LSLP allows multiple traffic patterns to coexist without manually tuning the demotion thresholds. Furthermore, it reduces the tail drop in the highest priority queue when many short flows overwhelm the highest priority queue. The performance evaluation shows that it works equally well for different traffic patterns without operator intervention.

Pernerapan Algoritma Multilevel Feedback Queue (MFQ) & First In First Out (FIFO) pada Studi Kasus di Toko Bunga ARTHA PUSPA

Artha Puspa is a flower shop in Madiun which sells plant needs. This very rapid business development encourages every entrepreneur to create a form that is different from other businesses, one of which is the Arta Puspa Flower Shop, a common problem that is often experienced by Artha Puspa, namely direct purchase queues and orders. With that we use a combination of MFQ and FIFO. The application of a combination of Multilevel Feedback Queue (MFQ) and First In First Out (FIFO) algorithms is a strategy that can be used to manage processes that occur in computer systems. MFQ is a scheduling algorithm that uses several levels of queues, each of which has a different priority. Each process will be placed in a queue level according to its priority. Meanwhile, FIFO is a scheduling algorithm that manages processes by issuing processes that apply a combination of multilevel feedback queue (MFQ) & first in first out (FIFO) algorithms in the case study of the Bunga Artha Puspa store to enter the system for the first time. The combination of the two algorithms can provide a more effective solution in managing processes in computer systems. MFQ is a scheduling algorithm that uses several levels of queues, each of which has a different priority. Each process will be placed in a queue level according to its priority. Meanwhile, FIFO is a scheduling algorithm that manages processes by issuing processes that apply a combination of multilevel feedback queue (MFQ) & first in first out (FIFO) algorithms in the case study of the Bunga Artha Puspa store to enter the system for the first time. The combination of the two algorithms can provide a more effective solution in managing processes in computer systems. MFQ can be used to manage high priority processes, while FIFO can be used to manage low priority processes. Thus, processes that have high priority will always be processed first, thereby increasing system efficiency. In addition, the combination of MFQ and FIFO can also help reduce the waiting time for processes that have low priority, thereby increasing user satisfaction.

WSMP: a warp scheduling strategy based on MFQ and PPF

Normally, 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.

Multi‐objective strategy‐based resource allocation and performance improvements in 5G and beyond wireless networks

SummaryThe increase in mobile communication has been caused the network traffic problem. For that, phantom cell is used for further spectrum efficiency enhancements. In this paper, improved cat swarm optimization clustering (ICSOC)‐based energy aware load balancing is made for both macro cell and small cell to improve the broadband network efficiency using 5G and beyond wireless networks. Then, Multilevel Feedback Queue with Round Robin (MFQ‐RR) scheduling is used to provide superior channel quality and well improved resource sharing for mobile phones. In this wireless network, the phantom cells provided a feasible and cost‐effective way of refining the network coverage and capacity for both local and wide area networks because of their low cost and easy deployment. This system has highly efficient, cost effective, and flexible that will fulfill the end user expectations, and it enhances the capacity and flexible multi‐RAT access to support the diverse applications. This system performance is evaluated using MATLAB 12 software. From experimental results, it is observed that the throughput and spectral efficiency are increased by 20% and 35%, respectively. As well as the packet delay is reduced to 12% and packet loss ratio is 33% decreased when compared with the existing techniques.

M-DRL: Deep Reinforcement Learning Based Coflow Traffic Scheduler with MLFQ Threshold Adaption

The coflow scheduling in data-parallel clusters can improve application-level communication performance. The existing coflow scheduling method without prior knowledge usually uses multi-level feedback queue (MLFQ) with fixed threshold parameters, which is insensitive to coflow traffic characteristics. Manual adjustment of the threshold parameters for different application scenarios often has long optimization period and is coarse in optimization granularity. We propose M-DRL, a deep reinforcement learning based coflow traffic scheduler by dynamically setting thresholds of MLFQ to adapt to the coflow traffic characteristics, and reduces the average coflow completion time. Trace-driven simulations on the public dataset show that coflow communication stages using M-DRL complete 2.08x(6.48x) and 1.36x(1.25x) faster on average coflow completion time (95-th percentile) in comparison to per-flow fairness and Aalo, and is comparable to SEBF with prior knowledge.

A novel evolutionary algorithm (MFO) with rate monotonic scheduling for load balancing in LTE advanced networks

In recent days, long term evolution (LTE)-advanced is the newest 4G technology. In this cellular network, the femto cells offered a viable and cost-effective method of enhancing the cellular reporting and capability for each WLAN and WiMAX. However, network allocates resource dissimilar to each and every users. To overcome this difficulty, moth flame optimisation (MFO) technique is used to provide equal resource allocation to user. Using this MFO method to balance the load for routing process. Rate-monotonic scheduling (RMS) algorithm is applied to share the load equally to available user, which increases the throughput and control the overload during communication time instance. The simulation results show that the proposed MFO technique has achieved improved network performance than existing hybrid multilevel feedback queue with round robin (HMFQ-RR) and hybrid weighted round robin with shortest job first (HWRR-SJF) in terms to reduce packet loss rate, packet delay and end to end delay.

A novel evolutionary algorithm (MFO) with rate monotonic scheduling for load balancing in LTE advanced networks

In recent days, long term evolution (LTE)-advanced is the newest 4G technology. In this cellular network, the femto cells offered a viable and cost-effective method of enhancing the cellular reporting and capability for each WLAN and WiMAX. However, network allocates resource dissimilar to each and every users. To overcome this difficulty, moth flame optimisation (MFO) technique is used to provide equal resource allocation to user. Using this MFO method to balance the load for routing process. Rate-monotonic scheduling (RMS) algorithm is applied to share the load equally to available user, which increases the throughput and control the overload during communication time instance. The simulation results show that the proposed MFO technique has achieved improved network performance than existing hybrid multilevel feedback queue with round robin (HMFQ-RR) and hybrid weighted round robin with shortest job first (HWRR-SJF) in terms to reduce packet loss rate, packet delay and end to end delay.

DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing

By providing the flexible and shared computing and communication resources along with the cloud services, the fog computing became an attractive paradigm to support delay-sensitive tasks in the Internet of Things (IoT). The existing researches for offloading delay-sensitive tasks in a hierarchical fog-cloud environment mostly focused on minimizing the overall communication delay. However, a fair offloading strategy selects a suitable computing device in terms of fog node or cloud server based on the resource requirements of the task while meeting the deadline. In this article, we design a new delay-dependent priority-aware task offloading (DPTO) strategy for scheduling and processing the tasks, generated from the IoT devices to suitable computing devices. The proposed strategy assigns a priority on each task based on its deadline and assigns it to a suitable multilevel-feedback queue. This schema reduces the waiting time of the delay-sensitive tasks on the queue and minimizes the starvation problem of the low priority tasks. Moreover, the DPTO strategy selects an optimal computing device for each task based on its resource availability and transmission time from the IoT device. This strategy minimizes the overall offloading time of the tasks while meeting the deadlines. Finally, the extensive simulation results with various performance parameters show the effectiveness of the proposed strategy over the existing baseline algorithms.

JAMS-SG

Time-triggered automotive networks use time-triggered protocols (FlexRay, TTEthernet, etc.) for periodic message transmissions that often originate from safety and time-critical applications. One of the major challenges with time-triggered transmissions is jitter, which is the unpredictable delay-induced deviation from the actual periodicity of a message. Failure to account for jitter can be catastrophic in time-sensitive systems, such as automotive platforms. In this article, we propose a novel scheduling framework (JAMS-SG) that satisfies timing constraints during message delivery for both jitter-affected time-triggered messages and high-priority event-triggered messages in automotive networks. At design time, JAMS-SG performs jitter-aware frame packing (packing of multiple signals from Electronic Control Units (ECUs) into messages) and schedules synthesis with a hybrid heuristic. At runtime, a Multi-Level Feedback Queue (MLFQ) handles jitter-affected time-triggered messages and high-priority event-triggered messages that are scheduled using a runtime scheduler. Our simulation results, based on messages and network traffic data from a real vehicle, indicate that JAMS-SG is highly scalable and outperforms the best-known prior work in the area in the presence of jitter.

Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

Dynamic adjustment of a MLFQ flow scheduler to improve cloud applications performance

Las soluciones presentes actualmente en el área de conmutación de flujos proponen el uso del concepto de Colas Multinivel con Realimentación (MLFQ por su sigla en inglés) como mecanismo para evitar el requerimiento de información previa (mecanismo agnóstico) con respecto al tamaño de los flujos de datos. Este es un aspecto importante para el logro de las metas de desempeño de alta capacidad de respuesta y alto rendimiento, esperadas en las aplicaciones en la nube (Por ejemplo, motores de búsqueda, redes sociales y sitios de comercio electrónico). Estas metas están estrechamente asociadas a la priorización de los flujos cortos (con tamaños de unos pocos KB), mayoritarios en estas aplicaciones, sobre los flujos largos (con tamaños de varios MB). Sin embargo, estas aplicaciones usualmente no son capaces de proporcionar de antemano la información acerca del tamaño de los flujos. En este artículo, analizamos la viabilidad de proporcionar ajuste dinámico a un esquema de conmutación basado en MLFQ, de tal manera que éste sea capaz de adaptarse a las variaciones espacio temporales que se observan en el tráfico presente en las redes de centro de datos, sin que se requiera información previa sobre las propiedades de las cargas de trabajo.

A Research in Real Time Scheduling Policy for Embedded System Domain

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.

FMD: A DoS mitigation scheme based on flow migration in software‐defined networking

SummarySoftware‐defined networking (SDN) emerges as the next generation of networking architecture, aiming to improve the network manageability and adaptability. However, because of the centralized control policy, SDN is liable to suffering from the denial of service attack in both the data plane and the control plane. To resist the attack and prevent the network from being paralyzed, we propose a novel mitigation scheme named flow migration defense, which uses a slave controller as a substitution to endure flooding requests mitigated from the master controller. Considering the special case that the normal requests may be regarded as the malicious ones, these requests are reforwarded back to the master controller on the basis of the round‐robin scheduling. To prevent the master controller from being flooded by the reforwarded requests, we design the adaptive rate adjustment method to adjust the reforwarding rate. Compared with multilevel feedback queue and FloodDefender, simulations demonstrate that flow migration defense can mitigate the SDN‐aimed denial of service attack efficiently with a better performance in terms of request response time, packet loss rate, and mitigation time.

Design and Implementation of Food & Beverage Order Applications Using Multilevel Feedback Queue Method Based Android (case Study : Bonk Cafe Krian)

With the development of information technology that exists today everyone can perform data processing easily, canproduce an information we need accurately and save time, as well as cost. Then we need a menu applicationapplication program using smartphone technology and web based to facilitate the ordering of food and drinks atBonk Cafe Krian. With the features that exist in this application, the user can make the ordering process usingAndroid and from the order will be stored into the database server so that the ordering process will appear on themonitor the kitchen along with the cashier. This Android-based service application has a significant time differenceso that this application can save time and speed up the service process.Multilevel Feedback Queue algorithm as a method that can increase the efesiesi time and speed up serviceprocess to the customer. With this algorithm can be built an application ordering android-based food and beveragethat is light and reliable.Functionally in Results in this test requires 4 customer data with different kinds of menus that have differentorder times, scheduling results in estimated waiting time and presentation time to customers with an average of 3.25minutes. So the food and beverage system is declared feasible test and feasible to be used functionally.

Design and Performance Evaluation of Smart Job First Multilevel Feedback Queue (SJFMLFQ) Scheduling Algorithm with Dynamic Smart Time Quantum

Multilevel feedback queue scheduling (MLFQ) algorithm is based on the concept of several queues in which a process moves. In earlier scenarios there are three queues defined for scheduling. The two higher level queues are running on Round Robin scheduling and last level queue is running on FCFS (First Come First Serve). A fix time quantum is defined for RR scheduling and scheduling of process depends upon the arrival time in ready queue. Previously a lot of work has been done in MLFQ. In our propose algorithm Smart Job First Multilevel feedback queue (SJFMLFQ) with smart time quantum (STQ), the processes are arranged in ascending order of their CPU execution time and calculate a Smart Priority Factor SPF on which processes are scheduled in queue. The process which has lowest SPF value will schedule first and the process which has highest SF value will schedule last in queue. Then a smart time quantum (STQ) is calculated for each queue. As a result, we found decreasing in turnaround time, average waiting time and increasing throughput as compared to the previous approaches and hence increase in the overall performance.

Designing of vague logic based multilevel feedback queue scheduler

Multilevel feedback queue scheduler suffers from major issues of scheduling such as starvation for long tasks, fixed number of queues, and static length of time quantum in each queue. These factors directly affect the performance of the scheduler. At many times impreciseness exists in attributes of tasks which make the performance even worse. In this paper, our intent is to improve the performance by providing a solution to these issues. We design a multilevel feedback queue scheduler using a vague set which we call as VMLFQ scheduler. VMLFQ scheduler intelligently handles the impreciseness and defines the optimum number of queues as well as the optimal size of time quantum for each queue. It also resolves the problem of starvation. This paper simulates and analyzes the performance of VMLFQ scheduler with the other multilevel feedback queue techniques using MatLab.

New MLFQ Scheduling Algorithm for Operating Systems Using Dynamic quantum

The new design of multilevel feedback queue will depend on usage new technique in computing the quantum to produce (ADQ) Auto Detect Quantum which is relied on the burst of each process has enrolled to the system. By summating the burst time of each process has arrived and dividing it by the number of available processes, we can obtained the dynamic quantum in each level of scheduling. The processes are scheduled and shifted down from queue to other according to their remaining bursts time that should be updated periodically. Every queue has a unique auto detected quantum which is gradually increased or decreased from top level to bottom level queues according to the case of arriving processes. Depending on the results of graphical simulating algorithm on cases study, we can discover that a dynamic quantum is very suitable to accommodate low priority processes that still for a long duration to complete their requests, i.e. avoid the starvation of CPU- bounded processes. Although, it stills compatible with high priority processes (I/O-Bounded) to provide a fair interactivity with them. In comparison to traditional MLFQ the performance of the new scheduling technique is better and practical according to the applied results. Additional, we developed suitable software to simulate the new design and test it on different cases to prove it.

Mobility-aware balanced scheduling algorithm in mobile Grid based on mobile agent

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.

English

Response time is one of the characteristics of scheduler, happens to be a prominent attribute of any CPU scheduling algorithm. The proposed New Multi Level Feedback Queue [NMLFQ] Scheduler is compared with dynamic, real time, Dependent Activity Scheduling Algorithm (DASA) and Lockes Best Effort Scheduling Algorithm (LBESA). We abbreviated beneficial result of NMLFQ scheduler in comparison with dynamic best effort schedulers with respect to response time.