CPU Scheduling Research Articles

Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique

Minimizing time cost in time-shared operating system is the main aim of the researchers interested in CPU scheduling. CPU scheduling is the basic job within any operating system. Scheduling criteria (e.g., waiting time, turnaround time and number of context switches (NCS)) are used to compare CPU scheduling algorithms. Round robin (RR) is the most common preemptive scheduling policy used in time-shared operating systems. In this paper, a modified version of the RR algorithm is introduced to combine the advantageous of favor short process and low scheduling overhead of RR for the sake of minimizing average waiting time, turnaround time and NCS. The proposed work starts by clustering the processes into clusters where each cluster contains processes that are similar in attributes (e.g., CPU service period, weights and number of allocations to CPU). Every process in a cluster is assigned the same time slice depending on the weight of its cluster and its CPU service period. The authors performed comparative study of the proposed approach and popular scheduling algorithms on nine groups of processes vary in their attributes. The evaluation was measured in terms of waiting time, turnaround time, and NCS. The experiments showed that the proposed approach gives better results.

Heuristic algorithms for diversity-aware balanced multi-way number partitioning

Number partitioning is a classic problem in artificial intelligence. And balanced multi-way number partitioning problem (BMNP) aims to partition a set of numbers into multiple subsets, such that (1) each subset contains the same number of numbers and (2) the subset sums are equal. The BMNP problem has various applications in real world scenarios, including task allocation, CPU scheduling, file placement in data center, multi-source data processing, etc. In this paper, we consider the problem of diversity-aware balanced multi-way number partitioning (DBMNP). DBMNP differs from BMNP, in that each number is associated with a type attribute. In addition to the two goals of BMNP, DBMNP also requires that the types of numbers in each subset are as diversified as possible. To solve the problem, we propose three heuristic algorithms to minimize the difference between subset sums and at the same time maximize diversify of each subset. Extensive experiments are conducted to evaluate the effectiveness of our proposed algorithms.

VCSP: virtual CPU scheduling for post-copy live migration of virtual machines

The live migration of virtual machines among physical machines aims at efficient utilization of resources, load balancing, maintenance, energy management, fault tolerance, sharing resources and mobile computing. There are several methods for the live migration of virtual machines. In the post-copy approach, a virtual machine starts working in a target host, when data that is not present in the target machine is requested, a page fault occurs and the processing stops in the target until the arrival of the requested information. Certainly, the number of stops and lags negatively affects the system downtime. The reduction of physical or virtual CPU frequency is one of the techniques recommended to efficient migration of virtual machines. The modification of the frequency of physical and/or virtual CPU has already been used in the improvement of the pre-copy method to manage the speed of changes in the source machine. This paper presents the virtual CPU scheduling for post-copy (VSCP) framework that reduces the speed of the virtual CPU in post-copy to make a balance between processing speed of pages in the target machine and their transmission speed in the source machine. The VCSP is also compared with the baseline methods of the post-copy-prefetching and post-copy. In the experiments, the system downtime, total migration time, total pages transferred and throughput of system are evaluated. The results indicate that the proposed method improves the system downtime up to 8.17%, total migration time up to 30.33%, total pages transferred up to 54.49% and throughput of system up to 23.65%.

An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler.

The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

CPU Scheduling Techniques: A Review on Novel Approaches Strategy and Performance Assessment

The operating system (OS) is now being widely used in many of the various modern applications in recent years, ranging from diabetic monitoring to other strategic planning. The main function of any OS is to control and coordinate all hardware resources among the commands of the same/different user, which increases the efficiency of advanced comprehensive applications. In an autonomous computer system, the CPU is one of the important resources to manage and process all activities which require scheduling techniques on a processor. Since the early days of computing and other multi-programming OS, various studies have been assigned to CPU scheduling techniques based on processes management and performance evaluation. Thus, outlining the many issues related to scheduling methodologies and the weaknesses that need to be addressed. This review paper is organized based on two distinct perspectives: the implement strategies of CPU scheduling technique and criteria-based measures used, which assess how the strategies are analyzed and used under performance evaluation.

Analysis of Preemptive Shortest Job First (SJF) Algorithm in CPU Scheduling

Analysis of Preemptive Shortest Job First (SJF) Algorithm in CPU Scheduling

Smoothed Graphic User Interaction on Smartphones With Motion Prediction

The smoothness of human-smartphone interaction directly influences users experience and affects their purchase decisions. A commonly used method to improve user interaction of smartphones is to optimize the CPU scheduler. However, optimizing the CPU scheduler requires a modification of operating system. In addition, the improvement of the smoothness of human-smartphone interaction may be limited because the display subsystem is not optimized. Therefore, in this paper, we design a motion prediction queuing system, named MPQS, to improve the smoothness of human-smartphone interaction. For this, we use the information of vector, speed, movement, provided by the queuing mechanism of Android, to predict the movement of user-smartphone interaction. Based on the prediction, we then utilize available execution time between frames to perform image processing. We conducted a set of experiments on beagleboard-xM to evaluate the performance of MPQS. Our experiment results show that the proposed method can reduce the number of jank by up to 21.75%.

Efficient Hybrid Central Processing Unit/ Input–Output Resource Scheduling for Virtual Machines

The use of virtualization technology in industrial control is increasing. However, virtual instances or virtual machines (VMs) are confronted with the unreasonable resource allocation in the industrial control cyber range, thereby highlighting the increasing importance of resource scheduling. In the Xen open source system, the input-output (IO)-intensive task response is extended because the system does not distinguish between CPU- and IO-intensive tasks. Therefore, this article presents an improved task performance through the hybrid scheduling of CPU and network IO resources. This method uses Cap- and Timeslice-scheduling algorithms for CPU resource scheduling. First, the Cap-scheduling algorithm uses historical data to train a recurrent neural network model for classification and then utilizes the heuristic method to set the upper limit of cap value for VMs. Second, the Timeslice-scheduling algorithm adjusts the timeslice based on Q-learning to shorten the execution time of the overall tasks. This article proposes an IOb-scheduling algorithm for network bandwidth scheduling. The part that does not exceed the average bandwidth is eliminated and distributed to other VMs by monitoring the bandwidth usage of each VM, thereby improving the utilization of bandwidth. Experimental results showed that the proposed CPU/IO scheduling algorithms improved the overall benchmark performance substantially.

Interactive Context for Mobile OS Resource Management

This paper presents an application-transparent execution context for mobile operating systems that reflects the criticality of current execution on the user interactivity. We track Android / Linux system-level events including semantic syscalls, UI actions, and standard inter-process and intra-process communication interfaces that signal the initiation and propagation of interactivity-related executions. This interactive context enables new optimizations in CPU scheduling and power state management. We recognize that online resource scheduling is susceptible to inaccurate contexts—e.g., misidentifying an interactive execution as background leads to possible priority inversion by schedulers. In particular, low-level OS events such as kernel block / wake-up carry ambiguous semantics that could induce false context propagations. We selectively expose such uncertainties to the schedulers so that executions with uncertain contexts are prioritized behind interactive executions but otherwise run as fast as possible to minimize the priority inversion. We further recognize the importance of prompt propagation of the interactivity context at the earliest possible moment to facilitate immediate prioritization. We utilize the interactivity context to enable differential per-core frequency control and background workload consolidation. Experiments on a quad-core smartphone with a dozen mobile workload scenarios show that we can, on average, save 13 percent energy with only 1 percent degradation on the interactive performance.

Comparative Performance Evaluation of Popular Virtual Private Servers

Virtual Private Server (VPS) enables user access to an operating system instance with a fine-grained control of private software and hardware resources. Many various factors can affect VPS performance and they primarily include physical server hardware specifications, installed operating system, virtualization layer, and the underlying network infrastructure. Therefore, it is very important to properly select a VPS host that meets users, applications and services resource and performance requirements. This paper presents a performance evaluation of three popular VPS hosts; namely Digital Ocean, Linode and VULTR. Performance measurement was conducted under the same controlled conditions for all three VPS hosts using a popular benchmark application for Unix operating systems - UnixBench. Three performance evaluation experiments with a focus on examining and studying key performance metrics which include CPU scheduling, memory management, hard disk drive management and Unix operating system task scheduling, were conducted. Performance easurement results show that VULTR achieves the best results under most of the tests in the first two experiments making it the best choice for low demand users, while DigitalOcean achieves the best results in the third experiment making it the best solution for high demand users who are looking for a high performance VPS.

BoR: Toward High-Performance Permissioned Blockchain in RDMA-Enabled Network

Known as a distributed ledger, blockchain is becoming prevalent due to its decentralization, traceability and tamper resistance. Particularly, permissioned blockchain such as Hyperledger Fabric shows great application prospects as the infrastructure of IoT security, credit management, etc. Many cloud platforms like AWS, Azure, Oracle and IBM cloud currently provide blockchain as a service, in which tenants can quickly build permissioned blockchain and run smart contract based applications. However, the transactions throughput and scalability in the permissioned blockchain are not ideal, despite many optimization efforts in consensus protocol and parallel chain. Existing solutions still reveals some limitations like excessive CPU scheduling, inefficient block broadcast and high latency of initial blocks synchronization when new nodes join blockchain network. Inspired by the emerging RDMA (Remote Direct Memory Access) network, we propose BoR, an RDMA-based permissioned blockchain framework. By offloading the block transfer transaction into RDMA NICs, it can increase block broadcast speed and reduce block sync delay. We exploit the RDMA primitives to redesign the block synchronization protocol and accelerate DPoS (Delegated Proof of Stake) consensus process for higher throughput and lower latency in kernel-bypass manner. As demonstrated in our evaluation with different workloads, BoR with lower CPU utilization significantly outperforms the state-of-the-art EoS blockchain.

Supporting the Priorities in the Multi-queue Block I/O Layer for NVMe SSDs

Recently, a multi-queue block I/O layer has been introduced in the Linux kernel to support NVMe SSDs efficiently. It can provide a high I/O bandwidth by employing several queues but does not provide a differentiated I/O services yet. In this paper, we present a new scheme that supports the priorities in the multi-queue block I/O layer. Our scheme brings the CFS (Completely Fair Scheduler) technique that is one of CPU scheduler into the I/O layer. In this way, we can provide a differentiated I/O service regardless of whether processes are CPU-intensive or I/O intensive. Besides, by considering the I/O request type, read requests are preferentially handled over write requests. Through implementation on the Linux kernel, we show that the proposed scheme appropriately services the I/O requests according to priority and request type and improves runtimes of read-intensive and write-intensive processes by 33% and 15%, respectively.

Design of Optimal Scheduler for Process Scheduling

CPU Scheduling takes plays an important role in multiprogramming systems. There are several programs present in memory. It is the responsibility of operating systems to select the process and assign it to CPU. There are various algorithms available for CPU Scheduling. The algorithm’s performance depends on various factors like arrival time, priority etc. This paper helps to select the best algorithm by comparing various algorithms under the same condition and analyzed them based on various factors like waiting time, turnaround time, CPU utilization, Throughput.

An intuitionistic based novel approach to highest response ratio next CPU scheduler

An intuitionistic based novel approach to highest response ratio next CPU scheduler

SchedCust: Design & Development of scheduling policy customization framework for ARM based System on Chip

Abstract Selecting a right scheduling strategy for the specific embedded application has a significant impact on system’s performance. To adapt to the requirements of a particular embedded application, the embedded operating system can be customized manually or automatically. In this paper, the standard process for customizing the CPU scheduling policy in an open source Linux operating system (OS) has been discussed. The paper also focuses on the design & development of the CPU scheduling customization framework for ARM based Raspberry Pi 3, tagged as SchedCust, that allows the CPU scheduler to be configured to execute a particular scheduling policy. The SchedCust framework helps the users to automatically develop the scheduling policy specific custom operating system (OS) for the selected processor. The experimental results have been computed for FIFO and RR scheduling policy, with respect to four distinct parameters. The data collected from user testing showed that the manual process of developing a custom OS takes more time and efforts when compared to the automated process.

Performance Improvement of Linux CPU Scheduler Using Policy Gradient Reinforcement Learning for Android Smartphones

The Energy Aware Scheduler (EAS) was developed and applied to the Linux kernel of recent Android smartphones in order to exploit the ARM big.LITTLE processing architecture efficiently. EAS organizes CPU hardware information into Energy Model which are used to improve CPU scheduling performance. In particular, it reduces power consumption and improves process scheduling performance. However, EAS has limitations in improving CPU scheduling performance, because the Energy Model configures the CPU hardware information to fixed values, which does not reflect the characteristics of running tasks, such as the workload changes and the transition between running state and sleep state. To solve this problem, this paper introduces the Learning Energy Aware Scheduler (Learning EAS). The Learning EAS adjusts the TARGET_LOAD used to set the CPU frequency and the sched_migration_cost used as the task migration criteria according to the characteristics of the running task through the policy gradient reinforcement learning. In LG G8 ThinQ, Learning EAS improved power consumption by 2.3% - 5.7%, hackbench results for process scheduling performance by 2.8% - 25.5%, applications entry time by 4.4% - 6.1%, and applications entry time under high CPU workload by 9.6% - 12.5%, respectively compared with EAS. This paper also showed that the Learning EAS is scalable by applying the Learning EAS to high-end and low-end chipset platforms of Qualcomm.Inc and MediaTek.Inc and improving power consumption by 2.8% - 7.8%, application entry time by 2.2% - 7.2%, respectively compared with EAS. Finally, this paper showed that the performance of CPU scheduling is improved gradually by the repetition of reinforcement learning.

Research and implementation on high-speed transmission performance optimization technology of ten gigabit firewall UDP multicast

Under the development trend of high throughput, large capacity and strong time effective data security transmission of the second generation data relay system in China, the high-performance processing requirements for the firewall of the backbone network security protection equipment are constantly improving. Focusing on the characteristics of the second generation data relay system, the key technologies of the transmission processing of the 10 Gigabit firewall based on the x86 architecture for different data flows are researched, the optimization design of the CPU allocation and scheduling mode, the function judgment mechanism of the network card characteristics and the kernel state write operation function of the 10 Gigabit firewall are completed. The test results show that the optimized 10 Gigabit firewall gives full play to its maximum transmission performance, and provides a better security transmission service guarantee for the integrated data relay users.

The Impact of Teaching Operating Systems using Two Different Teaching Modalities

This paper presents a preliminary look at the performance of two cohorts enrolled in an Operating System course which was taught using two different teaching delivery methods. Operating systems is a technical, senior-level, undergraduate course that includes abstract concepts, mechanisms, and their implementations. This course exposes students to a UNIX-based operating system and includes concurrent programming (threads and synchronization), inter-process communication, CPU scheduling main memory, and virtual memory management. Technical courses present an additional dimension of difficulty when compared to non-technical courses which are more focused on soft skills because they require strong technical skills such as programming and problem-solving. This paper discusses other research studies and statistical data which underscore some of the challenges and differences encountered when teaching a traditional face-to-face versus an online course and the impact on student success. In this work, the 2019 cohort was taught operating systems in the traditional face-to-face modality, while the 2020 cohort was taught the course using the synchronous online modality. The synchronous online modality is very similar to the face-to-face traditional class, in that, lectures are delivered in real-time; this allows students to ask the instructor questions in real-time. Each cohort was tested on the same course objectives (topics) over one semester in 2019 and 2020. The instructor presents the students’ performance on three(3) course exams and discusses the differences and similarities in their overall performance between the two groups.

An efficient virtual CPU scheduling in cloud computing

In cloud computing, fine-grained virtual CPU scheduling techniques are essential in hiding physical resources from running applications and mitigating the decrease in performance upon virtualization. However, evaluating and predicting the behaviors of virtual processors is getting harder because of the diverse QoS requirements of cloud applications. In this paper, we propose a novel virtual CPU scheduling scheme to provide a high I/O performance for cloud applications. We present an evaluation function that evaluates the task characteristics of virtual machines by observing the amount of resource consumption of each virtual processor. Based on the evaluation function, the proposed scheduling scheme controls the priorities of virtual machines adaptively for fair distribution in handling I/O requests. Because our scheme evaluates both CPU-intensiveness and I/O-intensiveness of virtual machines, it provides high performance in terms of responsiveness even for various types of tasks. We implemented and experimented the proposed scheme on a virtual machine monitor. The experimental results showed that the proposed scheme increases the responsiveness and I/O bandwidth of virtual machines.

Round Robin Algorithm With Average Quantum Dynamic Time Based on Multicore Processor

Modern operating systems that are widely used today, have used a multiprogramming system and use a multicore system to solve multiprogramming. Multicore systems provide greater efficiency than a single processor running on a single chip. The efficiency of the multicore system in completing its tasks is very dependent on the CPU scheduling algorithm used by the operating system. Round Robin algorithm is one of the CPU scheduling that is widely used in multiprogramming. With this algorithm, the CPU will divide the time for each process given the same time until the entire process in the queue is complete. In this study, the researcher improvised the Round Robin algorithm on a multicore processor system to execute the processes contained in a single queue queue that is to give a quantum time based on the average bursttime of the entire process in the queue. In order to minimize waiting time and turnaround time throughout the process.