Scheduling and Synchronization Algorithms in Operating System: A Survey

CPU scheduling is the basis of multi-programmed operating systems. The scheduler is accountable for multiplexing processes on the CPU. By switching the CPU among processes, the operating system results in the computer more prolific. Various CPU scheduling algorithms exist for a multi-programmed operating system like First Come First Served (FCFS), Shortest Job First (SJF), Shortest Remaining Time Next (SRTN) and Round Robin (RR) scheduling algorithm. This paper has threefold objective. Firstly, it throws light on a new-fangled CPU scheduling algorithm called MIN-MAX whichbehaves as both preemptive and non-preemptive algorithm basis on the burst time. Secondly, it would helps to understand how MIN-MAXalgorithm improves the CPU efficiency in multi programming operating system and also trims down the starvation problem amongprocesses. Thirdly, it focuses on the comparative study of the existing algorithms on basis of various scheduling parameters with the proposed algorithm MIN-MAX.

The performance of the multiprocessor system and time-sharing system rely upon CPU scheduling algorithm. Some of the well known efficient CPU scheduling algorithms are Round Robin scheduling (RR), Shortest job first(SJF), Preemptive version of SJF(SRTF), First Come First Serve (FCFS) etc. As of now, the Round Robin scheduling algorithm is considered as the efficient process scheduling algorithm among all the existing CPU scheduling algorithms. However, in RR the shortest one have to wait for a longer time and in SRTF longer process behaves as a suspended process as short tasks keep on executing. This paper proposes a new scheduling process algorithm ESRR (Efficient Shortest Remaining Time Round Robin) that consolidate two of the preemptive version of existing scheduling algorithms namely RR and SRTF. ESRR reduces total waiting time and turn around time in compare to RR and reduces the waiting time of the shorter process and it also provides a longer process to execute faster than SRTF.

In the realm of real-time and multitasking environments, the Round Robin (RR) CPU scheduling algorithm is extensively utilized. Renowned for its fairness and avoidance of process starvation, RR allocates a specific time quantum to each process. However, the frequent context switching between processes during CPU scheduling, owing to the short time quantum, can adversely impact system performance by increasing average waiting time and response waiting time. To address these concerns, this paper introduces "Improving Round Robin Scheduling Using Dynamic Time Quantum (IRRDQ)," a modified version of RR aimed at diminishing turnaround time, average waiting time, and context switching. In our proposed approach, we arrange all incoming processes based on minimum burst time and dynamically assign an optimal time quantum to each process using the Shortest Job First (SJF) algorithm. The computation of time quantum is tailored to the burst times of individual processes. Through experimental demonstrations, we showcase the significant performance improvement of our algorithm compared to RR and other existing algorithms.

A multitasking operating system allows many programs to run at the same time. We need to schedule our Central Processing Units (CPUs) efficiently to handle these services. CPU scheduling algorithms come in a score of forms and any strategy has advantages and disadvantages. We implemented a Multi-Level Feedback Queue (MLFQ) algorithm with static time quantum for the first queue, making an efficient scheduling algorithm of MLFQ dynamic time quantum used in the second queue. Other than that, we compared both results and observed which dynamic time quantum delivers the best result. In the first queue, we used the Round Robin (RR) algorithm with less quantum time for processes that have a short burst time. The process that had more burst time than the defined quantum in the first queue was allowed to move into the second queue. Here, we calculated the average burst time of all remaining processes and defined that burst time as a quantum for the second queue after the Shortest Job First (SJF) scheme was applied in this queue. As a result, 70% of the processes in the second queue was received CPU. Other processes moved into the lower queue (the last queue). Hence, this technique prevented the starvation problem for those processes which have large burst times.

The Shortest Job First (SJF) algorithm reduces the average waiting time of processes in a CPU environment; however, it may lead to the starvation of long processes. The Round Robin (RR) algorithm solves starvation by equally distributing processing time. This paper explores user-friendly improvements by allowing users to specify which processes should be finished first. The SJF, RR, and the user-priority SJFRR were compared using four test cases in the following criteria: context switching, average waiting time, and average turnaround time. The user-priority SJFRR was found to have reduced average waiting time and average turnaround time in all test cases except for the first. Further research is also recommended in order to find out whether the algorithm's performance is attributed to the randomized assignment of priority and to find a proper basis for user designation of priority tasks.

Load balancing in cloud computing assigning the all load over each node of a mutual or shared system to make resource usage effective and to perform the response time to the job. This has used Round Robin (RR) and shortest job first (SJF) algorithm. Scheduling is the main part of cloud computing and jobs or workflow scheduling under the platform as a service. The work under infrastructure as a service work to virtual machine scheduling which machine is decided by scheduler should go on which job or virtual machine. Simply RR is a time quantum-based preemptive algorithm, and SJF is a burst time-based non-preemptive and preemptive algorithm. But RR work in two-phase like expected finish time adapted for each process. Each process with generally minimum completion times scheduled. This algorithm result as no longer waiting for the smallest task.

These days, the problems of scheduling and security are increasingly pressing as a result of the rapidly growing number of implementations that are operated in operating systems. Process scheduling and scheduling security is the most effective methodology of operating a system which can improve and degrade the performance of the computer system. The performance of scheduling algorithm in operating system can be measured by scheduling principles like average waiting time, average turnaround time, response time, throughput etc. Much research carried out over operating system security and round robin scheduling used in time sharing operating system in previous years to resolve the issues regarding to improvement in the performance of round robin (RR) scheduling and improving the security in the scheduling approach. This study discussed the security issues in operating systems, scheduling security and discrete structure of a new approach for RR scheduling. The researcher has suggested a list of policy for scheduling method security and proposed a new approach for RR scheduling and investigated the scheduling principles. The proposed methodology covers distinctive strategy for selection manner of process from ready queue than RR and used diverges time quantum. The proposed methodology has been validated experimentally by comparing with a numerous scheduling algorithm namely as Round Robin (RR), Adaptive Round Robin (ARR), Round Robin Remaining time (RRRT), improved Round Robin (IRR), an additional improvement on the improved Round Robin (AAAIRR), and an Enhanced Round Robin (ERR). Numerous simulations have been conducted to validate the effectiveness of the proposed algorithm to compelling the performance of system. At the conclusion the performance of computing system is dependent on security of operating system as well as secluding methods.

Cloud Computing provides services, on-demand access, infrastructure, storage of data and application. It possesses the reliability, availability and the scalability. One of the issues in cloud computing is Energy Saving. In this paper, the proposed work is Energy Saving Task Scheduling (ESTS) methodology. The aim of this methodology is to show the performance comparison of all the task scheduling types. Task scheduling or Job scheduling is referred to as policies that control the work order to be performed by a computer system. Types of Task Scheduling are Shortest Job First (SJF), First Come First Serve (FCFS), Round Robin (RR) and Priority Scheduling. In each type of schedule, the processes used by the parameters were calculated. Finally, the performance comparison is made in scheduling algorithms and shows better results. This method is implemented in net beans toolkit.

Load sharing is a technique to improve the performance of distributed systems by distributing the system workload from heavily loaded nodes, where service is poor, to lightly loaded nodes in the system. Previous studies have considered two adaptive load sharing policies: sender-initiated and receiver-initiated. In the sender-initiated policy, a heavily loaded node attempts to transfer work to a lightly loaded node and in the receiver-initiated policy a lightly loaded node attempts to get work from a heavily loaded node. Almost all the previous studies assumed the first-come/first-served node scheduling policy; furthermore, analysis and simulations in these studies have been done under the assumption that the job service times are exponentially distributed and the job arrivals form a Poisson process (i.e., job inter-arrival times are exponentially distributed). The goal of this paper is to fill the void in the existing literature. We study the impact of these assumptions on the performance of the sender-initiated and receiver initiated policies. We consider three node scheduling policies-first-come/first-served (FCFS), shortest job first (SJF), and round robin (RR) policies. Furthermore, we also look at the impact of variance in the inter-arrival times and in the job service times. Our results show that: (i) When non-preemptive node scheduling policies (FCFS and SJF) are used, the receiver-initiated policy is (substantially) more sensitive to variance in inter-arrival times than the sender-initiated policies and the sender-initiated policies are relatively more sensitive to the variance in job service times; (ii) When the preemptive node scheduling policy (RR) is used, the sender-initiated policy provides a better performance than the receiver-initiated policy.

Developing a new scheduling algorithm and conducting the performance analysis to recognize its effect in practice can be a laborious task. CPU scheduling is crucial in achieving the operating system's (OS) design goals. There exists a variety of scheduling algorithms in the field and in this paper, a performance comparison of different existing scheduling algorithms by simulating the same bundle of tasks is carried out. A variety of algorithms under batch OS and time-sharing OS are considered. Upon the analysis, a novel task scheduling algorithm incorporating the merits of existing algorithms is proposed for a single CPU system. The performance of various algorithms is compared with the proposed algorithm for parameters viz., throughput, CPU utilization, average turnaround time, waiting time, and response time. Extensive simulation analysis for the various bundle of tasks is conducted and the proposed algorithm is found to outperform the other algorithms in terms of guaranteed reduced average response time. Thus, an efficient CPU scheduler is proposed to accommodate varying workloads at run-time making the best use of the CPU in a particular execution scenario.

In this paper, we focus on batch state estimation for linear systems. This problem is important in applications such as environmental field estimation, robotic navigation, and target tracking. Its difficulty lies on that limited operational resources among the sensors, e.g., shared communication bandwidth or battery power, constrain the number of sensors that can be active at each measurement step. As a result, sensor scheduling algorithms must be employed. Notwithstanding, current sensor scheduling algorithms for batch state estimation scale poorly with the system size and the time horizon. In addition, current sensor scheduling algorithms for Kalman filtering, although they scale better, provide no performance guarantees or approximation bounds for the minimization of the batch state estimation error. In this paper, one of our main contributions is to provide an algorithm that enjoys both the estimation accuracy of the batch state scheduling algorithms and the low time complexity of the Kalman filtering scheduling algorithms. In particular: 1) our algorithm is near-optimal: it achieves a solution up to a multiplicative factor 1/2 from the optimal solution, and this factor is close to the best approximation factor 1/e one can achieve in polynomial time for this problem; 2) our algorithm has (polynomial) time complexity that is not only lower than that of the current algorithms for batch state estimation; it is also lower than, or similar to, that of the current algorithms for Kalman filtering. We achieve these results by proving two properties for our batch state estimation error metric, which quantifies the square error of the minimum variance linear estimator of the batch state vector: a) it is supermodular in the choice of the sensors; b) it has a sparsity pattern (it involves matrices that are block tri-diagonal) that facilitates its evaluation at each sensor set.

Cloud computing has become inevitable in own way with its offering multi-services, deployment, resource provisioning, data management, etc. Efficient resource consumption proves the dignity of a reliable cloud service provider in the market. However, the cloud environment often witnesses the uncertainties with respect to the resource provisioning. The most significant uncertainty encountered is the fault that paves way for a failure in the total process. Existing methods fail to incorporate such uncertainty into the picture leading to performance degradation of the cloud service requested by cloud user. The proposed scheme, robust fault detection and recuperation (RFDR), is a reactive fault-tolerant strategy that monitors the system continuously and recovers it from failures caused by VMs, host, and PEs. The proposed mechanism is run through baseline scheduling algorithm such as first come first service (FCFS), shortest job first (SJF), priority scheduling (PS), min-min, and max–min scheduling for the performance evaluation. The experiments indicate that the proposed method efficiently handles the fault detection and recover with 99% of resource availability and reliability.

The paper focusses on priority based round robin scheduling algorithm for scheduling jobs in Hadoop environment. By Using this Proposed Scheduling Algorithm it reduces the starvation of jobs. And the advantage of priority scheduling is that the process with the highest priority will be executed first. Combining the both strategies of round robin and priority scheduling algorithm a optimized algorithm is to be implemented. Which works more efficiently even after considering all the parameters of scheduling algorithm. This proposed algorithm is also compared with existing round robin and priority scheduling algorithms.

The primary objective of this proposal is to minimize the battery drain and to maximize the compatibility of the device so that the complexity of the device is much more reduced to enhance the battery period considerably. In this paper we adopted a method for increasing the response time using a priority based algorithm along with Field Programmable Gate Array (FPGA) in Reconfigurable Computing System (RCS) to handle complex applications and to increase the efficiency and throughput of the system. A primary comparison was made with all existing algorithms namely First Come First Serve (FCFS), Round Robin, Shortest Job First (SJF) for a given set of processes by taking process delay as a core parameter. The result obtained from this analysis gave a clear picture that the objective can be achieved by decreasing the waiting time and by increasing the throughput. By achieving this task, multitasking between the applications or processes can be done frequently. By adopting these kind of scheduling algorithms with programmable gate array, there is a considerable improvement and performance of the device. Also, the life span of the device is increased which is considered as most important criteria for any users in computing environment.

A flexible manufacturing system (FMS) is designed to achieve good productivity and low cost. The success of an FMS depends largely on effective production scheduling and control. However, it has been found that current manufacturing scheduling and control algorithms lack the flexibility to handle interruptions or resource breakdowns; hence, system performance drops dramatically and abruptly when interruptions occur. This research develops a computer-simulation-based framework of FMS scheduling and control system using the holonic concept. This framework can maintain stability and flexibility while accommodating system disturbance, increase throughput, reduce part flow-time and work-in-process inventory, and balance workload among identical workstations. The significance of this research is the investigation of an innovative approach to revolutionary advances of control technologies for advanced manufacturing systems, and to the revitalisation of control and scheduling algorithms used by existing FMSs. A case study has been provided to substantiate the effectiveness of this proposed framework.