Performance Analysis of OS Scheduling for a Reconfigurable Computing Environment

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.

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.

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 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.

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 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.

In order to fully utilize the Grid resources, implementing a good scheduling algorithm is really important. Currently, some of the well-known researches and enterprise schedulers have applied Priority Rule (PR) schedulers to manage the Grid jobs because they are simple and easy to implement. To date, there is no strong performance justification or proof given on why these specific PR algorithms are preferred compared to other PR algorithms. For example, five PR algorithms; First Come First Serve (FCFS), Longest Job First (LJF), Shortest Job First (SJF), Earliest Deadline First (EDF) and Minimum Time To Deadline (MTTD) have been proposed. However, none of these algorithms perform well in every metrics of performance. An attempt to improve their performance has been made using Combinatorial Rule (CR) which is the combination of more than one PR. Unfortunately, the current implementation of CR only focuses on two performance metrics which are flowtime and makespan, while sacrificing the others. In order to tackle the aforementioned problem, this paper introduces Minimum Time To Deadline to Fastest Resource - Shortest Job to Fastest Resource (MTTDFR-SJFR), a new CR scheduler that performs better than other CR algorithms tested within the scope. To achieve high performance, a combination of PR with Fastest Resource (FR) resource selection scheme that further improve the performance is proposed. Graphical results obtained from experimental simulation showed the superiority of the proposed CR algorithm in term of overall performance, compared to other CR as well as the original PR.

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 popularity of fog-enabled smart cities is increasing due to the advantages provided by modern communication and information technologies, which contribute to an improved quality of life. Wireless networks make them more vulnerable when the network is under malicious attacks that cause a collision in the medium. Furthermore, diverse applications of smart cities demand a contention-free medium access control (MAC) protocol to meet adaptive data requirements. In this work, a time-slot-based medium access control protocol to meet adaptive data requirements (TMPAD) for IoT nodes in fog-enabled smart cities is proposed. TMPAD proposes a trust mechanism to differentiate malicious and legitimate data requests. In addition, it accommodates more legitimate data-requesting nodes to transfer their data during a session by applying the technique for order performance by similarity to ideal solution (TOPSIS) and 0/1 knapsack algorithm. The performance of TMPAD is compared with well-known techniques such as first come first serve (FCFS), shortest job first (SJF), and longest job first (LJF) in different prospective scenarios. The results show that TMPAD scrutinizes more data-requesting nodes in slot allocation, allowing more data transmission in a session, with better mean trust value, as compared to other algorithms.

Recently, high-end reconfigurable computing systems have been built that employ Field Programmable Gate Arrays (FPGAs) as hardware accelerators for general-purpose processors. These systems not only provide new opportunities for high-performance computing, but also pose new challenges to application developers. In this paper, we build a design model for hybrid designs that utilize both the processors and the FPGAs. The model characterizes a reconfigurable computing system using various parameters. Based on the model, we propose a design methodology for hardware/software co-design. The methodology partitions workload between the processors and the FPGAs, maintains load balance in the system, and realizes scalability over multiple nodes. Designs are proposed for several computationally intensive applications: matrix multiplication, matrix factorization and the Floyd-Warshall algorithm for the all-pairs shortest-paths problem. To illustrate our ideas, the proposed hybrid designs are implemented on a Cray XD1. Experimental results show that our designs utilize both the processors and the FPGAs efficiently, and overlap most of the data transfer overheads and network communication costs with the computations. Our designs achieve up to 90% of the total performance of the nodes, and 90% of the performance predicted by the design model. In addition, our designs scale over a large number of nodes.

The paper covers the reconfigurable computer systems based on high integration field programmable gate arrays (FPGAs) of Xilinx Virtex UltraScale+ series, where immersion liquid cooling system is used for cooling of all electronic components. The original engineering and technological solutions for thermal interface, cooling liquid, radiators and the entire reconfigurable computer system are considered. Owing to these features it is possible to achieve unprecedented layout density, up to 128 FPGAs for a 3U computational module placed into a standard \(19^{{\prime }{\prime }}\) computer rack. The paper presents results of prototyping and experimental verification of energy-efficient computational module with immersion liquid cooling system. On the base of the new computational module it is possible to achieve the performance of 1 Pflops in a standard 47U rack with total power consumption not more than 150 kW. The designed immersion liquid cooling system has power reserve for already produced and for next-generation high integration FPGA series, resistance to leaks and their consequences, and compatibility with traditional water cooling systems based on industrial chillers.

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.

Purpose of research. Computing networks of a plurality of processor modules have existed for a long time, however, with the development of wireless technologies, the possibility of high-quality and inexpensive implementation of the idea of real-time reconfigurable computing networks (RRTCN) has emerged. The purpose of this work is to select a wireless data transmission network for the RRTCN and create an algorithm for the dynamic selection of the "master" processor module.Methods. The research methods of the work are based on the definitions of the theory of sets and graphs. In particular, a grapho-theoretical approach to distribution is taken as a basis, supplemented by the introduction of a system of criteria for a reconfigurable real-time computing system based on a wireless data transmission protocol.Results. In this paper, the most suitable data transfer protocol for the local RCSRV is selected, a developed mathematical model of changing a real-time computing system built on a wireless protocol is presented, and an algorithm for selecting the processor module (PM) of the RCSRV "master" is developed. The novelty of the work is the use of the distance matrix for the mathematical description of the change in the RRTCN and its use to determine the PM of the "master" in the aircraft.Conclusion. The developed algorithm for choosing the PM of the "master" RRTCN and the mathematical model of changing the position of the PM in a real-time computer system built on a wireless protocol allows organizing the optimal choice of the PM of the "master" in a reconfigurable computing system. On the basis of the presented mathematical model, in the future, it is possible to create a full-fledged mathematical model of the RRTCN and an algorithm for placing tasks in it.

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.

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