Priority Based Dynamic Round Robin (PBDRR) Algorithm with Intelligent Time Slice for Soft Real Time Systems

This study proposes a new algorithm which is a logical extension of the popular Round Robin CPU scheduling algorithm. The Round Robin algorithm can be effective only if the time quantum is chosen accurately. Even by taking mean average of burst times as time quantum, the performance of the RR cannot be improved beyond a certain point. However, the novel method proposed here, suggests that a priority be assigned to each process based on balanced precedence factor. The novel method also uses mean average as a time quantum. Experiments are conducted in order to measure the effectiveness of this novel method. The results clearly showed that EPSADTQ is superior to RR and PSMTQ and its variants. EPSADTQ resulted in a significant reduction of the no. of context switches, average waiting time and average turnaround time.

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.

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

Cloud computing is one of the most emerging technologies which has created a revolution in the High performance Computing (HPC) domain. The term Quality of Service (QoS) plays a vital role in the formation of more flexible integration of various technologies. The Waiting Time (WT), Turnaround Time (TAT), Context Switching (CS) and Makespan (MS) are the primary parameter that has great impact on the scheduling of cloudlets. The Proposed algorithm has improved the resource utilization system of the existing Round Robin Algorithm (RRA) and Improved Round Robin Cloudlet Scheduling Algorithm (IRRCSA) by introducing the concept of dynamically calculated Time Quantum (TQ) for each virtual machine (VM) according to the allocated cloudlets. This new approach in cloudlet scheduling drastically reduced average WT, average TAT and Number of CS of the VMs, which further enhanced the capability of cloud service providers (CSPs) to provide better QoS.

Scheduling is one of the most important factors used in scheduling processes insideCPU. CPU scheduling is a concept of multiprogramming, where the CPU is used to schedule the incoming processes alternately. Many algorithms can be used to schedule processesinside CPU, but not all can be real-time. Long waiting times and response times often be problems in scheduling processes in realtime. FIFO and Round Robin algorithms can be implemented to schedule the processes in realtime. In this paper, the authors schedule the process of several sensors that are used to collect landslide data. The data processing is sent by FIFO scheduling and Round Robin scheduling separately on the Internet of Things (IoT) device. The author only analyzes the performance of FIFO and Round Robin algorithms in scheduling the incoming processes in real-time on the IoT operating system by considering the waiting time and response time. The analysis is expected to create a quick response time and waiting time so that proper algorithm is decided to complement the IoT architecture in landslide detection. The FIFO and Round Robin algorithms are implementedat the Raspbian and Arch Linux operating systems in the IoT device, the Raspberry Pi 3 Model B, which uses a 64-bit 64-bit ARM Cortex-AS3 64-bit process at 1.2GHz.

Server cluster systems equipped with virtual machines are widely used to realise scalable and high-performance computing systems. In order to satisfy application requirements like response time, processing loads of virtual machines have to balance with one another in a server cluster. In addition to achieve the performance objectives, the total electric energy of a server cluster to perform application processes has to be reduced. In this paper, the Energy Consumption Laxity Based (ECLB) algorithm is proposed to allocate computation type application processes to virtual machines in a server cluster so that the total electric energy of the server cluster and response time of each process can be reduced. We evaluate the ECLB algorithm compared with basic Round-Robin (RR) algorithm. Evaluation results show the average total electric energy of a server cluster and average response time of each process in the ECLB algorithm can be more reduced than in the RR algorithm.

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.

Delivery of goods will normally be conducted in accordance with the queuing time of booking. Sometimes, it is inefficient and results in a delay on the delivery of goods. Therefore, to make a better scheduling system, the Shortest Job First and Round Robin algorithms been implemented. From the results of experiments, Shortest Job First and Round Robin algorithms successfully applied to the scheduling delivery application. Shortest Job First algorithm is better than Round Robin scheduling in the case of delivery of goods, because the algorithm execution process which takes small time will be moved before the process which takes much time, so it needs smaller time than using Round Robin algorithm. Index Terms - Round Robin, Scheduling, Shipping, Shortest Job First

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.

Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest Transactions Per Second (TPS) of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58, 60 and 48 ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

In distributed computing cloud computing is an emerging technology which provides pay per model as per user demand or requirement. Cloud has a collection of virtual machines which facilities both computational and storage requirement. Scheduling and Load balancing are the main challenges in the cloud computing on which we are emphasizing. Scheduling is the process to control the order of work going to be performed by computer system. Load balancing has an important role in the performance in cloud computing. Better load balancing will make cloud computing more efficient and will also increase user satisfaction. It provides a way to handle several inquiries residing inside cloud computing environment set. Complete balancing acquires two tasks, one is resource provisioning/resource allocation and task scheduling throughout the system. In the proposed research paper, we are presenting a hybrid algorithm created by FCFS and Round Robin algorithms. As the Round Robin is the easiest algorithm that's why it is frequently used and the first preference for implementing easy schedulers. The Round Robin algorithm only requires a list of nodes. In the proposed solution we have eliminated the drawbacks of simple Round Robin algorithm by introducing assignment of time slices to different processes depending upon priorities.

Background: The most popular time-sharing operating systems scheduling technique, whose efficiency heavily dependent on time slice selection, is the round robin CPU scheduling algorithm. The time slice works similar to the First-Come-First-Serve (FCFS) scheduling or processor sharing algorithm if it is large or extremely too small. Some of the existing research papers have an algorithm called Improved Round Robin with Highest Response Ratio Next (IRRHRRN) which made use of response ratio with a predefined time quantum of 10ms with the major aim of avoiding starvation. However, the IRRHRRN algorithm favors processes with shorter burst time than the ones with longer burst time, and gave no regard to the process arrival time, thus leading to starvation. Aim: This study tries to improve on the IRRHRRN algorithm by proposing the Modified Round Robin with Highest Response Ratio Next (MRRHRRN) CPU Scheduling Algorithm using Dynamic Time Quantum in order to reduce the problem of starvation. Method: Dynamic method of determining the time quantum was adopted. Results: The proposed algorithm was compared with four other existing algorisms such as Standard Round Robin (RR), Improved Round Robin (IRR), An Additional Improvement in Round Robin (AAIRR), and the Improved Round Robin with Highest Response Ratio Next (IRRHRRN) and it provided some promising results in terms of the Average Waiting Time of 35407.6 ms, Average Turnaround Time of 36117.6 ms, Average Response Time of 10894.8 ms and Number of Context Switch of 301for the Non-Zero Arrival Times Processes

As can be expected in a few years the fifth generation (5G) networks will support several thousands of User Equipment (UEs). The traditional scheme of Random Access (RA) under such circumstances would fail to reduce the large delay in access. After the concept of Non-Orthogonal Multiple Access, the idea of Non-Orthogonal Random Access technique based on Successive Interference Cancellation (SIC) was proposed. This paper proposes an improvement in the delay reduction in the SIC-based NORA scheme by evolving the Preamble transmission with round-robin scheduling to improve the fast communication of NORA. The paper demonstrates how NORA with Round Robin Scheduling of Preamble is superior to simple NORA. Round Robin (RR) is one of the fundamental and widely used scheduling algorithms. The RR algorithm assigns equal portions of packet transmission time to each user in a circular order. RR algorithm achieves the best fairness performance if the users have similar channel conditions and similarly sized packet arriving at their buffers. It is on a first come first served basis. This paper attempts to provide a comparison of access delay improvement in 5G by comparing results of previous techniques to the proposed NORA with Round Robin Technique.

Fog computing idea is presented to reduce the burden on cloud and deliver similar facilities as cloud. However, fog encompasses small area relatively to cloud by saving the data for shorter amount of time and sending it to cloud for permanent storage. In this paper, a joint cloud and fog centered environment for efficient energy supervision of buildings is proposed. It caters for the data of groups of buildings at buyers’ end. 12 fogs are utilized for 6 different regions in the world which are based on 6 continents. Additionally, each fog is linked to a group of buildings and two fogs are linked to two groups. Each group comprises of multiple smart buildings and these buildings has at least 100 apartments. To manage the energy requirement of consumers, micro grids (MGs) are available near the buildings and accessible by the fogs. Energy is managed for the apartments and fog helps the consumers to fulfill their load demands through nearby MGs and cloud servers’ communication. So, the load on cloud and fog should be balanced and load balancing algorithms are used to manage the load using VMs. These algorithms are round robin (RR) and throttled and Priority Based load balancing and these algorithms are compared for a single service broker policy. Service broker policy considered in this paper is; dynamically reconfigure with load. Priority based load balancing is proposed for balancing the load on fog and results of proposed balancing algorithm are compared with other algorithms. While considering the proposed algorithm, results are compared with two load balancing algorithms and from this, the proposed algorithm gives better results than RR algorithm rather than throttled.

This paper puts forward a new dynamic round-robin (DYRR) packet scheduling algorithm with high efficiency and good fairness. DYRR algorithm introduces dynamic round-robin concept, that is, the allowance given to each of the flows in a given round is not fixed, but is related with the number of bytes sent of this and other flows of the last round scheduling. The time complexity of the DYRR algorithm is O(1). Results from performance simulation analysis shows that DYRR algorithm can effectively smooth output burst, realize fair scheduling, and have a good time delay characteristic.