An Approach Towards Development of a New Cloudlet Allocation Policy with Dynamic Time Quantum

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.

In this paper, a new variant of Round Robin (RR) algorithm is proposed which is suitable for soft real time systems. RR algorithm performs optimally in timeshared systems, but it is not suitable for soft real time systems. Because it gives more number of context switches, larger waiting time and larger response time. We have proposed a novel algorithm, known as Priority Based Dynamic Round Robin Algorithm(PBDRR), which calculates intelligent time slice for individual processes and changes after every round of execution. The proposed scheduling algorithm is developed by taking dynamic time quantum concept into account. Our experimental results show that our proposed algorithm performs better than algorithm in [8] in terms of reducing the number of context switches, average waiting time and average turnaround time.

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

Cloud environment uses virtualization technology to increase the resource utilization and scalability. Due to the concurrent running of co-allocated tasks with virtual machines on top a physical machine, performance of some tasks suffers significantly because of interference. To improve the quality of service and reduce the service level agreement violation between user and cloud service provider, we presented an interference aware scheduling approach. Interference aware scheduling approach predicts the effect of interference for execution of a task with respect to the resource usage by co-scheduled tasks in a virtualized environment. This prediction model is being used to schedule tasks to achieve better quality of service in term of task guarantee ratio and priority guarantee ratio. We implemented and validated our approach and the results show that our approach performs significantly better than other approaches.

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.

Cloud Computing is known as a provider of dynamic services. It utilizes a very large, scalable and virtualized resource over the Internet. So many industries have joined this bandwagon nowadays. One of the major research issues is to maintain good Quality of Service (QoS) of a Cloud Service Provider (CSP). The QoS encompasses different parameters, like, smart job allocation strategy, efficient load balancing, response time optimization, reduction in wastage of bandwidth, accountability of the overall system, etc. The efficient allocation strategy of the independent computational jobs among different Virtual Machines (VM) in a Datacenter (DC) is a distinguishable challenge in the Cloud Computing domain and finding out an optimal job allocation strategy guided by a good scheduling heuristic for such an environment is an NP-complete problem. So different heuristic approaches may be used for better result. The related works with other NP- complete problems have shown that solutions guided by heuristic approaches can often be improved by applying local scheduling procedure for allocating independent jobs in the virtual machines (VM) inside a Datacenter (DC), which, when combined with fast construction heuristics, can find out the shorter schedules on benchmark problems than the other solution methodologies found in different literatures, and in significant less time. This paper highlights a smart job allocation strategy for a CSP by applying Round-Robin (RR) scheduling policy.

Cloud computing offers the proficiency to use computing and storage resources on a metered basis and reduces the investments in Information Technology domain. This paper highlights a major research issue, i.e., providing good quality of service (QoS) to the cloud users. The QoS is associated with several parameters such as completion time, response time, turnaround time (TAT), waiting time (WT), bandwidth. A new cloudlet scheduling algorithm--improved round robin cloudlet scheduling algorithm--has been proposed which improves the TAT, WT and number of context switching. It enhances the resource utilization. The experimental results are obtained by CloudSim toolkit extending few base classes and compared by classical round robin algorithm.

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.

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.

Round Robin (RR) is a kind of process algorithms, where the time quantum is fixed along the processes execution. In the other hand it depending on the First Come First Serve (FCFS) algorithm. Also RR performs in timesharing system by given each process static Time Quantum (TQ). In this paper, The TQ studied to improve the efficiency of RR and performs the degrades with respect to Context Switching (CS), Average Wait Time (AWT) and Average Turned Around Time (ATAT) that an overhead on the system. Thus, the new approach was proposed to calculate the TQ, known as Ascending Quantum and Minumim-Maxumum Round Robin (AQMMRR). The processes were ascending with shortest remaining burst time and calculate the TQ from multiply the summation of minimum and maximum BT by (80) percentage. The experimental result shows that AQMMRR performs better than RR and comparing with other two related works.

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.

In order to minimize user perceived latency while ensuring high data availability, cloud applications desire to select servers from one of the multiple data centers (i.e., server clusters) in different geographical locations, which are able to provide desired services with low latency and low cost. This paper presents CloudGPS, a new server selection scheme of the cloud computing environment that achieves high scalability and ISP-friendliness. CloudGPS proposes a configurable global performance function that allows Internet service providers (ISPs) and cloud service providers (CSPs) to leverage the cost in terms of inter-domain transit traffic and the quality of service in terms of network latency. CloudGPS bounds the overall burden to be linear with the number of end users. Moreover, compared with traditional approaches, CloudGPS significantly reduces network distance measurement cost (i.e., from O(N) to O(1) for each end user in an application using N data centers). Furthermore, CloudGPS achieves ISP-friendliness by significantly decreasing inter-domain transit traffic.

Scalable, high performance, and fault-tolerant distributed applications are realized with virtual machines in server cluster systems. Application processes are performed on virtual machines in each server. Processing load of virtual machines to perform application processes has to be balanced in a server cluster system to satisfy the application requirements like response time. On the other hand, a server cluster system consumes a large amount of electric energy since multiple servers consume electric energy to perform application processes. It is critical to discuss how to reduce the total electric energy consumption of a server cluster to perform application processes on virtual machines. In our previous studies, the transmission model and power consumption model of a server to perform communication processes on multiple virtual machines are proposed. In this paper, we newly propose the transmission energy consumption laxity based (TECLB) algorithm to allocate communication processes to virtual machines in a server cluster based on the proposed transmission model and power consumption model of a server so that the total energy consumption of a server cluster can be reduced. We evaluate the TECLB algorithm in terms of the total energy consumption of a server cluster and transmission time of each process compared with the basic round-robin (RR) algorithm. The evaluation results show the average total energy consumption of a server cluster is maximumly reduced to 9% of the RR algorithm.

Der Ausdruck Quality of Service (QoS) beschreibt die Güte eines erbrachten Dienstes. QoS beschreibt Eigenschaften wie Antwortzeit, Verfügbarkeit, den Grad der Nachrichten-Verschlüsselung oder den Durchsatz. QoS ist sowohl für Multi-Media Anwendungen (Audio- und Video-Übertragung) als auch für Geschäftsanwendungen wichtig. Die vorgelegte Arbeit konzentriert sich dabei auf das Feld der Geschäftsanwendungen. Solche Anwendungen arbeiten mit Transaktionen wie etwa dem Bestellen eines Produkts oder der Überweisung von Geld zwischen Konten. QoS Eigenschaften solcher Anwendungen sind beispielsweise die Anzahl an Transaktionen während eines Zeitintervalls, die maximale Anzahl gleichzeitig mit dem System arbeitender Nutzer, oder die zu erwartende Verfügbarkeit. Durch Middleware kann die Realisierung von Verteilung vor dem Programmierer verborgen werden. In einer idealen Welt würde Middleware dasselbe für QoS leisten. Das heißt, der Entwickler spezifiziert nur die gewünschte Dienstgüte und kümmert sich nicht darum, wie sie realisiert wird. Das ist so aber nicht möglich, denn die zu erbringende Dienstgüte hat massive Auswirkungen auf das Design einer Applikation. Daher muss QoS schon in der Design-Phase angegangen werden, was mit einer rein Middleware-gestützten Herangehensweise nicht möglich ist. Die vorgelegte Arbeit zeigt, wie dieses Problem durch einen modellgetriebenen Ansatz behoben werden kann. Die Entwicklung beginnt dann mit einem Plattform-unabhängigen Modell (PIM), welches das Verhalten und die QoS Eigenschaften der Applikation beschreibt. Dieses Modell wird automatisch durch eine Modell-Transformation in ein Plattform-spezifisches Modell (PSM) umgewandelt. Das PSM beschreibt das Design einer Implementierung für eine spezifische Plattform und berücksichtigt dabei die im PIM beschriebenen QoS-Eigenschaften. Um dieses Szenario in der Praxis anwenden zu können, müssen zwei Probleme gelöst werden: das Modellieren von QoS Eigenschaften und die Modell-Transformation. Gegenwärtig erfordert das Erstellen einer Modell-Transformation detailliertes Wissen über die Modellierungssprache und ihre Interna. Um das zu vereinfachen, wurde im Rahmen dieser Arbeit die visuelle Regelbasierte Transformationssprache Kafka entwickelt. Kafka und die zugehörigen Werkzeuge bauen auf Graphtransformations-Theorie auf. Kafka vereinfacht das Erstellen von Transformationen weil es auf der Notation des PIM und PSM aufbaut. Daher muss der Entwickler nichts von den Interna (etwa dem Meta-Modell) verstehen. Um PIMs von QoS-fähigen Applikationen zu erstellen, wurde die Modellierungssprache PIQML entwickelt, welche auf UML 2.0 Komponenten, Hierarchischen Nachrichten Sequenz Diagrammen (HMSCs) und einer neuen Meta-Modell Erweiterung für QoS Verträge basiert. Die vorgelegte Arbeit zeigt, wie PIQML Modelle auf verschiedene Zielplattformen (Programmiersprachen und Middleware Produkte) abgebildet werden können. Basierend auf PIQML und Kafka wurde das Werkzeug Kase entwickelt, welches das Editieren von PIQML Modellen (PIMs), UML Modellen (PSMs) und Kafka Transformationen integriert. Das Ergebnis ist eine integrierte Werkzeug-Kette für das modellgetriebene Entwickeln QoS-fähiger verteilter Applikationen.