Improved Round Robin CPU Scheduling Algorithm with Different Arrival Times Based on Dynamic Quantum

Modern operating systems are based on the principle of time-sharing in executing simultaneous operations. Determining the length of the time slice, and the times when processes arrive at the ready queue are problems that affect metrics such as the average waiting time (AWT), average turnaround time (ATAT), response time (RT), and number of context switches (NCS) of the time-sharing round robin RR algorithms. The research aims to propose an algorithm that achieves a short waiting time while maintaining a reasonable response time, which is the most important characteristic of time-sharing algorithms. The Different Arrival-Dynamic Quantum Round Robin (DADQRR) algorithm bases its work on different parameters to adjust the time slice value dynamically. The algorithm has been compared to three other algorithms that are similar in terms of dealing with different arrival times, namely AN, MARR, and RR. The algorithm outperformed the three algorithms at a range from 6.155% to 31.409% in terms of AWT. It achieved an outperformance of 5.924% to 30.850%, considering the TAT. The ranges of outperformance values resulted from the difference in the ranges of arrival times, as well as in the ranges of burst times

An Attempt to Set Standards for Studying and Comparing the Efficiency of Round Robin Algorithms

With the advent of the need for interactive systems, the urgent need for time-sharing systems has emerged. Round-robin algorithms have emerged to achieve time-sharing. The degree of performance of time-sharing systems depends largely on the length of the time slice in the round-robin algorithms. The length of the time slice affects the measuring criteria of the performance of the algorithms. Researchers suggested and are continuing suggesting algorithms in order to obtain the best values for the time slice. Adopting one algorithm over another in a system and for a class of applications requires choosing the best performing algorithm. This research is an attempt to develop an objective approach for accurate comparison between algorithms. For the sake of objectivity in comparison, five algorithms similar in their general characteristics were chosen; Modified Median Round Robin Algorithm(MMRRA), A New Median-Average Round Robin Scheduling Algorithm(NMARR), An Improved Round Robin Scheduling Algorithm with Varying Time Quantum (IRRVQ), A Modified Round Robin CPU Scheduling Algorithm with Dynamic Time Quantum (RRDT), Improved Round Robin Algorithm with Progressive Dynamic Quantum (IRRPDQ). The results showed that the outperformance of an algorithm over a group of algorithms according to a specific criterion is not permanent and fixed in value, and that resorting to statistical measures is the best way to clarify the degree of performance of the algorithms

Task scheduling for application integration: A strategy for large volumes of data

Enterprise Application Integration is the research field, which provides methodologies, techniques and tools for modelling and implementing integration processes. An integration process performs the orchestration of a set of applications to keep them synchronised or to allow the creation of new features. It can be represented by a workflow composed of tasks and communication channels. Integration platforms are tools for the design and execution of integration processes in which, the runtime system is the component responsible for execution time of the tasks and the allocation of computational resources that perform them. The processing of a large volume of data, corresponding to execution of millions of tasks, can cause situations of overload, characterised by the accumulation of tasks in internal queues awaiting computational resources in the runtime systems, resulting in unacceptable response time for the external applications and users. Our research hypothesis is that the runtime systems of the integration platforms use simplistic heuristics for scheduling tasks, which does not allow them to maintain acceptable levels of performance when there are overload situations. In this research work, we developed (i) a representation for integration processes, (ii) a characterisation for your task schedules, (iii) a heuristic to deal with situations of overload, (iv) a mathematical model for a performance metric of the execution of integration processes and (v) a simulation tool for task scheduling heuristics. Our research results indicate that, in situations of overload, our heuristic promotes a balanced workload distribution and an increase in the performance of the execution of the integration processes.Integração de Aplicações Empresariais é o campo de pesquisa, que fornece metodologias, técnicas e ferramentas para modelar e implementar processos de integração. Um processo de integração executa a orquestração de um conjunto de aplicações para mantê-las sincronizadas ou para permitir a criação de novas funcionalidades. Ele pode ser representado por um fluxo de trabalho composto por tarefas e canais de comunicação. Plataformas de integração são ferramentas para projetar e executar processos de integração, nas quais o motor de execução é o componente responsável pelo tempo de execução das tarefas e pela alocação de recursos computacionais que as executam. O processamento de um grande volume de dados, correspondendo a execução de milhões de tarefas, pode causar situações de sobrecarga, caracterizadas pelo acúmulo de tarefas em filas internas que aguardam recursos computacionais nos motores de execução, resultando em tempos de resposta inaceitáveis para aplicações e usuários externos. Nossa hipótese de pesquisa é que os motores de execução das plataformas de integração usam heurísticas simplistas para agendar tarefas, o que não lhes permitem manter níveis aceitáveis de desempenho em situações de sobrecarga. Neste trabalho de pesquisa, desenvolvemos (i) uma representação para processos de integração, (ii) uma caracterização para seus agendamentos de tarefas, (iii) uma heurística para lidar com situações de sobrecarga, (iv) um modelo matemático para uma métrica de desempenho da execução de processos de integração e (v) uma ferramenta de simulação para heurísticas de agendamento de tarefas. Nossos resultados de pesquisa indicam que, em situações de sobrecarga, nossa heurística promove uma distribuição equilibrada da carga de trabalho e um aumento no desempenho da execução dos processos de integração

Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool. The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction

Adaptive Dual-Mode Arbitration for High-Performance Real-Time Embedded Systems

Multi-core platforms can deliver substantial computational power together with minimum costs, compact size, weight, and power usage. However, multi-core architectures are shaking the very foundation of modern real-time systems, i.e. deriving the Worst-Case Execution Time (WCET) of the tasks. Modern embedded systems such as those deployed in the automotive and avionic fields face two difficult-to-resolve conflicting requirements due to the interference problem on the shared hardware components amongst cores: delivering high average-case performance and providing tight WCET. This challenge exists in different shared hardware resources including on-chip shared cache, hardware prefetchers, buses, and memory controller. The problem is mainly because various cores in the system interfere with each other while competing to access the aforementioned hardware components. While dedicated real-time controllers provide timing guarantees, they do so at the cost of significantly degrading system performance. This dissertation overcomes this trade-off by introducing Duetto, a general hardware resource management paradigm that pairs a real-time arbiter with a high-performance arbiter and a latency estimator module. Based on the observation that the resource is rarely overloaded, Duetto executes the high-performance arbiter most of the time, switching to the real-time arbiter only in the rare cases when the latency estimator deems that timing guarantees risk being violated. In this thesis, the Duetto paradigm is realized for different shared hardware resources. In the first part, I demonstrate Duetto on the case study of a multi-bank on-chip memory and discuss the foundation of the methodology. The methodology is concerned about designing the real-time arbiter in such a way that it is compatible with Duetto, deriving latency analysis, and designing the latency estimator module. In the second part, this thesis addresses the trade-off between maintaining cache coherence in multi-core real-time systems and improving average-case performance by proposing a novel coherency arbiter infrastructure and employing it in the context of Duetto. This is achieved by precisely engineering the multi-core hardware architecture and its underlying interconnect infrastructure such that data sharing is feasible for real-time systems in a manner amenable for timing analysis. The proposed solution provides near-to Commercial-Off-The-Shelf (COTS) performance and does not impose any coherency protocol modifications. The third part of this dissertation proposes DuoMC by applying Duetto to off-chip Memory Controller (MC) which is crucial since Dynamic Random-Access Memory (DRAM) main memory is one of the most complex shared resources in multi-core architectures and it is one of the critical bottlenecks both from latency as well as performance perspectives. As part of the MC evaluation, we release MCsim, an open-source, cycle-accurate simulator for memory controllers

Academic Catalog: 2011-2012

https://digitalcommons.esf.edu/acadcat/1024/thumbnail.jp

Academic Catalog: 2009-2010

https://digitalcommons.esf.edu/acadcat/1022/thumbnail.jp

Academic Catalog: 2010-2011

https://digitalcommons.esf.edu/acadcat/1023/thumbnail.jp

Academic Catalog: 2008-2009

https://digitalcommons.esf.edu/acadcat/1021/thumbnail.jp