Mean Number Of Tasks Research Articles

Performance Analysis and Evaluation of Ternary Optical Computer Based on a Queueing System With Synchronous Multi-Vacations

The ternary optical computer (TOC) has attracted increasing attention from its providers and potential customers because of the advantages of its optical processor, such as low power consumption, numerous trits, parallelism, dynamical reconfigurability and bitwise allocability. The analysis of its performance has become an urgent problem to be solved in recent years. This paper builds a four-stage TOC service model by introducing synchronous multi-vacations and tandem queueing. Here, a vacation refers specifically to an optical processor vacation. Additionally, we propose a processor-divided-equally(PDE) strategy and a task scheduling and optical processor allocation algorithm under this strategy. Assuming that the intervals of task arrival follow a homogeneous Poisson process, we obtain some important performance indicators, such as the mean response time, mean number of tasks, and optical processor utilization under the PDE strategy, which are based on the M/M/1 and M/M/n queueing systems with exhaustive service and synchronous multi-vacations. The numerical results illustrate that the number of small optical processors has an important effect on the performance of TOC and that high vacation rate can improve the system performance to some extent.

Performance Analysis of Cloud Computing Center with Queueing Model

Cloud computing is a novel raising computing resource allocation. Successful development of cloud computing paradigm necessitates explicit performance evaluation of cloud data centers. The computing resource allocation and performance managing have been one of the most important septets of cloud computing. In this paper, we consider the cloud center as a queuing system with single task arrivals and a task request buffer of infinite capacity. We assess the performance of queuing system by using an analytical model and solve it to obtain important performance factors like mean number of tasks in the system. Using this model in order to evaluate the performance analysis of cloud server farms and obtained solved it to obtain accurate estimation of complete probability distribution of the request response time and other paramount performance indicators.

Performance prediction of parallel computing models to analyze cloud-based big data applications

Performance evaluation of cloud center is a necessary prerequisite to fulfilling contractual quality of service, particularly in big data applications. However, effectively evaluating performance of cloud services is challenging due to the complexity of cloud services and the diversity of big data applications. In this paper, we propose a performance evaluation model for parallel computing models deployed in cloud centers to support big data applications. In this evaluation model, a big data application is divided into lots of parallel tasks and the task arrivals follow a general distribution. In our approach, we also consider factors associated with resource heterogeneity, resource contention among cloud nodes, and data storage strategy, which have an impact on the performance of parallel computing models. Our model also allows us to calculate key performance indicators of cloud center such as mean number of tasks in the system, probability that a task obtains immediate service, and task waiting time. The model can also be used to predict the time of performing applications. We then demonstrate the utility of the model based on simulations and benchmarking using WordCount and TeraSort applications.

MMPP/G/m/m+r Queuing System Model to Analytically Evaluate Cloud Computing Center Performances

In the last decades cloud computing has been the focus of a lot of research in both academic and industrial fields, however, implementation-related issues have been developed and have received more attention than performance analysis which is an important aspect of cloud computing and it is of crucial interest for both cloud providers and cloud users. Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. Because of the nature of cloud centers and the diversity of user requests, an exact modeling of cloud centers is not practicable; in this work we report an approximate analytical model based on an approximate Markov chain model for performance evaluation of a cloud computing center. Due to the nature of the cloud environment, we considered, based on queuing theory, a MMPP task arrivals, a general service time for requests as well as large number of physical servers and a finite capacity. This makes our model more flexible in terms of scalability and diversity of service time. We used this model in order to evaluate the performance analysis of cloud server farms and we solved it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators such as: the Mean number of Tasks in the System, the distribution of Waiting Time, the Probability of Immediate Service, the Blocking Probability and Buffer Size...

Immediate Care of Open Extremity Fractures: Where Can We Improve?

Clear guidelines are set by the British Orthopaedic Association (BOA) and British Association of Plastic, Reconstructive and Aesthetic Surgeons (BAPRAS) on the preoperative management of open fractures. This as well as the clinical consequences of poor management of open fractures means the patient workup for surgery is important as well as the timing of surgery. Experience suggests few patients are managed 100% as per the guidelines and we look to test this hypothesis. A retrospective analysis was undertaken of all open long bone fractures (total 133), excluding hand injuries, which presented to a district general hospital over a 5-year period. The implementation of 7 defined key tasks for initial management was recorded. 101 cases were eligible, with the majority of cases (71.4%) having initial orthopaedic assessment outside normal working hours. The mean number of tasks completed was 3.23/7. Assessment out of hours was associated with less tasks being implemented but doctor seniority and the presence of polytrauma made no difference to the quality of acute care. Staff involved in the acute care of open fractures require targeted education to improve the delivery of initial preoperative care. We recommend that other centres assess their performance against this data.

Performance Analysis of Cloud Computing Centers for Bulk Services

Cloud is a service oriented platform where all kinds of virtual resources are treated as services to users. Several cloud service providers have offered different capabilities for a variety of market segments over the past few years. The most important aspects of cloud computing are resource scheduling, performance measures, and user requests. Sluggish access to data, applications, and web pages spoils employees and customers alike, as well as cause application crashes and data losses. In this paper, the authors propose an analytical queuing model for performance evaluation of cloud server farms for processing bulk data. Some important performance measures such as mean number of tasks in the queue, blocking probability, and probability of immediate service, and waiting-time distribution in the system have also been discussed. Finally, a variety of numerical results showing the effect of model parameters on key performance measures are presented.

Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems

Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other.

Task allocation in a multi-server system

We consider a slotted queueing system with C servers (processors) that can handle tasks (jobs). Tasks arrive in batches of random size at the start of every slot. Any task can be executed by any server in one slot with success probability α. If a task execution fails, then the task must be handled in some later time slot until it has been completed successfully. Tasks may be processed by several servers simultaneously. In that case, the task is completed successfully if the task execution is successful on at least one of the servers.We examine the impact of various allocation strategies on the mean number of tasks in the system and the mean response time of tasks. It is proven that both these performance measures are minimized by the strategy which always distributes the tasks over the servers as evenly as possible. Subsequently, we determine the distribution of the number of tasks in the system for a broad class of task allocation strategies, which includes the above optimal strategy as a special case. Some numerical experiments are performed to illustrate the performance characteristics of the various strategies.

Performance analysis and buffer allocations in some open assembly systems

This paper considers a finite capacity fork–join queueing model for open assembly systems with arrival and departure synchronizations and presents an approach for enumerating the state space and obtaining the steady state probabilities of the same for such a model under exponential assumptions. Several performance measures like the throughput of the system, fraction of arrivals that actually enter into the system, utilizations of the work stations, etc., are obtained. Further, design of such fork–join systems in terms of buffer size configurations for maximizing the throughput of the system, or for minimizing the mean waiting time of a typical job, or for minimizing the Work-In-Process of the system is done using the above performance prediction approach. Such optimal configurations exhibit some interesting features. Scope and purpose Analytical models are often used to understand the performance of manufacturing systems and also the associated decision making trade-offs that arise in their operations. We consider a model of a system with two assembly lines and a single join operation. It takes into account the finite capacity of the system and considers certain types of randomness in the inter-arrival times of the orders and processing and assembly of the tasks. Most models either consider that there are no external arrivals that correspond to orders or assume that assembly is instantaneous. We propose an analytical model to numerically obtain useful performance measures like fraction of orders that are actually accepted, mean number of tasks in the system, etc. We next use this technique to design such systems by finding buffer configurations that maximizes the throughput of the system, etc. We note some interesting features of such optimal designs.