Batch Processor Sharing with Hyper-Exponential Service Time

We study Batch Processor-Sharing (BPS) queuing model with hyper-exponential service time distribution and Poisson batch arrival process. One of the main goals to study BPS is the possibility of its application in size-based scheduling, which is used in differentiation between Short and Long flows in the Internet. In the case of hyper-exponential service time distribution we find an analytical expression of the expected conditional response time for the BPS queue. We show, that the expected conditional response time is a concave function of the service time. We apply the received results to the Two Level Processor-Sharing (TLPS) model with hyper-exponential service time distribution and find the expression of the expected response time for the TLPS model. TLPS scheduling discipline can be applied to size-based differentiation in TCP/IP networks and Web server request handling.Comment: Sophia Antipolis, France, 03 May 200

A Novel Workload Allocation Strategy for Batch Jobs

The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach

Size-based scheduling vs fairness for datacenter flows: a queuing perspective

Contrary to the conclusions of a recent body of work where approximate shortest remaining processing time first (SRPT) flow scheduling is advocated for datacenter networks, this paper aims to demonstrate that per-flow fairness remains a preferable objective. We evaluate abstract queuing models by analysis and simulation to illustrate the non-optimality of SRPT under the reasonable assumptions that datacenter flows occur in batches and bursts and not, as usually assumed, individually at the instants of a Poisson process. Results for these models have significant implications for the design of bandwidth sharing strategies for datacenter networks. In particular, we propose a novel "virtual fair scheduling" algorithm that enforces fairness between batches and is arguably simple enough to be implemented in high speed devices.Comment: 16 pages, 5 figure

Optimal policy for multi-class scheduling in a single server queue

In this paper we apply the Gittins optimality result to characterize the optimal scheduling discipline in a multi-class M/G/1 queue. We apply the general result to several cases of practical interest where the service time distributions belong to the set of decreasing hazard rate distributions, like Pareto or hyper-exponential. When there is only one class it is known that in this case the Least Attained Service policy is optimal. We show that in the multi-class case the optimal policy is a priority discipline, where jobs of the various classes depending on their attained service are classified into several priority levels. Using a tagged-job approach we obtain, for every class, the mean conditional sojourn time. This allows us to compare numerically the mean sojourn time in the system between the Gittins optimal and popular policies like Processor Sharing, First Come First Serve and Least Attained Service (LAS). We implement the Gittins' optimal algorithm in NS-2 and we perform numerical experiments to evaluate the achievable performance gain. We find that the Gittins policy can outperform by nearly 10% the LAS policy

The preemptive repeat hybrid server interruption model

We analyze a discrete-time queueing system with server interruptions and a hybrid preemptive repeat interruption discipline. Such a discipline encapsulates both the preemptive repeat identical and the preemptive repeat different disciplines. By the introduction and analysis of so-called service completion times, we significantly reduce the complexity of the analysis. Our results include a.o. the probability generating functions and moments of queue content and delay. Finally, by means of some numerical examples, we assess how performance measures are affected by the specifics of the interruption discipline

Sabisu kiritsu o koryoshita GI/GI/1 machi gyoretsu shisutemu no kakusan kinjiho ni kansuru kenkyu

制度:新 ; 報告番号:甲3539号 ; 学位の種類:博士(工学) ; 授与年月日:2012/2/25 ; 早大学位記番号:新587

Performance Evaluation of Adaptive Scheduling Algorithm for Shared Heterogeneous Cluster Systems

Cluster computing systems have recently generated enormous interest for providing easily scalable and cost-effective parallel computing solution for processing large-scale applications. Various adaptive space-sharing scheduling algorithms have been proposed to improve the performance of dedicated and homogeneous clusters. But commodity clusters are naturally non-dedicated and tend to be heterogeneous over the time as cluster hardware is usually upgraded and new fast machines are also added to improve cluster performance. The existing adaptive policies for dedicated homogeneous and heterogeneous parallel systems are not suitable for such conditions. Most of the existing adaptive policies assume a priori knowledge of certain job characteristics to take scheduling decisions. However such information is not readily available without incurring great cost. This paper fills these gaps by designing robust and effective space-sharing scheduling algorithm for non-dedicated heterogeneous cluster systems, assuming no job characteristics to reduce mean job response time. Evaluation results show that the proposed algorithm provide substantial improvement over existing algorithms at moderate to high system utilizations

Performance and Reliability Evaluation of Apache Kafka Messaging System

Streaming data is now flowing across various devices and applications around us. This type of data means any unbounded, ever growing, infinite data set which is continuously generated by all kinds of sources. Examples include sensor data transmitted among different Internet of Things (IoT) devices, user activity records collected on websites and payment requests sent from mobile devices. In many application scenarios, streaming data needs to be processed in real-time because its value can be futile over time. A variety of stream processing systems have been developed in the last decade and are evolving to address rising challenges. A typical stream processing system consists of multiple processing nodes in the topology of a DAG (directed acyclic graph). To build real-time streaming data pipelines across those nodes, message middleware technology is widely applied. As a distributed messaging system with high durability and scalability, Apache Kafka has become very popular among modern companies. It ingests streaming data from upstream applications and store the data in its distributed cluster, which provides a fault-tolerant data source for stream processors. Therefore, Kafka plays a critical role to ensure the completeness, correctness and timeliness of streaming data delivery. However, it is impossible to meet all the user requirements in real-time cases with a simple and fixed data delivery strategy. In this thesis, we address the challenge of choosing a proper configuration to guarantee both performance and reliability of Kafka for complex streaming application scenarios. We investigate the features that have an impact on the performance and reliability metrics. We propose a queueing based prediction model to predict the performance metrics, including producer throughput and packet latency of Kafka. We define two reliability metrics, the probability of message loss and the probability of message duplication. We create an ANN model to predict these metrics given unstable network metrics like network delay and packet loss rate. To collect sufficient training data we build a Docker-based Kafka testbed with a fault injection module. We use a new quality-of-service metric, timely throughput to help us choosing proper batch size in Kafka. Based on this metric, we propose a dynamic configuration method, which reactively guarantees both performance and reliability of Kafka under complex operation conditions