Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

Performance Modeling of the PeopleSoft Multi-Tier Remote Computing Architecture

Complex client-server configurations being designed today require a new and closely coordinated approach to analytic modeling and measurement. A closed queuing network model for a two-tiered PeopleSoft 6 client-server system with an Oracle database server is demonstrated using a new performance modeling tool that applies mean value analysis. The focus of this work is on the measurement and modeling of the PeopleSoft architecture to provide useful capacity planning insights for an actual large-scale university-wide deployment. A testbed and database exerciser are then developed to measure model parameters and perform the initial validation tests. The testbed also provides preliminary test data on a proposed three-tiered deployment architecture that includes the Citrix WinFrame environment as an intermediate level between the client and the Oracle server.http://deepblue.lib.umich.edu/bitstream/2027.42/107929/1/citi-tr-97-5.pd

Final Report E2DIS Architectural Analysis: An Engineering Change Proposal To The Dynamic Terrain Testbed Project

Report on analysis of the simulation architecture for environment server in distributed interactive simulation

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

Performance Modeling and Analysis of a Database Server with Write-Heavy Workload

Resource-optimization of the infrastructure for service oriented applications require accurate performance models. In this paper we investigate the performance dynamics of a MySQL/InnoDB database server with write-heavy workload. The main objective of our investigation was to understand the system dynamics due to the buffering of disk operations that occurs in database servers with write-heavy workload. In the paper, we characterize the traffic and its periodic anomalies caused by flushing of the buffer. Further, we present a performance model for the response time of the requests and show how this model can be configured to fit with actual database measurements. Also, we show that window-based admission control outperforms rate-based admission control for these types of systems

Modeling and Control of Server-based Systems

When deploying networked computing-based applications, proper resource management of the server-side resources is essential for maintaining quality of service and cost efficiency. The work presented in this thesis is based on six papers, all investigating problems that relate to resource management of server-based systems. Using a queueing system approach we model the performance of a database system being subjected to write-heavy traffic. We then evaluate the model using simulations and validate that it accurately mimics the behavior of a real test bed. In collaboration with Ericsson we model and design a per-request admission control scheme for a Mobile Service Support System (MSS). The model is then validated and the control scheme is evaluated in a test bed. Also, we investigate the feasibility to estimate the state of a server in an MSS using an event-based Extended Kalman Filter. In the brownout paradigm of server resource management, the amount of work required to serve a client is adjusted to compensate for temporary resource shortages. In this thesis we investigate how to perform load balancing over self-adaptive server instances. The load balancing schemes are evaluated in both simulations and test bed experiments. Further, we investigate how to employ delay-compensated feedback control to automatically adjust the amount of resources to deploy to a cloud application in the presence of a large, stochastic delay. The delay-compensated control scheme is evaluated in simulations and the conclusion is that it can be made fast and responsive compared to an industry-standard solution