Synthetic Workload Generation for Capacity Planning of Virtual Server Environments (original) (raw)

Synthetic Workload Generation for Cloud Computing Applications

Journal of Software Engineering and Applications, 2011

We present techniques for characterization, modeling and generation of workloads for cloud computing applications. Methods for capturing the workloads of cloud computing applications in two different models-benchmark application and workload models are described. We give the design and implementation of a synthetic workload generator that accepts the benchmark and workload model specifications generated by the characterization and modeling of workloads of cloud computing applications. We propose the Georgia Tech Cloud Workload Specification Language (GT-CWSL) that provides a structured way for specification of application workloads. The GT-CWSL combines the specifications of benchmark and workload models to create workload specifications that are used by a synthetic workload generator to generate synthetic workloads for performance evaluation of cloud computing applications.

REAL WORKLOAD CHARACTERIZATION AND SYNTHETIC WORKLOAD GENERATION

In computing, the workload is the amount of processing that the computer has been given to do at a given time. Workloads are two types namely synthetic and real workload. Real workloads are not publicly available and some workloads are available in the internet like Google trace, world cup 98 trace and Clark Net trace. Synthetic workloads are generated based on our experiments. The real trace is downloaded from Google cluster data which consists of two workloads. In first trace it refers to 7 hours period with set of tasks. In second trace it refers to 30 days period of work. Each dataset is packed as set of one or more files, each provided in compressed Common Separated Values(CSV) format. In this paper we are analyzing the Google cluster data version 2 trace in IBM SPSS statistics and generating another workload called synthetic workload with the same characteristics and behavior of real workload based on formulae which is generated using linear regression in IBM SPSS statistics.

SYNTHETIC WORKLOAD GENERATION IN CLOUD

User requests, together with arrival timestamp is called Workload. The workload can be either the synthetic or real workload. The synthetic workloads are useful to carry out the controlled experiment. For performance evaluation of complex multitier applications the synthetic workload generation techniques are required such as in Banking , E-Commerce , Business deployed in the cloud computing environments. In each class the application has its own characteristics of workload. The important requirement is that the generated workload called synthetic workload should maintain the same characteristics and behavior of real workload. Techniques to generate synthetic workload are discussed in this paper.

LoadBuilder: a tool for generating and modeling workloads in distributed workstation environments

1996

This report presents LoadBuilder, a distributed environment designed to provide a portable way of empirically studying the effects of various kinds of workloads in local area networks of heterogeneous workstations. This tool is especially intended to build distributed experimentations including composite workload setting, statistics collecting and performance evaluations. The statistical analysis of the experimental results will help the dynamic load

Virtual Systems Workload Characterization: An Overview

2009 18th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, 2009

Virtual systems and virtualization technology are taking the momentum nowadays in data centers and IT infrastructure models. Performance analysis of such systems is very invaluable for enterprises but yet is not a deterministic process. Singleworkload benchmark is useful in quantifying the virtualization overhead within a single VM, but not useful in whole virtualized environment with multiple isolated VM and varying workload on each and can't capture the system behavior. We need a common workload model and methodology for virtualized systems so that benchmark results can be compared across different platforms. In this paper we will present an overview of the key requirements and characteristics of virtual systems performance metrics and workload characterization which can be considered one step further in implementing virtual systems benchmark and performance model that describe the effect of the applications, host operating system and the hypervisor layer on the performance metrics of virtual workloads. An overview of Intel® vCon model and VMware VMmark will be introduced as examples for the consolidated servers' workload evaluation.

Characterizing Workload of Web Applications on Virtualized Servers

Lecture Notes in Computer Science, 2014

With the ever increasing demands of cloud computing services, planning and management of cloud resources has become a more and more important issue which directed affects the resource utilization and SLA and customer satisfaction. But before any management strategy is made, a good understanding of applications' workload in virtualized environment is the basic fact and principle to the resource management methods. Unfortunately, little work has been focused on this area. Lack of raw data could be one reason; another reason is that people still use the traditional models or methods shared under non-virtualized environment. The study of applications' workload in virtualized environment should take on some of its peculiar features comparing to the non-virtualized environment. In this paper, we are open to analyze the workload demands that reflect applications' behavior and the impact of virtualization. The results are obtained from an experimental cloud testbed running web applications, specifically the RUBiS benchmark application. We profile the workload dynamics on both virtualized and non-virtualized environments and compare the findings. The experimental results are valuable for us to estimate the performance of applications on computer architectures, to predict SLA compliance or violation based on the projected application workload and to guide the decision making to support applications with the right hardware.

Synthetic workload generation for load-balancing experiments

IEEE Parallel & Distributed Technology: Systems & Applications, 1995

This paper describes Dynamic Workload Generator (DWG), a facility for generating realistic and reproducible synthetic workloads for use in load-balancing experiments. For such experiments, the generated workload must not only mimic the highly dynamic resource-utilization patterns found on today's distributed systems but also behave as a real workload does when test jobs are run concurrently with it. The latter requirement is important in testing alternative load-balancing strategies, a process that requires running the same job multiple times, each time at a di erent site but under an identical network-wide workload. Parts of DWG are implemented inside the operating-system kernel and have complete control over the utilization levels of four key resources: CPU, memory, disk, and network. Besides accurately replaying network-wide load patterns recorded earlier, DWG gives up a fraction of its resources each time a new job arrives and reclaims these resources upon job completion. The latter operation is controlled by pattern-doctoring rules implemented in DWG. We present DWG's architecture, its doctoring rules, systematic methods for adjusting and evaluating doctoring rules, and experimental results on a network of Sun workstations.

Generating Representative Web Workloads for Network and Server Performance Evaluation

Sigmetrics Performance Evaluation Review, 1998

One role for workload generation is as a means for understanding how servers and networks respond to variation in load. This enables management and capacity planning based on current and projected usage. This paper applies a number of observations of Web server usage to create a realistic Web workload generation tool which mimics a set of real users accessing a server. The tool, called Surge (Scalable URL Reference Generator) generates references matching empirical measurements of 1) server le size distribution 2) request size distribution 3) relative le popularity 4) embedded le references 5) temporal locality of reference and 6) idle periods of individual users. This paper reviews the essential elements required in the generation of a representative Web workload. It also addresses the technical challenges to satisfying this large set of simultaneous constraints on the properties of the reference stream, the solutions we adopted, and their associated accuracy. Finally, we present evidence that Surge exercises servers in a manner signi cantly di erent from other Web server benchmarks.

Workload modeling and prediction for resources provisioning in cloud

2017

The evaluation of resource management policies in cloud environments is challenging since clouds are subject to varying demand coming from users with different profiles and Quality de Service (QoS) requirements. Factors as the virtualization layer overhead, insufficient trace logs available for analysis, and mixed workloads composed of a wide variety of applications in a heterogeneous environment frustrate the modeling and characterization of applications hosted in the cloud. In this context, workload modeling and characterization is a fundamental step on systematizing the analysis and simulation of the performance of computational resources management policies and a particularly useful strategy for the physical implementation of the clouds. In this doctoral thesis, we propose a methodology for workload modeling and characterization to create resource utilization profiles in Cloud. The workload behavior patterns are identified and modeled in the form of statistical distributions whi...

IJERT-Load Prediction of Virtual Machines in a Cloud Environment

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/load-prediction-of-virtual-machines-in-a-cloud-environment https://www.ijert.org/research/load-prediction-of-virtual-machines-in-a-cloud-environment-IJERTV4IS020686.pdf Cloud computing has many business customers uses resources usage based on their needs. Through virtualization technology which come from resource multiplexing many touted gains in the cloud model. In this paper, system uses virtualization technology to allocate data centre resources dynamically based on its application demands and it supports green computing by optimizing the number of servers in use. The concept of "skewness" is used to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness and combining different types of workloads nicely and improve the overall utilization of server resources. The development of a set of heuristics that prevent overload in the system effectively while saving of energy is used. Trace driven simulation and the experiment results that algorithm achieves good performance.