Workload Characterization Study Research Articles

Energy characterization of graph workloads

Graph algorithms are critical components of the big-data analysis workflow. The graph kernel performance is highly dependent on the input data graphs. The inherently sparse nature of the input graphs often results in irregular memory access patterns, which may not suit the data-locality based cache optimizations featured in current high-performance processors. Much prior research has identified several optimization opportunities by characterizing graph kernels on existing hardware. However, current graph workload characterization studies focus on performance-related observations and optimizations, overlooking the energy implications. In this paper, we address this technology gap by presenting an exhaustive and systematic energy characterization study of graph kernels. We characterize the six GAP benchmark suite kernels with a variety of input graphs on a dual-socket x86-based system. We then analyze how the algorithms, graph characteristics (like graph scale and degree), and system effects (like parallelism, simultaneous multithreading, and multiprocessing) impact the energy. Based on our analysis, we derive observations and insights to develop a basic energy model. The discussions in the paper can enable researchers to advance new models and energy-efficient architectures for graph workloads.

Virtualization in the Private Cloud: State of the Practice

Virtualization has become a mainstream technology that allows efficient and safe resource sharing in data centers. In this paper, we present a large scale workload characterization study of 90K virtual machines hosted on 8K physical servers, across several geographically distributed corporate data centers of a major service provider. The study focuses on 19 days of operation and focuses on the state of the practice, i.e., how virtual machines are deployed across different physical resources with an emphasis on processors and memory, focusing on resource sharing and usage of physical resources, virtual machine life cycles, and migration patterns and their frequencies. This paper illustrates that indeed there is a huge tendency in over-provisioning CPU and memory resources while certain virtualization features (e.g., migration and collocation) are used rather conservatively, showing that there is significant room for the development of policies that aim to reduce operational costs in data centers.

Workload generation for YouTube

This paper introduces a workload characterization study of the most popular short video sharing service of Web 2.0, YouTube. Based on a vast amount of data gathered in a five-month period, we analy...

Performance impacts of autocorrelated flows in multi-tiered systems

We focus on the general problem of capacity planning and performance prediction of multi-tiered systems. Workload characterization studies of such systems usually examine the stochastic characteristics of arrivals to the system and wait/service times at various tiers aiming at bottleneck identification, diagnosing the conditions under which bottlenecks are triggered, and assisting the development of resource management policies to improve performance or provide service level provisioning.

Addressing a workload characterization study to the design of consistency protocols

Shared Virtual Memory (SVM) provides a low-cost and effective way to implement the shared-memory programming paradigm. SVMs utilize a number of concepts that include consistency models/protocols, sharing patterns, false sharing, and fragmentation issues. The range of issues encountered in an SVM introduces a level of complexity and presents a challenge to many SVM researchers. This paper presents a careful study of SVM systems focusing on how the workload characteristics can affect the performace of consistency protocols. This knowledge is used to propose a novel consistency protocol that improves the system performance. This paper pursues two main goals: (i) to illustrate how different SVM workload characteristics are interrelated, and (ii) to motivate the design of a new multiple-writer memory consistency protocol. To achieve the first goal, we provide a detailed workload characterization analysis and discussion on how consistency models and protocols work. To achieve the second goal, we describe a software-based SVM protocol that achieves better performance than a hardware protocol proposed in the literature. In some workloads, the speedup obtained over the baseline protocol is more than 20%.

Dynamic scheduling of multiclass open queueing networks in a slowly changing environment

The popularity and importance of Web have increased dramatically in the past few years as well as the complexity of Web server systems. Workload characterization studies reveal that there exist strong time-of-day effects in the Web traffic. Many Web sites have sustained and higher hit rates during certain time periods of a day than other time periods. During the peak hours, the Web servers may even be overloaded. Simple stochastic processes with a fixed rate fails to capture this time varying characteristic of the Web systems. Therefore, we herein consider that the Web system is operating in a changing environment. Whenever the environment changes state, the arrival rates of user requests change as well as the service rates and the routing decisions.

Workload characterization

Analyzing an e-commerce site's scalability requires a solid understanding of its workload characteristics, but this characterization must occur at multiple levels and time scales. The author and colleagues have performed several workload characterization studies, including auction sites. Here, I present the general method we adopted and a few examples of the workload characterizations we studied.

Exploiting fine-grained idle periods in networks of workstations

Studies have shown that for a significant fraction of the time, workstations are idle. In this paper, we present a new scheduling policy called Linger-Longer that exploits the fine-grained availability of workstations to run sequential and parallel jobs. We present a two-level workload characterization study and use it to simulate a cluster of workstations running our new policy. We compare two variations of our policy to two previous policies: Immediate-Eviction and Pause-and-Migrate. Our study shows that the Linger-Longer policy can improve the throughput of foreign jobs on a cluster by 60 percent with only a 0.5 percent slowdown of local jobs. For parallel computing, we show that the Linger-Longer policy outperforms reconfiguration strategies when the processor utilization by the local process is 20 percent or less in both synthetic bulk synchronous and real data-parallel applications.

A workload characterization study of the 1998 World Cup Web site

This article presents a detailed workload characterization study of the 1998 World Cup Web site. Measurements from this site were collected over a three-month period. During this time the site received 1.35 billion requests, making this the largest Web workload analyzed to date. By examining this extremely busy site and through comparison with existing characterization studies, we are able to determine how Web server workloads are evolving. We find that improvements in the caching architecture of the World Wide Web are changing the workloads of Web servers, but major improvements to that architecture are still necessary. In particular, we uncover evidence that a better consistency mechanism is required for World Wide Web caches.

Workload characterization of a Web proxy in a cable modem environment

This paper presents a detailed workload characterization study of a World-Wide Web proxy. Measurements from a proxy within an Internet Service Provider (ISP) environment were collected. This ISP allows clients to access the Web using high-speed cable modems rather than traditional dialup modems. By examining this site we are able to evaluate the effects that cable modems have on proxy workloads.This paper focuses on workload characteristics such as file type distribution, file size distribution, file referencing behaviour and turnover in the active set of files. We find that when presented with faster access speeds users are willing to download extremely large files. A widespread increase in the transfer of these large files would have a significant impact on the Web. This behaviour increases the importance of caching for ensuring the scalability of the Web.

Internet Web servers: workload characterization and performance implications

This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in the study: three from academic environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year. The workload characterization focuses on the document type distribution, the document size distribution, the document referencing behavior, and the geographic distribution of server requests. Throughout the study, emphasis is placed on finding workload characteristics that are common to all the data sets studied. Ten such characteristics are identified. The paper concludes with a discussion of caching and performance issues, using the observed workload characteristics to suggest performance enhancements that seem promising for Internet Web servers.

Web server workload characterization

The phenomenal growth in popularity of the World Wide Web (WWW, or the Web) has made WWW traffic the largest contributor to packet and byte traffic on the NSFNET backbone. This growth has triggered recent research aimed at reducing the volume of network traffic produced by Web clients and servers, by using caching, and reducing the latency for WWW users, by using improved protocols for Web interaction.Fundamental to the goal of improving WWW performance is an understanding of WWW workloads. This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in this study: three from academic (i.e., university) environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year of activity.Throughout the study, emphasis is placed on finding workload invariants : observations that apply across all the data sets studied. Ten invariants are identified. These invariants are deemed important since they (potentially) represent universal truths for all Internet Web servers. The paper concludes with a discussion of caching and performance issues, using the invariants to suggest performance enhancements that seem most promising for Internet Web servers.

Workload characterization using the CRAY hardware performance monitor

This paper describes some preliminary results about a workload characterization study at a national supercomputer center. This study is part of a larger project to develop an analytic methodology for the construction of arepresentative benchmark set. Our study reports on 292,254 user jobs during the period from June 1992 to July 1993 and represents about one-half of the wall clock time available on the four-processor CRAY Y-MP at the National Center for Supercomputing Applications during this time. The performance statistics we gathered in some sensedefine the workload because of the large fraction of the available time that was recorded. These statistics will allow us to “reverse engineer” a set of benchmarks that analytically represents the workload at this site.

A workload characterization study of Internet Web servers

This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in this study: three from academic environments, two from scientific research organizations, and one from a commercial Internet provider. Throughout the study, emphasis is placed on finding workload invariants : observations that apply, or seem to apply, across all the data sets studied. Ten invariants are identified. These invariants are deemed important since they potentially represent universal truths for all Internet Web servers.

Cluster analysis and workload classification

Clustering techniques are widely recommended tools for workload classification. The k-means algorithm is widely accepted as the "standard" technique of detecting workload classes automatically from measurement data. This paper examines validity of the obtained workload classes, when the current system and workload is analyzed by a queueing network model and mean value analysis. Our results, based on one week's accounting data of a VAX 8600, indicate that the results of queueing network analysis are not stable when the classes of workload are constructed through the k-means algorithm. Therefore, we cannot recommended that the most widely used clustering technique should be used in any workload characterization study without careful validation.

A static and dynamic workload characterization study of the San Diego Supercomputer center Cray X-MP

The San Diego Supercomputer Center is one of four NSF sponsored national supercomputer centers. Up until January of 1990, its workhorse was a Cray X-MP, which served 2700 researchers from 170 institutions, spanning 44 states. In order to better understand how this supercomputer was utilized by its diverse community of users, we undertook a workload characterization study of the Cray X-MP. The goals of our study were twofold. First, we wished to characterize the workload at both the functional and resource levels. The functional level represents the user point of view: what types of programs users are running on the system. The resource level represents the system point of view: how the systems resources (CPU, memory, I/O bandwidth) are being used. Second, we wanted to see how the workload changed over an average weekday. Thus, we conducted a static characterization to understand its global attributes over the entire measurement period, as well as a dynamic workload characterization to understand the time behavior of the workload over a weekday cycle.