توضیحات
ABSTRACT
Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a largescale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, joband task-level workload behavior, and how the overall cluster resources are utilized
INTRODUCTION
The last decade has seen a surge of interest and commercial developments in cloud computing (large-scale distributed data processing). Companies like Google, Facebook and Amazon routinely process petabytes of web and user data using distributed computing frameworks such as MapReduce and Hadoop [1], [2]. They expose ample coarse-grain parallelism and harness large clusters of machines. Cloud computing services are also available to enterprise users and individuals, like Amazon’s EC2. The low cost, elastic scalability and robust performance makes cloud computing fast become a backbone of the society and necessity for everyday Internet uses
Year : 2012
Publisher : International Conference on Parallel Processing Workshops
By : Zitao Liu and Sangyeun Cho
File Information : English Language / 7 Page/ Size : 1.1 M
Download : click
سال : 2012
ناشر : International Conference on Parallel Processing Workshops
کاری از : Zitao Liu and Sangyeun Cho
لینک دانلود : روی همین لینک کلیک کنید
نقد و بررسیها
هیچ دیدگاهی برای این محصول نوشته نشده است.