Cloud Workload Characterization
In this chapter, we describe various Cloud workloads and optimization issues from the points of view of various players involved in Cloud Computing. A comprehensive categorization of various types of diverse workloads is proposed, and nature of stress that each of these places on the resources in a data center is described. These categorizations extend beyond the Cloud for completeness. The Cloud workload categories proposed in this chapter are big streaming data, big database creation and calculation, big database search and access, big data storage, in-memory database, many tiny tasks (Ants), high-performance computing (HPC), highly interactive single person, highly interactive multi-person jobs, single computer intensive jobs, private local tasks, slow communication, real-time local tasks, location aware computing, real-time geographically dispersed, access control, and voice or video over IP. We evaluate causes of resource contention in a multi-tenanted data center and conclude by suggesting remedial measures that both Cloud service providers and Cloud customers can undertake to minimize their pain points. This chapter identifies the relationship of critical computer resources to various workload categories. Low-level hardware measurements can be used to distinguish job transitions between categories and within phases of categories. This relationship with the categories allows a technical basis for SLAs, capital purchase decisions, and future computer architecture design decisions. A better understanding of these pain points, underlying causes, and suggested remedies will help IT managers to make intelligent decisions about moving their mission critical or enterprise class jobs into Public Cloud.
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