No abstract

Managed runtime environments (MREs) employ garbage collection (GC) for automatic memory management. However, GC induces pressure on the virtual memory (VM) manager, since it may touch pages that are not related to the working set of the application. Paging due to GC can significantly hurt performance, even when the application's working set fits into physical memory. We present a feedback-directed heap resizing mechanism to avoid GC-induced paging, using information from the operating system (OS). We avoid costly GCs when there is physical memory available, and trade off GC for paging when memory is constrained Our mechanism is simple and uses allocation stall events during GC alone to trigger heap resizing, without user participation or OS kernel modification. Our system enables significant performance improvements when real memory is restricted and similar to, or better performance than, the current state-of-the-art MRE, when memory is unconstrained.

Utility computing, elastic computing, and cloud computing are all terms that refer to the concept of dynamically provisioning processing time and storage space from a ubiquitous "cloud" of computational resources. Such systems allow users to acquire and release the resources on demand and provide ready access to data from processing elements, while relegating the physical location and exact parameters of the resources. Over the past few years, such systems have become increasingly popular, but nearly all current cloud computing offerings are either proprietary or depend upon software infrastructure that is invisible to the research community. In this work, we present Eucalyptus, an open-source software implementation of cloud computing that utilizes compute resources that are typically available to researchers, such as clusters and workstation farms. In order to foster community research exploration of cloud computing systems, the design of Eucalyptus emphasizes modularity, allowing researchers to experiment with their own security, scalability, scheduling, and interface implementations. In this paper, we outline the design of Eucalyptus, describe our own implementations of the modular system components, and provide results from experiments that measure performance and scalability of a Eucalyptus installation currently deployed for public use. The main contribution of our work is the presentation of the first research-oriented opensource cloud computing system focused on enabling methodical investigations into the programming, administration, and deployment of systems exploring this novel distributed computing model. 1 The term "cloud computing" is considered by some to be synonymous with the terms "elastic computing," "utility computing," and occasionally "grid computing." For the purposes of this paper, we will use the term "cloud computing" to refer to cloud, elastic, or utility computing but not to grid computing. The difference is explained in Section 4.