MOON: MapReduce On Opportunistic eNvironments

Abstract—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments

A Comparison of wide area network performance using virtualized and non-virtualized client architectures

The goal of this thesis is to determine if there is a significant performance difference between two network computer architecture models. The study will measure latency and throughput for both client-server and virtualized client architectures. In the client server environment, the client computer performs a significant portion of the work and frequently requires downloading uploading files to and from a remote location. Virtual client architecture turns the client machine into a terminal, sending only keystrokes and mouse clicks and receiving only display pixel or sound changes. I accomplished the goal of comparing these architectures by comparing completion times for ping reply, file download, a small set of common work tasks, and a moderately large SQL database query. I compared these tasks using simulated wide area network, local area network, and virtual client network architectures. The study limits the architecture to one where the virtual client and server are in the same data center

A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

On the Fly Orchestration of Unikernels: Tuning and Performance Evaluation of Virtual Infrastructure Managers

Network operators are facing significant challenges meeting the demand for more bandwidth, agile infrastructures, innovative services, while keeping costs low. Network Functions Virtualization (NFV) and Cloud Computing are emerging as key trends of 5G network architectures, providing flexibility, fast instantiation times, support of Commercial Off The Shelf hardware and significant cost savings. NFV leverages Cloud Computing principles to move the data-plane network functions from expensive, closed and proprietary hardware to the so-called Virtual Network Functions (VNFs). In this paper we deal with the management of virtual computing resources (Unikernels) for the execution of VNFs. This functionality is performed by the Virtual Infrastructure Manager (VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. We discuss the instantiation process of virtual resources and propose a generic reference model, starting from the analysis of three open source VIMs, namely OpenStack, Nomad and OpenVIM. We improve the aforementioned VIMs introducing the support for special-purpose Unikernels and aiming at reducing the duration of the instantiation process. We evaluate some performance aspects of the VIMs, considering both stock and tuned versions. The VIM extensions and performance evaluation tools are available under a liberal open source licence

Venice: Exploring Server Architectures for Effective Resource Sharing

Consolidated server racks are quickly becoming the backbone of IT infrastructure for science, engineering, and business, alike. These servers are still largely built and organized as when they were distributed, individual entities. Given that many fields increasingly rely on analytics of huge datasets, it makes sense to support flexible resource utilization across servers to improve cost-effectiveness and performance. We introduce Venice, a family of data-center server architectures that builds a strong communication substrate as a first-class resource for server chips. Venice provides a diverse set of resource-joining mechanisms that enables user programs to efficiently leverage non-local resources. To better understand the implications of design decisions about system support for resource sharing we have constructed a hardware prototype that allows us to more accurately measure end-to-end performance of at-scale applications and to explore tradeoffs among performance, power, and resource-sharing transparency. We present results from our initial studies analyzing these tradeoffs when sharing memory, accelerators, or NICs. We find that it is particularly important to reduce or hide latency, that data-sharing access patterns should match the features of the communication channels employed, and that inter-channel collaboration can be exploited for better performance