Åge Kvalnes

DAVVI: a prototype for the next generation multimedia entertainment platform

In this demo, we present DAVVI, a prototype of the next generation multimedia entertainment platform. It delivers multi-quality video content in a torrent-similar way like known systems from Move Networks, Microsoft and Apple do. However, it also provides a brand new, personalized user experience. Through applied search, personalization and recommendation technologies, end-users can efficiently search and retrieve highlights and combine arbitrary events in a customized manner using drag and drop. The created playlists of video segments are then delivered back to the system to improve future search and recommendation results. Here, we demonstrate this system using a soccer example.

Balava: Federating Private and Public Clouds

Balava is a new system for managing computations that span multiple clouds and involve data with confidentiality constraints. This paper describes the design, implementation and initial performance evaluation of Balava building-blocks. We detail the run-time developed to interconnect private and public clouds, and present a storage overlay built on top of this run-time. To support low-overhead execution of Balava computations, we are investigating alternative approaches to virtualization. We present a new hyper visor that supports light-weight virtual environments, while also preserving application binary interfaces.

Search-based composition, streaming and playback of video archive content

Locating content in existing video archives is both a time and bandwidth consuming process since users might have to download and manually watch large portions of superfluous videos. In this paper, we present two novel prototypes using an Internet based video composition and streaming system with a keyword-based search interface that collects, converts, analyses, indexes, and ranks video content. At user requests, the system can automatically sequence out portions of single videos or aggregate content from multiple videos to produce a single, personalized video stream on-the-fly.

Cogset: a high performance MapReduce engine

Cogset is a generic and efficient engine for reliable storage and parallel processing of distributed data sets. It supports a number of high‐level programming interfaces, including a MapReduce interface compatible with Hadoop. In this paper, we present Cogsets architecture and evaluate its performance as a MapReduce engine, comparing it with Hadoop. Our results show that Cogset generally outperforms Hadoop by a significant margin. We investigate the underlying causes of this difference in performance and demonstrate some relatively minor modifications that markedly improve Hadoops performance, closing some of the gap. Copyright

Paravirtualizing TCP

Virtualization has proven consolidation and isolation benefits, but invariably incurs an overhead. This overhead especially penalizes latency sensitive tasks such as tcp processing, because such processing only occurs when a guest is scheduled. We evaluate a system where tcp is paravirtualized. All guest tcp processing below the socket interface takes place in the hypervisor, which enables rapid response to protocol state changes. We have built Leulo, a prototype hypervisor, and a modified Linux guest kernel that outperforms the Kernel Virtual Machine (KVM) and approaches native performance for certain workloads in an http bench-mark.

Cogset vs. Hadoop: Measurements and Analysis

Cog set is an efficient and generic engine for reliable storage and parallel processing of data. It supports a number of high-level programming interfaces, including a MapReduce interface compatible with Hadoop. In this paper, we evaluate Cogset’s performance as a MapReduce engine, comparing it to Hadoop. Our results show that Cog set generally outperforms Hadoop by a significant margin. We investigate the causes of this gap in performance and demonstrate some relatively minor modifications that markedly improveHadoop’s performance, closing some of the gap.

Streaming as a hypervisor service

Interference from resource sharing can cause unpredictable performance when virtual machines are consolidated on a single machine. We demonstrate performance problems with HTTP streaming in a virtual machine environment, even when no other virtual machines compete for network bandwidth. The paper suggests and evaluates a novel in-hypervisor data streaming service as a remedy to the performance problem. By modifying a Linux guest to exploit this new hypervisor interface, significant performance gains are experienced from a hosted Apache HTTP server.

Jovaku: Globally Distributed Caching for Cloud Database Services Using DNS

Cloud database services are a convenient building block for emerging mobile cloud applications. A central database can simplify application architectures by serving both as a reliable point of contact and as a repository for critical state. Meanwhile, the issues of availability and scalability can be delegated to the cloud service provider. The convenience of this approach is balanced by associated costs, both in terms of latency and financial expenses. Hence, an attractive middle ground is to employ caching of data in a layer between applications and the cloud, to reduce the load imposed on the cloud database service. This paper presents Jovaku, a generic caching layer for cloud database services that can induce significant performance improvements and cost savings. Jovaku demonstrates the viability of a truly global caching infrastructure by building on the existing DNS system. Database operations are relayed through the DNS protocol, allowing results to be cached in DNS servers close to client devices. This greatly simplifies deployment, and offers supreme availability, allowing devices anywhere to benefit from database caching. Our evaluation shows that the latency to access Amazon DynamoDB is significantly reduced for requests that hit the cache, and that applications can benefit from caching with hit rates as low as 5%.

Position paper: elastic processing and storage at the edge of the cloud

Cloud services traditionally have a centralized architecture, where all clients communicate individually with the central service, and not directly with each other. Data is primarily stored in the cloud, and computations that touch data are performed in the cloud. We present Rusta, a platform that allows cloud services to deploy in a more flexible and decentralized manner, potentially involving the client machines at the edge of the cloud both for storage and processing of data. This can reduce operational costs both by leveraging freely available client resources, and by reducing data traffic to and from the cloud. Rusta includes a group abstraction to delineate webs of trusted peers, a light-weight process abstraction based on asynchronous message passing, and a distributed data storage layer. For elasticity, processes may migrate freely among the clients of a group, and can be replicated in a transparent manner. A central hub service executes in the cloud and maintains critical system state, while delegating work to clients as appropriate. This paper describes the design and current implementation of Rusta, its high-level programming model, and some of its potential applications, in particular as a foundation for highly elastic computations at the edge of the cloud.

Cloud-side Execution of Database Queries for Mobile Applications

We demonstrate a practical way to reduce latency for mobile .NET applications that interact with cloud database services. We provide a programming abstraction for location-independent code, which has the potential to execute either locally or at a satellite execution environment in the cloud, in close proximity to the database service. This preserves a programmatic style of database access, and maintains a simple deployment model, but allows applications to offload latency-sensitive code to the cloud. Our evaluation shows that this approach can significantly improve the response time for applications that execute dependent queries, and that the required cloud-side resources are modest.