Big Data platforms often need to support emerging data sources and applications while accommodating existing ones. Since different data and applications have varying requirements, multiple types of data stores (e.g. file-based and object-based) frequently co-exist in the same solution today without proper integration. Hence cross-store data access, key to effective data analytics, can not be achieved without laborious application re-programming, prohibitively expensive data migration, and/or costly maintenance of multiple data copies. We address this vital issue by introducing a first unified big data platform over heterogeneous storage. In particular, we present a prototype joining Apache Hadoop MapReduce with OpenStack's open-source object store Swift and IBM's cluster file system GPFSTM. A sentiment analysis application using 3 months of real Twitter data is employed to test and showcase our prototype. We have found that our prototype achieves 50% data capacity savings, eliminates data migration overhead, offers stronger reliability and enterprise support. Through our case study, we have learned important theoretical lessons concerning performance and reliability, as well as practical ones related to platform configuration. We have also identified several potentially high-impact research directions.

Cloud data centers will contain tens of thousands of servers with massive aggregate bandwidth requirements for generating, accessing, and analyzing immense amounts of data. The I/O requirements of the myriad applications that these data centers must support run the gamut from extreme IOPS intensive to extreme bandwidth intensive. Delivering high performance with unreliable commodity hardware for this range of workloads is truly a grand challenge. ZoneFS is a parallel file system that targets cloud data center infrastructures built up of commodity network switches. ZoneFS employs a highly-available and flexible storage architecture that divides a cluster switch hierarchy into zones and stripes data across servers and disks to maximize aggregate I/O throughput and avoid storage server hotspots. In this paper, we present the overall design and implementation of ZoneFS and evaluate its key features with several cloud computing workloads. Our experimental results show that ZoneFS can improve application runtime performance by up to 76% over standard parallel file systems and by up to 85% over Internet-scale file systems.

In this paper we present C2Cfs - a decentralized collective caching architecture for distributed file systems. C2Cfs diverges from the traditional client-server model and advocates decoupling the consistency management role of the central server from the data serving role. Our design enables multiple client-side caches to share data and efficiently propagate updates via direct client-to-client transfers, while maintaining the standard consistency semantics. We present an NFSv4-based implementation of our architecture, which works with unmodified NFS servers and requires no changes to the protocol. Finally, we evaluate the implementation and demonstrate the performance benefits of decentralized data access enabled by our approach.

Semantic caches, originally proposed for client-server database systems, are being recently deployed to accelerate the serving of dynamic Web content by transparently caching data on edge servers. Such caches require fast query containment tests to determine if a new query is contained in the results of cached queries. Query containment checking algorithms have been studied in the context of query optimization and materialized view selection, but their scalability remains a serious limitation. We argue that application queries are usually instantiations of a smaller number of base templates and show how this can be exploited to scale up containment checking. Our contributions include (i) algorithms to detect similarity between query predicates; (ii) efficient algorithms for proving containment among similar query predicates; (iii) a technique to dynamically aggregate similar queries in the cache to support efficient search; and (iv) integration of these schemes into a two-level containment checker. We describe our approach, report on its implementation in a dynamic Web data cache, and show that it can reduce query containment cost by an order of magnitude for Web workloads.

The majority of web pages served today are generated dynamically, usually by an application server querying a back-end database. To enhance the scalability of dynamic content serving in large sites, application servers are offloaded to front-end nodes, called edge servers. The improvement from such application offloading is marginal, however, if data is still fetched from the origin database system. To further improve scalability and cut response times, data must be effectively cached on such edge servers. The scale of deployment of edge servers and the rising costs of their administration demand that such caches be self-managing and adaptive. In this paper, we describe DBProxy, an edge-of-network semantic data cache for web applications. DBProxy is designed to adapt to changes in the workload in a transparent and graceful fashion by caching a large number of overlapping and dynamically changing "materialized views". New "views" are added automatically while others may be discarded to save space. In this paper, we discuss the challenges of designing and implementing such a dynamic edge data cache, and describe our proposed solutions.

Managed hosting and enterprise wide resource consolidation trends are increasingly leading to sharing of storage resources across multiple classes, corresponding to different applications/customers, each with a possibly different Quality of Service (QoS) requirement. To enable a storage system to meet diverse QoS requirements, we present two algorithms for dynamically allocating cache space among multiple classes of workloads. Our algorithms dynamically adapt the cache space allocated to each class in response to the observed response time, the temporal locality of reference, and the arrival pattern for each class. Using trace driven simulations collected from large storage system installations, we experimentally demonstrate that the algorithms not only meet the QoS requirements, but also increase the throughput by achieving a higher hit rate whenever feasible.

We argue that weak cache consistency mechanisms supported by existing Web proxy caches must be augmented by strong consistency mechanisms to support the growing diversity in application requirements. Existing strong consistency mechanisms are not appealing for Web environments due to their large state space or control message overhead. We focus on the lease approach that balances these trade-offs and present analytical models and policies for determining the optimal lease duration. We present extensions to the HTTP protocol to incorporate leases and then implement our techniques in the Squid proxy cache and the Apache Web server. Our experimental evaluation of the leases approach shows that: 1) our techniques impose modest overheads even for long leases (a lease duration of 1 hour requires state to be maintained for 1030 leases and imposes an per-object overhead of a control message every 33 minutes), 2) leases yields a 138-425 percent improvement over existing strong consistency mechanisms, and 3) the implementation overhead of leases is comparable to existing weak consistency mechanisms.

We argue that cache consistency mechanisms designed for stand-alone proxies do not scale to the large number of proxies in a content distribution network and are not flexible enough to allow consistency guarantees to be tailored to object needs. To meet the twin challenges of scalability and flexibility, we introduce the notion of cooperative consistency along with a mechanism, called cooperative leases, to achieve it. By supporting /spl Delta/-consistency semantics and by using a single lease for multiple proxies, cooperative leases allow the notion of leases to be applied in a flexible, scalable manner to CDNs. Further, the approach employs application-level multicast to propagate server notifications to proxies in a scalable manner. We implement our approach in the Apache Web server and the Squid proxy cache and demonstrate its efficacy using a detailed experimental evaluation. Our results show a factor of 2.5 reduction in server message overhead and a 20 percent reduction in server state space overhead when compared to original leases albeit at an increased interproxy communication overhead.

Database caching at proxy servers enables dynamic content to be generated at the edge of the network, thereby improving the scalability and response time of web applications. The scale of deployment of edge servers coupled with the rising costs of their administration demand that such caching middleware be adaptive and self-managing. To achieve this, a cache must be dynamically populated and pruned based on the application query stream and access pattern. In this paper, we describe such a cache which maintains a large number of materialized views of previous query results. Cached "views" share physical storage to avoid redundancy, and are usually added and evicted dynamically to adapt to the current workload and to available resources. These two properties of large scale (large number of cached views) and overlapping storage introduce several challenges to query matching and storage management which are not addressed by traditional approaches. In this paper, we describe an edge data cache architecture with a flexible query matching algorithm and a novel storage management policy which work well in such an environment. We perform an evaluation of a prototype of such an architecture using the TPC-W benchmark and find that it reduces query response times by up to 75%, while reducing network and server load.

In this paper, we argue that cache consistency mechanisms designed for stand-alone proxies do not scale to the large number of proxies in a content distribution network and are not flexible enough to allow consistency guarantees to be tailored to object needs. To meet the twin challenges of scalability and flexibility, we introduce the notion of cooperative consistency along with a mechanism, called cooperative leases, to achieve it. By supporting Δ-consistency semantics and by using a single lease for multiple proxies, cooperative leases allows the notion of leases to be applied in a flexible, scalable manner to CDNs. Further, the approach employs application-level multicast to propagate server notifications to proxies in a scalable manner. We implement our approach in the Apache web server and the Squid proxy cache and demonstrate its efficacy using a detailed experimental evaluation. Our results show a factor of 2.5 reduction in server message overhead and a 20% reduction in server state space overhead when compared to original leases albeit at an increased inter-proxy communication overhead.

The rapid growth of the Internet in users and content has fueled extensive efforts to improve the user's overall Internet experience. A growing number of providers deliver content from multiple servers or proxies to reduce response time by moving content closer to end users. An increasingly popular mechanism to direct clients to the closest point of service is DNS-based redirection, due to its transparency and generality. This paper draws attention to two of the main issues in using DNS: (1) the negative effects of reducing or eliminating the cache lifetimes of DNS information, and (2) the implicit assumption that client nameservers are indicative of actual client location and performance. We quantify the impact of reducing DNS TTL values on Web access latency and show that it can increase name resolution latency by two orders of magnitude. Using HTTP and DNS server logs, as well as a large number of dial-up ISP clients, we measure client-nameserver proximity and show that a significant fraction are distant, more than 8 hops apart. Finally, we suggest protocol modifications to improve the accuracy of DNS-based redirection schemes.

In this paper, we argue that weak cache consistency mechanisms supported by existing Web proxy caches must be augmented by strong consistency mechanisms to support the growing diversity in application requirements. Existing strong consistency mechanisms are not appealing for Web environments due to their large state space or control message overhead. We focus on the lease approach that balances these tradeoffs and present analytical models and policies for determining the optimal lease duration. We present extensions to HTTP to incorporate leases and then implement our techniques in the Squid proxy cache and the Apache Web server. Our experimental evaluation of the leases approach shows that: (i) our techniques impose modest overheads even for long leases (a lease duration of 1 hour requires state to be maintained for 1030 leases and imposes a per object overhead of a control message every 33 minutes); (ii) leases yield a 138-425% improvement over existing strong consistency mechanisms; and (iii) the implementation overhead of leases is comparable to existing weak consistency mechanisms.

We describe the design and implementation of an integrated architecture for cache systems that scale to hundreds or thousands of caches with thousands to millions of users. Rather than simply try to maximize hit rates, we take an end-to-end approach to improving response time by also considering hit times and miss times. We begin by studying several Internet caches and workloads, and we derive three core design principles for large scale distributed caches: minimize the number of hops to locate and access data on both hits and misses; share data among many users and scale to many caches; and cache data close to clients. Our strategies for addressing these issues are built around a scalable, high-performance data-location service that tracks where objects are replicated. We describe how to construct such a service and how to use this service to provide direct access to remote data and push-based data replication. We evaluate our system through trace-driven simulation and find that these strategies together provide response time speedups of 1.27 to 2.43 compared to a traditional three-level cache hierarchy for a range of trace workloads and simulated environments.

Clustered multimedia servers, consisting of interconnected nodes and disks, have been proposed for large-scale servers that are capable of supporting multiple concurrent streams which access the video objects stored in the server. As the number of disks and nodes in the cluster increases, so does the probability of a failure. With data striped across all disks in a cluster, the failure of a single disk or node results in the disruption of many or all streams in the system. Guaranteeing high availability in such a cluster becomes a primary requirement to ensure continuous service. In this paper, we study mirroring and software RAID schemes with different placement strategies that guarantee high availability in the event of disk and node failures while satisfying the real-time requirements of the streams. We examine various declustering techniques for spreading the redundant information across disks and nodes and show that random declustering has good real-time performance. Finally, we compare the overall cost per stream for different system configurations. We derive the parameter space where mirroring and software RAID apply, and determine optimal parity group sizes.

Abstract The paper describes the approach taken for configuration management in the Nexus distributed operating system. Nexus uses kernel‐level support for monitoring the failure or termination status of distributed components of an application. Periodic user‐level messages are not required for status monitoring. Group and dependency relationships between such components can be defined by the programmer for the purpose of configuration monitoring and management. An object belonging to a distributed application can be monitored by its host kernel for system‐defined exception conditions. When any of these conditions arise, other objects are notified through signals or messages, as specified by the programmer.

This paper describes the approach taken for configuration management in the Nexus distributed operating system. Nexus uses kernel-level support for monitoring status of distributed components of an application. Periodic user-level messages are no longer required for status monitoring. Group and dependency relationships between such components can be defined by the programmer for the purpose of configuration monitoring and management. An object belonging to a distributed application can be monitored by its host kernel for some system-defined exception conditions. When any of these conditions arise, other objects are notified through signals or messages, as specified by the programmer.>