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Distributed, structured data stores such as Big Table, HBase, and Cassandra use a cluster of machines, each running a database-like software system called the Tablet Server Storage Layer or TSSL. A TSSL's performance on each node directly impacts the ...

In this paper, we propose an unorthodox topology for datacenters that eliminates all hierarchical switches in favor of connecting nodes at random according to a small-world-inspired distribution. Specifically, we examine topologies where the underlying ...

Evaluating the performance of large compute clusters requires benchmarks with representative workloads. At Google, performance benchmarks are used to obtain performance metrics such as task scheduling delays and machine resource utilizations to assess ...

Traditional parallel processing models, such as BSP, are "scale up" based, aiming to achieve high performance by increasing computing power, interconnection network bandwidth, and memory/storage capacity within dedicated systems, while big data ...

Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant ...

Modern data centers can consist of hundreds of thousands of servers and millions of virtualized end hosts. Managing address assignment while simultaneously enabling scalable communication is a challenge in such an environment. We present ALIAS, an ...

Many online data sets evolve over time as new entries are slowly added and existing entries are deleted or modified. Taking advantage of this, systems for incremental bulk data processing, such as Google's Percolator, can achieve efficient updates. To ...

Enterprises today face several challenges when hosting line-of-business applications in the cloud. Central to many of these challenges is the limited support for control over cloud network functions, such as, the ability to ensure security, performance ...

Inspired by Google's BigTable, a variety of scalable, semi-structured, weak-semantic table stores have been developed and optimized for different priorities such as query speed, ingest speed, availability, and interactivity. As these systems mature, ...

By offering high availability and elastic access to resources, third-party cloud infrastructures such as Amazon EC2 are revolutionizing the way today's businesses operate. Unfortunately, taking advantage of their benefits requires businesses to accept a ...

We present CoScan, a scheduling framework that eliminates redundant processing in workflows that scan large batches of data in a map-reduce computing environment. CoScan merges Pig programs from multiple users at runtime to reduce I/O contention while ...

MapReduce has been widely recognized as an efficient tool for large-scale data analysis. It achieves high performance by exploiting parallelism among processing nodes while providing a simple interface for upper-layer applications. Some vendors have ...

Iterative computations are pervasive among data analysis applications in the cloud, including Web search, online social network analysis, recommendation systems, and so on. These cloud applications typically involve data sets of massive scale. Fast ...

We present a technique for partially replicating data items at scale according to expressive policy specifications. Recent projects have addressed the challenge of policy-based replication of personal data (photos, music, etc.) within a network of ...

The system overload is a common problem in a Database-as-a-Serice (DaaS) environment because of unpredictable and bursty workloads from various clients. Due to the service delivery nature of DaaS, such system overload usually has direct economic impact ...

Cloud computing is a new computing paradigm, combining diverse client devices -- PCs, smartphones, sensors, single-function, and embedded -- with computation and data storage in the cloud. As with every advance in computing, programming is a fundamental ...

Disaster Recovery (DR) is a desirable feature for all enterprises, and a crucial one for many. However, adoption of DR remains limited due to the stark tradeoffs it imposes. To recover an application to the point of crash, one is limited by financial ...

Infrastructure-as-a-Service (IaaS) cloud platforms have brought two unprecedented changes to cluster provisioning practices. First, any (nonexpert) user can provision a cluster of any size on the cloud within minutes to run her data-processing jobs. The ...

Virtualized datacenters strive to reduce costs through workload consolidation. Workloads exhibit a diverse set of IO behaviors and varying IO load that makes it difficult to estimate the IO performance on shared storage. As a result, system ...

Scientists are currently evaluating the cloud as a new platform. Many important scientific applications, however, perform poorly in the cloud. These applications proceed in highly parallel discrete time-steps or "ticks," using logical synchronization ...

MapReduce is becoming ubiquitous in large-scale data analysis. Several recent works have shown that the performance of Hadoop MapReduce could be improved, for instance, by creating indexes in a non-invasive manner. However, they ignore the impact of the ...

Workload consolidation is very attractive for cloud platforms due to several reasons including reduced infrastructure costs, lower energy consumption, and ease of management. Advances in virtualization hardware and software continue to improve resource ...

Load balancing requests across a cluster of back-end servers is critical for avoiding performance bottlenecks and meeting service-level objectives (SLOs) in large-scale cloud computing services. This paper shows how a small, fast popularity-based front-...

Virtualization is a key technology that powers cloud computing platforms such as Amazon EC2. Virtual machine (VM) consolidation, where multiple VMs share a physical host, has seen rapid adoption in practice with increasingly large number of VMs per ...

We believe datacenters can benefit from more focus on per-node efficiency, performance, and predictability, versus the more common focus so far on scalability to a large number of nodes. Improving per-node efficiency decreases costs and fault recovery ...

As cloud computing becomes widely deployed, one of the challenges faced involves the ability to orchestrate a highly complex set of subsystems (compute, storage, network resources) that span large geographic areas serving diverse clients. To ease this ...

Low-latency, high-throughput web services are typically achieved through partitioning, replication, and caching. Although these strategies and the general design of large-scale distributed search systems are well known, the academic literature provides ...

We report on our experience scaling up the Mobile Millennium traffic information system using cloud computing and the Spark cluster computing framework. Mobile Millennium uses machine learning to infer traffic conditions for large metropolitan areas ...

In this paper we aim to understand the types of applications for which cloud computing is economically tenable, i.e., for which the cost savings associated with cloud placement outweigh any associated deployment costs.

We discover two scenarios. (i) In ...

Recent proposals to build hybrid electrical (packet-switched) and optical (circuit switched) data center interconnects promise to reduce the cost, complexity, and energy requirements of very large data center networks. Supporting realistic traffic ...