DryadLINQ is a system and a set of language extensions that enable a new programming model for large scale distributed computing. It generalizes previous execution environments such as SQL, MapReduce, and Dryad in two ways: by adopting an expressive data model of strongly typed.NET objects; and by supporting general-purpose imperative and declarative operations on datasets within a traditional high-level programming language. A DryadLINQ program is a sequential program composed of LINQ expressions performing arbitrary sideeffect-free transformations on datasets, and can be written and debugged using standard.NET development tools. The DryadLINQ system automatically and transparently translates the data-parallel portions of the program into a distributed execution plan which is passed to the Dryad execution platform. Dryad, which has been in continuous operation for several years on production clusters made up of thousands of computers, ensures efficient, reliable execution of this plan. We describe the implementation of the DryadLINQ compiler and runtime. We evaluate DryadLINQ on a varied set of programs drawn from domains such as web-graph analysis, large-scale log mining, and machine learning. We show that excellent absolute performance can be attained—a general-purpose sort of 1012 Bytes of data executes in 319 seconds on a 240-computer, 960disk cluster—as well as demonstrating near-linear scaling of execution time on representative applications as we vary the number of computers used for a job. 1

Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, processing is typically done on large clusters of shared-nothing commodity machines. It is imperative to develop a programming model that hides the complexity of the underlying system but provides flexibility by allowing users to extend functionality to meet a variety of requirements. In this paper, we present a new declarative and extensible scripting language, SCOPE (Structured Computations Optimized for Parallel Execution), targeted for this type of massive data analysis. The language is designed for ease of use with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. SCOPE borrows several features from SQL. Data is modeled as sets of rows composed of typed columns. The select statement is retained with inner joins, outer joins, and aggregation allowed. Users can easily define their own functions and implement their own versions of operators: extractors (parsing and constructing rows from a file), processors (row-wise processing), reducers (group-wise processing), and combiners (combining rows from two inputs). SCOPE supports nesting of expressions but also allows a computation to be specified as a series of steps, in a manner often preferred by programmers. We also describe how scripts are compiled into efficient, parallel execution plans and executed on large clusters. 1.

Multi-tier Internet service clusters often contain complex calling dependencies among service components spreading across cluster nodes. Without proper handling, partial failure or overload at one component can cause cascading performance degradation in the entire system. While dependency management may not present significant challenges for even-driven services (particularly in the context of staged event-driven architecture), there is a lack of system support for thread-based online services to achieve dependency isolation automatically. To this end, we propose dependency capsule, a new mechanism that supports automatic recognition of dependency states and per-dependency management for threadbased services. Our design employs a number of dependency capsules at each service node: one for each remote service component. Dependency capsules monitor and manage threads that block on supporting services and isolate their performance impact on the capsule host and the rest of the system. In addition to the failure and overload isolation, each capsule can also maintain dependency-specific feedback information to adjust control strategies for better availability and performance.