MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.

/pdf/spark-cluster-computing-with-working-sets-2mqxkxxuqt.pdf

Spark: cluster computing with working sets

http://emamispnu.persiangig.com/Projects/Cloud%20Computing/science_2.pdf

Review: A survey on security issues in service delivery models of cloud computing

This paper presents a new technique for disk storage management called a log-structured file system. A log-structured file system writes all modifications to disk sequentially in a log-like structure, thereby speeding up both file writing and crash recovery. The log is the only structure on disk; it contains indexing information so that files can be read back from the log efficiently. In order to maintain large free areas on disk for fast writing, we divide the log intosegmentsand use a segment cleaner to compress the live information from heavily fragmented segments. We present a series of simulations that demonstrate the efficiency of a simple cleaning policy based on cost and benefit. We have implemented a prototype log-structured file system called Sprite LFS; it outperforms current Unix file systems by an order of magnitude for small-file writes while matching or exceeding Unix performance for reads and large writes. Even when the overhead for cleaning is included, Sprite LFS can use 70% of the disk bandwidth for writing, whereas Unix file systems typically can use only 5–10%.

The design and implementation of a log-structured file system

Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems.  These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years.  However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers. In this work, we present Eucalyptus -- an open-source software framework for cloud computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the system, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings.  Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.

/pdf/the-eucalyptus-open-source-cloud-computing-system-2ahud2ixme.pdf

The Eucalyptus Open-Source Cloud-Computing System

This survey covers rollback-recovery techniques that do not require special language constructs. In the first part of the survey we classify rollback-recovery protocols into checkpoint-based and log-based.Checkpoint-based protocols rely solely on checkpointing for system state restoration. Checkpointing can be coordinated, uncoordinated, or communication-induced. Log-based protocols combine checkpointing with logging of nondeterministic events, encoded in tuples called determinants. Depending on how determinants are logged, log-based protocols can be pessimistic, optimistic, or causal. Throughout the survey, we highlight the research issues that are at the core of rollback-recovery and present the solutions that currently address them. We also compare the performance of different rollback-recovery protocols with respect to a series of desirable properties and discuss the issues that arise in the practical implementations of these protocols.

/pdf/a-survey-of-rollback-recovery-protocols-in-message-passing-bnrpate93a.pdf

A survey of rollback-recovery protocols in message-passing systems

Shared memory facilitates the transition from sequential to parallel processing. Since most data structures can be retained, simply adding synchronization achieves correct, efficient programs for many applications. We discuss our experience with parallel computing on networks of workstations using the TreadMarks distributed shared memory system. DSM allows processes to assume a globally shared virtual memory even though they execute on nodes that do not physically share memory. We illustrate a DSM system consisting of N networked workstations, each with its own memory. The DSM software provides the abstraction of a globally shared memory, in which each processor can access any data item without the programmer having to worry about where the data is or how to obtain its value.

/pdf/treadmarks-shared-memory-computing-on-networks-of-f8d1jl4sga.pdf

TreadMarks: shared memory computing on networks of workstations

Munin is a distributed shared memory (DSM) system that allows shared memory parallel programs to be executed efficiently on distributed memory multiprocessors. Munin is unique among existing DSM systems in its use of multiple consistency protocols and in its use of release consistency. In Munin, shared program variables are annotated with their expected access pattern, and these annotations are then used by the runtime system to choose a consistency protocol best suited to that access pattern. Release consistency allows Munin to mask network latency and reduce the number of messages required to keep memory consistent. Munin's multiprotocol release consistency is implemented in software using a delayed update queue that buffers and merges pending outgoing writes. A sixteen-processor prototype of Munin is currently operational. We evaluate its implementation and describe the execution of two Munin programs that achieve performance within ten percent of message passing implementations of the same programs. Munin achieves this level of performance with only minor annotations to the shared memory programs.

/pdf/implementation-and-performance-of-munin-13txcqpmmh.pdf

Implementation and performance of Munin

TreadMarks is a distributed shared memory (DSM) system for standard Unix systems such as SunOS and Ultrix. This paper presents a performance evaluation of TreadMarks running on Ultrix using DECstation-5000/240's that are connected by a 100-Mbps switch-based ATM LAN and a 10-Mbps Ethernet. Our objective is to determine the efficiency of a user-level DSM implementation on commercially available workstations and operating systems.

We achieved good speedups on the 8-processor ATM network for Jacobi (7.4), TSP (7.2), Quicksort (6.3), and ILINK (5.7). For a slightly modified version of Water from the SPLASH benchmark suite, we achieved only moderate speedups (4.0) due to the high communication and synchronization rate. Speedups decline on the 10-Mbps Ethernet (5.5 for Jacobi, 6.5 for TSP, 4.2 for Quicksort, 5.1 for ILINK, and 2.1 for Water), reflecting the bandwidth limitations of the Ethernet. These results support the contention that, with suitable networking technology, DSM is a viable technique for parallel computation on clusters of workstations.

To achieve these speedups, TreadMarks goes to great lengths to reduce the amount of communication performed to maintain memory consistency. It uses a lazy implementation of release consistency, and it allows multiple concurrent writers to modify a page, reducing the impact of false sharing. Great care was taken to minimize communication overhead. In particular, on the ATM network, we used a standard low-level protocol, AAL3/4, bypassing the TCP/IP protocol stack. Unix communication overhead, however, remains the main obstacle in the way of better performance for programs like Water. Compared to the Unix communication overhead, memory management cost (both kernel and user level) is small and wire time is negligible.

This research was supported in part by the National Science Foundation under Grants CCR-9116343, CCR-9211004, CDA-9222911, and CDA-9310073, by the Texas Advanced Technology Program under Grant 003604014, and by a NASA Graduate Fellowship.

TreadMarks: distributed shared memory on standard workstations and operating systems

This paper describes the architecture of eNVy, a large non-volatile main memory storage system built primarily with Flash memory. eNVy presents its storage space as a linear, memory mapped array rather than as an emulated disk in order to provide an efficient and easy to use software interface.Flash memories provide persistent storage with solid-state memory access times at a lower cost than other solid-state technologies. However, they have a number of drawbacks. Flash chips are write-once, bulk-erase devices whose contents cannot be updated in-place. They also suffer from slow program times and a limit on the number of program/erase cycles. eNVy uses a copy-on-write scheme, page remapping, a small amount of battery backed SRAM, and high bandwidth parallel data transfers to provide low latency, in-place update semantics. A cleaning algorithm optimized for large Flash arrays is used to reclaim space. The algorithm is designed to evenly wear the array, thereby extending its lifetime.Software simulations of a 2 gigabyte eNVy system show that it can support I/O rates corresponding to approximately 30,000 transactions per second on the TPC-A database benchmark. Despite the added work done to overcome the deficiencies associated with Flash memories, average latencies to the storage system are as low as 180ns for reads and 200ns for writes. The estimated lifetime of this type of storage system is in the 10 year range when exposed to a workload of 10,000 transactions per second.

eNVy: a non-volatile, main memory storage system

Relaxed memory consistency models, such as release consistency, were introduced in order to reduce the impact of remote memory access latency in both software and hardware distributed shared memory (DSM). However, in a software DSM, it is also important to reduce the number of messages and the amount of data exchanged for remote memory access. Lazy release consistency is a new algorithm for implementing release consistency that lazily pulls modifications across the interconnect only when necessary. Trace-driven simulation using the SPLASH benchmarks indicates that lazy release consistency reduces both the number of messages and the amount of data transferred between processors. These reductions are especially significant for programs that exhibit false sharing and make extensive use of locks.

/pdf/lazy-release-consistency-for-software-distributed-shared-8y0stx2kvc.pdf

Willy Zwaenepoel

Papers

TreadMarks: shared memory computing on networks of workstations

Implementation and performance of Munin

TreadMarks: distributed shared memory on standard workstations and operating systems

eNVy: a non-volatile, main memory storage system

Lazy release consistency for software distributed shared memory