MapReduce has emerged as a prevailing distributed computation paradigm for enterprise and large-scale data-intensive computing. The model is also increasingly used in the massively-parallel cloud environment, where MapReduce jobs are run on a set of virtual machines (VMs) on pay-as-needed basis. However, MapReduce jobs suffer from performance degradation when running in the cloud due to inefficient resource allocation. In particular, the MapReduce model is designed for and leverages information from the native clusters to operate efficiently, whereas the cloud presents a virtual cluster topology overlying or hiding actual network information. This results in two placement anomalies: loss of data locality and loss of job locality, where jobs are placed physically away from their data or other associated jobs, adversely affecting their performance.

This paper presents VMFlockMS, a migration service optimized for cross-datacenter transfer and instantiation of groups of virtual machine (VM) images that comprise an application-level solution (e.g., a three-tier web application). We dub these groups of related VM images VMFlocks. VMFlockMS employs two main techniques: first, data deduplication within the VMFlock to be migrated and between the VMFlock and the data already present at the destination datacenter, and, second, accelerated instantiation of the application at the target datacenter after transferring only a partial set of data blocks and prioritization of the remaining data based on previously observed access patterns originating from the running VMs. VMFlockMS is designed to be deployed as a set of virtual appliances which make efficient use of the available cloud resources to locally access and deduplicate the images and data in a distributed fashion with minimal requirements imposed on the cloud API to access the VM image repository. VMFlockMS provides an incrementally scalable and high-performance migration service. Our evaluation shows that VMFlockMS can reduce the data volumes to be transferred over the network to as low as 3% of the original VMFlock size, enables the complete transfer of the VM images belonging to a VMFlock over transcontinental link up to 3.5x faster than alternative approaches, and enables booting these VM images with as little as 5% of the compressed VMFlock data available at the destination.

Software bugs that occur in production are often difficult to reproduce in the lab due to subtle differences in the application environment and nondeterminism. To address this problem, we present Transplay, a system that captures production software bugs into small per-bug recordings which are used to reproduce the bugs on a completely different operating system without access to any of the original software used in the production environment. Transplay introduces partial checkpointing, a new mechanism that efficiently captures the partial state necessary to reexecute just the last few moments of the application before it encountered a failure. The recorded state, which typically consists of a few megabytes of data, is used to replay the application without requiring the specific application binaries, libraries, support data, or the original execution environment. Transplay integrates with existing debuggers to provide standard debugging facilities to allow the user to examine the contents of variables and other program state at each source line of the application's replayed execution. We have implemented a Transplay prototype that can record unmodified Linux applications and replay them on different versions of Linux as well as Windows. Experiments with several applications including Apache and MySQL show that Transplay can reproduce real bugs and be used in production with modest recording overhead.

This paper presents CARP, an integrated program and storage replication solution. CARP extends program replication systems which do not currently address storage errors, builds upon a record-and-replay scheme that handles nondeterminism in program execution, and uses a scheme based on recorded program state and I/O logs to enable efficient detection of silent data errors and efficient recovery from such errors. CARP is designed to be transparent to applications with minimal run-time impact and is general enough to be implemented on commodity machines. We implemented CARP as a prototype on the Linux operating system and conducted extensive sensitivity analysis of its overhead with different application profiles and system parameters. In particular, we evaluated CARP with standard unmodified email, database, and web server benchmarks and showed that it imposes acceptable overhead while providing sub-second program state recovery times on detecting a silent data error.

Record and replay (RR) is a software based state replication solution designed to support recording and subsequent replay of the execution of unmodified applications running on multiprocessor systems for fault-tolerance. Multiple instances of the application are simultaneously executed in separate virtualized environments called containers. Containers facilitate state replication between the application instances by resolving the resource conflicts and providing a uniform view of the underlying operating system across all clones. The virtualization layer that creates the container abstraction actively monitors the primary instance of the application and synchronizes its state with that of the clones by transferring the necessary information to enforce identical state among them. In particular, we address the replication of relevant operating system state, such as network state to preserve network connections across failures, and the state that results from nondeterministic interleaved accesses to shared memory in SMP systems. We have implemented RR's state replication mechanisms in the Linux operating system by making novel use of existing features on the Intel and PowerPC architectures.

We present a new distributed checkpoint-restart mechanism, Cruz, that works without requiring application, library, or base kernel modifications. This mechanism provides comprehensive support for checkpointing and restoring application state, both at user level and within the OS. Our implementation builds on Zap, a process migration mechanism, implemented as a Linux kernel module, which operates by interposing a thin layer between applications and the OS. In particular, we enable support for networked applications by adding migratable IP and MAC addresses, and checkpoint-restart of socket buffer state, socket options, and TCP state. We leverage this capability to devise a novel method for coordinated checkpoint-restart that is simpler than prior approaches. For instance, it eliminates the need to flush communication channels by exploiting the packet re-transmission behavior of TCP and existing OS support for packet filtering. Our experiments show that the overhead of coordinating checkpoint-restart is negligible, demonstrating the scalability of this approach.