Predicting Intermediate Storage Performance for Workflow Applications

Configuring a storage system to better serve an application is a challenging task complicated by a multidimensional, discrete configuration space and the high cost of space exploration (e.g., by running the application with different storage configurations). To enable selecting the best configuration in a reasonable time, we design an end-to-end performance prediction mechanism that estimates the turn-around time of an application using storage system under a given configuration. This approach focuses on a generic object-based storage system design, supports exploring the impact of optimizations targeting workflow applications (e.g., various data placement schemes) in addition to other, more traditional, configuration knobs (e.g., stripe size or replication level), and models the system operation at data-chunk and control message level. This paper presents our experience to date with designing and using this prediction mechanism. We evaluate this mechanism using micro- as well as synthetic benchmarks mimicking real workflow applications, and a real application.. A preliminary evaluation shows that we are on a good track to meet our objectives: it can scale to model a workflow application run on an entire cluster while offering an over 200x speedup factor (normalized by resource) compared to running the actual application, and can achieve, in the limited number of scenarios we study, a prediction accuracy that enables identifying the best storage system configuration

Sharing the Burden of GHG Reductions

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).The G8 countries propose a goal of a 50% reduction in global emissions by 2050, in an effort that needs to take account of other agreements specifying that developing countries are to be provided with incentives to action and protected from the impact of measures taken by others. To help inform international negotiations of measures to achieve these goals we develop a technique for endogenously estimating the allowance allocations and associated financial transfers necessary to achieve predetermined distributional outcomes and apply it in the MIT Emissions Prediction and Policy Analysis (EPPA) model. Possible burden sharing agreements are represented by different allowance allocations (and resulting financial flows) in a global cap-and-trade system. Cases studied include agreements that allocate the burden based on simple allocation rules found in current national proposals and alternatives that specify national equity goals for both developing and developed countries. The analysis shows the ambitious nature of this reduction goal: universal participation will be necessary and the welfare costs can be both substantial and wildly different across regions depending on the allocation method chosen. The choice of allocation rule is shown to affect the magnitude of the task and required emissions price because of income effects. If developing countries are fully compensated for the costs of mitigation then the welfare costs to developed countries, if shared equally, are around 2% in 2020, rising to some 10% in 2050, and the implied financial transfers are large—over $400 billion per year in 2020 and rising to around$3 trillion in 2050. For success in dealing with the climate threat any negotiation of long-term goals and paths to achievement need to be grounded in a full understanding of the substantial amounts at stake.Development of the EPPA model used has been supported by the U.S. Department of Energy, U.S. Environmental Protection Agency and U.S. National Science Foundation, and by a consortium of industry and foundation sponsors of the MIT Joint Program on the Science and Policy of Global Change

Extending Message Passing Interface Windows to Storage

This work presents an extension to MPI supporting the one-sided communication model and window allocations in storage. Our design transparently integrates with the current MPI implementations, enabling applications to target MPI windows in storage, memory or both simultaneously, without major modifications. Initial performance results demonstrate that the presented MPI window extension could potentially be helpful for a wide-range of use-cases and with low-overhead