“Almost” Subsidy-free Spatial Pricing in a Multi-dimensional Setting

Consider a population of citizens uniformly spread over the entire plane, that faces a problem of locating public facilities to be used by its members. The cost of every facility is financed by its users, who also face an idiosyncratic private access cost to the facility. We assume that the facilities’ cost is independent of location and access costs are linear with respect to the Euclidean distance. We show that an external intervention that covers 0.19% of the facility cost is sufficient to guarantee secession-proofness or no cross-subsidization, where no group of individuals is charged more than its stand alone cost incurred if it had acted on its own. Moreover, we demonstrate that in this case the Rawlsian access pricing is the only secession-proof allocation.Secession-Proofness, Optimal Jurisdictions, Rawlsian Allocation, Hexagonal Partition, Cross-Subsidization

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model") and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries. Through extensive benchmarking, we show that implementing complex objects manipulation and non-trivial, library-style computations on top of PlinyCompute can result in a speedup of 2x to more than 50x or more compared to equivalent implementations on Spark.Comment: 48 pages, including references and Appendi

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

Optimizing egalitarian performance in the side-effects model of colocation for data center resource management

In data centers, up to dozens of tasks are colocated on a single physical machine. Machines are used more efficiently, but tasks' performance deteriorates, as colocated tasks compete for shared resources. As tasks are heterogeneous, the resulting performance dependencies are complex. In our previous work [18] we proposed a new combinatorial optimization model that uses two parameters of a task - its size and its type - to characterize how a task influences the performance of other tasks allocated to the same machine. In this paper, we study the egalitarian optimization goal: maximizing the worst-off performance. This problem generalizes the classic makespan minimization on multiple processors (P||Cmax). We prove that polynomially-solvable variants of multiprocessor scheduling are NP-hard and hard to approximate when the number of types is not constant. For a constant number of types, we propose a PTAS, a fast approximation algorithm, and a series of heuristics. We simulate the algorithms on instances derived from a trace of one of Google clusters. Algorithms aware of jobs' types lead to better performance compared with algorithms solving P||Cmax. The notion of type enables us to model degeneration of performance caused by using standard combinatorial optimization methods. Types add a layer of additional complexity. However, our results - approximation algorithms and good average-case performance - show that types can be handled efficiently.Comment: Author's version of a paper published in Euro-Par 2017 Proceedings, extends the published paper with addtional results and proof

A support architecture for reliable distributed computing systems

The Clouds kernel design was through several design phases and is nearly complete. The object manager, the process manager, the storage manager, the communications manager, and the actions manager are examined

Real-time disk scheduling in a mixed-media file system

This paper presents our real-time disk scheduler called the Delta L scheduler, which optimizes unscheduled best-effort disk requests by giving priority to best-effort disk requests while meeting real-time request deadlines. Our scheduler tries to execute real-time disk requests as much as possible in the background. Only when real-time request deadlines are endangered, our scheduler gives priority to real-time disk requests. The Delta L disk scheduler is part of our mixed-media file system called Clockwise. An essential part of our work is extensive and detailed raw disk performance measurements. The Delta L disk scheduler for its real-time schedulability analysis and to decide whether scheduling a best-effort request before a real-time request violates real-time constraints uses these raw performance measurements. Further, a Clockwise off-line simulator uses the raw performance measurements where a number of different disk schedulers are compared. We compare the Delta L scheduler with a prioritizing Latest Start Time (LST) scheduler and non-prioritizing EDF scheduler. The Delta L scheduler is comparable to LST in achieving low latencies for best-effort requests under light to moderate real-time loads and better in achieving low latencies for best-effort requests for extreme real-time loads. The simulator is calibrated to an actual Clockwise. Clockwise runs on a 200MHz Pentium-Pro based PC with PCI bus, multiple SCSI controllers and disks on Linux 2.2.x and the Nemesis kernel. Clockwise performance is dictated by the hardware: all available bandwidth can be committed to real-time streams, provided hardware overloads do not occur