Light Reading: Optimizing Reader/Writer Locking for Read-Dominant Real-Time Workloads (Artifact)

This paper is directed at reader/writer locking for read-dominant real-time workloads. It is shown that state-of-the-art real-time reader/writer locking protocols are subject to performance limitations when reads dominate, and that existing schedulability analysis fails to leverage the sparsity of writes in this case. A new reader/writer locking-protocol implementation and new inflation-free schedulability analysis are proposed to address these problems. Overhead evaluations of the new implementation show a decrease in overheads of up to 70% over previous implementations, leading to throughput for read operations increasing by up to 450%. Schedulability experiments are presented that show that the analysis results in schedulability improvements of up to 156.8% compared to the existing state-of-the-art approach

Scopes Describe Frames: A Uniform Model for Memory Layout in Dynamic Semantics (Artifact)

Our paper introduces a systematic approach to the alignment of names in the static structure of a program, and memory layout and access during its execution. We develop a uniform memory model consisting of frames that instantiate the scopes in the scope graph of a program. This provides a language-independent correspondence between static scopes and run-time memory layout, and between static resolution paths and run-time memory access paths. The approach scales to a range of binding features, supports straightforward type soundness proofs, and provides the basis for a language-independent specification of sound reachability-based garbage collection. This Coq artifact showcases how our uniform model for memory layout in dynamic semantics provides structure to type soundness proofs. The artifact contains type soundness proofs mechanized in Coq for (supersets of) all languages in the paper. The type soundness proofs rely on a language-independent framework formalizing scope graphs and frame heaps

Concurrent Data Structures Linked in Time (Artifact)

This artifact provides the full mechanization in FCSL of the developments in the companion paper, "Concurrent Data Structures Linked in Time". In the latter, we propose a new method, based on a separation-style logic, for reasoning about concurrent objects with such linearization points. We embrace the dynamic nature of linearization points, and encode it as part of the data structure's auxiliary state, so that it can be dynamically modified in place by auxiliary code, as needed when some appropriate run-time event occurs. We illustrate the method by verifying (mechanically in FCSL) an intricate optimal snapshot algorithm due to Jayanti, as well as some clients. FCSL is the first completely formalized framework for mechanized verification of full functional correctness of fine-grained concurrent programs. It is implemented as an embedded domain-specific language (DSL) in the dependently-typed language of the Coq proof assistant, and is powerful enough to reason about programming features such as higher-order functions and local thread spawning. By incorporating a uniform concurrency model, based on state-transition systems and partial commutative monoids, FCSL makes it possible to build proofs about concurrent libraries in a thread-local, compositional way, thus facilitating scalability and reuse: libraries are verified just once, and their specifications are used ubiquitously in client-side reasoning

Towards Strong Normalization for Dependent Object Types (DOT) (Artifact)

This artifact provides the fully mechanized proof of strong normalization for D_{<:}a variant of (Dependent Object Types) DOT (Rompf & Amin, 2016) that excludes recursive functions and recursive types. The intersection type and recursive self type are further integrated, moving towards DOT. The key proof idea follows the method of Girard (Girard, 1972) and Tait (Tait, 1967)

Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact)

This artifact supports our research in self-adaptation in large-scale software-intensive distributed systems. The main problem in making such systems self-adaptive is that their adaptation needs to consider the current situation in the whole system. However, developing a complete and accurate model of such systems at design time is very challenging. We are instead investigating a novel approach where the system model consists only of the essential input and output parameters and Big Data analytics is used to guide self-adaptation based on a continuous stream of operational data. In this artifact, we provide a concrete model problem that can be used as a case study for evaluating different self-adaptation techniques pertinent to complex large-scale distributed systems. We also provide an extensible tool-based framework for endorsing an arbitrary system with self-adaptation based on analysis of operational data coming from the system. The model problem (CrowdNav) and the framework (RTX) have been packaged together in this artifact, but can also work independently

Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact)

Arising with the popularity of Hadoop, optimizing Hadoop executions has grabbed lots of attention from research community. Many research contributions are proposed to elevate Hadoop performance, particularly in the domain of self-adaptive software systems. However, due to the complexity of Hadoop operation and the difficulty to reproduce experiments, the efforts of these Hadoop-related research are hard to be evaluated. To address this limitation, we propose a research acceleration platform for rapid prototyping and evaluation of self-adaptive behavior in Hadoop clusters. It provides an automated manner to quickly and easily provision reproducible Hadoop environments and execute acknowledged benchmarks. This platform is based on the state-of-the-art container technology that supports both distributed configurations as well as standalone single-host setups. We demonstrate the approach on a complete implementation of a concrete Hadoop self-adaptive case study

Lifestate: Event-Driven Protocols and Callback Control Flow (Artifact)

Developing interactive applications (apps) against event-driven software frameworks such as Android is notoriously difficult. To create apps that behave as expected, developers must follow complex and often implicit asynchronous programming protocols. Such protocols intertwine the proper registering of callbacks to receive control from the framework with appropriate application-programming interface (API) calls that in turn affect the set of possible future callbacks. An app violates the protocol when, for example, it calls a particular API method in a state of the framework where such a call is invalid. What makes automated reasoning hard in this domain is largely what makes programming apps against such frameworks hard: the specification of the protocol is unclear, and the control flow is complex, asynchronous, and higher-order. In this paper, we tackle the problem of specifying and modeling event-driven application-programming protocols. In particular, we formalize a core meta-model that captures the dialogue between event-driven frameworks and application callbacks. Based on this meta-model, we define a language called lifestate that permits precise and formal descriptions of application-programming protocols and the callback control flow imposed by the event-driven framework. Lifestate unifies modeling what app callbacks can expect of the framework with specifying rules the app must respect when calling into the framework. In this way, we effectively combine lifecycle constraints and typestate rules. To evaluate the effectiveness of lifestate modeling, we provide a dynamic verification algorithm that takes as input a trace of execution of an app and a lifestate protocol specification to either produce a trace witnessing a protocol violation or a proof that no such trace is realizable

Scheduling Self-Suspending Tasks: New and Old Results (Artifact)

In computing systems, a job may suspend itself (before it finishes its execution) when it has to wait for certain results from other (usually external) activities. For real-time systems, such self-suspension behavior has been shown to induce performance degradation. Hence, the researchers in the real-time systems community have devoted themselves to the design and analysis of scheduling algorithms that can alleviate the performance penalty due to self-suspension behavior. As self-suspension and delegation of parts of a job to non-bottleneck resources is pretty natural in many applications, researchers in the operations research (OR) community have also explored scheduling algorithms for systems with such suspension behavior, called the master-slave problem in the OR community. This paper first reviews the results for the master-slave problem in the OR literature and explains their impact on several long-standing problems for scheduling self-suspending real-time tasks. For frame-based periodic real-time tasks, in which the periods of all tasks are identical and all jobs related to one frame are released synchronously, we explore different approximation metrics with respect to resource augmentation factors under different scenarios for both uniprocessor and multiprocessor systems, and demonstrate that different approximation metrics can create different levels of difficulty for the approximation. Our experimental results show that such more carefully designed schedules can significantly outperform the state-of-the-art