4,687 research outputs found
Fault Injection for Embedded Microprocessor-based Systems
Microprocessor-based embedded systems are increasingly used to control safety-critical systems (e.g., air and railway traffic control, nuclear plant control, aircraft and car control). In this case, fault tolerance mechanisms are introduced at the hardware and software level. Debugging and verifying the correct design and implementation of these mechanisms ask for effective environments, and Fault Injection represents a viable solution for their implementation. In this paper we present a Fault Injection environment, named FlexFI, suitable to assess the correctness of the design and implementation of the hardware and software mechanisms existing in embedded microprocessor-based systems, and to compute the fault coverage they provide. The paper describes and analyzes different solutions for implementing the most critical modules, which differ in terms of cost, speed, and intrusiveness in the original system behavio
Designing Software Architectures As a Composition of Specializations of Knowledge Domains
This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: âA software architecture should be a composition of specializations of knowledge domainsâ. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience
Adjusting process count on demand for petascale global optimizationâ
There are many challenges that need to be met before efficient and reliable computation at the
petascale is possible. Many scientific and engineering codes running at the petascale are likely to
be memory intensive, which makes thrashing a serious problem for many petascale applications.
One way to overcome this challenge is to use a dynamic number of processes, so that the total
amount of memory available for the computation can be increased on demand. This paper
describes modifications made to the massively parallel global optimization code pVTdirect in
order to allow for a dynamic number of processes. In particular, the modified version of the
code monitors memory use and spawns new processes if the amount of available memory is
determined to be insufficient. The primary design challenges are discussed, and performance
results are presented and analyzed
D-SPACE4Cloud: A Design Tool for Big Data Applications
The last years have seen a steep rise in data generation worldwide, with the
development and widespread adoption of several software projects targeting the
Big Data paradigm. Many companies currently engage in Big Data analytics as
part of their core business activities, nonetheless there are no tools and
techniques to support the design of the underlying hardware configuration
backing such systems. In particular, the focus in this report is set on Cloud
deployed clusters, which represent a cost-effective alternative to on premises
installations. We propose a novel tool implementing a battery of optimization
and prediction techniques integrated so as to efficiently assess several
alternative resource configurations, in order to determine the minimum cost
cluster deployment satisfying QoS constraints. Further, the experimental
campaign conducted on real systems shows the validity and relevance of the
proposed method
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Theory and Practice of Transactional Method Caching
Nowadays, tiered architectures are widely accepted for constructing large
scale information systems. In this context application servers often form the
bottleneck for a system's efficiency. An application server exposes an object
oriented interface consisting of set of methods which are accessed by
potentially remote clients. The idea of method caching is to store results of
read-only method invocations with respect to the application server's interface
on the client side. If the client invokes the same method with the same
arguments again, the corresponding result can be taken from the cache without
contacting the server. It has been shown that this approach can considerably
improve a real world system's efficiency.
This paper extends the concept of method caching by addressing the case where
clients wrap related method invocations in ACID transactions. Demarcating
sequences of method calls in this way is supported by many important
application server standards. In this context the paper presents an
architecture, a theory and an efficient protocol for maintaining full
transactional consistency and in particular serializability when using a method
cache on the client side. In order to create a protocol for scheduling cached
method results, the paper extends a classical transaction formalism. Based on
this extension, a recovery protocol and an optimistic serializability protocol
are derived. The latter one differs from traditional transactional cache
protocols in many essential ways. An efficiency experiment validates the
approach: Using the cache a system's performance and scalability are
considerably improved
Preemptive Software Transactional Memory
In state-of-the-art Software Transactional Memory (STM) systems, threads carry out the execution of transactions as non-interruptible tasks. Hence, a thread can react to the injection of a higher priority transactional task and take care of its processing only at the end of the currently executed transaction. In this article we pursue a paradigm shift where the execution of an in-memory transaction is carried out as a preemptable task, so that a thread can start processing a higher priority transactional task before finalizing its current transaction. We achieve this goal in an application-transparent manner, by only relying on Operating System facilities we include in our preemptive STM architecture. With our approach we are able to re-evaluate CPU assignment across transactions along a same thread every few tens of microseconds. This is mandatory for an effective priority-aware architecture given the typically finer-grain nature of in-memory transactions compared to their counterpart in database systems. We integrated our preemptive STM architecture with the TinySTM package, and released it as open source. We also provide the results of an experimental assessment of our proposal based on running a port of the TPC-C benchmark to the STM environment
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