A Framework for Verification and Debugging of Resource Usage Properties: Resource Usage Verification

The estimation and control of resource usage is now an important challenge in an increasing number of computing systems. In particular, requirements on timing and energy arise in a wide variety of applications such as internet of things, cloud computing, health, transportation, and robots. At the same time, parallel computing, with (heterogeneous) multi-core platforms in particular, has become the dominant paradigm in computer architecture. Predicting resource usage on such platforms poses a difficult challenge. Most work on static resource analysis has focused on sequential programs, and relatively little progress has been made on the analysis of parallel programs, or more specifically on parallel logic programs. We propose a novel, general, and flexible framework for setting up cost equations/relations which can be instantiated for performing resource usage analysis of parallel logic programs for a wide range of resources, platforms, and execution models. The analysis estimates both lower and upper bounds on the resource usage of a parallel program (without executing it) as functions on input data sizes. In addition, it also infers other meaningful information to better exploit and assess the potential and actual parallelism of a system. We develop a method for solving cost relations involving the max function that arise in the analysis of parallel programs. Finally, we instantiate our general framework for the analysis of logic programs with Independent And-Parallelism, report on an implementation within the CiaoPP system, and provide some experimental results. To our knowledge, this is the first approach to the cost analysis of parallel logic programs.

Since distributed software systems are ubiquitous, their correct functioning is crucially important. Static verification is possible in principle, but requires high expertise and effort which is not feasible in many eco-systems. Runtime verification can serve as a lean alternative, where monitoring mechanisms are automatically generated from property specifications, to check compliance at runtime. This paper contributes a practical solution for powerful and flexible runtime verification of distributed, object-oriented applications, via a combination of the runtime verification tool Larva and the active object framework ProActive. Even if Larva supports in itself only the generation of local, sequential monitors, we empower Larva for distributed monitoring by connecting monitors with active objects, turning them into active, communicating monitors. We discuss how this allows for a variety of monitoring architectures. Further, we show how property specifications, and thereby the generated monitors, provide a model that splits the blame between the local object and its environment. While Larva itself focuses on monitoring of control-oriented properties, we use the Larva front-end StaRVOOrS to also capture data-oriented (pre/post) properties in the distributed monitoring. We demonstrate this approach to distributed runtime verification with a case study, a distributed key/value store.

The vision of the Semantic Web is to reduce manual discovery and usage of Web resources (documents and services) and to allow software agents to automatically identify these Web resources, integrate them and execute them for achieving the intended goals of the user. Such a composed Web service may be represented as a workflow, called service flow. Current studies of Web services are not sufficient for automatic composition. This paper presents different types of compositional knowledge required for Web service discovery and composition: syntactic, semantic and pragmatic knowledge. As a proof of concept, we have implemented our framework in a cardiovascular domain which requires advanced service discovery and composition across heterogeneous platforms of multiple organizations. Within this framework, we describe (1) How to represent the compositional knowledge, which plays a role in service discovery and composition, in DAML-S; (2) How heterogeneous medical services intemperate in a composed medical service flow. (3) To solve this knowledge level integration, we build on an ontology integration method called SEMIO (Semantic Interoperability).

As a novel decentralized computing paradigm, blockchain is expected to disrupt the existing e-commerce architecture and process. Secure smart contracts are the crucial foundation for e-commerce based on blockchain. However, vulnerabilities in smart contracts occur from time to time and cause significant financial losses in e-commerce. Some static verification methods have been developed to guarantee security for e-commerce smart contracts at design time, but they cannot support complex scenarios at runtime. As a lightweight verification method, runtime verification is a potential method for secure e-commerce smart contracts. The existing runtime verification methods are based on the manual instrument, which leads to additional overheads and gas consumption. To deal with this, we propose a passive learning-based runtime verification framework for e-commerce smart contracts. Firstly, by exploring the Genetic algorithm to evolve state merging and automaton reorganizing in order to simultaneously split time and gas behaviors, we propose a passive learning method to model runtime information for e-commerce smart contracts (PL4ESC). It directly learns P2TA (priced probabilistic timed automaton) from runtime traces without any prior knowledge. Then, we integrate PL4ESC with the open-source PAT (Process Analysis Toolkit) to automatically verify the security of runtime e-commerce smart contracts. The experiments show that PL4ESC is better at accuracy and precision than state-of-the-art passive learning methods. It improves accuracy by 1 to 4 percent compared to TAG and RTI+. As far as we know, it is not only the first learning method that can learn a P2TA from traces, but it is also the first automated runtime verification framework for e-commerce smart contracts. This will provide security guarantees for blockchain-based e-commerce.

Resource-intensive applications such as scientific applications require the architecture or system on which they execute to display a very high level of dependability to reduce the impact of faults. Typically, the state of the underlying system is captured through messages that are recorded in a log file, which has been proven useful to system administrators in understanding the root-causes of system failures (and for their subsequent debugging). However, the time window between when the first error message is detected in the log file and time of the ensuing failure may not be large enough to allow the administrators to save the state of the running application, which will result in lost execution time. We thus address this fundamental question: Is it possible to extend this time window? The answer is positive: We show that, by using (i) resource usage logs to track anomalous resource usage and (ii) error logs to identify root-causes of system failures, it is possible to increase the time window, on average, by 50 minutes. These files were those obtained for the Ranger Supercomputer from TACC. We achieve this by applying anomaly detection techniques on resource usage data and conducting a root-cause analysis on error log files.

Considering resource usage is a powerful insight in the analysis of many phenomena in the sciences. Much of the current research on these resource theories focuses on the analysis of specific resources such quantum entanglement, purity, randomness or asymmetry. However, the mathematical foundations of resource theories are at a much earlier stage, and there has been no satisfactory account of quantitative aspects such as costs, rates or probabilities. We present a categorical foundation for quantitative resource theories, derived from enriched category theory. Our approach is compositional, with rich algebraic structure facilitating calculations. The resulting theory is parameterized, both in the quantities under consideration, for example costs or probabilities, and in the structural features of the resources such as whether they can be freely copied or deleted. We also achieve a clear separation of concerns between the resource conversions that are freely available, and the costly resources that are typically the object of study. By using an abstract categorical approach, our framework is naturally open to extension. We provide many examples throughout, emphasising the resource theoretic intuitions for each of the mathematical objects under consideration.

Ensuring the correctness of a distributed program is not an easy task. Testing and formal methods can play a significant role in this regard. However, testing does not allow for formal specification of the properties to be checked. On the other hand, formal methods verify that a model of a distributed system satisfies certain properties that are formally specified. However, formal methods do not guarantee that a particular implementation of the system under consideration will also satisfy all of the necessary properties. Runtime verification tries to combine some of the advantages of testing and some of the advantages of formal methods. Runtime verification works on a particular implementation of the system and at the same time it allows us to specify the properties of interest formally. In this chapter we have talked about runtime verification of distributed programs. The main components of a runtime verification framework have been presented and discussed in some details. The reasons that make runtime verification of distributed programs a difficult task have been discussed. A summarization of some of the techniques presented in the literature to simplify runtime verification of distributed programs has also been presented.

Static verification of software is becoming ever more effective and efficient. Still, static techniques either have high precision, in which case powerful judgements are hard to achieve automatically, or they use abstractions supporting increased automation, but possibly losing important aspects of the concrete system in the process. Runtime verification has complementary strengths and weaknesses. It combines full precision of the model (including the real deployment environment) with full automation, but cannot judge future and alternative runs. Another drawback of runtime verification can be the computational overhead of monitoring the running system which, although typically not very high, can still be prohibitive in certain settings. In this paper we propose a framework to combine static analysis techniques and runtime verification with the aim of getting the best of both techniques. In particular, we discuss an instantiation of our framework for the deductive theorem prover KeY, and the runtime verification tool Larva. Apart from combining static and dynamic verification, this approach also combines the data centric analysis of KeY with the control centric analysis of Larva. An advantage of the approach is that, through the use of a single specification which can be used by both analysis techniques, expensive parts of the analysis could be moved to the static phase, allowing the runtime monitor to make significant assumptions, dropping parts of expensive checks at runtime. We also discuss specific applications of our approach.

Interrupts have been widely used in safety-critical computer systems to handle outside stimuli and interact with the hardware, but reasoning about interrupt-driven software remains a difficult task. Although a number of static verification techniques have been proposed for interrupt-driven software, they often rely on constructing a monolithic verification model. Furthermore, they do not precisely capture the complete execution semantics of interrupts such as nested invocations of interrupt handlers. To overcome these limitations, we propose an abstract interpretation framework for static verification of interrupt-driven software that first analyzes each interrupt handler in isolation as if it were a sequential program, and then propagates the result to other interrupt handlers. This iterative process continues until results from all interrupt handlers reach a fixed point. Since our method never constructs the global model, it avoids the up-front blowup in model construction that hampers existing, non-modular, verification techniques. We have evaluated our method on 35 interrupt-driven applications with a total of 22,541 lines of code. Our results show the method is able to quickly and more accurately analyze the behavior of interrupts.

The Resource-Constrained Project Scheduling Problem (RCPSP) is a general problem class of scheduling problems. It even contains the well-known job shop scheduling problem as a special case. In these problems, jobs demand different amounts from multiple resources at once. The goal is to minimize completion time while satisfying resource capacity constraints throughout the schedule. The RCPSP has several variations and generalizations, one of which is the multi-mode problem. Multiple operation modes are given for jobs, differing in resource usages and execution times. Another generalization is where resources have time-varying capacities instead of a constant limit. The S-graph framework was developed for batch process scheduling and used successfully in various applications. The advantages of this approach motivate research to extend its capabilities to more general problem classes and apply it in various specialized case studies. The authors have previously presented an extension of the S-graph framework for RCPSP and its multi-mode generalization. In this work, a further extension is proposed for handling time-varying capacities. The method relies on a model transformation technique and recent algorithmic developments of the framework.

In railways, computer-based interlocking (CBI) systems are safety critical systems, and should be proved that there is no defect leading to catastrophes during their whole life cycles. Runtime verification is able to provide on-going protection during the operational phase. Unfortunately, as being a variantrich system, it is hard to build monitors for CBI with standard runtime verification frameworks. To overcome this shortcoming, we propose parametric timed automata (PTA) to build feature models for the system. We prove that the language of PTA has more expressive power than timed automata. Based on PTA, we build monitors for a concrete CBI used in Beijing Metro Line 7. The experiment results show that complicated variant-rich CBI systems can be specified with a unique PTA feature model, and monitors can be quickly derived from the model by assign values to parameters. Therefore, this approach is feasible for monitoring CBI systems.

Topicality. The tourist and recreational sphere is one of the largest users of natural resources. Hence, there are urgent questions on how not only to increase the efficiency of their usage but also to ensure accessibility, preservation of qualitative and quantitative characteristics for future generations; also, the formation of recreational services for urgent recreational needs of all segments of the population. Aim and tasks. The purpose of the study is to form the conceptual basis for the development of inclusive recreational nature usage. The following tasks for achieving this goal: to define the conceptual provisions of inclusive development that are relevant to the recreational sphere and recreational nature management; to offer an interpretation of inclusive recreational nature usage; specifying its features, principles, goals, criteria. Research results. Based on the dominants of inclusive development, the basic provisions of inclusive recreational nature management have been developed: equal opportunities in access to recreational resources; equal opportunities for the right to rest, restoration of psycho-physical conditions; equal opportunities to ensure the harmonious development of personality; improving the quality of life of people through the availability of recreational services; ensuring socio-economic activity, well-being and employment opportunities in the field of tourism and recreation and related businesses and fair distribution of results; improving the efficiency of the use of publicly available natural resources and conditions. The essence of inclusive recreational nature is to provide equal opportunities for the usage of natural recreational resources and areas to meet socially significant and individual recreational needs that positively affect the well-being and quality of life. Conclusion. It is advisable to use an inclusive approach to equalize the opportunities for the use and consumption of valuable recreational resources and conditions for recreation, health, comprehensive human, and economic development. The formulated essence, features, principles, criteria, and goals of inclusive recreational nature usage have indicated the directions of transformation processes designed to ensure the necessary changes to guarantee equal opportunities for the population of the country.

Runtime verification (RV) monitors program executions for conformance with formal specifications (specs). This paper concerns Monitoring-Oriented Programming (MOP), the only RV approach shown to scale to thousands of open-source GitHub projects when simultaneously monitoring passing unit tests against dozens of specs. Explicitly storing traces—sequences of spec-related program events—can make it easier to debug spec violations or to monitor tests against hyperproperties, which requires reasoning about sets of traces. But, most online MOP algorithms are implicit trace, i.e. they work event by event to avoid the time and space costs of storing traces. Yet, TraceMOP, the only explicit-trace online MOP algorithm, is often too slow and often fails. We propose LazyMOP, a faster explicit-trace online MOP algorithm for RV of tests that is enabled by three simple optimizations. First, whereas all existing online MOP algorithms eagerly monitor all events as they occur, LazyMOP lazily stores only unique traces at runtime and monitors them just before the test run ends. Lazy monitoring is inspired by a recent finding: 99.87% of traces during RV of tests are duplicates. Second, to speed up trace storage, LazyMOP encodes events and their locations as integers, and amortizes the cost of looking up locations across events. Lastly, LazyMOP only synchronizes accesses to its trace store after detecting multi-threading, unlike TraceMOP’s eager and wasteful synchronization of all accesses. On 179 Java open-source projects, LazyMOP is up to 4.9x faster and uses 4.8x less memory than TraceMOP, finding the same traces (modulo test non-determinism) and violations. We show LazyMOP’s usefulness in the context of software evolution, where tests are re-run after each code change. LazyMOP e optimizes LazyMOP in this context by generating fewer duplicate traces. Using unique traces from one code version, LazyMOP e finds all pairs of method 𝑚 and spec 𝑠, where all traces for 𝑠 in 𝑚 are identical. Then, in a future version, LazyMOP e generates and monitors only one trace of 𝑠 in 𝑚. LazyMOP e is up to 3.9x faster than LazyMOP and it speeds up two recent techniques that speed up RV during evolution by up to 4.6x with no loss in violations.

Local people's knowledge with regard to land use activities in southwest Madagascar – Conceptual insights for sustainable land management

With the increasing human population and urbanization, the demand for animal origin products is going to grow, especially in the developing nations till the 2050s and the production needs to be escalated and optimized with the changing climate. Heat stress is known to reduce the animal performance, production, shelf life and meat quality in all animals. The beef cattle are globally reared, following different managemental practices, so the usage of natural resources like land and water, manpower, fodders, production systems and the environmental impact also varies profoundly. Recent changes in the climate, global warming and depletion of resources have severely affected the production and heat stress is now a common constraint all over the world. Due to evolutionary diversification the tropical and temperate breeds are comparatively more thermotolerant, but the beef cattle in the colder regions are vulnerable to high environmental temperatures. Also, the production of beef increases the carbon footprint and is much less eco-friendly than growing plant-based protein. So, we comprehended the environmental temperature variation over the continents and impact of heat stress on beef cattle. Also, other factors like cattle population, land and pasture usage, livestock units in trade, methane emissions and gross beef production value were examined to evaluate the collective impact of all these on the beef sector. Our findings and predictions reveal that, in the advent of climate change, depleting natural resources and rise in the greenhouse gases, beef production will be a constant challenge, which can be only achieved by maintaining a healthy cattle population and optimum usage of natural resources. Only then can the beef sector be efficient, sustainable, and a profitable enterprise in future.