Cordie␙06: concurrency, real-time and distribution in eiffel␓like languages

Ada is a structuredstatically typedimperativeand object-oriented high-level programming languageextended from Pascal and other languages. It has built-in language support for design by contract DbCextremely strong typingexplicit concurrency, tasks, synchronous message passing, protected objects, and non-determinism.

Ada improves code safety and maintainability by using the compiler to find errors in favor of runtime errors. Ada was originally designed for embedded and real-time systems. Tucker Taft of Intermetrics between andimproved support for systems, numerical, financial, and object-oriented programming OOP. Features of Ada include: strong typingmodular programming mechanisms packagesrun-time checkingparallel processing taskssynchronous message passingprotected objects, and nondeterministic select statementsexception handlingand generics.

Code blocks are delimited by words such as "declare", "begin", and "end", where the "end" in most cases is followed by the identifier of the block it closes e. In the case of conditional blocks this avoids a dangling else that could pair with the wrong nested if-expression in other languages like C or Java. Ada is designed for developing very large software systems. Ada packages can be compiled separately.

Ada package specifications the package interface can also be compiled separately without the implementation to check for consistency.

This makes it possible to detect problems early during the design phase, before implementation starts. A large number of compile-time checks are supported to help avoid bugs that would not be detectable until run-time in some other languages or would require explicit checks to be added to the source code.

For example, the syntax requires explicitly named closing of blocks to prevent errors due to mismatched end tokens. The adherence to strong typing allows detecting many common software errors wrong parameters, range violations, invalid references, mismatched types, etc. As concurrency is part of the language specification, the compiler can in some cases detect potential deadlocks. Ada also supports run-time checks to protect against access to unallocated memory, buffer overflow errors, range violations, off-by-one errorsarray access errors, and other detectable bugs.

These checks can be disabled in the interest of runtime efficiency, but can often be compiled efficiently. It also includes facilities to help program verification. For these reasons, Ada is widely used in critical systems, where any anomaly might lead to very serious consequences, e. Examples of systems where Ada is used include avionicsair traffic controlrailways, banking, military and space technology.

Ada's dynamic memory management is high-level and type-safe. Ada has no generic or untyped pointers ; nor does it implicitly declare any pointer type. Instead, all dynamic memory allocation and deallocation must occur via explicitly declared access types.Application security testing continues to be the fastest growing of all tracked information security segments. Fortify has been known for its depth of coverage and innovation for more than a decade.

Feature updates for the latest on-premise release, Our May updates include some great new features. My favorite new feature is Security Assistant for Visual Studio it even has its own blog post!

Fortify Security Assistant provides real-time-as-you-type security analysis on your code, and provides immediate results in the IDE Integrated development environment!

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Token Management This release includes a new user interface for managing tokens. You no longer have to use the CLI to create, extend, or revoke tokens. When a token is about to expire, a notification is sent, making interruptions due to expired tokens less likely.

The token management interface can be accessed from the Administration section under Users. This results from an increase in the maximum number of processing threads enabled by the enhanced DB Access concurrency. Predictions that fall within the confidence threshold are automatically audited.

Ada (programming language)

Consolidated Proxy Settings Fortify Software Security Center now uses a consolidated proxy configuration section that can be re-used throughout the application instead of having to individually configure proxy configurations for things like Audit Assistant, bug trackers, etc.

The JIRA plugin has been rewritten with better comments in cleaner code. Fortify WebInspect This release helps r educe friction with improved automationincluding these new features for WebInspect Enterprise:. The standalone proxy enables Fortify WebInspect Enterprise users to spin up and work with the WebInspect proxy without requiring WebInspect licenses to operate.

This is particularly useful for automating workflows via traffic capture. If you are a current customer and have questions, contact Micro Focus Fortify Customer Support using one of the following options.

If your organization is interested in secure application development, security testing, and continuous monitoring and protection of apps and the valuable data they contain, check out Micro Focus Fortify. This website uses cookies.

By continuing to browse or login to this website, you consent to the use of cookies. Learn more. Code: github. Clients can specify page size and start, and also whether to filter completed scans.

This is the full metadata of the request form.Working with Java applications has a lot of benefits. In the majority of cases, you get interoperability between operating systems and various environments. You can move your applications from server to server, from operating system to operating system, without major effort or in rare cases with minor changes. One of the most interesting benefits of running a JVM based application is automatic memory handling.

When you create an object in your code it is assigned on a heap and stays there until it is referenced from the code. When it is no longer needed it needs to be removed from the memory to make room for new objects. Garbage Collection GC tuning is the process of adjusting the startup parameters of your JVM-based application to match the desired results. Nothing more and nothing less. Which is by the way what you should start with. Or it can be as complicated as tuning all the advanced parameters to adjust the different heap regions.

Everything depends on the situation and your needs. There are resources that need to be designated for the garbage collector so it can do its work.

You can imagine that instead of handling the business logic of our application the CPU can be busy handling the removal of unused data from the heap. The GC process can be heavy. This can lead to your users not being able to properly use your application at all. Your distributed systems can collapse because of elements not responding in a timely manner. To avoid that we need to ensure that the garbage collector that is running for our JVM applications is well configured and is doing its job as good as it can.

The first thing that you should know is that tuning the garbage collection should be one of the last operations you do. To be blunt, there are numerous situations where the way how the garbage collector works only highlights a bigger problem. You will most likely be more effective in refactoring the code to be more efficient.

So how do we say that the garbage collector does a good job? We can look into our monitoring, like our own Sematext Cloud. It will provide you information regarding your JVM memory utilization, the garbage collector work and of course the overall performance of your application. For example, have a look at the following chart:.

Usually, it is a sign of a healthy JVM heap. The largest portion of the memory, called the old generation, gets filled up and then is cleared by the garbage collector.

If we would correlate that with the garbage collector timings we would see the whole picture. Knowing all of that we can judge if we are satisfied with how the garbage collection is working or if tuning is needed. There is also one more thing that you should consider when thinking about garbage collection performance tuning.

The default Java garbage collection settings may not be perfect for your application, so to speak. Meaning, instead of going for more hardware or for more beefy machines you may want to look into how your memory is managed. Sometimes tuning can decrease the operation cost lowering your expenses and allowing for growth without growing the environment. Once you are sure that the garbage collector is to blame and you want to start optimizing its parameters we can start working on the JVM startup parameters.

A Study of Lock-Free Based Concurrent Garbage Collectors for Multicore Platform

When talking about the procedure you should take when tuning the garbage collector you have to remember that there are more garbage collectors available in the JVM world.Concurrent garbage collectors CGC have recently obtained extensive concern on multicore platform. Excellent designed CGC can improve the efficiency of runtime systems by exploring the full potential processing resources of multicore computers.

Two major performance critical components for designing CGC are studied in this paper, stack scanning and heap compaction. Since the lock-based algorithms do not scale well, we present a lock-free solution for constructing a highly concurrent garbage collector.

The evaluation results of this study demonstrate that our approach achieves competitive performance. Garbage collection mechanism has been widely used in modern object oriented programming languages such as Java or C. The garbage collectors can guarantee the security and reliability of the runtime systems, but they also introduce additional performance overhead. Traditional garbage collectors perform the entire memory reclamation by suspending the running programs [ 1 ].

It is obvious that such a strategy will seriously affect performance and responsiveness of the systems, and this is especially unacceptable for a real-time system. The emergence of multicore architectures has a profound impact on the implementation of programming languages, and the languages that support garbage collection mechanism should make use of this parallel processing capacity [ 2 ].

With the development of multicore architectures, the designing of parallel and concurrent garbage collector faces a number of chances and challenges. Kliot et al. Most of the garbage collectors will interrupt the mutator threads when performing the scanning of the runtime stack in order to obtain an accurate heap snapshot which will be used in the subsequent marking phase.

Traditional blocking stack scanning strategy may lead to a long and unpredictable pause time. Another noteworthy performance issue is heap fragmentation. As we know, most of modern on-the-fly garbage collectors adopt the strategy that does not move the objects in order to obtain a less pause time during the collection process. One of the major drawbacks of that strategy is that the heap becomes fragmented and the allocation becomes more costly for long time running programs.

To solve fragmentation problem, an extra heap compaction function is added into the garbage collection. Apparently, this compaction function should be carefully designed to avoid creating more overhead. In this paper we mainly study two issues of designing concurrent garbage collectors, stack scanning and heap compaction.

Distributed Systems - OS - Lec-6 - Bhanu Priya

In the multicore computing environments, lock-based synchronization mechanism does not scale well with concurrent execution threads. To solve this problem, lock-free algorithms are designed for highly concurrent systems. Compare and swap CAS primitive can be used to execute atomic read-modify-write operations on shared data in lock-free algorithms.

We first use CAS to design a concurrent stack scanning mechanism. Then for heap compaction, we use MCAS synchronization mechanism to design a lock-free and concurrent object copying process. The rest of this paper is organized as follows. In Section 2 we describe a methodology of concurrent stack scanning. In Section 3 we present a design of concurrent heap compaction.

Measurements are reported in Section 4 and related work is discussed in Section 5and finally we conclude in Section 6. In this section we present a concurrent stack scanning mechanism using CAS synchronization primitive, which allows collector threads to scan the stack concurrently with mutator threads in lock-free manner.Cloud Functions and Cloud Run both provide good solutions for hosting your webhook targets.

Generally, Cloud Functions is quick to set up, good for prototyping, and ideal for lower volume workflows. Cloud Run provides more flexibility and is able to handle larger volumes with concurrency. Using Cloud Run, you can define a webhook target in any language you choose. You only need to create an HTTP endpoint that can accept the data. Typically this is done with a POSTfor example:. This is usually done by sending some kind of token, message, or secret and expecting a valid response.

You'll need to obtain these requirements from the service provider. Using the same example above, this could look like:. After the provider verifies your ownership, you'll need to add authorization on your end as well. A webhook target is an open and public URL.

Most services provide a token or a secret to ensure that the incoming requests are from authorized services. Because the URL is public, you cannot prevent malicious attempts to send data to the webhook target. However, using tokens or secrets ensures you only process data from authorized sources. In order to verify the request, you need to store your copy of the secret either as an environment variable or using some kind of key management system. Each request should have a secret or token in the request headers or the JSON payload, and you must check it to ensure the source is valid.

If the webhook provider does not support a secret or other authentication mechanism, anyone with the URL of your webhook target will be able to send messages. In this case, your webhook implementation should be safe to expose to the public internet. Most services require you to respond to a request within a set amount of time, as specified by the service. Some webhooks have built-in retry methods if there is an error response, such as an HTTP status code of 4xx or 5xx, so you'll need to return a successful status code 2xx to let the service know the event was processed properly.

Both Cloud Run and the webhooks provider have timeouts. The shorter of the two will apply to your application. These products allow you to quickly hand off the data, immediately return a success response to the webhooks provider, and continue the processing without the timeout concern.

These are also good options for handling failures and retries.I defended my PhD thesis Practical framework for contract-based concurrent object-oriented programming in Februaryand moved to Bath, U.

The model takes advantage of the inherent concurrency implicit in object-oriented programming to provide programmers with a simple extension enabling them to produce parallel applications with little more effort than sequential ones. The basic idea is to take object-oriented programming as given, and extend it in a minimal way to cover concurrency and distribution.

The mechanism is based on the principles of Design by Contract: it largely derives from examining the semantics of contracts in a non-sequential setting. Writing concurrent applications with SCOOP is extremely simple, since it does not require the usual baggage of concurrent and multithreaded programming semaphores, rendezvous, monitors, etc.

The model is applicable to many different physical setups, from multithreading to highly parallel scientific computation, to distributed systems and Web programming. Dissertation PDF. I also enjoy learning foreign languages.

My favourite colour is blue and my favourite island in the Indian Ocean is Mauritius. Piotr Nienaltowski. Nienaltowski at praxis-his. Nienaltowski P. Arslan V. Paper PDF. Slides PPS. Eds : Journal of Object Technology, vol. NET Technologies Skala V. Concurrent Object-Oriented Programming. Introduction to programming.Ada is a structuredstatically typedimperativeand object-oriented high-level programming languageextended from Pascal and other languages.

It has built-in language support for design by contract DbCextremely strong typingexplicit concurrency, tasks, synchronous message passing, protected objects, and non-determinism. Ada improves code safety and maintainability by using the compiler to find errors in favor of runtime errors.

Ada was originally designed for embedded and real-time systems. Tucker Taft of Intermetrics between andimproved support for systems, numerical, financial, and object-oriented programming OOP. Features of Ada include: strong typingmodular programming mechanisms packagesrun-time checkingparallel processing taskssynchronous message passingprotected objects, and nondeterministic select statementsexception handlingand generics.

Code blocks are delimited by words such as "declare", "begin", and "end", where the "end" in most cases is followed by the identifier of the block it closes e. In the case of conditional blocks this avoids a dangling else that could pair with the wrong nested if-expression in other languages like C or Java.

Ada is designed for developing very large software systems. Ada packages can be compiled separately. Ada package specifications the package interface can also be compiled separately without the implementation to check for consistency.

This makes it possible to detect problems early during the design phase, before implementation starts. A large number of compile-time checks are supported to help avoid bugs that would not be detectable until run-time in some other languages or would require explicit checks to be added to the source code. For example, the syntax requires explicitly named closing of blocks to prevent errors due to mismatched end tokens.

The adherence to strong typing allows detecting many common software errors wrong parameters, range violations, invalid references, mismatched types, etc. As concurrency is part of the language specification, the compiler can in some cases detect potential deadlocks.

Ada also supports run-time checks to protect against access to unallocated memory, buffer overflow errors, range violations, off-by-one errorsarray access errors, and other detectable bugs. These checks can be disabled in the interest of runtime efficiency, but can often be compiled efficiently. It also includes facilities to help program verification.

For these reasons, Ada is widely used in critical systems, where any anomaly might lead to very serious consequences, e. Examples of systems where Ada is used include avionicsair traffic controlrailways, banking, military and space technology.

Ada's dynamic memory management is high-level and type-safe. Ada has no generic or untyped pointers ; nor does it implicitly declare any pointer type. Instead, all dynamic memory allocation and deallocation must occur via explicitly declared access types. Each access type has an associated storage pool that handles the low-level details of memory management; the programmer can either use the default storage pool or define new ones this is particularly relevant for Non-Uniform Memory Access.

It is even possible to declare several different access types that all designate the same type but use different storage pools. Also, the language provides for accessibility checksboth at compile time and at run time, that ensures that an access value cannot outlive the type of the object it points to.

Though the semantics of the language allow automatic garbage collection of inaccessible objects, most implementations do not support it by default, as it would cause unpredictable behaviour in real-time systems. Ada does support a limited form of region-based memory management ; also, creative use of storage pools can provide for a limited form of automatic garbage collection, since destroying a storage pool also destroys all the objects in the pool.

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