Comparison of Functions, Threads, Objects, Components, & Services

For example components can be optimized for better performance through a compiler, inside an interpreter, or from the outside via the environment. Whereas services can only be optimized via the environment. Rarely does a service have a very low-level interface for tuning internals.

	Functions	Threads	Objects	Components	Services
Locality	Same	Same Different	Same Different	Different	Different
Environment	Same	Same	Same	Same Different	Different
Overhead	Max	Min	Min	Min Max Distributed	Max Distributed
Speed	Very Fast IPC	Very Fast IPC	Fast IPC	Slow IPC	Very Slow IPC
Optimization	Compiler	Compiler	Interpreter Environment	Compiler Interpreter Environment	Environment
Debugging	Min	Long-term	Short-term Long-term	Long-term	Long-term

Do you have a suggestion about how to improve this blog? Let's talk about it. Contact me at David.Brenner.Jr@Gmail.com or 720-584-5229.

Comments

The meaning of time in reinforcement learning

Reinforcement learning (RL) is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning is concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward through the process of trial and error. In reinforcement learning an agent starts at an empty state then analyzes the available datasets according to a policy of positive states and negative states. Rather than being explicitly taught as in supervised learning the correct set of actions for performing a task, reinforcement learning uses rewards as signals for positive states and punishments as signals for negative states. The agent obtains the best path to a desirable reward as a cumulation of positive states and negative states. As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences...

Threat hunting polymorphic malware in Linux with Python

You can investigate suspicious activity that could be polymorphic malware by collecting relevant machine data from your endpoint. You can use the machine data to create your own analysis. Before you start your investigation you will need to determine normal activity on your endpoint. Normal activity is the scope of functionality of the software on your endpoint during periods of low activity and high activity. You will need some kind of software that periodically collects specific machine data from your endpoint like my software developed in Python that's available for free download at https://github.com/davidbrennerjr/server-stats-collector Ingest one or more of the following machine data from Category #1. Ingest one or more of the following machine data from Category #2. And ingest one or more of the following machine data from Category #3. Category #1 General system-wide error messages from /var/log/syslog Auditing logs of application rule...

Application behavior monitoring in Linux with Python

You can monitor application behaviors by collecting relevant machine data from your endpoint. You can use the machine data to investigate suspicious activity and create your own analysis. Before you start your investigation you will need to determine normal activity on your endpoint. Normal activity is the scope of functionality of the software on your endpoint during periods of low activity and high activity. You will need some kind of software that periodically collects specific machine data from your endpoint like my software developed in Python that's available for free download at https://github.com/davidbrennerjr/server-stats-collector Ingest one or more of the following machine data from Category #1. Ingest one or more of the following machine data from Category #2. Category #1 General system-wide error messages from /var/log/syslog Auditing logs of application rulesets Auditing logs of security contexts Auditing logs of ...

David Brenner Jr

Search This Blog