Memory Resource Management for Real-Time Systems

18th Euromicro Conference on Real-Time Systems (ECRTS'06), 2000

As real-time and embedded systems become increasingly large and complex, the traditional strictly static approach to memory management begins to prove untenable. The challenge is to provide a dynamic memory model that guarantees tight and bounded time and space requirements without overburdening the developer with memory concerns. This paper provides an analysis of memory management approaches in order to characterise the tradeoffs across three semantic domains: space, time and a characterisation of memory usage information such as the lifetime of objects. A unified approach to distinguishing the merits of each memory model highlights the relationship across these three domains, thereby identifying the class of applications that benefit from targeting a particular model. Crucially, an initial investigation of this relationship identifies the direction future research must take in order to address the requirements of the next generation of complex embedded systems. Some initial suggestions are made in this regard and the memory model proposed in the Real-Time Specification for Java is evaluated in this context.

Different memory allocation algorithms have been devised to organize memory efficiently in different timestamps according to the needs and scenario of usage yet there are issues and challenges of these allocators to provide full support for real time needs. Real time systems require memory on priority otherwise program may crash or may be unresponsive if demanded memory is not allocated with quick response. Besides the timing constraints, memory allocator algorithms must minimize the memory loss which comes in the form of fragmentation, the unusable memory in response to the memory allocation needs because memory is allocated in the form of blocks. Our focus would be to analyse traditional dynamic memory management algorithms with respect to their functionality, response time and efficiency to find out the issues and challenges with these allocators to sum up the knowledge to know the limitations of these algorithm which might reduce the performance of real time systems. This research paper will give a comparative analysis of some well known memory management techniques to highlight issues for real time systems and innovative techniques suitable for these applications will be argued.

From Model-Driven Design to Resource …, 2006

Dynamic memory storage has been widely used during years in computer science. However, its use in real-time systems has not been considered as an important issue because the spatial and temporal worst case for allocation and deallocation operations were unbounded or bounded but with a very large bound.

2014 IEEE 20th International Conference on Embedded and Real-Time Computing Systems and Applications, 2014

In this work we describe a new memory management concept which allows the use of both virtual and dynamic memory management at the same time in the context of realtime systems. For a fixed size of the virtual address space, the operations of memory allocation, de-allocation and access have a constant complexity. Therefore our approach is highly suited for real-time environments with hard deadlines. We employ efficient data-structures to yield runtimes that are close to traditional static memory management concepts, and-at the same time-provide the user with the full flexibility of both virtual and dynamic memory management. Our approach is based on novel operating system components and a novel real-time aware virtual memory management unit (RTMMU) in hardware. Our experimental results demonstrate the applicability of our concept and compare its performance with a classical approach. The results show that our new approach does not only provide constant-time memory management operations, but is also able to reduce the memory footprint to a large extent.

A dynamic memory management system has to take care of the allocation and deallocation of memory blocks in a software system. Real-time embedded systems add some more constraints to the design and the implementation of dynamic memory management systems if compared with the PC world. An increasing number of features are added to embedded mobile devices, however, resources like dynamic memory are limited. In addition, in real-time systems, real-time deadlines must be respected and allocations and deallocations must be done in due time. In this paper we present a case study on evaluating dynamic memory management in embedded real-time systems. We have used a scenario-based approach and used a simulation environment to evaluate the performance of different dynamic memory management systems. Our contribution is to present a practical approach, the tools and the rationales to evaluate dynamic memory management in embedded real-time systems.

Proc. IEEE-'Real-Time Systems Symposium

Traditional dynamic memory management techniques for imperative programming lan-guages are unsuitable for reliable real-time applications because their worst-case time and space requirements are insufficiently bounded. This is demonstrated by detailed measurements of sev-eral ...

Abstract – Real Time Operating System (RTOS) is an operating system which supports real-time application by giving correct result within given dead line. There are so many features supported by RTOS like synchronization, clock and timer support, interrupt handling, memory management, task preemption etc. Among these all memory management is a crucial component. Memory management is nothing but memory allocation/ deallocation technique for the process. This paper presents different memory management algorithms for RTOS and comparison between them. Keywords – Memory management, Memory Allocation, Real time operating system

Real-Time Systems, 2000

This paper addresses the issue of improving the performance of memory management for real-time Java applications, building upon the real-time speci®cation for Java (RTSJ) from the Real-Time Java Expert Group. In a ®rst step, a collecting dynamic memory solution including both a real-time garbage collector and region-based memory management, is proposed. A thorough analysis of the parameters in¯uencing the performance of write barriers in memory management, together with ways of improvement are then presented. Finally, the implementation of a memory management solution compliant with the RTSJ and integrating the proposed improvements is sketched.

Annals of Emerging Technologies in Computing, 2019

This paper presents novel hardware architecture of dynamic memory manager providing memory allocation and deallocation operations that are suitable for hard real-time and safety-critical systems due to very high determinism of these operations. The proposed memory manager implements Worst-Fit algorithm for selection of suitable free block of memory that can be used by the external environment, e.g. CPU. The deterministic timing of the memory allocation and deallocation operations is essential for hard real-time systems. The proposed memory manager performs these operations in nearly constant time thanks to the adoption of hardware-accelerated max queue, which is a data structure that continuously provides the largest free block of memory in two clock cycles regardless of actual number or constellation of existing free blocks of memory. In order to minimize the overhead caused by implementing the memory management in hardware, the max queue was optimized by developing a new sorting a...

The Journal of Supercomputing, 2006

The dynamic distributed real-time applications run on clusters with varying execution time, so reallocation of resources is critical to meet the applications's deadline. In this paper we present two adaptive recourse management techniques for dynamic real-time applications by employing the prediction of responses of real-time tasks that operate in time sharing environment and run-time analysis of scheduling policies. Prediction of response time for resource reallocation is accomplished by historical profiling of applications' resource usage to estimate resource requirements on the target machine and a probabilistic approach is applied for calculating the queuing delay that a process will experience on distributed hosts. Results show that as compared to statistical and worst-case approaches, our technique uses system resource more efficiently.