Toward Specialization of Memory Management in Unikernels
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Memory Management and Garbage Collection
Overview Memory Management Stack: Data on stack (local variables on activation records) have lifetime that coincides with the life of a procedure call. Memory for stack data is allocated on entry to procedures ::: ::: and de-allocated on return. Heap: Data on heap have lifetimes that may differ from the life of a procedure call. Memory for heap data is allocated on demand (e.g. malloc, new, etc.) ::: ::: and released Manually: e.g. using free Automatically: e.g. using a garbage collector Compilers Memory Management CSE 304/504 1 / 16 Overview Memory Allocation Heap memory is divided into free and used. Free memory is kept in a data structure, usually a free list. When a new chunk of memory is needed, a chunk from the free list is returned (after marking it as used). When a chunk of memory is freed, it is added to the free list (after marking it as free) Compilers Memory Management CSE 304/504 2 / 16 Overview Fragmentation Free space is said to be fragmented when free chunks are not contiguous. Fragmentation is reduced by: Maintaining different-sized free lists (e.g. free 8-byte cells, free 16-byte cells etc.) and allocating out of the appropriate list. If a small chunk is not available (e.g. no free 8-byte cells), grab a larger chunk (say, a 32-byte chunk), subdivide it (into 4 smaller chunks) and allocate. When a small chunk is freed, check if it can be merged with adjacent areas to make a larger chunk. Compilers Memory Management CSE 304/504 3 / 16 Overview Manual Memory Management Programmer has full control over memory ::: with the responsibility to manage it well Premature free's lead to dangling references Overly conservative free's lead to memory leaks With manual free's it is virtually impossible to ensure that a program is correct and secure. -
The Case for Compressed Caching in Virtual Memory Systems
THE ADVANCED COMPUTING SYSTEMS ASSOCIATION The following paper was originally published in the Proceedings of the USENIX Annual Technical Conference Monterey, California, USA, June 6-11, 1999 The Case for Compressed Caching in Virtual Memory Systems _ Paul R. Wilson, Scott F. Kaplan, and Yannis Smaragdakis aUniversity of Texas at Austin © 1999 by The USENIX Association All Rights Reserved Rights to individual papers remain with the author or the author's employer. Permission is granted for noncommercial reproduction of the work for educational or research purposes. This copyright notice must be included in the reproduced paper. USENIX acknowledges all trademarks herein. For more information about the USENIX Association: Phone: 1 510 528 8649 FAX: 1 510 548 5738 Email: [email protected] WWW: http://www.usenix.org The Case for Compressed Caching in Virtual Memory Systems Paul R. Wilson, Scott F. Kaplan, and Yannis Smaragdakis Dept. of Computer Sciences University of Texas at Austin Austin, Texas 78751-1182 g fwilson|sfkaplan|smaragd @cs.utexas.edu http://www.cs.utexas.edu/users/oops/ Abstract In [Wil90, Wil91b] we proposed compressed caching for virtual memory—storing pages in compressed form Compressed caching uses part of the available RAM to in a main memory compression cache to reduce disk pag- hold pages in compressed form, effectively adding a new ing. Appel also promoted this idea [AL91], and it was level to the virtual memory hierarchy. This level attempts evaluated empirically by Douglis [Dou93] and by Russi- to bridge the huge performance gap between normal (un- novich and Cogswell [RC96]. Unfortunately Douglis’s compressed) RAM and disk. -
Memory Protection at Option
Memory Protection at Option Application-Tailored Memory Safety in Safety-Critical Embedded Systems – Speicherschutz nach Wahl Auf die Anwendung zugeschnittene Speichersicherheit in sicherheitskritischen eingebetteten Systemen Der Technischen Fakultät der Universität Erlangen-Nürnberg zur Erlangung des Grades Doktor-Ingenieur vorgelegt von Michael Stilkerich Erlangen — 2012 Als Dissertation genehmigt von der Technischen Fakultät Universität Erlangen-Nürnberg Tag der Einreichung: 09.07.2012 Tag der Promotion: 30.11.2012 Dekan: Prof. Dr.-Ing. Marion Merklein Berichterstatter: Prof. Dr.-Ing. Wolfgang Schröder-Preikschat Prof. Dr. Michael Philippsen Abstract With the increasing capabilities and resources available on microcontrollers, there is a trend in the embedded industry to integrate multiple software functions on a single system to save cost, size, weight, and power. The integration raises new requirements, thereunder the need for spatial isolation, which is commonly established by using a memory protection unit (MPU) that can constrain access to the physical address space to a fixed set of address regions. MPU-based protection is limited in terms of available hardware, flexibility, granularity and ease of use. Software-based memory protection can provide an alternative or complement MPU-based protection, but has found little attention in the embedded domain. In this thesis, I evaluate qualitative and quantitative advantages and limitations of MPU-based memory protection and software-based protection based on a multi-JVM. I developed a framework composed of the AUTOSAR OS-like operating system CiAO and KESO, a Java implementation for deeply embedded systems. The framework allows choosing from no memory protection, MPU-based protection, software-based protection, and a combination of the two. -
An In-Depth Study with All Rust Cves
Memory-Safety Challenge Considered Solved? An In-Depth Study with All Rust CVEs HUI XU, School of Computer Science, Fudan University ZHUANGBIN CHEN, Dept. of CSE, The Chinese University of Hong Kong MINGSHEN SUN, Baidu Security YANGFAN ZHOU, School of Computer Science, Fudan University MICHAEL R. LYU, Dept. of CSE, The Chinese University of Hong Kong Rust is an emerging programing language that aims at preventing memory-safety bugs without sacrificing much efficiency. The claimed property is very attractive to developers, and many projects start usingthe language. However, can Rust achieve the memory-safety promise? This paper studies the question by surveying 186 real-world bug reports collected from several origins which contain all existing Rust CVEs (common vulnerability and exposures) of memory-safety issues by 2020-12-31. We manually analyze each bug and extract their culprit patterns. Our analysis result shows that Rust can keep its promise that all memory-safety bugs require unsafe code, and many memory-safety bugs in our dataset are mild soundness issues that only leave a possibility to write memory-safety bugs without unsafe code. Furthermore, we summarize three typical categories of memory-safety bugs, including automatic memory reclaim, unsound function, and unsound generic or trait. While automatic memory claim bugs are related to the side effect of Rust newly-adopted ownership-based resource management scheme, unsound function reveals the essential challenge of Rust development for avoiding unsound code, and unsound generic or trait intensifies the risk of introducing unsoundness. Based on these findings, we propose two promising directions towards improving the security of Rust development, including several best practices of using specific APIs and methods to detect particular bugs involving unsafe code. -
Project Snowflake: Non-Blocking Safe Manual Memory Management in .NET
Project Snowflake: Non-blocking Safe Manual Memory Management in .NET Matthew Parkinson Dimitrios Vytiniotis Kapil Vaswani Manuel Costa Pantazis Deligiannis Microsoft Research Dylan McDermott Aaron Blankstein Jonathan Balkind University of Cambridge Princeton University July 26, 2017 Abstract Garbage collection greatly improves programmer productivity and ensures memory safety. Manual memory management on the other hand often delivers better performance but is typically unsafe and can lead to system crashes or security vulnerabilities. We propose integrating safe manual memory management with garbage collection in the .NET runtime to get the best of both worlds. In our design, programmers can choose between allocating objects in the garbage collected heap or the manual heap. All existing applications run unmodified, and without any performance degradation, using the garbage collected heap. Our programming model for manual memory management is flexible: although objects in the manual heap can have a single owning pointer, we allow deallocation at any program point and concurrent sharing of these objects amongst all the threads in the program. Experimental results from our .NET CoreCLR implementation on real-world applications show substantial performance gains especially in multithreaded scenarios: up to 3x savings in peak working sets and 2x improvements in runtime. 1 Introduction The importance of garbage collection (GC) in modern software cannot be overstated. GC greatly improves programmer productivity because it frees programmers from the burden of thinking about object lifetimes and freeing memory. Even more importantly, GC prevents temporal memory safety errors, i.e., uses of memory after it has been freed, which often lead to security breaches. Modern generational collectors, such as the .NET GC, deliver great throughput through a combination of fast thread-local bump allocation and cheap collection of young objects [63, 18, 61]. -
Ensuring the Spatial and Temporal Memory Safety of C at Runtime
MemSafe: Ensuring the Spatial and Temporal Memory Safety of C at Runtime Matthew S. Simpson, Rajeev K. Barua Department of Electrical & Computer Engineering University of Maryland, College Park College Park, MD 20742-3256, USA fsimpsom, [email protected] Abstract—Memory access violations are a leading source of dereferencing pointers obtained from invalid pointer arith- unreliability in C programs. As evidence of this problem, a vari- metic; and dereferencing uninitialized, NULL or “manufac- ety of methods exist that retrofit C with software checks to detect tured” pointers.1 A temporal error is a violation caused by memory errors at runtime. However, these methods generally suffer from one or more drawbacks including the inability to using a pointer whose referent has been deallocated (e.g. with detect all errors, the use of incompatible metadata, the need for free) and is no longer a valid object. The most well-known manual code modifications, and high runtime overheads. temporal violations include dereferencing “dangling” point- In this paper, we present a compiler analysis and transforma- ers to dynamically allocated memory and freeing a pointer tion for ensuring the memory safety of C called MemSafe. Mem- more than once. Dereferencing pointers to automatically allo- Safe makes several novel contributions that improve upon previ- ous work and lower the cost of safety. These include (1) a method cated memory (stack variables) is also a concern if the address for modeling temporal errors as spatial errors, (2) a compati- of the referent “escapes” and is made available outside the ble metadata representation combining features of object- and function in which it was defined. -
Memory Safety Without Garbage Collection for Embedded Applications
Memory Safety Without Garbage Collection for Embedded Applications DINAKAR DHURJATI, SUMANT KOWSHIK, VIKRAM ADVE, and CHRIS LATTNER University of Illinois at Urbana-Champaign Traditional approaches to enforcing memory safety of programs rely heavily on run-time checks of memory accesses and on garbage collection, both of which are unattractive for embedded ap- plications. The goal of our work is to develop advanced compiler techniques for enforcing memory safety with minimal run-time overheads. In this paper, we describe a set of compiler techniques that, together with minor semantic restrictions on C programs and no new syntax, ensure memory safety and provide most of the error-detection capabilities of type-safe languages, without using garbage collection, and with no run-time software checks, (on systems with standard hardware support for memory management). The language permits arbitrary pointer-based data structures, explicit deallocation of dynamically allocated memory, and restricted array operations. One of the key results of this paper is a compiler technique that ensures that dereferencing dangling pointers to freed memory does not violate memory safety, without annotations, run-time checks, or garbage collection, and works for arbitrary type-safe C programs. Furthermore, we present a new inter- procedural analysis for static array bounds checking under certain assumptions. For a diverse set of embedded C programs, we show that we are able to ensure memory safety of pointer and dy- namic memory usage in all these programs with no run-time software checks (on systems with standard hardware memory protection), requiring only minor restructuring to conform to simple type restrictions. Static array bounds checking fails for roughly half the programs we study due to complex array references, and these are the only cases where explicit run-time software checks would be needed under our language and system assumptions. -
Extracting Compressed Pages from the Windows 10 Virtual Store WHITE PAPER | EXTRACTING COMPRESSED PAGES from the WINDOWS 10 VIRTUAL STORE 2
white paper Extracting Compressed Pages from the Windows 10 Virtual Store WHITE PAPER | EXTRACTING COMPRESSED PAGES FROM THE WINDOWS 10 VIRTUAL STORE 2 Abstract Windows 8.1 introduced memory compression in August 2013. By the end of 2013 Linux 3.11 and OS X Mavericks leveraged compressed memory as well. Disk I/O continues to be orders of magnitude slower than RAM, whereas reading and decompressing data in RAM is fast and highly parallelizable across the system’s CPU cores, yielding a significant performance increase. However, this came at the cost of increased complexity of process memory reconstruction and thus reduced the power of popular tools such as Volatility, Rekall, and Redline. In this document we introduce a method to retrieve compressed pages from the Windows 10 Memory Manager Virtual Store, thus providing forensics and auditing tools with a way to retrieve, examine, and reconstruct memory artifacts regardless of their storage location. Introduction Windows 10 moves pages between physical memory and the hard disk or the Store Manager’s virtual store when memory is constrained. Universal Windows Platform (UWP) applications leverage the Virtual Store any time they are suspended (as is the case when minimized). When a given page is no longer in the process’s working set, the corresponding Page Table Entry (PTE) is used by the OS to specify the storage location as well as additional data that allows it to start the retrieval process. In the case of a page file, the retrieval is straightforward because both the page file index and the location of the page within the page file can be directly retrieved. -
Memory Leak Or Dangling Pointer
Modern C++ for Computer Vision and Image Processing Igor Bogoslavskyi Outline Using pointers Pointers are polymorphic Pointer “this” Using const with pointers Stack and Heap Memory leaks and dangling pointers Memory leak Dangling pointer RAII 2 Using pointers in real world Using pointers for classes Pointers can point to objects of custom classes: 1 std::vector<int> vector_int; 2 std::vector<int >* vec_ptr = &vector_int; 3 MyClass obj; 4 MyClass* obj_ptr = &obj; Call object functions from pointer with -> 1 MyClass obj; 2 obj.MyFunc(); 3 MyClass* obj_ptr = &obj; 4 obj_ptr->MyFunc(); obj->Func() (*obj).Func() ↔ 4 Pointers are polymorphic Pointers are just like references, but have additional useful properties: Can be reassigned Can point to ‘‘nothing’’ (nullptr) Can be stored in a vector or an array Use pointers for polymorphism 1 Derived derived; 2 Base* ptr = &derived; Example: for implementing strategy store a pointer to the strategy interface and initialize it with nullptr and check if it is set before calling its methods 5 1 #include <iostream > 2 #include <vector > 3 using std::cout; 4 struct AbstractShape { 5 virtual void Print() const = 0; 6 }; 7 struct Square : public AbstractShape { 8 void Print() const override { cout << "Square\n";} 9 }; 10 struct Triangle : public AbstractShape { 11 void Print() const override { cout << "Triangle\n";} 12 }; 13 int main() { 14 std::vector<AbstractShape*> shapes; 15 Square square; 16 Triangle triangle; 17 shapes.push_back(&square); 18 shapes.push_back(&triangle); 19 for (const auto* shape : shapes) { shape->Print(); } 20 return 0; 21 } 6 this pointer Every object of a class or a struct holds a pointer to itself This pointer is called this Allows the objects to: Return a reference to themselves: return *this; Create copies of themselves within a function Explicitly show that a member belongs to the current object: this->x(); 7 Using const with pointers Pointers can point to a const variable: 1 // Cannot change value , can reassign pointer. -
Memory Management
Memory Management Reading: Silberschatz chapter 9 Reading: Stallings chapter 7 EEL 358 1 Outline ¾ Background ¾ Issues in Memory Management ¾ Logical Vs Physical address, MMU ¾ Dynamic Loading ¾ Memory Partitioning Placement Algorithms Dynamic Partitioning ¾ Buddy System ¾ Paging ¾ Memory Segmentation ¾ Example – Intel Pentium EEL 358 2 Background ¾ Main memory → fast, relatively high cost, volatile ¾ Secondary memory → large capacity, slower, cheaper than main memory and is usually non volatile ¾ The CPU fetches instructions/data of a program from memory; therefore, the program/data must reside in the main (RAM and ROM) memory ¾ Multiprogramming systems → main memory must be subdivided to accommodate several processes ¾ This subdivision is carried out dynamically by OS and known as memory management EEL 358 3 Issues in Memory Management ¾ Relocation: Swapping of active process in and out of main memory to maximize CPU utilization Process may not be placed back in same main memory region! Ability to relocate the process to different area of memory ¾ Protection: Protection against unwanted interference by another process Must be ensured by processor (hardware) rather than OS ¾ Sharing: Flexibility to allow several process to access the same portions of the main memory ¾ Efficiency: Memory must be fairly allocated for high processor utilization, Systematic flow of information between main and secondary memory EEL 358 4 Binding of Instructions and Data to Memory Compiler → Generates Object Code Linker → Combines the Object code into -
Intra-Unikernel Isolation with Intel Memory Protection Keys
Intra-Unikernel Isolation with Intel Memory Protection Keys Mincheol Sung Pierre Olivier∗ Virginia Tech, USA The University of Manchester, United Kingdom [email protected] [email protected] Stefan Lankes Binoy Ravindran RWTH Aachen University, Germany Virginia Tech, USA [email protected] [email protected] Abstract ACM Reference Format: Mincheol Sung, Pierre Olivier, Stefan Lankes, and Binoy Ravin- Unikernels are minimal, single-purpose virtual machines. dran. 2020. Intra-Unikernel Isolation with Intel Memory Protec- This new operating system model promises numerous bene- tion Keys. In 16th ACM SIGPLAN/SIGOPS International Conference fits within many application domains in terms of lightweight- on Virtual Execution Environments (VEE ’20), March 17, 2020, Lau- ness, performance, and security. Although the isolation be- sanne, Switzerland. ACM, New York, NY, USA, 14 pages. https: tween unikernels is generally recognized as strong, there //doi.org/10.1145/3381052.3381326 is no isolation within a unikernel itself. This is due to the use of a single, unprotected address space, a basic principle 1 Introduction of unikernels that provide their lightweightness and perfor- Unikernels have gained attention in the academic research mance benefits. In this paper, we propose a new design that community, offering multiple benefits in terms of improved brings memory isolation inside a unikernel instance while performance, increased security, reduced costs, etc. As a keeping a single address space. We leverage Intel’s Memory result, -
HALO: Post-Link Heap-Layout Optimisation
HALO: Post-Link Heap-Layout Optimisation Joe Savage Timothy M. Jones University of Cambridge, UK University of Cambridge, UK [email protected] [email protected] Abstract 1 Introduction Today, general-purpose memory allocators dominate the As the gap between memory and processor speeds continues landscape of dynamic memory management. While these so- to widen, efficient cache utilisation is more important than lutions can provide reasonably good behaviour across a wide ever. While compilers have long employed techniques like range of workloads, it is an unfortunate reality that their basic-block reordering, loop fission and tiling, and intelligent behaviour for any particular workload can be highly subop- register allocation to improve the cache behaviour of pro- timal. By catering primarily to average and worst-case usage grams, the layout of dynamically allocated memory remains patterns, these allocators deny programs the advantages of largely beyond the reach of static tools. domain-specific optimisations, and thus may inadvertently Today, when a C++ program calls new, or a C program place data in a manner that hinders performance, generating malloc, its request is satisfied by a general-purpose allocator unnecessary cache misses and load stalls. with no intimate knowledge of what the program does or To help alleviate these issues, we propose HALO: a post- how its data objects are used. Allocations are made through link profile-guided optimisation tool that can improve the fixed, lifeless interfaces, and fulfilled by