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At least 343 records · Page 19Linked to original sources

arXiv2019

MultiK: A Framework for Orchestrating Multiple Specialized Kernels

We present, MultiK, a Linux-based framework 1 that reduces the attack surface for operating system kernels by reducing code bloat. MultiK "orchestrates" multiple kernels that are specialized for individual applications in a transparent manner. This framework is flexible to accommodate different kernel code reduction techniques and, most importantly, run the specialized kernels with near-zero additional runtime overheads. MultiK avoids the overheads of virtualization and runs natively on the system. For instance, an Apache instance is shown to run on a kernel that has (a) 93.68% of its code reduced, (b) 19 of 23 known kernel vulnerabilities eliminated and (c) with negligible performance overheads (0.19%). MultiK is a framework that can integrate with existing code reduction and OS security techniques. We demonstrate this by using D-KUT and S-KUT -- two methods to profile and eliminate unwanted kernel code. The whole process is transparent to the user applications because MultiK does not require a recompilation of the application.

cs.OS↗

arXiv2019

A WCET-aware cache coloring technique for reducing interference in real-time systems

The predictability of a system is the condition to give saferbound on worst case execution timeof real-time tasks which are running on it. Commercial off-the-shelf(COTS) processors are in-creasingly used in embedded systems and contain shared cache memory. This component hasa hard predictable behavior because its state depends of theexecution history of the systems.To increase predictability of COTS component we use cache coloring, a technique widely usedto partition cache memory. Our main contribution is a WCET aware heuristic which parti-tion task according to the needs of each task. Our experiments are made with CPLEX an ILPsolver with random tasks set generated running on preemptive system scheduled with earliestdeadline first(EDF).

cs.OS↗

arXiv2019

Understanding and taming SSD read performance variability: HDFS case study

In this paper we analyze the influence that lower layers (file system, OS, SSD) have on HDFS' ability to extract maximum performance from SSDs on the read path. We uncover and analyze three surprising performance slowdowns induced by lower layers that result in HDFS read throughput loss. First, intrinsic slowdown affects reads from every new file system extent for a variable amount of time. Second, temporal slowdown appears temporarily and periodically and is workload-agnostic. Third, in permanent slowdown, some files can individually and permanently become slower after a period of time. We analyze the impact of these slowdowns on HDFS and show significant throughput loss. Individually, each of the slowdowns can cause a read throughput loss of 10-15%. However, their effect is cumulative. When all slowdowns happen concurrently, read throughput drops by as much as 30%. We further analyze mitigation techniques and show that two of the three slowdowns could be addressed via increased IO request parallelism in the lower layers. Unfortunately, HDFS cannot automatically adapt to use such additional parallelism. Our results point to a need for adaptability in storage stacks. The reason is that an access pattern that maximizes performance in the common case is not necessarily the same one that can mask performance fluctuations.

cs.OS↗

arXiv2019

KEY-SSD: Access-Control Drive to Protect Files from Ransomware Attacks

Traditional techniques to prevent damage from ransomware attacks are to detect and block attacks by monitoring the known behaviors such as frequent name changes, recurring access to cryptographic libraries and exchange keys with remote servers. Unfortunately, intelligent ransomware can easily bypass these techniques. Another prevention technique is to recover from the backup copy when a file is infected with ransomware. However, the data backup technique requires extra storage space and can be removed with ransomware. In this paper, we propose to implement an access control mechanism on a disk drive, called a KEY-SSD disk drive. KEY-SSD is the data store and the last barrier to data protection. Unauthorized applications will not be able to read file data even if they bypass the file system defense, thus denying the block request without knowing the disk's registered block key and completely eliminating the possibility of the file becoming hostage to ransomware. We have prototyped KEY-SSD and validated the usefulness of KEY-SSD by demonstrating 1) selective block access control, 2) unauthorized data access blocking and 3) negligible performance overhead. Our comprehensive evaluation of KEY-SSD for various workloads show the KEY-SSD performance is hardly degraded due to OS lightweight key transmission and access control drive optimization. We also confirmed that KEY-SSD successfully protects the files in the actual ransomware sample.

cs.CR↗

arXiv2019

IOArbiter: Dynamic Provisioning of Backend Block Storage in the Cloud

With the advent of virtualization technology, cloud computing realizes on-demand computing. The capability of dynamic resource provisioning is a fundamental driving factor for users to adopt the cloud technology. The aspect is important for cloud service providers to optimize the expense for running the infrastructure as well. Despite many technological advances in related areas, however, it is still the case that the infrastructure providers must decide hardware configuration before deploying a cloud infrastructure, especially from the storage's perspective. This static nature of the storage provisioning practice can cause many problems in meeting tenant requirements, which often come later into the picture. In this paper, we propose a system called IOArbiter that enables the dynamic creation of underlying storage implementation in the cloud. IOArbiter defers storage provisioning to the time at which a tenant actually requests a storage space. As a result, an underlying storage implementation, e.g., RAID-5, 6 or Ceph storage pool with 6+3 erasure coding, will be materialized at the volume creation time. Using our prototype implementation with Openstack Cinder, we show that IOArbiter can simultaneously satisfy a number of different tenant demands, which may not be possible with a static configuration strategy. Additionally QoS mechanisms such as admission control and dynamic throttling help the system mitigate a noisy neighbor problem significantly.

cs.DC↗

arXiv2019

A Survey on Tiering and Caching in High-Performance Storage Systems

Although every individual invented storage technology made a big step towards perfection, none of them is spotless. Different data store essentials such as performance, availability, and recovery requirements have not met together in a single economically affordable medium, yet. One of the most influential factors is price. So, there has always been a trade-off between having a desired set of storage choices and the costs. To address this issue, a network of various types of storing media is used to deliver the high performance of expensive devices such as solid state drives and non-volatile memories, along with the high capacity of inexpensive ones like hard disk drives. In software, caching and tiering are long-established concepts for handling file operations and moving data automatically within such a storage network and manage data backup in low-cost media. Intelligently moving data around different devices based on the needs is the key insight for this matter. In this survey, we discuss some recent pieces of research that have been done to improve high-performance storage systems with caching and tiering techniques.

cs.AR↗

arXiv2019

MANA for MPI: MPI-Agnostic Network-Agnostic Transparent Checkpointing

Transparently checkpointing MPI for fault tolerance and load balancing is a long-standing problem in HPC. The problem has been complicated by the need to provide checkpoint-restart services for all combinations of an MPI implementation over all network interconnects. This work presents MANA (MPI-Agnostic Network-Agnostic transparent checkpointing), a single code base which supports all MPI implementation and interconnect combinations. The agnostic properties imply that one can checkpoint an MPI application under one MPI implementation and perhaps over TCP, and then restart under a second MPI implementation over InfiniBand on a cluster with a different number of CPU cores per node. This technique is based on a novel "split-process" approach, which enables two separate programs to co-exist within a single process with a single address space. This work overcomes the limitations of the two most widely adopted transparent checkpointing solutions, BLCR and DMTCP/InfiniBand, which require separate modifications to each MPI implementation and/or underlying network API. The runtime overhead is found to be insignificant both for checkpoint-restart within a single host, and when comparing a local MPI computation that was migrated to a remote cluster against an ordinary MPI computation running natively on that same remote cluster.

cs.DC↗

arXiv2019

Programming Unikernels in the Large via Functor Driven Development

Compiling applications as unikernels allows them to be tailored to diverse execution environments. Dependency on a monolithic operating system is replaced with linkage against libraries that provide specific services. Doing so in practice has revealed a major barrier: managing the configuration matrix across heterogenous execution targets. A realistic unikernel application depends on hundreds of libraries, each of which may place different demands on the different target execution platforms (e.g.,~cryptographic acceleration). We propose a modular approach to structuring large scale codebases that cleanly separates configuration, application and operating system logic. Our implementation is built on the \mirage unikernel framework, using the \ocaml language's powerful abstraction and metaprogramming facilities. Leveraging modules allows us to build many components independently, with only loose coupling through a set of standardised signatures. Components can be parameterized by other components and composed. Our approach accounts for state, dependency ordering, and error management, and our usage over the years has demonstrated significant efficiency benefits by leveraging compiler features such as global link-time optimisation during the configuration process. We describe our application architecture and experiences via some practical applications of our approach, and discuss how library development in \mirage can facilitate adoption in other unikernel frameworks and programming languages.

cs.PL↗

arXiv2019

Neverland: Lightweight Hardware Extensions for Enforcing Operating System Integrity

The security of applications hinges on the trustworthiness of the operating system, as applications rely on the OS to protect code and data. As a result, multiple protections for safeguarding the integrity of kernel code and data are being continuously proposed and deployed. These existing protections, however, are far from ideal as they either provide partial protection, or require complex and high overhead hardware and software stacks. In this work, we present Neverland: a low-overhead, hardware-assisted, memory protection scheme that safeguards the operating system from rootkits and kernel-mode malware. Once the system is done booting, Neverland's hardware takes away the operating system's ability to overwrite certain configuration registers, as well as portions of its own physical address space that contain kernel code and security-critical data. Furthermore, it prohibits the CPU from fetching privileged code from any memory region lying outside the physical addresses assigned to the OS kernel and drivers (regardless of virtual page permissions). This combination of protections makes it extremely hard for an attacker to tamper with the kernel or introduce new privileged code into the system -- even in the presence of kernel vulnerabilities. Our evaluations show that the extra hardware required to support these protections incurs minimal silicon and energy overheads. Neverland enables operating systems to reduce their attack surface without having to rely on complex integrity monitoring software or hardware.

cs.CR↗

arXiv2019

Secure Extensibility for System State Extraction via Plugin Sandboxing

We introduce a new mechanism to securely extend systems data collection software with potentially untrusted third-party code. Unlike existing tools which run extension modules or plugins directly inside the monitored endpoint (the guest), we run plugins inside a specially crafted sandbox, so as to protect the guest as well as the software core. To get the right mix of accessibility and constraints required for systems data extraction, we create our sandbox by combining multiple features exported by an unmodified kernel. We have tested its applicability by successfully sandboxing plugins of an opensourced data collection software for containerized guest systems. We have also verified its security posture in terms of successful containment of several exploits, which would have otherwise directly impacted a guest, if shipped inside third-party plugins.

cs.CR↗

arXiv2019

MemoryRanger Prevents Hijacking FILE_OBJECT Structures in Windows Kernel

Windows OS kernel memory is one of the main targets of cyber-attacks. By launching such attacks, hackers are succeeding in process privilege escalation and tampering with users data by accessing kernel mode memory. This paper considers a new example of such an attack, which results in access to the files opened in an exclusive mode. Windows built-in security features prevent such legal access, but attackers can circumvent them by patching dynamically allocated objects. The research shows that the Windows 10, version 1809 x64 is vulnerable to this attack. The paper provides an example of using MemoryRanger, a hypervisor-based solution to prevent such attack by running kernel-mode drivers in isolated kernel memory enclaves.

cs.CR↗

arXiv2019

Scan-and-Pay on Android is Dangerous

Mobile payments have increased significantly in the recent years and one-to-one money transfers are offered by a wide variety of smartphone applications. These applications usually support scan-and-pay -- a technique that allows a payer to easily scan the destination address of the payment directly from the payee's smartphone screen. This technique is pervasive because it does not require any particular hardware, only the camera, which is present on all modern smartphones. However, in this work we show that a malicious application can exploit the overlay feature on Android to compromise the integrity of transactions that make use of the scan-and-pay technique. We implement Malview, a proof-of-concept malicious application that runs in the background on the payee's smartphone and show that it succeeds in redirecting payments to a malicious wallet. We analyze the weaknesses of the current defense mechanisms and discuss possible countermeasures against the attack.

cs.CR↗

arXiv2019

Avoiding Scalability Collapse by Restricting Concurrency

Saturated locks often degrade the performance of a multithreaded application, leading to a so-called scalability collapse problem. This problem arises when a growing number of threads circulating through a saturated lock causes the overall application performance to fade or even drop abruptly. This problem is particularly (but not solely) acute on oversubscribed systems (systems with more threads than available hardware cores). In this paper, we introduce GCR (generic concurrency restriction), a mechanism that aims to avoid the scalability collapse. GCR, designed as a generic, lock-agnostic wrapper, intercepts lock acquisition calls, and decides when threads would be allowed to proceed with the acquisition of the underlying lock. Furthermore, we present GCR-NUMA, a non-uniform memory access (NUMA)-aware extension of GCR, that strives to ensure that threads allowed to acquire the lock are those that run on the same socket. The extensive evaluation that includes more than two dozen locks, three machines and three benchmarks shows that GCR brings substantial speedup (in many cases, up to three orders of magnitude) in case of contention and growing thread counts, while introducing nearly negligible slowdown when the underlying lock is not contended. GCR-NUMA brings even larger performance gains starting at even lighter lock contention.

cs.OS↗

arXiv2020

ExplFrame: Exploiting Page Frame Cache for Fault Analysis of Block Ciphers

Page Frame Cache (PFC) is a purely software cache, present in modern Linux based operating systems (OS), which stores the page frames that are recently being released by the processes running on a particular CPU. In this paper, we show that the page frame cache can be maliciously exploited by an adversary to steer the pages of a victim process to some pre-decided attacker-chosen locations in the memory. We practically demonstrate an end-to-end attack, ExplFrame, where an attacker having only user-level privilege is able to force a victim process's memory pages to vulnerable locations in DRAM and deterministically conduct Rowhammer to induce faults. We further show that these faults can be exploited for extracting the secret key of table-based block cipher implementations. As a case study, we perform a full-key recovery on OpenSSL AES by Rowhammer-induced single bit faults in the T-tables. We propose an improvised fault analysis technique which can exploit any Rowhammer-induced bit-flips in the AES T-tables.

cs.CR↗

arXiv2019

Neural Heterogeneous Scheduler

Access to parallel and distributed computation has enabled researchers and developers to improve algorithms and performance in many applications. Recent research has focused on next generation special purpose systems with multiple kinds of coprocessors, known as heterogeneous system-on-chips (SoC). In this paper, we introduce a method to intelligently schedule--and learn to schedule--a stream of tasks to available processing elements in such a system. We use deep reinforcement learning enabling complex sequential decision making and empirically show that our reinforcement learning system provides for a viable, better alternative to conventional scheduling heuristics with respect to minimizing execution time.

cs.LG↗

arXiv2019

Slicing the IO execution with ReLayTracer

Analyzing IO performance anomalies is a crucial task in various computing environments, ranging from large-scale cloud applications to desktop applications. However, the IO stack of modern operating systems is complicated, making it hard to understand the performance anomalies with existing tools. Kernel IO executions are frequently interrupted by internal kernel activities, requiring a sophisticated IO profile tool to deal with the noises. Furthermore, complicated interactions of concurrent IO requests cause different sources of tail latencies in kernel IO stack. As a consequence, developers want to know fine-grained latency profile across IO layers, which may differ in each IO requests. To meet the requirements, this paper suggests ReLayTracer, a per-request, per-layer IO profiler. ReLayTracer enables a detailed analysis to identify root causes of IO performance anomalies by providing per-layer latency distributions of each IO request, hardware performance behavior, and time spent by kernel activities such as an interrupt.

cs.OS↗

arXiv2019

Lawn: an Unbound Low Latency Timer Data Structure for Large Scale, High Throughput Systems

As demand for Real-Time applications rises among the general public, the importance of enabling large-scale, unbound algorithms to solve conventional problems with low to no latency is critical for product viability. Timer algorithms are prevalent in the core mechanisms behind operating systems, network protocol implementation, stream processing, and several database capabilities. This paper presents a field-tested algorithm for low latency, unbound range timer structure, based upon the well excepted Timing Wheel algorithm. Using a set of queues hashed by TTL, the algorithm allows for a simpler implementation, minimal overhead no overflow and no performance degradation in comparison to the current state of the algorithms under typical use cases.

cs.DS↗

arXiv2019

HTS: A Hardware Task Scheduler for Heterogeneous Systems

As the Moore's scaling era comes to an end, application specific hardware accelerators appear as an attractive way to improve the performance and power efficiency of our computing systems. A massively heterogeneous system with a large number of hardware accelerators along with multiple general purpose CPUs is a promising direction, but pose several challenges in terms of the run-time scheduling of tasks on the accelerators and design granularity of accelerators. This paper addresses these challenges by developing an example heterogeneous system to enable multiple applications to share the available accelerators. We propose to design accelerators at a lower abstraction to enable applications to be broken down into tasks that can be mapped on several accelerators. We observe that several real-life workloads can be broken down into common primitives that are shared across many workloads. Finally, we propose and design a hardware task scheduler inspired by the hardware schedulers in out-of-order superscalar processors to efficiently utilize the accelerators in the system by scheduling tasks in out-of-order and even speculatively. We evaluate the proposed system on both real-life and synthetic benchmarks based on Digital Signal Processing~(DSP) applications. Compared to executing the benchmark on a system with sequential scheduling, proposed scheduler achieves up to 12x improvement in performance.

cs.OS↗