Efficient and predictable high-speed storage access for real-time embedded systems

As the speed, size, reliability and power efficiency of non-volatile storage media increases, and the data demands of many application domains grow, operating systems are being put under escalating pressure to provide high-speed access to storage. Traditional models of storage access assume devices to be slow, expecting plenty of slack time in which to process data between requests being serviced, and that all significant variations in timing will be down to the storage device itself. Modern high-speed storage devices break this assumption, causing storage applications to become processor-bound, rather than I/O-bound, in an increasing number of situations. This is especially an issue in real-time embedded systems, where limited processing resources and strict timing and predictability requirements amplify any issues caused by the complexity of the software storage stack. This thesis explores the issues related to accessing high-speed storage from real-time embedded systems, providing a thorough analysis of storage operations based on metrics relevant to the area. From this analysis, a number of alternative storage architectures are proposed and explored, showing that a simpler, more direct path from applications to storage can have a positive impact on efficiency and predictability in such systems

Doctor of Philosophy

dissertationIn the past few years, we have seen a tremendous increase in digital data being generated. By 2011, storage vendors had shipped 905 PB of purpose-built backup appliances. By 2013, the number of objects stored in Amazon S3 had reached 2 trillion. Facebook had stored 20 PB of photos by 2010. All of these require an efficient storage solution. To improve space efficiency, compression and deduplication are being widely used. Compression works by identifying repeated strings and replacing them with more compact encodings while deduplication partitions data into fixed-size or variable-size chunks and removes duplicate blocks. While we have seen great improvements in space efficiency from these two approaches, there are still some limitations. First, traditional compressors are limited in their ability to detect redundancy across a large range since they search for redundant data in a fine-grain level (string level). For deduplication, metadata embedded in an input file changes more frequently, and this introduces more unnecessary unique chunks, leading to poor deduplication. Cloud storage systems suffer from unpredictable and inefficient performance because of interference among different types of workloads. This dissertation proposes techniques to improve the effectiveness of traditional compressors and deduplication in improving space efficiency, and a new IO scheduling algorithm to improve performance predictability and efficiency for cloud storage systems. The common idea is to utilize similarity. To improve the effectiveness of compression and deduplication, similarity in content is used to transform an input file into a compression- or deduplication-friendly format. We propose Migratory Compression, a generic data transformation that identifies similar data in a coarse-grain level (block level) and then groups similar blocks together. It can be used as a preprocessing stage for any traditional compressor. We find metadata have a huge impact in reducing the benefit of deduplication. To isolate the impact from metadata, we propose to separate metadata from data. Three approaches are presented for use cases with different constrains. For the commonly used tar format, we propose Migratory Tar: a data transformation and also a new tar format that deduplicates better. We also present a case study where we use deduplication to reduce storage consumption for storing disk images, while at the same time achieving high performance in image deployment. Finally, we apply the same principle of utilizing similarity in IO scheduling to prevent interference between random and sequential workloads, leading to efficient, consistent, and predictable performance for sequential workloads and a high disk utilization

Workload Interleaving with Performance Guarantees in Data Centers

In the era of global, large scale data centers residing in clouds, many applications and users share the same pool of resources for the purposes of reducing energy and operating costs, and of improving availability and reliability. Along with the above benefits, resource sharing also introduces performance challenges: when multiple workloads access the same resources concurrently, contention may occur and introduce delays in the performance of individual workloads. Providing performance isolation to individual workloads needs effective management methodologies. The challenges of deriving effective management methodologies lie in finding accurate, robust, compact metrics and models to drive algorithms that can meet different performance objectives while achieving efficient utilization of resources. This dissertation proposes a set of methodologies aiming at solving the challenging performance isolation problem in workload interleaving in data centers, focusing on both storage components and computing components. at the storage node level, we focus on methodologies for better interleaving user traffic with background workloads, such as tasks for improving reliability, availability, and power savings. More specifically, a scheduling policy for background workload based on the statistical characteristics of the system busy periods and a methodology that quantitatively estimates the performance impact of power savings are developed. at the storage cluster level, we consider methodologies on how to efficiently conduct work consolidation and schedule asynchronous updates without violating user performance targets. More specifically, we develop a framework that can estimate beforehand the benefits and overheads of each option in order to automate the process of reaching intelligent consolidation decisions while achieving faster eventual consistency. at the computing node level, we focus on improving workload interleaving at off-the-shelf servers as they are the basic building blocks of large-scale data centers. We develop priority scheduling middleware that employs different policies to schedule background tasks based on the instantaneous resource requirements of the high priority applications running on the server node. Finally, at the computing cluster level, we investigate popular computing frameworks for large-scale data intensive distributed processing, such as MapReduce and its Hadoop implementation. We develop a new Hadoop scheduler called DyScale to exploit capabilities offered by heterogeneous cores in order to achieve a variety of performance objectives

TACKLING PERFORMANCE AND SECURITY ISSUES FOR CLOUD STORAGE SYSTEMS

Building data-intensive applications and emerging computing paradigm (e.g., Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT) in cloud computing environments is becoming a norm, given the many advantages in scalability, reliability, security and performance. However, under rapid changes in applications, system middleware and underlying storage device, service providers are facing new challenges to deliver performance and security isolation in the context of shared resources among multiple tenants. The gap between the decades-old storage abstraction and modern storage device keeps widening, calling for software/hardware co-designs to approach more effective performance and security protocols. This dissertation rethinks the storage subsystem from device-level to system-level and proposes new designs at different levels to tackle performance and security issues for cloud storage systems. In the first part, we present an event-based SSD (Solid State Drive) simulator that models modern protocols, firmware and storage backend in detail. The proposed simulator can capture the nuances of SSD internal states under various I/O workloads, which help researchers understand the impact of various SSD designs and workload characteristics on end-to-end performance. In the second part, we study the security challenges of shared in-storage computing infrastructures. Many cloud providers offer isolation at multiple levels to secure data and instance, however, security measures in emerging in-storage computing infrastructures are not studied. We first investigate the attacks that could be conducted by offloaded in-storage programs in a multi-tenancy cloud environment. To defend against these attacks, we build a lightweight Trusted Execution Environment, IceClave to enable security isolation between in-storage programs and internal flash management functions. We show that while enforcing security isolation in the SSD controller with minimal hardware cost, IceClave still keeps the performance benefit of in-storage computing by delivering up to 2.4x better performance than the conventional host-based trusted computing approach. In the third part, we investigate the performance interference problem caused by other tenants' I/O flows. We demonstrate that I/O resource sharing can often lead to performance degradation and instability. The block device abstraction fails to expose SSD parallelism and pass application requirements. To this end, we propose a software/hardware co-design to enforce performance isolation by bridging the semantic gap. Our design can significantly improve QoS (Quality of Service) by reducing throughput penalties and tail latency spikes. Lastly, we explore more effective I/O control to address contention in the storage software stack. We illustrate that the state-of-the-art resource control mechanism, Linux cgroups is insufficient for controlling I/O resources. Inappropriate cgroup configurations may even hurt the performance of co-located workloads under memory intensive scenarios. We add kernel support for limiting page cache usage per cgroup and achieving I/O proportionality

Optimizing Main Memory Usage in Modern Computing Systems to Improve Overall System Performance

Operating Systems use fast, CPU-addressable main memory to maintain an application’s temporary data as anonymous data and to cache copies of persistent data stored in slower block-based storage devices. However, the use of this faster memory comes at a high cost. Therefore, several techniques have been implemented to use main memory more efficiently in the literature. In this dissertation we introduce three distinct approaches to improve overall system performance by optimizing main memory usage. First, DRAM and host-side caching of file system data are used for speeding up virtual machine performance in today’s virtualized data centers. The clustering of VM images that share identical pages, coupled with data deduplication, has the potential to optimize main memory usage, since it provides more opportunity for sharing resources across processes and across different VMs. In our first approach, we study the use of content and semantic similarity metrics and a new algorithm to cluster VM images and place them in hosts where through deduplication we improve main memory usage. Second, while careful VM placement can improve memory usage by eliminating duplicate data, caches in current systems employ complex machinery to manage the cached data. Writing data to a page not present in the file system page cache causes the operating system to synchronously fetch the page into memory, blocking the writing process. In this thesis, we address this limitation with a new approach to managing page writes involving buffering the written data elsewhere in memory and unblocking the writing process immediately. This buffering allows the system to service file writes faster and with less memory resources. In our last approach, we investigate the use of emerging byte-addressable persistent memory technology to extend main memory as a less costly alternative to exclusively using expensive DRAM. We motivate and build a tiered memory system wherein persistent memory and DRAM co-exist and provide improved application performance at lower cost and power consumption with the goal of placing the right data in the right memory tier at the right time. The proposed approach seamlessly performs page migration across memory tiers as access patterns change and/or to handle tier memory pressure

INFERENCE-BASED FORENSICS FOR EXTRACTING INFORMATION FROM DIVERSE SOURCES

Digital forensics is tasked with the examination and extraction of evidence from a diverse set of devices and information sources. While digital forensics has long been synonymous with file recovery, this label no longer adequately describes the science’s role in modern investigations. Spurred by evolving technologies and online crime, law enforcement is shifting the focus of digital forensics from its traditional role in the final stages of an investigation to assisting investigators in the earliest phases — often before a suspect has been identified and a warrant served. Investigators need new forensic techniques to investigate online crimes, such as child pornography trafficking on peer-to-peer networks (p2p), and to extract evidence from new information sources, such as mobile phones. The traditional approach of developing tools tailored specifically to each source is no longer tenable given the diversity, volume of storage, and introduction rate of new devices and network applications. Instead, we propose the adoption of flexible, inference-based techniques to extract evidence from any format. Such techniques can be readily applied to a wide variety of different evidence sources without requiring significant manual work on the investigator’s part. The primary contribution of my dissertation is a set of novel forensic techniques for extracting information from diverse data sources. We frame the evaluation using two different, but increasingly important, forensic scenarios: mobile phone triage and network-based investigations. Via probabilistic descriptions of typical data structures, and using a classic dynamic programming algorithm, our phone triage techniques are able to identify user information in phones across varied models and manufacturers. We also show how to incorporate feedback from the investigator to improve the usability of extracted information. For network-based investigations, we quantify and characterize the extent of contraband trafficking on peer-to-peer networks. We suggest various techniques for prioritizing law enforcement’s limited resources. We finally investigate techniques that use system logs to generate and then analyze a finite state model of a protocol’s implementation. The objective is to infer behavior that an investigator can leverage to further law enforcement objectives. We evaluate all of our techniques using the real-world legal constraints and restrictions of investigators

Efficient Task-Local I/O Operations of Massively Parallel Applications

Applications on current large-scale HPC systems use enormous numbers of processing elements for their computation and have access to large amounts of main memory for their data. Nevertheless, they still need file-system access to maintain program and application data persistently. Characteristic I/O patterns that produce a high load on the file system often occurduring access to checkpoint and restart files, which have to be frequently stored to allow the application to be restarted after program termination or system failure. On large-scale HPC systems with distributed memory, each application task will often perform such I/O individually by creating task-local file objects on the file system. At large scale, these I/O patterns impose substantial stress on the metadata management components of the I/O subsystem. For example, the simultaneous creation of thousands of task-local files in the same directory can cause delays of several minutes. Also at the startup of dynamically linked applications, such metadata contention occurs while searching for library files and induces a comparably high metadata load on the file system. Even mid-scale applications cause in such load scenarios startup delays of ten minutes or more. Therefore, dynamic linking and loading is nowadays not applied on large HPC systems, although dynamic linking has many advantages for managing large code bases. The reason for these limitations is that POSIX I/O and the dynamic loader are implemented as serial components of the operating system and do not take advantage of the parallel nature of the I/O operations. To avoid the above bottlenecks, this work describes two novel approaches for the integration of locality awareness (e.g., through aggregation or caching) into the serial I/O operations of parallel applications. The underlying methods are implemented in two tools, $\textit{SIONlib}$ and $\textit{Spindle}$, which exploit the knowledge of application parallelism to coordinate access to file-system objects. In addition, the applied methods also use knowledge of the underlying I/O subsystem structure, the parallel file system configuration, and the network betweenHPC-system and I/O system to optimize application I/O. Both tools add layers between the parallel application and the POSIX-based standard interfaces of the operating system for I/O and dynamic loading, eliminating the need for modifying the underlying system software. SIONlib is already applied in several applications, including PEPC, muphi, and MP2C, to implement efficient checkpointing. In addition, SIONlib is integrated in the performance-analysis tools Scalasca and Score-P to efficiently store and read trace data. Latest benchmarks on the Blue Gene/Q in Jülich demonstrate that SIONlib solves the metadata problem at large scale by running efficiently up to 1.8 million tasks while maintaining high I/O bandwidths of 60-80% of file-system peak with a negligible file-creation time. The scalability of Spindle could be demonstrated by running the Pynamic benchmark, a proxy benchmark for a real application, on a cluster of Lawrence Livermore National Laboratory at large scale. The results show that the startup of dynamically linked applications is now feasible on more than 15000 tasks, whereas the overhead of Spindle is nearly constantly low. With SIONlib and Spindle, this work demonstrates how scalability of operating system components can be improved without modifying them and without changing the I/O patterns of applications. In this way, SIONlib and Spindle represent prototype implementations of functionality needed by next-generation runtime systems

ClusterRAID: Architecture and Prototype of a Distributed Fault-Tolerant Mass Storage System for Clusters

During the past few years clusters built from commodity off-the-shelf (COTS) components have emerged as the predominant supercomputer architecture. Typically comprising a collection of standard PCs or workstations and an interconnection network, they have replaced the traditionally used integrated systems due to their better price/performance ratio. As paradigms shift from mere computing intensive to I/O intensive applications, mass storage solutions for cluster installations become a more and more crucial aspect of these systems. The inherent unreliability of the underlying components is one of the reasons why no system has been established as a standard storage solution for clusters yet. This thesis sets out the architecture and prototype implementation of a novel distributed mass storage system for commodity off-the-shelf clusters and addresses the issue of the unreliable constituent components. The key concept of the presented system is the conversion of the local hard disk drive of a cluster node into a reliable device while preserving the block device interface. By the deployment of sophisticated erasure-correcting codes, the system allows the adjustment of the number of tolerable failures and thus the overall reliability. In addition, the applied data layout considers the access behaviour of a broad range of applications and minimizes the number of required network transactions. Extensive measurements and functionality tests of the prototype, both stand-alone and in conjunction with local or distributed file systems, show the validity of the concept

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Systems support for genomics computing in cloud environments

Genomics research has enormous applications in many areas such as health care, forensic, agriculture, etc. Most recent achievements in this field come from the availability of the unprecedented genomic data. However, new sequencing technologies in genomics keep producing data at a faster pace resulting a very huge amount of data. This poses great challenges on how to store, manage, process and analyze the data efficiently. To deal with these, genomics research groups often equip themselves with a small scale server room composed of high storage capacity and computing ability machines. This solution is not only costly, unscalable but also inefficient. A better solution would be the Cloud Computing with its elasticity and pay-as-you-go economic model. Nevertheless, Cloud Computing only provides the potential infrastructure solution. To address the high-throughput processing challenges, we need to have a suitable programming model. The fundamental idea is to process data in parallel. In existing models, MapReduce appears to be the best candidate because of its extremely scalability. In this work, we plan to develop a domain specific style system to support data management and analysis in genomics using Cloud Computing and MapReduce. Starting from the application layer, we developed a fundamental alignment tool called CloudAligner based on the MapReduce framework that outperformed its counterparts. After that, we continue seeking solutions to improve the system at the infrastructure level. Observing that scientists spend too much time on accessing data from low speed archives (tapes), we developed the Distributed Disk Cache (DiSK), and it was covered in a Master thesis. Another challenge is to enable the system to support differentiated services which are prevalent in Cloud Computing. To address this, we proposed a Differentiated Replication (DiR) mechanism allowing data to be inserted and retrieved with different availability. Another problem that greatly reduces the performance of the system is the heterogeneity of the Cloud. To tame it, we created an Open Reputation model called Opera. It employs vectors to record the behaviors (reputations) of nodes from different aspects. We modified the Hadoop MapReduce scheduler to make use of this information. The results proved that under heterogeneous environments, our system is better than the original Hadoop in terms of job execution time, number of failed/killed tasks, and energy consumption. The last challenge we have dealt with is the data movement since the data in our targeted domain (genomics) is extremely large and is generated with exponential rate. We divided the issue into two categories: internal and external movement. We have successfully developed a cached system to minimize the internal data movement and an easy-to-use tool called SPBD to handle external data movement with minimal respond time