Lawrence Chiu

Evaluating Phase Change Memory for Enterprise Storage Systems: A Study of Caching and Tiering Approaches

Storage systems based on Phase Change Memory (PCM) devices are beginning to generate considerable attention in both industry and academic communities. But whether the technology in its current state will be a commercially and technically viable alternative to entrenched technologies such as flash-based SSDs remains undecided. To address this, it is important to consider PCM SSD devices not just from a device standpoint, but also from a holistic perspective. This article presents the results of our performance study of a recent all-PCM SSD prototype. The average latency for a 4KiB random read is 6.7μs, which is about 16× faster than a comparable eMLC flash SSD. The distribution of I/O response times is also much narrower than flash SSD for both reads and writes. Based on the performance measurements and real-world workload traces, we explore two typical storage use cases: tiering and caching. We report that the IOPS/

Adaptive Data Migration in Multi-tiered Storage Based Cloud Environment

of a tiered storage system can be improved by 12--66% and the aggregate elapsed time of a server-side caching solution can be improved by up to 35% by adding PCM. Our results show that (even at current price points) PCM storage devices show promising performance as a new component in enterprise storage systems.

Automated lookahead data migration in SSD-enabled multi-tiered storage systems

Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated data migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective data migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated data migration in multi-tiered storage systems is how to fully release the power of data migration while guaranteeing the migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adaptive look ahead data migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive data migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of look ahead migration efficiency in our formal model develop. Second, our data migration model can adaptively determine the optimal look ahead window size, based on several parameters, to optimize the effectiveness of look ahead migration. Third, we formally and experimentally show that the adaptive data migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive look ahead migration approach with the basic migration model and show that the adaptive migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.

Software defined just-in-time caching in an enterprise storage system

The significant IO improvements of Solid State Disks (SSD) over traditional rotational hard disks makes it an attractive approach to integrate SSDs in tiered storage systems for performance enhancement. However, to integrate SSD into multi-tiered storage system effectively, automated data migration between SSD and HDD plays a critical role. In many real world application scenarios like banking and supermarket environments, workload and IO profile present interesting characteristics and also bear the constraint of workload deadline. How to fully release the power of data migration while guaranteeing the migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an automated, deadline-aware, lookahead migration scheme to address the data migration challenge. We analyze the factors that may impact on the performance of lookahead migration efficiency and develop a greedy algorithm to adaptively determine the optimal lookahead window size to optimize the effectiveness of lookahead migration, aiming at improving overall system performance and resource utilization while meeting workload deadlines. We compare our lookahead migration approach with the basic migration model and validate the effectiveness and efficiency of our adaptive lookahead migration approach through a trace driven experimental study.

A Fault-Tolerant Middleware Architecture for High-Availability Storage Services

A software defined storage environment is one in which logical storage resources and services are completely abstracted from physical storage systems. Therefore, not only can storage resources cross physical boundaries, but they can also be defined by software and provisioned automatically, for instance, by the applications that consume them. In this paper, we present a novel software defined cooperative caching (SDCC) framework that operates at the block layer and manages the placement of data in different tiers and caches that span multiple servers and storage systems in an integrated and coherent fashion. A programming interface complements the core framework by giving the applications an interface to control data organization across the storage, thereby allowing the block storage infrastructure to be software defined. The SDCC framework allows applications to actively influence the data layout while also benefitting from the system-wide knowledge and resource management capabilities of the storage system. We present an experimental study conducted using real workloads, and the results demonstrate the performance benefits gained with SDCC, as well as the potential for consolidating multiple different workloads that share the same storage server.

Phase change memory in enterprise storage systems: silver bullet or snake oil?

Today organizations and business enterprises of all sizes need to deal with unprecedented amounts of digital information, creating challenging demands for mass storage and on-demand storage services. The current trend of clustered scale-out storage systems use symmetric active replication based clustering middleware to provide continuous availability and high throughput. Such architectures provide significant gains in terms of cost, scalability and performance of mass storage and storage services. However, a fundamental limitation of such an architecture is its vulnerability to application-induced massive dependent failures of the clustering middleware. In this paper, we propose hierarchical middleware architectures that improve availability and reliability in scale-out storage systems while continuing to deliver the cost and performance advantages and a single system image (SSI). Hierarchical middleware architectures organize critical cluster management services into an overlay network that provides application fault isolation and eliminates symmetric clustering middleware as a single-point-of-failure. We present an in-depth evaluation of hierarchical middlewares based on an industry-strength storage system. Our results show that hierarchical architectures can significantly improve availability and reliability of scale-out storage clusters.

Analyzing Enterprise Storage Workloads With Graph Modeling and Clustering

Storage devices based on Phase Change Memory (PCM) devices are beginning to generate considerable attention in both industry and academic communities. But whether the technology in its current state will be a commercially and technically viable alternative to entrenched technologies such as flash-based SSDs still remains unanswered. To address this it is important to consider PCM SSD devices not just from a device standpoint, but also from a holistic perspective. This paper presents the results of our performance measurement study of a recent all-PCM SSD prototype. The average latency for 4 KB random read is 6.7 μs, which is about 16x faster than a comparable eMLC flash SSD. The distribution of I/O response times is also much narrower than the flash SSD for both reads and writes. Based on real-world workload traces, we model a hypothetical storage device which consists of flash, HDD, and PCM to identify the combinations of device types that offer the best performance within cost constraints. Our results show that - even at current price points - PCM storage devices show promise as a new component in multi-tiered enterprise storage systems.

GraphLens: Mining Enterprise Storage Workloads Using Graph Analytics

Utilizing graph analysis models and algorithms to exploit complex interactions over a network of entities is emerging as an attractive network analytic technology. In this paper, we show that traditional column or row-based trace analysis may not be effective in deriving deep insights hidden in the storage traces collected over complex storage applications, such as complex spatial and temporal patterns, hotspots and their movement patterns. We propose a novel graph analytics framework, GraphLens, for mining and analyzing real world storage traces with three unique features. First, we model storage traces as heterogeneous trace graphs in order to capture multiple complex and heterogeneous factors, such as diverse spatial/temporal access information and their relationships, into a unified analytic framework. Second, we employ and develop an innovative graph clustering method that employs two levels of clustering abstractions on storage trace analysis. We discover interesting spatial access patterns and identify important temporal correlations among spatial access patterns. This enables us to better characterize important hotspots and understand hotspot movement patterns. Third, at each level of abstraction, we design a unified weighted similarity measure through an iterative dynamic weight learning algorithm. With an optimal weight assignment scheme, we can efficiently combine the correlation information for each type of storage access patterns, such as random versus sequential, read versus write, to identify interesting spatial/temporal correlations hidden in the traces. Some optimization techniques on matrix computation are proposed to further improve the efficiency of our clustering algorithm on large trace datasets. Extensive evaluation on real storage traces shows GraphLens can provide broad and deep trace analysis for better storage strategy planning and efficient data placement guidance. GraphLens can be applied to both a single PC with multiple disks and a distributed network across a cluster of compute nodes to offer a few opportunities for optimization of storage performance.

Trillion Operations Key-Value Storage Engine: Revisiting the Mission Critical Analytics Storage Software Stack

Conventional methods used to analyze storage workloads have been centered on relational database technology combined with attributes-based classification algorithms. This paper presents a novel analytic architecture, GraphLens, for mining and analyzing real world storage traces. The design of our GraphLens system embodies three unique features. First, we model storage traces as heterogeneous trace graphs in order to capture diverse spatial correlations and storage access patterns using a unified analytic framework. Second, we employ and develop an innovative graph clustering method to discover interesting spatial access patterns. This enables us to better characterize important hotspots of storage access and understand hotspot movement patterns. Third, we design a unified weighted similarity measure through an iterative learning and dynamic weight refinement algorithm. With an optimal weight assignment scheme, we can efficiently combine the correlation information for each type of storage access patterns, such as random v.s. sequential, read v.s. write, to identify interesting spatial correlations hidden in the traces. Extensive evaluation on real storage traces shows GraphLens can provide scalable and reliable data analytics for better storage strategy planning and efficient data placement guidance.

Recovery scopes, recovery groups, and fine-grained recovery in enterprise storage controllers with multi-core processors

Data is the new natural resource of this century. As data volumes grow and applications aimed at monetizing the data continue to evolve, data processing platforms are expected to meet new scale, performance, reliability and data retention requirements. At the same time, storage hardware continues to improve in performance and price-performance. In this paper, we present TOKVS - Trillion Operation Key-Value Store, a NoSQL storage engine that redefines the storage software stack to meet the requirements of next-generation applications on next-generation hardware.