Sangeetha Seshadri

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/

Optimizing Multiple Queries in Distributed Data Stream Systems

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

We consider the problem of query optimization in distributed stream based systems where multiple continuous queries may be executing simultaneously. In such systems, distribution adds degrees of freedom to an already complex optimization problem. Thousands of network nodes may need to be considered for operator placements in order to support in-network processing - clearly overwhelming even from the perspective of distributed query optimization. Added to this complexity is the potential for significant savings by combining query plans in order to re-use the stream of intermediate results. These issues force us to develop new techniques for query optimization. We present a formal definition of the multi-query optimization problem in such systems and propose some initial directions.

Optimizing Multiple Distributed Stream Queries Using Hierarchical Network Partitions

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.

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

We consider the problem of query optimization in distributed data stream systems where multiple continuous queries may be executing simultaneously. In order to achieve the best performance, query planning (such as join ordering) must be considered in conjunction with deployment planning (e.g., assigning operators to physical nodes with optimal ordering). However, such a combination involves not only a large number of network nodes but also many query operators, resulting in an extremely large search space for optimal solutions. Our paper aims at addressing this problem by utilizing hierarchical network partitions. We propose two algorithms - top-down and bottom-up which utilize hierarchical network partitions to provide scalable query optimization. Formal analysis is presented to establish the bounds on the search-space and to show the sub-optimality of our algorithms. Through simulations and experiments using a prototype deployed on Emulab we demonstrate the effectiveness of our algorithms.

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.

Routing Queries through a Peer-to-Peer InfoBeacons Network Using Information Retrieval Techniques

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.

Scalable and Reliable Location Services through Decentralized Replication

In the InfoBeacons system, a peer-to-peer network of beacons cooperates to route queries to the best information sources. Many internet sources are unwilling to provide more cooperation than simple searching to aid in the query routing.We adapt techniques from information retrieval to deal with this lack of cooperation. In particular, beacons determine how to route queries based on information cached from sources’ responses to queries. In this paper, we examine alternative architectures for routing queries between beacons and to data sources. We also examine how to improve the routing by probing sources in an informed way to learn about their content. Results of experiments using a beacon network to search 2,500 information sources demonstrates the effectiveness of our system; for example, our techniques require contacting up to 71 percent fewer sources than existing peer-to-peer random walk techniques.

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

One of the critical challenges for service oriented computing systems is the capability to guarantee scalable and reliable service provision. This paper presents Reliable GeoGrid, a decentralized service computing architecture based on geographical location aware overlay network for supporting reliable and scalable mobile information delivery services. The reliable GeoGrid approach offers two distinct features. First, we develop a distributed replication scheme, aiming at providing scalable and reliable processing of location service requests in decentralized pervasive computing environments. Our replica management operates on a network of heterogeneous nodes and utilizes a shortcut-based optimization to increase the resilience of the system against node failures and network failures. Second, we devise a dynamic load balancing technique that exploits the service processing capabilities of replicas to scale the system in anticipation of unexpected workload changes and node failures by taking into account of node heterogeneity, network proximity, and changing workload at each node. Our experimental evaluation shows that the reliable GeoGrid architecture is highly scalable under changing service workloads with moving hotspots and highly reliable in the presence of both individual node failures and massive node failures.

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.