Data Access Patterns Of Applications Research Articles

Waffle: An Online Oblivious Datastore for Protecting Data Access Patterns

We present Waffle, a datastore that protects an application's data access patterns from a passive persistent adversary. Waffle achieves this without prior knowledge of the input data access distribution, making it the first of its kind to adaptively handle input sequences under a passive persistent adversary. Waffle maintains a constant bandwidth and client-side storage overhead, which can be adjusted to suit the application owner's preferences. This flexibility allows the owner to fine-tune system parameters and strike a balance between security and performance. Our evaluation, utilizing the Yahoo! Cloud Serving Benchmark (YCSB) benchmark and Redis as the backend storage, demonstrates promising results. The insecure baseline outperforms Waffle by a mere 5-6x, whereas Waffle outperforms Pancake-a state-of-the-art oblivious datastore under passive persistent adversaries-by 45-57%, and a concurrent ORAM system, TaoStore, by 102x.

Modeling Remote I/O versus Staging Tradeoff in Multi-Data Center Computing

In multi-data center computing, data to be processed is not always local to the computation. This is a major challenge especially for data-intensive Cloud computing applications, since large amount of data would need to be either moved the local sites (staging) or accessed remotely over the network (remote I/O). Cloud application developers generally chose between staging and remote I/O intuitively without making any scientific comparison specific to their application data access patterns since there is no generic model available that they can use. In this paper, we propose a generic model for the Cloud application developers which would help them to choose the most appropriate data access mechanism for their specific application workloads. We define the parameters that potentially affect the end-to-end performance of the multi-data center Cloud applications which need to access large datasets over the network. To test and validate our models, we implemented a series of synthetic benchmark applications to simulate the most common data access patterns encountered in Cloud applications. We show that our model provides promising results in different settings with different parameters, such as network bandwidth, server and client capabilities, and data access ratio.

Improving Networked File System Performance Using a Locality-Aware Cooperative Cache Protocol

In a distributed environment, the utilization of file buffer caches in different clients may greatly vary. Cooperative caching has been proposed to increase cache utilization by coordinating the shared usage of distributed caches. It allows clients that would more greatly benefit from larger caches to forward data objects to peer clients with relatively underutilized caches. To support such coordination, global cache utilization must be dynamically evaluated. This, in turn, requires an effective analysis of application data access patterns. Existing coordination protocols are demonstrably suboptimal in this respect, exhibiting inefficient memory utilization and undue interference among clients. We propose a locality-aware cooperative caching protocol, called LAC, that is based on analysis and manipulation of data block reuse distance to effectively predict cache utilization and the probability of data reuse at each client. Using a dynamically adaptive synchronization technique, we keep local information up to date and consistently comparable across clients. The system is highly scalable in the sense that global coordination is achieved without centralized control. We have conducted thorough trace-driven simulation experiments to assess the performance differences between LAC and various existing protocols representative of the general class. Using a realistic and representative cost model, we show that the LAC protocol significantly and consistently outperforms existing cooperative caching protocols, demonstrating high and balanced utilization of caches across all clients. In our experiments, LAC reduces block access time by up to 36 percent, with an average of 31 percent, over the system without peer cache coordination, and reduces block access time by up to 22 percent, with an average of 13 percent, over the best performer of the existing protocols.

ILP-Based energy minimization techniques for banked memories

Main memories can consume a significant portion of overall energy in many data-intensive embedded applications. One way of reducing this energy consumption is banking, that is, dividing available memory space into multiple banks and placing unused (idle) memory banks into low-power operating modes. Prior work investigated code-restructuring- and data-layout-reorganization-based approaches for increasing the energy benefits that could be obtained from a banked memory architecture. This article explores different techniques that can potentially coexist within the same optimization framework for maximizing benefits of low-power operating modes. These techniques include employing nonuniform bank sizes, data migration, data compression, and data replication. By using these techniques, we try to increase the chances for utilizing low-power operating modes in a more effective manner, and achieve further energy savings over what could be achieved by exploiting low-power modes alone. Specifically, nonuniform banking tries to match bank sizes with application-data access patterns. The goal of data migration is to cluster data with similar access patterns in the same set of banks. Data compression reduces the size of the data used by an application, and thus helps reduce the number of memory banks occupied by data. Finally, data replication increases bank idleness by duplicating select read-only data blocks across banks. We formulate each of these techniques as an ILP (integer linear programming) problem, and solve them using a commercial solver. Our experimental analysis using several benchmarks indicates that all the techniques presented in this framework are successful in reducing memory energy consumption. Based on our experience with these techniques, we recommend to compiler writers for banked memories to consider data compression, replication, and migration.

Architectural support for uniprocessor and multiprocessor active memory systems

We introduce an architectural approach to improve memory system performance in both uniprocessor and multiprocessor systems. The architectural innovation is a flexible active memory controller backed by specialized cache coherence protocols that permit the transparent use of address remapping techniques. The resulting system shows significant performance improvement across a spectrum of machine configurations, from uniprocessors through single-node multiprocessors (SMPs) to distributed shared memory clusters (DSMs). Address remapping techniques exploit the data access patterns of applications to enhance their cache performance. However, they create coherence problems since the processor is allowed to refer to the same data via more than one address. While most active memory implementations require cache flushes, we present a new approach to solve the coherence problem. We leverage and extend the cache coherence protocol so that our techniques work transparently to efficiently support uniprocessor, SMP and DSM active memory systems. We detail the coherence protocol extensions to support our active memory techniques and present simulation results that show uniprocessor speedup from 1.3 to 7.6 on a range of applications and microbenchmarks. We also show remarkable performance improvement on small to medium-scale SMP and DSM multiprocessors, allowing some parallel applications to continue to scale long after their performance levels off on normal systems.

Adaptive prefetching and storage reorganization in a log-structured storage system

We present a storage management system that has the ability to adapt to the data access characteristics of the application that uses it based on collection and analysis of runtime statistics. This feature is especially useful in the storage management layer of database systems, where applications exhibit relatively predictable access patterns. Adaptive reorganization is performed by the storage management system in a manner that optimizes the access patterns of the system for which it is used. We enhance the log-structured storage system that naturally caters for write optimization, with the addition of a statistics collection mechanism to determine data access patterns of applications. The storage system can serve as a testbed for a variety of statistics analysis and clustering mechanisms. Higher level application-specific data clustering mechanisms can be used to override the storage system's low-level clustering mechanisms. In addition, the analysis techniques and reorganization scheme can be used in other storage systems. Performance results from our prototype show potential response time speedups of up to 83 percent over the basic log-structured file system in the best case, using a combination of storage reorganization and prefetching.