Bitcoin is the world's first blockchain-based, peer-to-peer cryptocurrency system. Being tremendously successful, the Bitcoin system is designed to support reliable, secure, and trusted transactions between untrusted peers. Since its release in 2009, the Bitcoin system has rapidly grown to an unprecedentedly large scale. However, the real-world behaviors of miners and users in the system and the efficacy of the original Bitcoin system design in the field deployment still remain unclear, hindering us from understanding its internals and developing the next-generation cryptocurrency system. In this paper, we study the behaviors of Bitcoin miners and users and their interactions based on quantitative analysis of more than nine years of Bitcoin transaction history, from its first release on January 3rd, 2009 to April 30th, 2018. We have analyzed over 300 million transaction records to study the transactions' processing, confirmation, and implementation. We have obtained several critical findings regarding how the miners and users exploit the high degree of freedom provided by the Bitcoin system to achieve their own interests. For example, we find that miners often attempt to maximize their profits even by sacrificing system performance; users could try to speed up the transaction processing by mistakenly trading off security for reduced latency. Such unexpected behaviors, to some degree, deviate from the original design purposes of the Bitcoin system and could bring undesirable consequences. Besides revealing several unexpected behaviors of the Bitcoin miners and users in the real world, we have also discussed the associated system implications as well as optimization opportunities in the future.

Block access patterns refer to the regularities of accessed blocks, and can be used to effectively enhance the intelligence of block storage systems. However, existing algorithms fail to uncover block access patterns in efficient ways. They either suffer high time and space overhead or only focus on the simplest patterns like sequential ones.

Object-based cloud storage presents an unconventional storage model. Exploiting its unique characteristics, such as the strong semantic correlations among objects and the high I/O parallelism potential, can greatly enhance user experience. Unfortunately, current storage optimization techniques, such as the caching and prefetching schemes, are designed for conventional storage and thus are sub-optimal for cloud storage services. In this paper, we propose a client-side cache management framework, called Pacaca, which integrates object clustering, parallelized prefetching, and cost-aware caching to exploit I/O parallelism and object correlations on cloud storage. We first develop an efficient mining scheme, called Frequent Cluster Mining (FCM), to discover object correlations from the access sequence, and then build a prefetching scheme to fetch the correlated objects in parallel. These two schemes are closely coordinated for achieving high prefetching accuracy, proper control on parallelism degree, and effective mis-prefetching detection and handling. After studying the impact of parallelized prefetching on cache management, we further present a cost-aware caching scheme to differentiate low-cost and high-cost objects for efficient caching by leveraging the awareness of parallelism and object correlations. Our experimental results show that our optimization schemes can effectively reduce the access latency, outperforming traditional schemes by up to 58%.

Successfully integrating cloud storage as a primary storage layer in the I/O stack is highly challenging. This is essentially due to two inherent critical issues: the high and variant cloud I/O latency and the per-I/O pricing model of cloud storage. To minimize the associated latency and monetary cost with cloud I/Os, caching is a crucial technology, as it directly influences how frequently the client has to communicate with the cloud. Unfortunately, current cloud caching schemes are mostly designed to optimize miss reduction as the sole objective and only focus on improving system performance while ignoring the fact that various cache misses could have completely distinct effects in terms of latency and monetary cost. In this article, we present a cost-aware caching scheme, called GDS-LC , which is highly optimized for cloud storage caching. Different from traditional caching schemes that merely focus on improving cache hit ratios and the classic cost-aware schemes that can only achieve a single optimization target, GDS-LC offers a comprehensive cache design by considering not only the access locality but also the object size, associated latency, and price, aiming at enhancing the user experience with cloud storage from two aspects: access latency and monetary cost. To achieve this, GDS-LC virtually partitions the cache space into two regions: a high-priority latency-aware region and a low-priority price-aware region. Each region is managed by a cost-aware caching scheme, which is based on GreedyDual-Size (GDS) and designed for a cloud storage scenario by adopting clean-dirty differentiation and latency normalization. The GDS-LC framework is highly flexible, and we present a further enhanced algorithm, called GDS-LCF , by incorporating access frequency in caching decisions. We have built a prototype to emulate a typical cloud client cache and evaluate GDS-LC and GDS-LCF with Amazon Simple Storage Services (S3) in three different scenarios: local cloud, Internet cloud, and heterogeneous cloud. Our experimental results show that our caching schemes can effectively achieve both optimization goals: low access latency and low monetary cost. It is our hope that this work can inspire the community to reconsider the cache design in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary layer.

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data is stored in the service provider’s data centers, and users access data via the network. For such a new storage model, our prior wisdom about conventional storage may not remain valid nor applicable to the emerging cloud storage. In this article, we present a comprehensive study to gain insight into the unique characteristics of cloud storage and optimize user experiences with cloud storage from a client’s perspective. Unlike prior measurement work that mostly aims to characterize cloud storage providers or specific client applications, we focus on analyzing the effects of various client-side factors on the user-experienced performance. Through extensive experiments and quantitative analysis, we have obtained several important findings. For example, we find that (1) a proper combination of parallelism and request size can achieve optimized bandwidths, (2) a client’s capabilities and geographical location play an important role in determining the end-to-end user-perceivable performance, and (3) the interference among mixed cloud storage requests may cause performance degradation. Based on our findings, we showcase a sampling- and inference-based method to determine a proper combination for different optimization goals. We further present a set of case studies on client-side chunking and parallelization for typical cloud-based applications. Our studies show that specific attention should be paid to fully exploiting the capabilities of clients and the great potential of cloud storage services.

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data is stored in the service provider's data centers, and users access data via the network. For such a new storage model, our prior wisdom about conventional storage may not remain valid nor applicable to the emerging cloud storage. In this paper, we present a comprehensive study and attempt to gain insight into the unique characteristics of cloud storage, primarily from the client's perspective. Through extensive experiments and quantitative analysis, we have acquired several interesting, and in some cases unexpected, findings. (1) Parallelizing I/Os and increasing request sizes are keys to improving the performance, but optimal bandwidth may only be achieved with a proper combination of parallelism and request size. (2) Client capabilities, including CPU, memory, and storage, play an unexpectedly important role in determining the achievable performance. (3) A geographically long distance affects client-perceived performance but does not always result in lower bandwidth and longer latency. Based on our experimental studies, we further present a case study on appropriate chunking and parallelization in a cloud storage client. Our studies show that specific attention should be paid to fully exploiting the capabilities of clients and the great potential of cloud storage services.

A unique merit of a solid-state drive (SSD) is its internal parallelism . In this article, we present a set of comprehensive studies on understanding and exploiting internal parallelism of SSDs. Through extensive experiments and thorough analysis, we show that exploiting internal parallelism of SSDs can not only substantially improve input/output (I/O) performance but also may lead to some surprising side effects and dynamics. For example, we find that with parallel I/Os, SSD performance is no longer highly sensitive to access patterns (random or sequential), but rather to other factors, such as data access interferences and physical data layout. Many of our prior understandings about SSDs also need to be reconsidered. For example, we find that with parallel I/Os, write performance could outperform reads and is largely independent of access patterns, which is opposite to our long-existing common understanding about slow random writes on SSDs. We have also observed a strong interference between concurrent reads and writes as well as the impact of physical data layout to parallel I/O performance. Based on these findings, we present a set of case studies in database management systems, a typical data-intensive application. Our case studies show that exploiting internal parallelism is not only the key to enhancing application performance, and more importantly, it also fundamentally changes the equation for optimizing applications. This calls for a careful reconsideration of various aspects in application and system designs. Furthermore, we give a set of experimental studies on new-generation SSDs and the interaction between internal and external parallelism in an SSD-based Redundant Array of Independent Disks (RAID) storage. With these critical findings, we finally make a set of recommendations to system architects and application designers for effectively exploiting internal parallelism.