Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency

Persistent memory provides high-performance data persistence at main memory. Memory writes need to be performed in strict order to satisfy storage consistency requirements and enable correct recovery from system crashes. Unfortunately, adhering to such a strict order significantly degrades system performance and persistent memory endurance. This paper introduces a new mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering requirements at significantly lower performance and endurance loss. LOC consists of two key techniques. First, Eager Commit eliminates the need to perform a persistent commit record write within a transaction. We do so by ensuring that we can determine the status of all committed transactions during recovery by storing necessary metadata information statically with blocks of data written to memory. Second, Speculative Persistence relaxes the write ordering between transactions by allowing writes to be speculatively written to persistent memory. A speculative write is made visible to software only after its associated transaction commits. To enable this, our mechanism supports the tracking of committed transaction ID and multi-versioning in the CPU cache. Our evaluations show that LOC reduces the average performance overhead of memory persistence from 66.9% to 34.9% and the memory write traffic overhead from 17.1% to 3.4% on a variety of workloads.Comment: This paper has been accepted by IEEE Transactions on Parallel and Distributed System

Providing Transaction Class-Based QoS in In-Memory Data Grids via Machine Learning

Elastic architectures and the ”pay-as-you-go” resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing platforms, mainly thanks to their ability to be dynamically (re-)sized and tuned. Anyway, when specific QoS requirements have to be met, this kind of architectures have revealed to be complex to be managed by humans. Particularly, their management is a very complex task without the stand of mechanisms supporting run-time automatic sizing/tuning of the data platform and the underlying (virtual) hardware resources provided by the cloud. In this paper, we present a neural network-based architecture where the system is constantly and automatically re-configured, particularly in terms of computing resources

Incremental Consistency Guarantees for Replicated Objects

Programming with replicated objects is difficult. Developers must face the fundamental trade-off between consistency and performance head on, while struggling with the complexity of distributed storage stacks. We introduce Correctables, a novel abstraction that hides most of this complexity, allowing developers to focus on the task of balancing consistency and performance. To aid developers with this task, Correctables provide incremental consistency guarantees, which capture successive refinements on the result of an ongoing operation on a replicated object. In short, applications receive both a preliminary---fast, possibly inconsistent---result, as well as a final---consistent---result that arrives later. We show how to leverage incremental consistency guarantees by speculating on preliminary values, trading throughput and bandwidth for improved latency. We experiment with two popular storage systems (Cassandra and ZooKeeper) and three applications: a Twissandra-based microblogging service, an ad serving system, and a ticket selling system. Our evaluation on the Amazon EC2 platform with YCSB workloads A, B, and C shows that we can reduce the latency of strongly consistent operations by up to 40% (from 100ms to 60ms) at little cost (10% bandwidth increase, 6% throughput drop) in the ad system. Even if the preliminary result is frequently inconsistent (25% of accesses), incremental consistency incurs a bandwidth overhead of only 27%.Comment: 16 total pages, 12 figures. OSDI'16 (to appear

Proceedings of the ECSCW'95 Workshop on the Role of Version Control in CSCW Applications

The workshop entitled "The Role of Version Control in Computer Supported Cooperative Work Applications" was held on September 10, 1995 in Stockholm, Sweden in conjunction with the ECSCW'95 conference. Version control, the ability to manage relationships between successive instances of artifacts, organize those instances into meaningful structures, and support navigation and other operations on those structures, is an important problem in CSCW applications. It has long been recognized as a critical issue for inherently cooperative tasks such as software engineering, technical documentation, and authoring. The primary challenge for versioning in these areas is to support opportunistic, open-ended design processes requiring the preservation of historical perspectives in the design process, the reuse of previous designs, and the exploitation of alternative designs. The primary goal of this workshop was to bring together a diverse group of individuals interested in examining the role of versioning in Computer Supported Cooperative Work. Participation was encouraged from members of the research community currently investigating the versioning process in CSCW as well as application designers and developers who are familiar with the real-world requirements for versioning in CSCW. Both groups were represented at the workshop resulting in an exchange of ideas and information that helped to familiarize developers with the most recent research results in the area, and to provide researchers with an updated view of the needs and challenges faced by application developers. In preparing for this workshop, the organizers were able to build upon the results of their previous one entitled "The Workshop on Versioning in Hypertext" held in conjunction with the ECHT'94 conference. The following section of this report contains a summary in which the workshop organizers report the major results of the workshop. The summary is followed by a section that contains the position papers that were accepted to the workshop. The position papers provide more detailed information describing recent research efforts of the workshop participants as well as current challenges that are being encountered in the development of CSCW applications. A list of workshop participants is provided at the end of the report. The organizers would like to thank all of the participants for their contributions which were, of course, vital to the success of the workshop. We would also like to thank the ECSCW'95 conference organizers for providing a forum in which this workshop was possible

Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed