Abstract

Abstract

Abstract
Massive-scale distributed computing is a challenge at our doorstep. The current exponential growth of data calls for massive-scale capabilities of storage and processing. This is being acknowledged by several major Internet players embracing the cloud computing model and offering first generation distributed tuple stores. Having all started from similar requirements, these systems ended up providing a similar service: A simple tuple store interface, that allows applications to insert, query, and remove individual elements. Furthermore, while availability is commonly assumed to be sustained by the massive scale itself, data consistency and freshness is usually severely hindered. By doing so, these services focus on a specific narrow trade-off between consistency, availability, performance, scale, and migration cost, that is much less attractive to common business needs. In this paper we introduce Data Droplets, a novel tuple store that shifts the current trade-off towards the needs of common business users, providing additional consistency guarantees and higher level data processing primitives smoothing the migration path for existing applications. We present a detailed comparison between Data Droplets and existing systems regarding their data model, architecture and trade-offs. Preliminary results of the system's performance under a realistic workload are also presented.

Abstract
Key-value stores hold the unprecedented bulk of the data produced by applications such as social networks. Their scalability and availability requirements often outweigh sacrificing richer data and processing models, and even elementary data consistency. Moreover, existing key-value stores have only random or order based placement strategies. In this paper we exploit arbitrary data relations easily expressed by the application to foster data locality and improve the performance of complex queries common in social network read-intensive workloads. We present a novel data placement strategy, supporting dynamic tags, based on multidimensional locality-preserving mappings. We compare our data placement strategy with the ones used in existing key-value stores under the workload of a typical social network application and show that the proposed correlation-aware data placement strategy offers a major improvement on the system's overall response time and network requirements.

Abstract

Abstract
Cloud computing infrastructures are the most recent approach to the development and conception of computational systems. Cloud infrastructures are complex environments with various subsystems, each one with their own challenges. Cloud systems should be able to provide the following fundamental property: elasticity. Elasticity is the ability to automatically add and remove instances according to the needs of the system. This is a requirement for pay-per-use billing models. Various open source software solutions allow companies and institutions to build their own Cloud infrastructure. However, in most of these, the elasticity feature is quite immature. Monitoring and timely adapting the active resources of a Cloud computing infrastructure is key to provide the elasticity required by diverse, multi-tenant and pay-per-use business models. In this paper, we propose Elastack, an automated monitoring and adaptive system, generic enough to be applied to existing IaaS frameworks, and intended to enable the elasticity they currently lack. Our approach offers any Cloud infrastructure the mechanisms to implement automated monitoring and adaptation as well as the flexibility to go beyond these. We evaluate Elastack by integrating it with the OpenStack showing how easy it is to add these important features with a minimum, almost imperceptible, amount of modifications to the default installation. © 2012 ACM.