With the advent of cloud computing model, distributed caches have become the cornerstone for building scalable applications. Popular systems like Facebook [1] or Twitter use Memcached [5], a highly scalable distributed object cache, to speed up applications by avoiding database accesses. Distributed object caches assign objects to cache instances based on a hashing function, and objects are not moved from a cache instance to another unless more instances are added to the cache and objects are redistributed. This may lead to situations where some cache instances are overloaded when some of the objects they store are frequently accessed, while other cache instances are less frequently used.In this paper we propose a multi-resource load balancing algorithm for distributed cache systems. The algorithm aims at balancing both CPU and Memory resources among cache instances by redistributing stored data. Considering the possible conflict of balancing multiple resources at the same time, we give CPU and Memory resources weighted priorities based on the runtime load distributions. A scarcer resource is given a higher weight than a less scarce resource when load balancing. The system imbalance degree is evaluated based on monitoring information, and the utility load of a node, a unit for resource consumption. Besides, since continuous rebalance of the system may affect the QoS of applications utilizing the cache system, our data selection policy ensures that each data migration minimizes the system imbalance degree and hence, the total reconfiguration cost can be minimized. An extensive simulation is conducted to compare our policy with other policies. Our policy shows a significant improvement in time efficiency and decrease in reconfiguration cost.

The need of systems able to deal with large amounts of data is increasing in the big data era. These systems run on top of a distributed system where failures will happen. In the last decade a large number of data stores have been developed for providing scalability. These systems avoid some of the properties traditional relational databases provided in order to achieve scalability producing a variety of data stores known as NoSQL data stores. In the last years, NewSQL systems have been developed in order to meet the best of both worlds: transactional processing and SQL language and scalability. In this paper we present a practical experience on how failures affect the regular operation of a scalable fault-tolerant relational database (NewSQL), CumuloNimbo. The paper evaluates the performance of CumuloNimbo using the TPC-C benchmark varying the number of nodes where the database runs under different failure scenarios.

One of the most demanding needs in cloud computing is that of having scalable and highly available databases. One of the ways to attend these needs is to leverage the scalable replication techniques developed in the last decade. These techniques allow increasing both the availability and scalability of databases. Many replication protocols have been proposed during the last decade. The main research challenge was how to scale under the eager replication model, the one that provides consistency across replicas. In this paper, we examine three eager database replication systems available today: Middle-R, C-JDBC and MySQL Cluster using TPC-W benchmark. We analyze their architecture, replication protocols and compare the performance both in the absence of failures and when there are failures.

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Companies have evolved from a world where they only had SQL databases to a world where they use different kinds of data stores such as key-value data stores, document-oriented data stores and graph databases. This scenario rose new challenges such as data model heterogeneity and data consistency. There could be inconsistencies in case of failures during business actions requiring to update data scattered across different data stores due to the lack of transactional consistency across data stores. In this paper we propose an ultra-scalable transactional management layer that can be integrated with any data store with multi-versioning capabilities. This layer has been developed in the context of the FP7 CoherentPaaS European Project where it was integrated with six different data stores, three NoSQL data stores and three SQL-like databases. We particularly focus on the ultra-scalable transaction management API and how it can be easily integrated in any versioned data store.