Linear Scalability of Distributed Applications

The explosion of social applications such as Facebook, LinkedIn and Twitter, of electronic commerce with companies like Amazon.com and Ebay.com, and of Internet search has created the need for new technologies and appropriate systems to manage effectively a considerable amount of data and users. These applications must run continuously every day of the year and must be capable of surviving sudden and abrupt load increases as well as all kinds of software, hardware, human and organizational failures. Increasing (or decreasing) the allocated resources of a distributed application in an elastic and scalable manner, while satisfying requirements on availability and performance in a cost-effective way, is essential for the commercial viability but it poses great challenges in today's infrastructures. Indeed, Cloud Computing can provide resources on demand: it now becomes easy to start dozens of servers in parallel (computational resources) or to store a huge amount of data (storage resources), even for a very limited period, paying only for the resources consumed. However, these complex infrastructures consisting of heterogeneous and low-cost resources are failure-prone. Also, although cloud resources are deemed to be virtually unlimited, only adequate resource management and demand multiplexing can meet customer requirements and avoid performance deteriorations. In this thesis, we deal with adaptive management of cloud resources under specific application requirements. First, in the intra-cloud environment, we address the problem of cloud storage resource management with availability guarantees and find the optimal resource allocation in a decentralized way by means of a virtual economy. Data replicas migrate, replicate or delete themselves according to their economic fitness. Our approach responds effectively to sudden load increases or failures and makes best use of the geographical distance between nodes to improve application-specific data availability. We then propose a decentralized approach for adaptive management of computational resources for applications requiring high availability and performance guarantees under load spikes, sudden failures or cloud resource updates. Our approach involves a virtual economy among service components (similar to the one among data replicas) and an innovative cascading scheme for setting up the performance goals of individual components so as to meet the overall application requirements. Our approach manages to meet application requirements with the minimum resources, by allocating new ones or releasing redundant ones. Finally, as cloud storage vendors offer online services at different rates, which can vary widely due to second-degree price discrimination, we present an inter-cloud storage resource allocation method to aggregate resources from different storage vendors and provide to the user a system which guarantees the best rate to host and serve its data, while satisfying the user requirements on availability, durability, latency, etc. Our system continuously optimizes the placement of data according to its type and usage pattern, and minimizes migration costs from one provider to another, thereby avoiding vendor lock-in.