Storage area network

Summary

A storage area network (SAN) or storage network is a computer network which provides access to consolidated, block-level data storage. SANs are primarily used to access data storage devices, such as disk arrays and tape libraries from servers so that the devices appear to the operating system as direct-attached storage. A SAN typically is a dedicated network of storage devices not accessible through the local area network (LAN). Although a SAN provides only block-level access, built on top of SANs do provide file-level access and are known as s. Newer SAN configurations enable hybrid SAN and allow traditional block storage that appears as local storage but also object storage for web services through APIs. Storage architectures Storage area networks (SANs) are sometimes referred to as network behind the servers and historically developed out of a centralized data storage model, but with its own data network. A SAN is, at its simplest, a dedicated network for data stor

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As storage hardware evolves, the systems and components which interact with them must adapt as well. Outdated assumptions at the operating system level necessitate a dramatic rethinking of the storage path as emerging storage technology offer performance speeds several orders of magnitude faster than traditional hard disks. This thesis investigates the design space of contemporary storage systems, in particular for non-volatile memory (NVM) technology, and presents a survey of the techniques currently being employed to build systems for NVM. We identify and examine relevant systems to determine features applicable to building a storage extension for IX, a data- plane operating system. We present the design and implementation of such a system and detail the assumptions and principles we strove to incorporate.

2017

Cuckoo Directory: A Scalable Directory for Many-Core Systems

Ken Balet, Babak Falsafi, Michael Ferdman, Pejman Lotfi Kamran

Growing core counts have highlighted the need for scalable on-chip coherence mechanisms. The increase in the number of on-chip cores exposes the energy and area costs of scaling the directories. Duplicate-tag based directories require highly associative structures that grow with core count, precluding scalability due to prohibitive power consumption. Sparse directories overcome the power barrier by reducing directory associativity, but require storage area over-provisioning to avoid high invalidation rates. We propose the Cuckoo directory, a power- and area-efficient scalable distributed directory. The cuckoo directory scales to high core counts without the energy costs of wide associative lookup and without gross capacity over-provisioning. Simulation of a 16-core CMP with commercial server and scientific workloads shows that the Cuckoo directory eliminates invalidations while being up to four times more power efficient than the Duplicate-tag directory and 24% more power-efficient and up to seven times more area efficient than the Sparse directory organization. Analytical projections indicate that the Cuckoo directory retains its energy and area benefits with increasing core count, efficiently scaling to at least 1024 cores.

IEEE Press2011

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.

EPFL2012