Low delay personal content storage in a multi-service access network

A current important trend is the introduction of new services in the access and aggregation network, close to the end user. A major opportunity for such service enabled access networks is providing users with fast and reliable storage, allowing them to transparently access and share their personal data anytime, anywhere, while guaranteeing data retention. An important issue in deploying such a service is where to put the storage servers, minimizing the deployment cost without sacrificing performance. This paper presents and evaluates an optimization algorithm for solving this storage server placement problem, respecting the bandwidth constraints of the access and aggregation network while guaranteeing a low delay for accessing the storage service from any access node. It will be shown that the algorithm produces close to optimal results relatively fast. Introduction The implementation of the “broadband for all” objective and the large-scale introduction of new services, such as (interactive) digital television, has triggered an evolution of the access network infrastructure [1]. Where current access networks are often separated per service, telecom research envisions offering a myriad of broadband services over a converged access network, as depicted in Figure 1, bringing IP-awareness closer to the end user and increasing functional intelligence of the access and aggregation nodes [2]. Figure 1: Evolution towards a converged, IP aware, full service access network. A major opportunity of such multi-service access networks is providing anytime, anywhere access to fast and reliable storage. End users have an increasing amount of digital data (digital photo albums, digital home videos, a digital music and movie collection, etc), while harddisks are failure prone and recordable optical media only have limited archival lifespan [3]. A high speed network storage service, enabling a user to access his personal data at all times, wherever he is, while relieving him of the burden of meticulously backing up all his precious photos, home videos and other important files, would make life a lot easier. When designing such a storage service, an important question that needs answering is where to put the storage servers. This paper presents and evaluates an optimization algorithm for determining the best locations for placing storage servers, minimizing the deployment cost while guaranteeing fast access from any access node. Both access and aggregation nodes are candidate storage server locations. A user can be served from any storage server. This requires a pervasive replication mechanism, as introduced in [13], creating a replica of a user’s data wherever and whenever it is accessed. In this paper, it is assumed that server nodes have enough processing power and storage capacity to handle all requests. Furthermore, a read mostly data storage service is assumed. In many typical end user applications, such as a digital media library, data is read much more often than it is written. Read mostly data implies no strong consistency is required between the different replicas. Updates can be propagated periodically, at the same time deciding whether a replica should be retained or deleted (e.g. based on last access time, access frequency, etc), taking into account that a minimum set of replicas should be maintained at all times in order to ensure resilience. Since this kind of read mostly data storage service puts most strain on downstream traffic, only downstream traffic measures are considered when determining the best locations for placing a storage server. The remainder of this paper is structured as follows: first, related work is presented. Then, the next section formalizes the problem description, after which the storage server placement algorithm is detailed. Next, the algorithm is evaluated using two typical access and aggregation network topologies. Finally concluding remarks are presented and some future work is discussed. Related work Many distributed filesystems exist today, ranging from client-server systems such as NFS [4], AFS [5] and Coda [6] over cluster filesystems (e.g. Lustre [7], GPFS [8] and the Google File System [9]) to global scale peer-to-peer filesystems (e.g. OceanStore [10], FARSITE [12] and Pangaea [13]). Deployment of a transparent, fast, scalable and reliable storage service in the access and aggregation network is a new idea, triggered by the increased functional intelligence