Dynamic, Scalable, and Efficient Content Replication Techniques
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Dynamic, Scalable, and Efficient Content Replication Techniques Yan Chen
3.1 Introduction Exponential growth in processor performance, storage capacity, and network bandwidth is changing our view of computing. Our focus has shifted away from centralized, hand-choreographed systems to global-scale, distributed, self-organizing complexes – composed of thousands or millions of elements. Unfortunately, large pervasive systems are likely to have frequent component failures and be easily partitioned by slow or failed network links. Thus, use of local resources is extremely important – both for performance and availability. Further, pervasive streaming applications must tune their communication structure to avoid excess resource usage. To achieve both local access and efficient communication, we require flexibility in the placement of data replicas and multicast nodes. One approach for achieving this flexibility while retaining strong properties of the data is to partition the system into two tiers of replicas [18] – a small, durable primary tier and a large, soft-state, second-tier. The primary tier could represent a Web server (for Web content delivery), the Byzantine inner ring of a storage system [6, 29], or a streaming media provider. The important aspect of the primary tier is that it must hold the most up-to-date copy of data and be responsible for serializing and committing updates. We will treat the primary tier as a black box, called simply “the data source”. The second-tier becomes soft-state and will be the focus of this chapter. Examples of second-tiers include Content Delivery Networks (CDNs), file system caches, or Web proxy caches. Because second-tier replicas (or just “replicas”) are soft-state, we can dynamically grow and shrink their numbers to meet constraints of the system. We may, for instance, wish to achieve a Quality of Service (QoS) guarantee that bounds the maximum network latency between each client and replicas of the data that it is accessing. Since replicas consume resources, we will seek to generate as few replicas as possible to meet this constraint. As a consequence, popular data items may
Yan Chen Department of EECS, Northwestern University, Evanston IL, USA, e-mail: [email protected] R. Buyya et al. (eds.), Content Delivery Networks, c Springer-Verlag Berlin Heidelberg 2008
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warrant hundreds or thousands of replicas, while unpopular items may require no replicas. One difficult aspect of unconstrained replication is ensuring that content does not become stale. Slightly relaxed consistency, such as in the Web [20], OceanStore [29], or Coda [26], allows delay between the commitment of updates at the data source and the propagation of updates to replicas. None-the-less, update propagation must still occur in a timely manner. The potentially large number of replicas rules out direct, point-to-point delivery of updates to replicas. In fact, the extremely fluid nature of the second tier suggests a need to self-organize replicas into a multicast tree; we call such a t
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