El asilo como transformador de la herencia republicana

In [46], the main idea of the Replica Distribution (RD) protocol is to disseminate object replicas on nodes at r-hop distance, where the value of r-hop depends on the replication degree of each object that is found. When the node joins the network, it communicates the description of its objects to the manager that de- cides the replication degree of each object on the basis of the provided descriptor and the estimated number of nodes in the dense region. When a node needs to replicate an object, it sends a replication packet specifying the number of replicas remaining to replicate and the desired r-hop distance between replicas. The replication packet propagates on nodes along an approximately straight line with a fixed direction. When the packet reaches the r-hop away node, it saves a copy and reiterates the process by decreasing the number of requested replicas. However, this placement does not consider object popularity in the placement decision, which leads to inefficient dissemination for the content.

The work in [47] proposed a self-stabilizing asynchronous, fully distributed, scalable protocol that places replicated resources in a network of arbitrary topol- ogy with the aim that the furthest distance to be traveled to find a content replica is slightly larger than optimal, and the distance between identical copies is large. They modeled the problem as a p-center problem, where the objective function is to locate p facilities such that the maximum distance is minimized.

M inX x

X i

dt(x, ci) (3.1)

where dt(x, ci)is the distance at time t from node x to center ci. The objective is to place different items in the vicinity of each node or to place the identical items as far away from each other as possible. This is similar to the p-center problem formulation and can be used for channel assignment to maximize channel reuse. They found the protocol is close to optimal in convergence time

and message overhead. However, in WMNs, the requirements are different for replication strategies since they are less constrained by energy consumption and node mobility. Furthermore, the p-center problem does not consider content popularity at each node. Therefore, our model is different, since we consider popularity as a factor affecting the placement.

Random-Walk Diffusion (RWD) mechanism was proposed in [48] in which a mobile device hosting a content replica, stores it for a storage time t. At the end of its storage time, the replica node selects with equal probability one of its neighbors to store the content for the following storage period. Therefore, content replicas roam the network by moving from one node to another, randomly, at each time step t. In [49], the main contribution is the design of a mechanism for content placement and replication that achieves load balancing according to the variations in the network topology and the query rate. The mechanism distributes the burden of storing and providing content on nodes to achieve load balancing. The replica nodes are responsible to decide whether to replicate, hand over or drop content based on local measurements of their workload. During storage time τ , the replica node counts the number of queries that it serves (i.e., Sv(j)). When the storage time expires, the replica node compares Sv(j) to a reference value SRfor the workload that node v(j)is willing to support. Decisions are taken as follows:

if (Sv(j)− SR)          >  replicate, < − drop, else handover (3.2)

Where  is a tolerance value to avoid replication/drop decisions in case of small changes in the node’s workload. In both [48] and [49], the mechanisms do not replicate the content according to the clients’ demands, where the former mechanism guarantees the existence of one replica at anytime and the latter

mechanism replicate, handover or drop the replica.

In [50], the authors propose the use of social networking concepts to place shared data efficiently in an opportunistic network. They introduced the concept of conditional betweenness centrality that measures the cost of accessing content on a particular node from any other node that has interest in the content. Initially, content is placed at a random node, then the algorithm finds the shortest paths from all nodes to the content location. Some nodes might have multiple shortest paths passing through them. A portion of the top nodes is selected and for each one of them, the centrality value is computed and the node with the lowest cost of data access is selected to be the location of the data. This process is repeated until there is no more movement of the data. However, Content is not replicated over multiple nodes (1-median), while we emphasize that content replication decreases communication overhead and increases the availability in presence of node failures. The authors in [51] propose a P2P content sharing protocol for wireless ad hoc networks. They study the best neighborhood selection strategy that suites the wireless multi-hop environment by organizing peers in a minimum spanning tree and define the neighborhood of a peer as being its neighborhood over the logical tree rather than its physical neighborhood.

In [52], SCALAR (SCAlable data Lookup And Replication framework) was proposed for MANETs. The framework does not depend on the underlying routing protocol and minimizes the number of nodes involved in the data lookup process by constructing a dynamic virtual backbone structure among the mobile nodes. In [53], the REDMAN (REplication in Dense MANETs) middleware solution was proposed. The main idea is to maintain a fixed replication degree for the needed resources regardless of replica server nodes exiting the dense region. Zone-Based Replication (ZBR) scheme was proposed in [54] for MANETs, where an object is replicated if it is not within the zone of the requesting node.

The replica placement gives priority to peripheral nodes along the route to access the object, such that enough storage is available. However, our scheme is different as we consider the factor of object popularity in the placement decision. In [55], the main contribution is the design of a mechanism for content placement and replication that achieves load balancing according to the variations in the network topology and the query rate. The mechanism distributes the burden of storing and providing content on nodes to achieve load balancing. The replica nodes are responsible to decide whether to replicate, hand over or drop content based on local measurements of their workload.