Optimization policy for file replica placement in fog domains

Abstract

Fog computing architectures distribute computational and storage resources along the continuum from the cloud to things. Therefore, the execution of services or the storage of files can be closer to the users. The main objectives of fog computing domains are to reduce the user latency and the network usage. Availability is also an issue in fog architectures because the topology of the network does not guarantee redundant links between devices. Consequently, the definition of placement polices is a key challenge. We propose a placement policy for data replication to increase data availability that contrasts with other storage policies that only consider a single replica of the files. The system is modeled with complex weighted networks and topological features, such as centrality indices. Graph partition algorithms are evaluated to select the fog devices that store data replicas. Our approach is compared with two other placement policies: one that stores only one replica and FogStore, which also stores file replicas but uses a greedy approach (the shortest path). We analyze 22 experiments with simulations. The results show that our approach obtains the shortest latency times, mainly for writing operations, a smaller network usage increase, and a similar file availability to FogStore.

How to place your apps in the fog: State of the art and open challenges

“…Fog computing architectures substantially differ from cloud architectures. In particular, according to recent literature: –Fog nodes feature limited and very heterogeneous resources, while data center nodes feature high (and virtually unbounded) computational, storage and power capabilities. –Fog devices are highly geographically distributed—often mobile—and possibly span wide large‐scale networks reaching closer to (human and machine) end users, while cloud data centers are located in few geographic locations all over the world and connect directly to fibre backbones. –End‐to‐end latency between cloud nodes within data centers is usually negligible and bandwidth availability is guaranteed via redundant links, while in fog domains, network QoS between nodes can largely vary due to the presence of a plethora of different (wired or wireless) communication and Internet access technologies. –Fog nodes are owned and managed by various service providers (from end users to Internet service providers to cloud operators) and might also opportunistically include available edge devices (eg, crowd computing, ad hoc networks) whereas the largest cloud data centers are in the hands of a few big players. …”

“…Fog computing architectures substantially differ from cloud architectures. In particular, according to recent literature [12][13][14][15][16] : -Fog nodes feature limited and very heterogeneous resources, while data center nodes feature high (and virtually unbounded) computational, storage and power capabilities. -Fog devices are highly geographically distributed-often mobile-and possibly span wide large-scale networks reaching closer to (human and machine) end users, while cloud data centers are located in few geographic locations all over the world and connect directly to fibre backbones.…”

A metaheuristic‐based data replica placement approach for data‐intensive IoT applications in the fog computing environment

“…Besides, we compare the proposed mechanism with two baseline mechanisms. The first mechanism is called “FFRPP,” 18 which is a graph portioning‐based approach, and the second mechanism is called “MCS,” 19 which is a greedy‐based solution.…”

“…Guerrero et al 18 developed a graph portioning approach for solving file replica placement. They use graph partition algorithms to select the best fog devices that store data replicas and increase data availability.…”

Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models

“…Replica placement in fog has been discussed both for data [16], and for services [12,7,5]. In a survey by Salaht et al [27] that presents an overview of service placement algorithms in fog computing, the authors note that most current service placement techniques are reactive, i.e., they do not anticipate client movement as we do in this work.…”