Design and Implementation of Fragmented Clouds for Evaluation of Distributed Databases

TL;DR

This paper introduces a Fragmented Hybrid Cloud (FHC) model that unifies geographically distributed private clouds, analyzing how node mobility impacts distributed database performance in terms of latency, throughput, and network stability.

Contribution

The paper presents a layered FHC architecture that considers mobility effects and evaluates its impact on popular distributed databases like Cassandra, MongoDB, Redis, and MySQL.

Findings

Mobility causes variable latency and bandwidth affecting database performance.

Performance insights for Cassandra, MongoDB, Redis, and MySQL on FHC.

Layered FHC implementation effectively models mobility impacts.

Abstract

In this paper, we present a Fragmented Hybrid Cloud (FHC) that provides a unified view of multiple geographically distributed private cloud datacenters. FHC leverages a fragmented usage model in which outsourcing is bi-directional across private clouds that can be hosted by static and mobile entities. The mobility aspect of private cloud nodes has important impact on the FHC performance in terms of latency and network throughput that are reversely proportional to time-varying distances among different nodes. Mobility also results in intermittent interruption among computing nodes and network links of FHC infrastructure. To fully consider mobility and its consequences, we implemented a layered FHC that leverages Linux utilities and bash-shell programming. We also evaluated the impact of the mobility of nodes on the performance of distributed databases as a result of time-varying latency and bandwidth, downsizing and upsizing cluster nodes, and network accessibility. The findings from our extensive experiments provide deep insights into the performance of well-known big data databases, such as Cassandra, MongoDB, Redis, and MySQL, when deployed on a FHC.