GEO-REPLICATION IN A REVIEW OF LATENCY AND COST-EFFECTIVENESS

Replication is a data distribution technique for synchronization between databases so that data remains consistent. Replication can overcome data loss problems and perform system recovery quickly if a problem occurs on one of the servers. One of the problems is when a natural disaster occurs at the server location. As a result, if you do not have data replication in different locations, it will cause the system to not run and possibly lose data. Then, geo-replication can reduce latency because the distance between the client and the data center is much closer. The application of geo-replication in general replicates data in all data centers. As a result, the cost of implementation is high because it requires a lot of resources. Because of the various advantages and disadvantages in its application, it is necessary to group geo-replication techniques to make it easier for researchers and technicians to adjust as needed. Therefore, this paper surveys the articles on Geo-replication techniques to implement cost-effectiveness and latency. The articles surveyed included a method for selecting replication sites, a method for reducing round trip time, a method according to data type, and selecting a leader to determine which server node to use. The results of the article survey show that implementing geo-replication for cost-effectiveness is more suitable for use in systems where all users do not need to access all data. Meanwhile, low latency is more suitable for systems used by various types of users. This paper can utilize the techniques that have been reviewed to overcome the problem of cost-effectiveness and latency in implementing Geo-replication

Cost-configurable cloud storage system designs

Today’s cloud storage systems lack flexible cost-performance trade-offs. For example, (a) in database systems, there are only a limited number of cost-performance options and they are not seamless, (b) in cloud caching systems, there is no flexibility in performance isolation, and (c) in geo-replication systems, the cost-performance trade-off is not optimal to various application types. In this thesis, we develop novel mechanisms that offer greater flexibility for making finer, online cost-performance trade-offs for data storage systems using (a) data access statistics and (b) models that capture information regarding cost and user experience. We specifically look at ways of achieving better cost-latency trade-offs in the following problem domains: (Mutant) NoSQL database systems, (SpaceLease) cloud caching systems, and (Acorn) geo-replicated, multi-data center systems. With NoSQL database storage systems, we observe the inflexibility in the cost and performance trade-offs: the trade-offs have limited options and the transition between different cost-performance points are not automatic. We address the inflexibility by proposing Mutant, a NoSQL database storage layer that seamlessly trades off cost and performance. We implemented Mutant by modifying RocksDB, a popular NoSQL database, and evaluated with both synthetic and real-world workloads to demonstrate the seamless and automatic cost-performance trade-offs. With edge cloud caching systems, we observe the unpredictable performance in public cloud cache services: CPs (content providers) pay the same amount of price, but they get unstable cache hit rate over time. We address the performance unpredictability by proposing SpaceLease, a performance-isolated cache architecture that uses dedicated resource for caching data in the edge cloud platform. We implemented SpaceLease and showed up to 79% reduction in the performance variability with a minimal cost overhead. In addition to the stable performance, SpaceLease also (a) provides a control that trades off cost and hit rate, (b) maximizes the aggregate cache utility across data centers, and (c) adapts quickly to changing workload patterns. With geo-distributed multi-data center replication systems, we observe that (a) better replication decisions can be made by using the “right” object attribute for each application type, such as topics for public video sharing applications and users for social network applications, and (b) using the combinations of the attributes and extra random replicas makes better replications under a cost or latency constraint. In response, we developed Acorn, an attribute-based partial geo-replication system, and showed that Acorn delivers up to a 90% cost reduction or a 91% latency reduction.Ph.D