The provision of Storage as a Service (STaaS) in many geo-distributed datacenters by several Cloud Storage Providers (CSPs) has made online cloud storage a great choice for replicating and distributing objects that are accessed worldwide. Online Social Networks (OSN) such as Facebook and Twitter have billions of active users worldwide accessing shared objects. These users expect to access these objects within a tolerable time. To minimize users’ access latency time of these objects, OSN service providers must host several replicas of objects in many datacenters. However, this replication process produces a higher monetary cost. This paper addresses crucial issues, including how many replicas are required to fulfil the expected workload of the object and the optimal datacenters to host these replicas to reduce latency time for users and monetary costs for OSN service providers. Two online algorithms are proposed to determine the suitable number of replicas for each object and the optimal placement of these replicas. The DTS algorithm establishes the replication and placement of objects using deterministic time slots, while the RTS algorithm is based on randomized time slots. Experimental results show the effectiveness of the proposed algorithms for producing latency time below certain thresholds and reducing the monetary cost.

In social media, a huge number of worldwide data objects are posted every day. The contents of these data objects include text, links, images, audio, and videos which could be small, medium, or large and accessed across the world. Moving these data objects into a single cloud service provider (CSP) is risky and results in four-fold obstacles: vendor lock-in, service availability, cost-ineffective use, and increasing latency. Using multiple CSPs to replicate and distribute the data object solves such obstacles. However, replicating data objects among multiple CSPs increases the cost of creating and maintaining this replication. This study focuses on three issues of Online Social Network (OSN) which include: (1) determining the appropriate number of replicas of each data object based on its popularity on the OSN, (2) identifying the suitable datacenters that host the replicas according to latency time of different regions, and (3) deciding the suitable storage class for the data object at a specific time of its lifetime. Two algorithms are proposed to adapt the replication and placement of the data object according to its popularity in the OSN. The first algorithm is Dynamic Fixed Time (DFT) which uses fixed time periods to adapt replication and placement. The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. A simulation using a synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a monetary cost savings of more than 23% compared to the Static Replication and Local Placement (SRLP) algorithm.

In the telerehabilitation system, the statistical data of the patients’ movement are stored in the temporary storage and synchronised to the storage service of online cloud data. Application providers faced a problem in reducing the monetary cost of the whole cloud service and reducing the footprint of the main memory space. In addition, users encounter long latency when the required data need to be read from the cloud via the internet and the hard disk drive (HDD) of the cloud servers. To solve this problem, an optimal data access framework is presented to cache the statistical data of the patients in the application server. The main memory database and cache use internal tracking in the main memory to track records that are not accessed by transferring the data to the disk. This mechanism retains the keys and all indexed fields of evicted records in the main memory which prevents potential memory space savings for the application that have many keys and secondary indexes. Therefore, to overcome the mentioned problems, the cloud database is categorised into three partitions (hot, warm, cold). In addition, a cache memory image in the application server is provided for the hot partition of the cloud database. The use of cache memory image reduces the number of reading operations from the cloud and saves the space of the main memory. The experimental results showed that the proposed framework can produce good quality solutions by utilising the main memory space and reducing the latency and read operations from the cloud that lead to reducing the monetary costs.

Advancements in internet, cloud, and business intelligence technologies, as well as the emergence of big data, have caused massive traffic in online transaction processing (OLTP) systems. All these impose the needs for effective and efficient data storage and processing which is not available in conventional Relational Database Management Systems (RDBMSs). In the year 2000, a new generation of database management systems (DBMSs) called Not Only Structured Query Language (NoSQL) has emerged to address the scalability of OLTP workloads in a way that was impossible for conventional relational database management systems (RDBMSs). Despite adequately addressing issues linked to scalability and producing better read-write performance than RDBMS, NoSQL DBMSs appear to lack in providing definite assurance of data consistency. Hence, a newer DBMS alternative called NewSQL has emerged. NewSQL DBMS has combined the advantages of both conventional RDBMS and NoSQL. They have the scalable performance of NoSQL DBMSs and the data consistency of traditional RDBMS. To date, there are tens of existing NewSQL-DBMSs, so it is difficult to understand the difference between them, and it is also quite challenging to decide the best solution for a specific task. Hence, this paper presents a comprehensive review concerning NewSQL DBMSs by emphasizing the following purposes:(1) providing researchers and practitioners guidance that may assist in selecting the most appropriate NewSQL-DBMS, (2) classifying NewSQL-DBMSs based on internal implantation and number of tiers, (3) providing comparisons and analyses of query capabilities, technical characteristics, and security attributes of the prominent NewSQL-DBMSs, and finally, (4) identifying issues and challenges raised with the emergence of NewSQL-DBMSs. In conclusion, NewSQL DBMSs can offers solutions for fault tolerance, horizontal scalability features.