# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 5 Conclusions and Future Work  
This study set out to investigate a unified vendor-agnostic solution to big data stream processing in a multi-cloud environment. As key beneficiaries of commercial cloud com \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{Windowing Rate versus Resource Utilisation Metrics} \\ \hline Windowing Rate (Period/Duration) & Three Workers (n = 3) & Six Workers (n = 6) & Ten Workers (n = 10) & Mann–Whitney U Test Results \\ \hline
0.1 & 600,000 & 600,000 & 600,000 & Three Workers and Six Workers \\ \hline
0.2 & 300,000 & 300,000 & 300,000 & \(p\)-value = 0.9161, accept Ho \\ \hline
1.0 & 60,000 & 60,000 & 60,000 & Three Workers and Ten Workers \\ \hline
1.5 & 39,945 & 40,106 & 39,920 & \(p\)-value = 0.9161, accept Ho \\ \hline
2.0 & 30,361 & 30,197 & 29,637 & Six Workers and Ten Workers \\ \hline Average & 206,061 & 206,060 & 205,911 & \(p\)-value = 0.9161, accept Ho \\ \hline \hline \multicolumn{5}{c}{Container Co-Location Metrics} \\ \hline Windowing Rate (Period/Duration) & Cluster (n = 1) & Cluster (n = 2) & Cluster (n = 4) & Mann–Whitney U Test Results \\ \hline
0.1 & 144,100 & 144,000 & 143,900 & Three Workers and Six Workers \\ \hline
0.2 & 72,150 & 72,090 & 72,140 & \(p\)-value = 0.8413, accept Ho \\ \hline Rate (Period/Duration) & 1.0 & 14,746 & 14,724 & 14,711 & Three Workers and Ten Workers \\ \hline
1.5 & 10,044 & 9935 & 9998 & \(p\)-value = 0.8413, accept Ho \\ \hline
2.0 & 7548 & 7569 & 7593 & Six Workers and Ten Workers \\ \hline Average & 49,717 & 49,663 & 49,668 & \(p\)-value = 1.0000, accept Ho \\ \hline \hline \multicolumn{5}{c}{Container Co-Location Metrics} \\ \hline Windowing Rate (Period/Duration) & Cluster (n = 1) & Cluster (n = 2) & Cluster (n = 4) & Mann–Whitney U Test Results \\ \hline Total Number of Records Ingested by Number of Containers per Node Winding Rate (Period/Duration) & (n = 3) & (n = 6) & (n = 10) & \\ \hline
0.1 & 600,000 & 600,000 & 600,000 & Three Workers and Six Workers \\ \hline
0.2 & 300,000 & 300,000 & 300,000 & \(p\)-value = 0.9161, accept Ho \\ \hline
1.0 & 60,000 & 60,000 & 60,000 & Three Workers and Ten Workers \\ \hline
1.5 & 39,827 & 39,869 & 40,073 & \(p\)-value = 0.9161, accept Ho \\ \hline
2.0 & 28,881 & 29,898 & 30,037 & Six Workers and Ten Workers \\ \hline Average & 205,741 & 205,953 & 206,022 & \(p\)-value = 0.9161, accept Ho \\ \hline \hline \end{tabular}
\end{table} 
Table 14: _Cont._puting, small and medium enterprises, organisations, or departments characterised by devolved administration, tight budgetary constraints, and a lack of specialised technical skills in-house (SMEODs) were selected as the target domain for MC-BDP, a new reference architecture for big data stream processing aimed at maximising the cloud's economies of scale while at the same reducing the risk of vendor lock-in. The majority of developments targeted at facilitating the adoption of big data analytics through cloud computing have focused on reducing the complexity of implementing and managing large distributed systems. Consequently, solutions are predominantly SaaS-based and associated with another major cause of apprehension: the risk of vendor lock-in. A number of authoritative studies have been conducted on possible ways of mitigating the risk of vendor lock-in [18; 32; 49]. However, no solutions were found which minimised the risk of vendor lock-in while simultaneously allowing implementers to maximise the cloud's economies of scale. Although the combination of containers, cloud computing, and big data streams is not new, the field lacked a domain-specific approach developed from a cloud consumer's perspective to enable implementers to ingress into big data stream analytics using a cloud model less restrictive than SaaS. As a response to the breadth, extensiveness, and complexity of big data research, recent developments have tended to focus on specific domains such as the biomedical sciences [9], the IoT [48], edge computing [50], national security [10], and scientific simulations [8].