Nowadays, enterprises are more and more inclined to take data as the driving force, because their data is the driving force for their development engine to create new products, surpass competitors and provide customers with better experience. Therefore, big data management and processing for different partners (such as data analysts, data engineers, and data operating companies) should be fast, automated, and scalable.
The company needs strong financial governance to process data, so that it can continuously monitor, prevent amazing expenditures, and directly prove the advantages it has gained against expenditures.
Compared with internal deployment, financial governance is fundamentally a unique challenge in cloud computing, including the cost of agreeing long-term commitments in advance. In essence, all cloud management services are on-demand services, a system based on usage, and financial governance ensures that inefficient expenditures are differentiated, and are inevitably eliminated when measuring the ROI of important expenditures.
Many challenges that affect the reliable financial governance of data processing platforms in cloud computing are equivalent to the challenges faced by delivering any cloud computing based framework. However, data platforms based on cloud computing face explicit challenges specific to information processing.
The comparison between local infrastructure costs (saving a lot of direct responsibility in the long run) and cloud infrastructure is the on-demand use of cloud computing resources (by instance). A rather confusing comparison is to seek a specially optimized packet from your ISP, but it consumes a lot of bandwidth usage without real-time checks and filters. This will cause unexpected surprises in your cloud bill. Governance is to maintain checks and balances. It is basically the development of daily tasks. It is important to maintain accountability and control cloud spending.
Nowadays, the danger of moving to cloud computing is getting smaller and smaller. The legal arrangement and the end of POC are very simple and not too complicated. Most cloud payment models are pay on demand, and pay on demand, so the company will not see strong direct bills. However, with the development of cloud projects, use cases and events become more and more complex. The danger of cloud bills getting out of control is rising.
It is easy to package some of the purposes behind it. Since the application requirements cannot be known in advance, allocating servers in advance will increase the running time of servers. Most Web applications are designed to reduce latency to provide a better customer experience, not cost. This means that we need to give up the on-demand advantage of changing the workload considered by ECS and resulting in poor performance optimization.
Although most applications plan to slowly increase and decrease the data processing requirements, in fact, the data may represent a burst, effectively expanding the demand for other servers. This is also related to idle time. Most Web applications can have consistent traffic, but as the day progresses, much of the work at hand can be dispersed, resulting in idle time (significantly reduced utilization).
At present, capacity management in cloud computing refers to simplifying the utilization of infrastructure through the barrier of financial governance, so that groups can quickly carry out activities without worrying about unpredictable bills. The goal of the company in the optimization process is to manufacture a system that can continuously provide sufficient capacity to slightly exceed the requirements, while maintaining the traceability and predictability of user, cluster and work cost index levels.
More developed applications can use today's technology platforms, including artificial intelligence and machine learning, to drive stronger governance. The company should study the platform that can realize the automatic expansion of Workload Aware, so as to strengthen the internal financial governance of the company. This will help support different teams to run big data in a shared cloud environment, as well as support independent teams to integrate, so as to save more costs without affecting performance.
In addition, it also needs to combine the solid principles of optimization and upgrading to restore and reallocate unused assets; Actively reduce the scale to prevent the cost caused by idle nodes from overwhelming; Packed as a container for resource allocation policies; And decentralized off the shelf, which reduces the probability of large-scale interference from off the shelf hubs provided by your cloud provider.
Uncontrolled spending brings more harm than the increased cloud costs. In general, governance is related to control, responsibility and accountability. Financial governance is the same. For a long time, we have always felt that the mysterious power of loneliness can cover up evil, which is called cost overrun. However, in fact, the common behavior of each stakeholder is the reason to prevent cost overruns.