The development of cloud computing has significantly altered how services are built, deployed, and made accessible to users outside of the organization. In actuality, the pay-as-you-go model of dispersed IT supported by the cloud computing paradigm calls for the outsourcing of software services and applications. In this situation, the capacity to ensure effective cloud performance management and to facilitate automated scalability become fundamental prerequisites. Users of the cloud are becoming more and more interested in a transparent and coherent image of the cloud, where performance is guaranteed in a variety of situations and under a variety of loads. In this essay, We examine the advantages of an integrated scalability approach at various cloud stack layers, concentrating on the database and compute infrastructure layers. In order to achieve this, we offer various performance measurements and a set of rules based on them to assess the cloud stack’s condition and scale it as needed to maintain stable performance. Then, using a proof-of-concept architecture, we empirically investigate three scaling scenarios for cloud performance: database only, computing infrastructure solely, and the scenario where computing infrastructure and database compete for resources.

Demand Response (DR) is quickly becoming a critical component of the contemporary energy industry, notably in EU energy markets. As a result, substantial work has gone into standardizing demand response data models. As a result, an increasing number of demand response concepts are based on these standards. As a result, an increasing number of demand response concepts are based on these standards. These approaches, however, are often centralized, and those that rely on cloud solutions employ the cloud as a centralized data repository, assuming that the data is already homogenised when saved, i.e. all data has the same structure and type. In practice, however, DR plans rely on a number of components that deliver data in a variety of forms and types. Furthermore, the various DR standards establish models for various data formats, which impede data sharing between different DR systems. This article introduces CIM, a generic technology that allows current disaster recovery systems to disperse their components in the cloud while providing a robust security and privacy foundation for data interaction. Furthermore, the CIM includes a semantic interoperability layer capable of transforming data into a normalised form when transferred, allowing it to be consumed transparently by DR components. Experiments support the CIM as a solution for DR systems to decentralize their designs and share heterogeneous data with other DR systems that adhere to other DR standards.

The Terasort benchmark and the YCSB benchmark are the two most used cloud computing benchmarks. Despite the fact that these benchmarks are quite helpful, data warehouse systems and associated OLAP technologies were not the focus of their creation. We initially introduce cloud computing and data warehouse systems in this essay. Then, we contend that the TPC-H benchmark, which is the most well- known benchmark for decision support systems, conflicts with the justifications for cloud computing (scalability, elasticity, pay-per-use, fault-tolerance characteristics), and customer relationship management (end- user satisfaction, Quality of Service features). Finally, we propose updated specifications for a benchmark for cloud data warehouse systems. The suggested specifications ought to make it possible to fairly compare the products offered by various cloud system providers .