Towards Robust Green Virtual Cloud Data Center Provisioning

Abstract

Cloud data center (CDC) network virtualization is being regarded as a promising technology to provide performance guarantee for cloud computing applications. One critical issue in CDC network virtualization technology is virtual data center (VDC) embedding, which deals with the CDC physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VDC embedding problem is NP-hard, even in the offline case. Node heterogeneity and CDC network scale bring challenges to the VDC embedding. This paper aims to embed a VDC in a robust and green way. We propose two effective, computation-efficient and energy-efficient embedding algorithms. Extensive simulations under various network scales and topologies are carried out to compare the proposed algorithms with the existing VDC embedding algorithms in terms of the VDC acceptance ratio, the long-term revenue of the cloud service provider (CSP), the CDC' long-term energy consumption in light-load CDCs, and in terms of CSP's long-term revenue in heavy-load CDCs. Index Terms-Cloud data center, Virtual data center embedding, Energy consumption, Mixed integer programming ---------- ----------2168-7161 (c)

Formally modeling and analyzing cost‐aware job scheduling for cloud data center

“…Therefore, the average resource utilization of ETC is not as good as the DJSA algorithm. (2) The total cost of the DJSA algorithm is lower than the ETC strategy ( Figure 12B). The reason is that the ETC strategy does not consider the delay cost.…”

“…(1) We extend traditional Petri nets to construct the job scheduling model of cloud data center based on the requirements, thus improving the reusability of the model. (2) We analyze the relationships between the reachable states of job scheduling model then analyze the relationship between energy cost, utility loss, and reachable states. The ADMM algorithm is used to decompose the scheduling optimization problem into a single cloud data center and then output the optimization scheduling scheme.…”

“…Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields . Under a cloud computing environment, the data center consists of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data . When the workload of the cloud data center increases rapidly, more hosts will be started on, and more virtual machines will be deployed to provide more available resources .…”

“…1 Under a cloud computing environment, the data center consists of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data. 2 When the workload of the cloud data center increases rapidly, more hosts will be started on, and more virtual machines will be deployed to provide more available resources. 3 How to lower the cost of those geographically distributed data centers has become a challenging issue.…”

“…In Definition 1, each Web portal also has a specific resource slot. For example, Web portal Wp i is mainly used to realize the function of compute-intensive tasks, the corresponding resource slot br (Wp i ) may be (2,2,1), that is, the required number of memory, CPU, and hard disk is 2, 2, and 1 (the actual unit can be set based on the specific requirement). If jobs Ja i, j and Ja e, f are compute-intensive tasks, the number of required resource slots is 5 and 7.…”