Toward SLAs Guaranteed Scalable VDC Provisioning in Cloud Data Centers

Abstract

Cloud computing has been a cost-efficient paradigm for deploying various applications in datacenters in recent years. Therefore, efficient provisioning for virtual data center (VDC) requests from different service providers (SPs) over physical data centers plays a vital role in improving the quality of service (QoS) and reducing the operational cost of SPs. Therefore, a significant attention has been paid for the VDC provisioning problem. However, few approaches have been proposed for the problem of reliable VDC embedding across multiple data centers, as most of them only consider the problem of VDC mapping within a single data center. In this paper, we study the problem of QoS-aware VDC provisioning across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized while satisfying the reliability requirement of each VDC request. We formulate this problem as a mathematical optimization problem by using integer linear programming (ILP) and propose an efficient heuristic algorithm called reliable VDC embedding (RVDCE) algorithm to solve this NP-hard problem. The simulation results show that the proposed algorithm performs better in terms of blocking ratio, CPU resource consumption, and bandwidth consumption of backbone network than the existing solution. In addition, this paper has also incorporated integrated security to minimize security vulnerabilities seen in other similar approaches. Apart from demonstrating how to resolve security challenges in our VDC proposal, cost calculations have been implemented to demonstrate the robustness, resiliency, validity, and effectiveness of the VDC provisioning solution for cloud computing.

Deadline-Aware Fast One-to-Many Bulk Transfers over Inter-Datacenter Networks

“…iv) Determining whether all receivers can obtain a replica before the deadline. the data size to be transferred t 1 the arrival time t 2 the deadline by which data transfer has to be completed (hard deadline)…”

“…We formulate the offline problem as a mixed integer linear program and use the commodity optimization solvers to solve it and find the offline-optimal solution. Since there are O(mn 2 kq max ) variables and O(mn 2 q max ) constraints in the formulation of the offline problem, 2 we can see that it is time intractable to find the offline-2. m, n, ρ, and qmax denote the number of transfer requests, number of datacenters, number of tunnel paths, and the latest deadline among all current transfers, respectively.…”

“…T ODAY, an unprecedented number of information technology companies offer Internet services to users worldwide. In order to guarantee quality and highly available services, these services are generally operated on top of multiple datacenters dispersed in different cities or countries close to regional users [1], [2], [3]. Given such architecture, it is necessary to replicate data, such as updated machine learning models and multimedia, across the datacenters that offer the same services to reduce the response latency to access services, enhance failure recovery, and expedite geo-distributed data analytics [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16].…”

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

“…They established an integer linear programming model and a column generation model to optimize the node resources and bandwidth resources consumed by the deployment. Sun et al [15] proposed a reliability-aware SFC deployment algorithm to select a less reliable deployment solution to achieve smaller deployment costs and obtain greater benefits while ensuring user reliability requirements. Liang et al [16] proposed a dynamic orchestration mechanism for the SFC in hybrid NFV networks.…”

“…Dividing ( < , I ) by the maximum delay allows the load rate to have greater weight when selecting nodes. We select the node with the minimum optimal selection factor that satisfies the resource constraint in Formulas (8) and (15) to deploy the current VNF (link). More details are shown in Algorithm 2.…”