SSUR An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center

Abstract : Cloud data centers provide services for an increasing number of applications. The virtual machines (VMs) that perform the corresponding application tasks need to be allocated to physical machines (PMs). For VM allocation, cloud service centers consider both energy consumption and quality of service (QoS), while cloud users are primarily concerned with their own needs, such as throughput and reliability. This paper proposes an allocation scheme for optimization based on user requirements in a cloud data center. First, various application requests from mobile phones and devices, which are regarded as a group of VM lists in the data center, are submitted to the cloud platform. Our method first allocates these arriving VMs to appropriate PMs based on their usage of hardware resources and the current throughput of the PMs in the data center. Second, due to dynamic workloads, the loads of the PMs that host these VMs may become very high. CPU utilization thresholds are set to determine whether migration is required, and the energy consumption before and after allocation is used to choose which VMs are reallocated. A suitable strategy for VM migration and PM shutdown can improve reliability and reduce energy consumption. Finally, it is shown through experimental simulations that compared with two existing algorithms, on the premise that the user requirements are met, the proposed method offers good performance in terms of total energy consumption, CPU utilization, number of PMs used and number of service-level agreement (SLA) violation.

 EXISTING SYSTEM :

 ? We focus on resource allocation strategies and the related existing solutions. ? Several works in the existing literature have addressed virtual machine placement issue. ? In existing literature, this approach has been well explored but it stills not clear what formula to use to generate this metric so that one-dimensional FFD can be performed. ? Another set of heuristics was proposed in the existing literature to achieve a better host utilization. ? Several blocking estimation models from the existing literature are subject to the same limitation; notably the singleresource case.

 DISADVANTAGE :

 ? The concept of VM placement algorithms originates from the consolidation of Operating Systems (OSs) within the same PM to avoid application compatibility issues. ? Despite these efforts, there are some remaining problems that need to be addressed. Amongst these are threats introduced by the cloud’s architectural vulnerabilities. ? The problem with their solution (Biran et al., 2012) is that it works only with small to medium sized cloud architectures. ? It is modelled using modified bin packing problem, where PMs are bins and VMs are items. ? This will ensure the availability of the critical VMs which if compromised, can cause massive harm to the users.

 PROPOSED SYSTEM :

 • To achieve optimal resource placement, an exact formulation that aims at finding the best placement of resources by maximizing the revenue and minimizing the corresponding costs is proposed in. • Optimization approaches can be very time consuming, several works addressing resource allocation in cloud systems proposed alternative approaches providing solutions in more reasonable time. • A general analytical model for evaluating task blocking probability in cloud computing system is proposed in . • The proposed model considers the concept of virtualization as well as heterogeneous server pools but this study is also limited to single-resource dimension.

 ADVANTAGE :

 ? This strives to avoid performance degradation which results from migrating data-intensive VMs away from the PMs that contain their images. ? The data-intensive VMs lose their performance up to 40 percent if they are migrated away from those PMs that contain their images. ? The VM placement algorithm discussed does not only save the costs but also strives to maintain the performance of the cloud environment even after any migrations. ? The idea is to evaluate the currently implemented VM placement algorithms to identify the algorithm that qualifies to be used in implementation of O-Sec VM Placement algorithm. ? Again the VM placement needs to ensure that the least number of PMs is used in order to minimize energy consumption.