# v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers +
## Data Center Design and the v-Mapper System Model
### VM Placement and the Data Center Model
### Baseline Strategies and Compared Scenarios

In this section, we report on the evaluation of our algorithms against various data center topologies. To provide benchmarks for our evaluations, we considered the following baseline strategies. Displacement strategy (ds): The displacement strategy selects a path with minimum end-to-end node congestion. It is frequently used in VM clusters handling small data loads. Blind placement (bp): In a blind placement strategy, the tenant VMs adopts the path with a minimum hop count. Sequential placement (sp): Here, the tenant VMs are placed sequentially in a server queue, where the VMs are ordered on the basis of their increasing machine-id order (lowest to highest order). This strategy attempts to balance the load resilience of applications by improving host subscription. Stochastic (Random) placement (stp): Also known as the random placement strategy, in SP, the VMs are distributed in stochastic order. The stochastic placement strategy randomly picks available host machines from within a specified availability zone for a job. ### Simulation Set-Up and Settings

We considered two types of topologies: physical topology and logical topology. To ensure optimal switching functions and traffic control, the physical cloud infrastructure used in our testing scenario consisted of eight hosts connected over seven SDN-enabled switches. The framework's prototype was implemented by using an API that uses SDN-enabled EPC Gateway to correspond with SDN-enabled switches. The simulations were performed through OpenDayLight controller and Mininet v.2.2.0 on an AMD Opteron(tm) 6300 Series Platform with 16-core x86 processor. For the logical topology, we constructed a workload emulating the DC traffic. We also ran our experiments using a real workload from large computing clusters at Wuhan University. The workload contained 6 days of cluster machine activity and included details like job number, arrival, waiting and service time, CPU and memory usage traces. To avoid resource contention issues, the workload was experimented in controlled environment and workload was submitted in batches. During test runs it was considered that all links are active. We compared five algorithms i.e., vmap (v-Mapper), sp-bp, sp-ds, stp-bp and stp-ds on two datacenter topologies: Fat-Tree and B-Cube. Fat-tree is a scalable design for network architecture. The unique feature of a fat-tree data center architecture is that for any switch, the number of links going down to its siblings is equal to the number of links going up to its parent in the upper level. Therefore, the links get \"fatter\" towards the top of the tree. The switch in the root of the tree has the most links compared to any other switch below it. B-Cube [31], on the other hand, is server-centric network architecture in which servers with multiple network ports connect to multiple layers of mini-switches, as shown in Figure 5. By employing a range of topology settings, traffic and data applications, we ensured maximum core switch use to estimate the network resources use. ## 6 Performance Results and Analysis

In this section, we present our findings to verify the effectiveness of the v-Mapper system. ### Impact on Data Center Topologies

Our proposed strategy takes into account the measured (real-time) communication dependencies among VMs and the underlying data center network topology as well as the capacity limits of the physical servers of the data center. Its major goal is to minimize the data center network traffic while satisfying all the server-side constraints. We assign every link in every topology the same capacity. To ensure uniformity, a synthesized traffic pattern is adopted. A detailed literature review on data traffic patterns and its challenges are presented in [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44] The VM resources are uniformly distributed and occur between range values of 0.2 and 0.8. From the results in Figure 6, vmap shows less congestion in the maximum core switch for the Fat-Tree and B-Cube topologies.