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We consider the assignment of enterprise applications in virtual machines to physical servers, also known as server consolidation problem. Data center operators try to minimize the number of servers, but at the same time provide sufficient computing resources at each point in time. While historical workload data would allow for accurate workload forecasting and optimal allocation of enterprise applications to servers, the volume of data and the large number of resulting capacity constraints in a mathematical problem formulation renders this task impossible for any but small instances. We use singular value decomposition (SVD) to extract significant features from a large constraint matrix and provide a new geometric interpretation of these features, which allows for allocating large sets of applications efficiently to physical servers with this new formulation. While SVD is typically applied for analytical purposes like time series decomposition, noise filtering, or clustering, in this paper features are used to transform the original allocation problem in a low-dimensional integer program with only the extracted features in a much smaller constraint matrix. We evaluate the approach using workload data from a large Data center and show that it leads to high solution quality, but at the same time allows for solving considerably larger problem instances than what would be possible without data reduction and model transform. The overall approach can also be applied to other large integer problems, as they can be found in applications of multiple or multi-dimensional knapsack or bin packing problems.

Abstract-Nowadays corporate data centers leverage virtualization technology to cut operational and management costs. Virtualization allows splitting and assigning physical servers to virtual machines (VM) that run particular business applications. This has led to a new stream in the capacity planning literature dealing with the problem of assigning VMs with volatile demands to physical servers in a static way such that energy costs are minimized. Live migration technology allows for dynamic resource allocation, where a controller responds to overload or underload on a server during runtime and reallocates VMs in order to maximize energy efficiency. Dynamic resource allocation is often seen as the most efficient means to allocate hardware resources in a data center. Unfortunately, there is hardly any experimental evidence for this claim. In this paper, we provide the results of an extensive experimental analysis of both capacity management approaches on a data center infrastructure. We show that with typical workloads of transactional business applications dynamic resource allocation does not increase energy efficiency over the static allocation of VMs to servers and can even come at a cost, because migrations lead to overheads and service disruptions.

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We introduce a novel method to discover beneficial time frames for adapting virtual machine (VM) assignments in consolidated enterprise data centers. Our key insight lies in learning an optimal orthonormal transform from the workload data of a set of enterprise applications hosted in VMs. The transform allows us to extract only a few indicators from long, time-varying and complex workload time series. The indicators represent the initially high-dimensional data set in a reduced form which allows for a concise identification of periods of relatively stable resource demands and turning points in the behavior of a set of VM workloads that require VM reassignments. In this work, we address one of the most pressing problems for data center operators, namely the reduction of managerial complexity of resource and workload management in data centers hosting thousands of applications with complex and varying workload behaviors. We demonstrate the decision support model using workload traces from a professional data center. 1