Modeling cloud resources using machine learning

Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version

Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis

Traditional data centers are designed with a rigid architecture of fit-for-purpose servers that provision resources beyond the average workload in order to deal with occasional peaks of data. Heterogeneous data centers are pushing towards more cost-efficient architectures with better resource provisioning. In this paper we study the feasibility of using disaggregated architectures for intensive data applications, in contrast to the monolithic approach of server-oriented architectures. Particularly, we have tested a proactive network analysis system in which the workload demands are highly variable. In the context of the dReDBox disaggregated architecture, the results show that the overhead caused by using remote memory resources is significant, between 66\% and 80\%, but we have also observed that the memory usage is one order of magnitude higher for the stress case with respect to average workloads. Therefore, dimensioning memory for the worst case in conventional systems will result in a notable waste of resources. Finally, we found that, for the selected use case, parallelism is limited by memory. Therefore, using a disaggregated architecture will allow for increased parallelism, which, at the same time, will mitigate the overhead caused by remote memory.Comment: 8 pages, 6 figures, 2 tables, 32 references. Pre-print. The paper will be presented during the IEEE International Conference on High Performance Computing and Communications in Bangkok, Thailand. 18 - 20 December, 2017. To be published in the conference proceeding

On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft

Predictive power management for multi-core processors

textEnergy consumption by computing systems is rapidly increasing due to the growth of data centers and pervasive computing. In 2006 data center energy usage in the United States reached 61 billion kilowatt-hours (KWh) at an annual cost of 4.5 billion USD [Pl08]. It is projected to reach 100 billion KWh by 2011 at a cost of 7.4 billion USD. The nature of energy usage in these systems provides an opportunity to reduce consumption. Specifically, the power and performance demand of computing systems vary widely in time and across workloads. This has led to the design of dynamically adaptive or power managed systems. At runtime, these systems can be reconfigured to provide optimal performance and power capacity to match workload demand. This causes the system to frequently be over or under provisioned. Similarly, the power demand of the system is difficult to account for. The aggregate power consumption of a system is composed of many heterogeneous systems, each with a unique power consumption characteristic. This research addresses the problem of when to apply dynamic power management in multi-core processors by accounting for and predicting power and performance demand at the core-level. By tracking performance events at the processor core or thread-level, power consumption can be accounted for at each of the major components of the computing system through empirical, power models. This also provides accounting for individual components within a shared resource such as a power plane or top-level cache. This view of the system exposes the fundamental performance and power phase behavior, thus making prediction possible. This dissertation also presents an extensive analysis of complete system power accounting for systems and workloads ranging from servers to desktops and laptops. The analysis leads to the development of a simple, effective prediction scheme for controlling power adaptations. The proposed Periodic Power Phase Predictor (PPPP) identifies patterns of activity in multi-core systems and predicts transitions between activity levels. This predictor is shown to increase performance and reduce power consumption compared to reactive, commercial power management schemes by achieving higher average frequency in active phases and lower average frequency in idle phases.Electrical and Computer Engineerin

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research