Alessandro Ferreira Leite

A Fine-grained Approach for Power Consumption Analysis and Prediction

Abstract Power consumption has became a critical concern in modern computing systems for various reasons including financial savings and environmental protection. With battery powered devices, we need to care about the available amount of energy since it is limited. For the case of supercomputers, as they imply a large aggregation of heavy CPU activities, we are exposed to a risk of overheating. As the design of current and future hardware is becoming more and more complex, energy prediction or estimation is as elusive as that of time performance. However, having a good prediction of power consumption is still an important request to the computer science community. Indeed, power consumption might become a common performance and cost metric in the near future. A good methodology for energy prediction could have a great impact on power-aware programming, compilation, or runtime monitoring. In this paper, we try to understand from measurements where and how power is consumed at the level of a computing node. We focus on a set of basic programming instructions, more precisely those related to CPU and memory. We propose an analytical prediction model based on the hypothesis that each basic instruction has an average energy cost that can be estimated on a given architecture through a series of micro-benchmarks. The considered energy cost per operation includes both the overhead of the embedding loop and associated (hardware/software) optimizations. Using these precalculated values, we derive a linear extrapolation model to predict the energy of a given algorithm expressed by means of atomic instructions. We then use three selected applications to check the accuracy of our prediction method by comparing our estimations with the corresponding measurements obtained using a multimeter. We show a 9.48% energy prediction on sorting.

Executing a biological sequence comparison application on a federated cloud environment

Smith-Waterman (SW) is a popular application in Bioinformatics which calculates the best score/alignment between two genomic sequences. Even though SW provides the best result, it is not widely used in genome projects due to huge requirements in computing power and memory space. Recently, Cloud Computing has been receiving a lot of attention since it is able to provide utility computing in an elastic environment. The advantages of Cloud Computing can be obtained at zero cost since many of the Public Clouds provide free usage slots, allowing users to run their applications for free in Cloud environments. Also, many Clouds can be put together and seen as a unique environment, creating Federated Clouds. In this paper, we propose and evaluate an approach to implement the SW algorithm in Federated Clouds. A hierarchical Multi-Cloud architecture is proposed which is able to transparently connect and manage several Clouds. The results obtained with our architecture and our MapReduce SW implementation in five Public Clouds show that, only by using the free quota, we were able to run the SW application over a huge genomic database in time that is comparable with the one obtained in multicore clusters, showing the appropriateness of our approach.

Excalibur: an autonomic cloud architecture for executing parallel applications

IaaS providers often allow the users to specify many requirements for their applications. However, users without advanced technical knowledge usually do not provide a good specification of the cloud environment, leading to low performance and/or high monetary cost. In this context, the users face the challenges of how to scale cloud-unaware applications without re-engineering them. Therefore, in this paper, we propose and evaluate a cloud architecture, namely Excalibur, to execute applications in the cloud. In our architecture, the users provide the applications and the architecture sets up the whole environment and adjusts it at run-time accordingly. We executed a genomics workflow in our architecture, which was deployed in Amazon EC2. The experiments show that the proposed architecture dynamically scales this cloud-unaware application up to 10 instances, reducing the execution time by 73% and the cost by 84% when compared to the execution in the configuration specified by the user.

Power-aware server consolidation for federated clouds

Cloud computing has evolved to provide computing resources on‐demand through a virtualized infrastructure, letting applications, computing power, data storage, and network resources to be provisioned and managed over private networks or over the Internet. Cloud services normally run on large data centers and demand a huge amount of electricity. Consequently, the electricity cost represents one of the major concerns of data centers, because it is sometimes nonlinear with the capacity of the data centers, and it is also associated with a high amount of carbon emission (CO2). However, energy‐saving schemes that result in too much degradation of the system performance or in violations of service‐level agreement (SLA) parameters would eventually cause the users to move to another cloud provider. Thus, there is a need to reach a balance between energy savings and the costs incurred by these savings in the execution of the applications. Therefore, in this paper, we propose and evaluate a power and SLA‐aware application consolidation solution for cloud federations. It comprises a multi‐agent system for server consolidation, taking into account SLA, power consumption, and carbon footprint. Different for similar solutions available in the literature, in our solution, when a cloud is overloaded, its data center needs to negotiate with other data centers before migrating the workload to another cloud. Simulation results show that our approach can reduce up to 46% of the power consumption while trying to meet performance requirements. Furthermore, we show that federated clouds can provide an adequate solution to deal with power consumption in the clouds. Copyright

Fairness analysis with cost impact for Brasilia's Flight Information Region using reinforcement learning approach

To analyze fairness between passengers and airlines considering financial cost, the management of the adaptation for air traffic flow in a heuristic and dynamically manner is studied in this research. Multi-Agent theory with reinforcement learning approach is used as a basic methodology integrated with a system of Decision Support System Applied to Tactical Air Traffic Flow Management (SISCONFLUX). The objective to develop this model is to increase the safety preserve and reduce the air traffic congestions. Reward structure with evaluation functions of financial cost and delays impact is proposed for related flights using real data from Brasilias Flight Information Region (FIR-BS). With the developed model, the experimental results show that the time delay is 25% less than the results computed only by Graph Theory with the same data, and fairness considering financial cost factor can be used together with congestion scenario in the air traffic management without affecting safety and flow factors.

A multi-agent planning model for airport ground handling management

Inefficiency airport ground handing operations is one of the main reasons for flight delays, as it comprises a series of processes and collaborations between various airports services. Using multi-agent planning (MAP) method, this paper proposes a framework as a management system to improve the airport ground handling management (GHM). With the identification of the services and resources related to GHM, the forward MAP approach is applied to coordinates the tasks and planning in order to reduce both the delays and the operating cost. In this case, the key contribution includes MAP model for airport ground handling operations under a unified framework compatible with the airport collaborative decision making (ACDM) strategy.

Energy-Aware Multi-Agent Server Consolidation in Federated Clouds

In this paper, we propose and evaluate a server consolidation approach for efficient power management in virtualized federated Data Centers. The main goal of our approach is to reduce power consumption, trying to meet QoS requirements with limited energy defined by a third party agent. In our model, we address application workload considering the costs due to turning servers on/off and Virtual Machine migrations in same Data Center and between different Data Centers. Our simulation results with 2 data centers and 400 simultaneous Virtual Machines show that our approach is able to reduce more than 50% of energy consumption, while still meeting the QoS requirements.

Towards Intelligent System Wide Information Management for Air Traffic Management

This paper briefly reviews the state-of-the-art in Artificial Intelligence (AI) applied to Air Traffic Management (ATM). The research topics include the application of semantic ontology, multi-agent systems, reinforcement learning (RL), and game theory in ATM. Likewise, this paper also highlights our research advances in this area. In this case, we describe a new Probabilistic Web Ontology Language (PR-OWL) algorithm to enable the reasoning on big datasets in polynomial time. Then, we present the use of both Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms in 4D trajectory management. Next, we describe the usage of Multi-agent Planning (MAP) theory on airport ground handling management. Finally, this paper envisions some research and development directions of AI applied to ATM. It includes: (a) mapping and reducing the gaps between advanced AI technologies and ATM; (b) considering uncertainty in Semantic Ontology for SWIM data exchanging models in ATM; (c) using big data analytics in SWIM; and (d) integrating collaborative ATM technologies towards intelligent SWIM (I-SWIM).