ECCO: An integrated solution for Environment Compatible COmputing systems. G. B. Barone, D. Bottalico, V. Boccia, L. Carracciuolo

Transcription

1 ECCO: An integrated solution for Environment Compatible COmputing systems G. B. Barone, D. Bottalico, V. Boccia, L. Carracciuolo

2 Outline Introduction, some remarks and general objectives The case study characteristics Our solution key-points Realization strategy and underlying technologies The ECCO solution description Some first results Conclusions and future works 2

3 Introduction Large Data Centers have costs of implementation and management (TCO - Total Cost of Ownership) generally very high: these costs relate largely energy consumption: Ethical issue: European Commission pushes to adopt an Action Plan for the promotion of energy efficiency in Europe Economic issue: Data Centers owners need to reduce the Total Cost od Ownership 3

4 Some remarks It is possible to implement energy-saving simple strategies in the management of big Data Centers, by means of resources availability reduction BUT Scientists (and also Industries) need to use big Data Centers to perform advancement of science and technology in strategic and generally mission-critical fields. The objective is: realize a system to reduce the energy costs, without too much neglecting the needs of users. 4

5 Reference case study: SCoPE data center Systems physical resources are part of a self contained Grid and Cloud infrastructure ; storage and computational resources are made available to national and international relevant distributed infrastructures and, thus, used not only from the local users CPU, 150TB SAN storage, Low Latency Infiniband connectivity

6 Reference case study: SCoPE data center Facilities S.Co.P.E. datacenter is based on a TIER III infrastructure design topology; Dedicated 1MW power plant equipped with diesel generator and redundant 1+1 ups; Fully redundant liquid cooling system capable of dissipating 2,000 Watt per cubic meter and 30,000 Watt per rack; Operational and safety parameters (i.e. temperature, power consumption) monitored through sensors;

7 Reference case study: SCoPE data center Other facilities details: The data related to the energy consumption of the data center IT systems and facilities equipment are provided by a number of wireless energy sensors installed on the main power lines; Energy data along with environmental and security parameters are collected by an industry standard Data Logger (SmartGate) and stored for statistical purpose; A System level degree of monitoring is achieved by separately tracking the energy spent to produce useful work (IT equipments) and the total energy consumed in the data center in order to monitoring the PUE 7

8 SCoPE: workflow characterization 1. The overall load (jobs ) can be highly variable with a variation percentage up to about 80%. 2. Workflow characterization in terms of CPU and I/O intensity for both sequential and parallel jobs. 3. jobs (of the same or different types) can have short (less than 2 hours), medium (up to 72 hours) and long execution times (some days)

9 that is: Objectives for our case study «Reducing the energy costs (without performing any change in facilities) without too much neglecting the needs of users» minimize the number of active physical resources; allocate physical resources in a suitable way, e.g. maximizing the number of active resources per rack; increase and reduce dynamically active resources on the basis of the users demand; to deliver as much as possible the maximum level of performance for both the system and applications. 9

10 Our solution key-points Much work already exists in literature, but our approach differs for these points: our use case, the SCoPE data center, is a part of a real hybrid production context our workflow is real, highly variable in time and heterogeneous the QoS metrics are not related to a pay-per-use model but a free-to-use one where SLAs constraints are related to user satisfaction at present, we are not able to implement any modification on both the power and the cooling system 10

11 Strategies and technologies we focus mainly on optimization strategies involving both sustainable and efficient resources management and allocation we use, among the others: Virtualization techniques (e.g. VMWARE and KVM) Cloud computing (based on Openstack) Data mining techniques for classification REST, Python, SNMP, 11

12 Our solution: the ECCO system Systems, Communications and Security (EASyCoSe 2014) 12

13 The ECCO components We designed the ECCO system as a combination of three kinds of modules: Modules that are able to detect the status of the data center (ECCO SENSORS) A module, which is the intelligence of the system, which identifies the best resource configuration for the data center (ECCO ENGINE) Modules that are able to apply to the data center the configuration chosen by the intelligence of the system(ecco ACTUATORS) 13

14 The ECCO sensors The ECCO SENSORS are asked to identify the state of the data center, from the following data: the users needs (in terms of QoS metrics) and the workflow characteristics; the actual overall data center energy consumption; all available resources characteristics; the actual resources load/availability ratio. 14

15 The ECCO actuators The ECCO ACTUATORS are asked to act on the data center configuration and on the workflow management on the basis of the directives received from the ECCO ENGINE. They perform: resources rationalization by turning on/off physical resource or by making available virtual resources of various kinds (compute, storage, etc.) in order to minimize energy consumption; resources aggregation in the same rack in order to maximize the cooling efficiency; partial or total workflow allocation on suitable resources able to guarantee the users satisfaction. 15

16 The core of the system: the ECCO ENGINE It is a black box responsible for processing the most sustainable resources arrangement for the data center. It can be formalized as a finite state machine (FSM), able to react to the changing in data provided by the sensors, by informing the suitable actuator about: the component to interact with (e.g. high-level services systems, resource manager, scheduler, etc.); the action to be taken (turn off/on physical/virtual resources to be allocated for each part of the workflow, etc.); the final resource where perform the action (compute, storage, etc.). 16

17 The core of the system: the ECCO ENGINE The ECCO ENGINE acts by means of its internal components: the historical database containing information about data center configurations previously used for already classified workflows and related delivered performances in terms of both users satisfaction and energy costs reduction; the ECCO OPT, that is asked to calculate, starting from information received by the ECCO SENSORS, the near-optimal configuration for the data center that matches the twofold aim to save energy and satisfy users; the workflow classifier for new workflows classification. 17

18 The ECCO OPT module The ECCO ENGINE FSM is modeled by a transfer function that, on the basis of both an optimization process performed by the ECCO OPT module, and actual/historical states, provides output to the suitable actuator. Our actual research activity is focused mainly on the mathematical formalization of the transfer function and the related optimization algorithm. 18

19 The workflow classifier To obtain an automatic jobs classification we used a Data Mining technique named data clustering that refers to the process aimed to divide into natural groups the instances represented by data. We develop the Workflow Classifier module using the Java APIs of the KMeans clustering algorithm implemented by the WEKA (Waikato Environment for Knowledge Analysis), a wellknown suite of machine learning software. At now, to perform workflow classification we considered two attributes: the jobs duration (tte - Total Time of Execution) and parallelism granularity (ntasks - the number of concurrent tasks of the job). 19

20 Workflow classification: first results January 2014 (a) Workflow classification: completed jobs (b) Weka clustering results: completed jobs The workflow classification performed on jobs completed on January 2014 provides results compliant to the real jobs classification 20

21 Realized sensors Energy consumption sensor: processes data retrieved from the hardware sensor for energy consumption measurement (the SmartGate device); Overall resources availability sensor: asks the suitable infrastructure Information System (TopBDII for Grid and OpenStack Nova for Cloud), for both resources kinds and availability; Resources state sensor: retrieves information about resources usage from the data center Resource Management System; Overall resources request sensor: retrieves information about users requirements from the data center Resource Management System. 21

22 The actuators We realized, at present only an actuator able to: power on/off physical systems by means of low level standard interfaces (e.g. SNMP); power on/off virtual computing/storage resources to be inserted into the Grid environment by means of the GaaS framework or general purpose Cloud resources via standard interfaces (e.g. OCCI). 22

23 Two solutions to help the ECCO realization GaaS (Grid as a Service) is a framework based on Cloud computing to make available virtualized Grid services (in particular computing and storage resources) on demand, instead of using a static set of physical resources. ASC (Adaptive Scheduling Controller), using an adaptive approach in job scheduling policy, allows a balanced, effective and efficient use of computational resources 23

24 Future works We are working to enrich the functionalities of ECCO ACTUATORS: partial or total workflow re-allocation for "migratable" applications (i.e. fully virtualized applications or checkpoint-enabled applications); GAAS ACTUATORS: full OCCI compliant cloud manager to enhance interoperability between different cloud provider; ECCO ENGINE: add the ability to choose the workflow classifier module that best match the long-term workflow to achieve better job classification and enhanced ECCO OPT module performance. 24

25 Acknowledgment This work has been realized thanks to the use of the S.Co.P.E computing infrastructure at the University of Naples in the framework of P.O.N. ReCaS Project 25

26 Thanks 26