Ariel Oleksiak

Cloud Computing: Survey on Energy Efficiency

Cloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions.

Grid scheduling simulations with GSSIM

Grid simulation tools provide frameworks for simulating application scheduling in various grid infrastructures. However, while experimenting with many existing tools, we have encountered two main shortcomings: (i) there are no tools for generating workloads, resources and events ; (ii) it is difficult and time consuming to model different grid levels, i.e. resource brokers, and local level scheduling systems. In this paper we present the grid scheduling simulator (GSSIM), a framework that addresses these shortcomings and provides an easy-to-use Grid scheduling framework for enabling simulations of a wide range of scheduling algorithms in multi-level, heterogeneous grid infrastructures. In order to foster more collaboration in the community at large, GSSIM is complemented with a portal (http://www.gssim.org) that provides a repository of grid scheduling algorithms, synthetic workloads and benchmarks for use with GSSIM.

Dynamic grid scheduling with job migration and rescheduling in the GridLab resource management system

Grid computing has become one of the most important research topics that appeared in the field of computing in the last years. Simultaneously, we have noticed the growing popularity of new Web-based technologies which allow us to create application-oriented Grid middleware services providing capabilities required for dynamic resource and job management, monitoring, security, etc. Consequently, end users are able to get easier access to geographically distributed resources. In this paper we present the results of our experiments with the Grid(Lab) Resource Management System (GRMS), which acts on behalf of end users and controls their computations efficiently using distributed heterogeneous resources. We show how resource matching techniques used within GRMS can be improved by the use of a job migration based rescheduling policy. The main aim of this policy is to shorten job pending times and reduce machine overloads. The influence of this method on application performance and resource utilization is studied in detail and compared with two other simple policies.

A multicriteria approach to two-level hierarchy scheduling in grids

In this paper we address a multicriteria scheduling problem for computational Grid systems. We focus on the two-level hierarchical Grid scheduling problem, in which at the first level (the Grid level) a Grid broker makes scheduling decisions and allocates jobs to Grid nodes. Jobs are then sent to the Grid nodes, where local schedulers generate local schedules for each node accordingly. A general approach is presented taking into account preferences of all the stakeholders of Grid scheduling (end-users, Grid administrators, and local resource providers) and assuming a lack of knowledge about job time characteristics. A single-stakeholder, single-criterion version of the approach has been compared experimentally with the existing approaches.

Practical power consumption estimation for real life HPC applications

Due to high energy costs, fine-grained power consumption accounting and capability of making users of High Performance Computing (HPC) clusters aware of the cost of their computation is becoming more and more important. Hardware power measurement solutions can be very expensive, hence the appeal of software-based estimation methods. In this paper we present a practical approach to power consumption estimation of both individual application executions and whole computing nodes. We compare it to existing state-of-the-art solutions, provide accuracy figures, and discuss possible deployment scenarios. Highlights? We explore existing methods for measuring HPC machine power use. ? We propose general method for software estimation of power consumption. ? We discuss both application and machine-level power estimation. ? We provide practical application scenarios for both methods.

Performance Evaluation and Energy Efficiency of High-Density HPC Platforms Based on Intel, AMD and ARM Processors

Due to growth of energy consumption by HPC servers and data centers many research efforts aim at addressing the problem of energy efficiency. Hence, the use of low power processors such as Intel Atom and ARM Cortex have recently gained more interest. In this article, we compare performance and energy efficiency of cutting-edge high-density HPC platform enclosures featuring either very high-performing processors such as Intel Core i7 or E7 yet having low power-efficiency, or the reverse i.e. energy efficient processors such as Intel Atom, AMD Fusion or ARM Cortex A9 yet with limited computing capacity. Our objective was to quantify in a very pragmatic way these general purpose CPUs using a set of reference benchmarks and applications run in an HPC environment, the trade-off that could exist between computing and power efficiency.

DCworms – A tool for simulation of energy efficiency in distributed computing infrastructures

Abstract In the recent years, energy-efficiency of computing infrastructures has gained a great attention. For this reason, proper estimation and evaluation of energy that is required to execute data center workloads became an important research problem. In this paper we present a Data Center Workload and Resource Management Simulator (DCworms) which enables modeling and simulation of computing infrastructures to estimate their performance, energy consumption, and energy-efficiency metrics for diverse workloads and management policies. We discuss methods of power usage modeling available in the simulator. To this end, we compare results of simulations to measurements of real servers. To demonstrate DCworms capabilities we evaluate impact of several resource management policies on overall energy-efficiency of specific workloads executed on heterogeneous resources.

Multi-Criteria Grid Resource Management Using Performance Prediction Techniques

To date, many of existing Grid resource brokers make their decisions concerning selection of the best resources for computational jobs using basic resource parameters such as, for instance, load. This approach may often be insufficient. Estimations of job start and execution times are needed in order to make more adequate decisions and to provide better quality of service for end-users. Nevertheless, due to heterogeneity of Grids and often incomplete information available the results of performance prediction methods may be very inaccurate. Therefore, estimations of prediction errors should be also taken into consideration during a resource selection phase. We present in this paper the multi-criteria resource selection method based on estimations of job start and execution times, and prediction errors. To this end, we use GRMS [28] and GPRES tools. Tests have been conducted based on workload traces which were recorded from a parallel machine at UPC. These traces cover 3 years of job information as recorded by the LoadLeveler batch management systems. We show that the presented method can considerably improve the efficiency of resource selection decisions.

Hierarchical scheduling strategies for parallel tasks and advance reservations in grids

Recently, the advance reservation functionality gained high importance in grids due to increasing popularity of modern applications that require interactive tasks, co-allocation of multiple resources, and performance guarantees. However, simultaneous scheduling, both advance reservations and batch tasks affects the performance. Advance reservations significantly deteriorate flow time of batch tasks and the overall resource utilization, especially in hierarchical scheduling structures. This is a consequence of unknown batch task processing times and the lack of possibility of altering allocations of advance reservations. To address these issues we present a common model for scheduling both computational batch tasks and tasks with advance reservation requests. We propose simple on-line scheduling policies and generic advices that reduce negative impact of advance reservations on a schedule quality. We also propose novel data structures and algorithms for efficient scheduling of advance reservations. A comprehensive experimental analysis is presented to show the influence of advance reservations on resource utilization, mean flow time, and mean tardiness—the criteria significant for administrators, users submitting batch tasks, and users requesting advance reservations, respectively. All experiments were performed with a well-known real workload using the GSSIM simulator.

GSSIM --A tool for distributed computing experiments

In this paper we present the Grid Scheduling Simulator GSSIM, a comprehensive and advanced simulation tool for distributed computing problems. Based on a classification of simulator features proposed in the paper, we define problems that can be simulated using GSSIM and compare it to other simulation tools. We focus on an extension of our previous works including advanced workload generation methods, simulation of a network with advance reservation features, handling specific application performance models and energy efficiency modeling. Some important features of GSSIM are demonstrated by three diverse experiments conducted with the use of the tool. We also present an advanced web tool for the remote management and execution of simulation experiments, which makes GSSIM the comprehensive distributed computing simulator available on the Web.