Network-aware heuristics for inter-domain meta-scheduling in Grids

AbstractGrid computing generally involves the aggregation of geographically distributed resources in the context of a particular application. As such resources can exist within different administrative domains, requirements on the communication network must also be taken into account when performing meta-scheduling, migration or monitoring of jobs. Similarly, coordinating efficient interaction between different domains should also be considered when performing such meta-scheduling of jobs. A strategy to perform peer-to-peer-inspired meta-scheduling in Grids is presented. This strategy has three main goals: (1) it takes the network characteristics into account when performing meta-scheduling; (2) communication and query referral between domains is considered, so that efficient meta-scheduling can be performed; and (3) the strategy demonstrates scalability, making it suitable for many scientific applications that require resources on a large scale. Simulation results are presented that demonstrate the usefulness of this approach, and it is compared with other proposals from literature

DECENTRALIZED RESOURCE ORCHESTRATION FOR HETEROGENEOUS GRIDS

Modern desktop machines now use multi-core CPUs to enable improved performance. However, achieving high performance on multi-core machines without optimized software support is still difficult even in a single machine, because contention for shared resources can make it hard to exploit multiple computing resources efficiently. Moreover, more diverse and heterogeneous hardware platforms (e.g. general-purpose GPU and Cell processors) have emerged and begun to impact grid computing. Given that heterogeneity and diversity are now a major trend going forward, grid computing must support these environmental changes. In this dissertation, I design and evaluate a decentralized resource management scheme to exploit heterogeneous multiple computing resources effectively. I suggest resource management algorithms that can efficiently utilize a diverse computational environment, including multiple symmetric computing entities and heterogeneous multi-computing entities, and achieve good load-balancing and high total system throughput. Moreover, I propose expressive resource description techniques to accommodate more heterogeneous environments, allowing incoming jobs with complex requirements to be matched to available resources. First, I develop decentralized resource management frameworks and job scheduling schemes to exploit multi-core nodes in peer-to-peer grids. I present two new load-balancing schemes that explicitly account for resource sharing and contention across multiple cores within a single machine, and propose a simple performance prediction model that can represent a continuum of resource sharing among cores of a CPU. Second, I provide scalable resource discovery and load balancing techniques to accommodate nodes with many types of computing elements, such as multi-core CPUs and GPUs, in a peer-to-peer grid architecture. My scheme takes into account diverse aspects of heterogeneous nodes to maximize overall system throughput as well as minimize messaging costs without sacrificing the failure resilience provided by an underlying peer-to-peer overlay network. Finally, I propose an expressive resource discovery method to support multi-attribute, range-based job constraints. The common approach of using simple attribute indexes does not suffice, as range-based constraints may be satisfied by more than a single value. I design a compact ID-based representation for resource characteristics, and integrate this representation into the decentralized resource discovery framework. By extensive experimental results via simulation, I show that my schemes can match heterogeneous jobs to heterogeneous resources both effectively (good matches are found, load is balanced), and efficiently (the new functionality imposes little overhead)

EECluster: An Energy-Efficient Tool for managing HPC Clusters

High Performance Computing clusters have become a very important element in research, academic and industrial communities because they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nevertheless, this high performance comes at the price of consuming large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, driving up power and cooling costs and forcing IT companies to reduce operation costs. To reduce the high energy consumptions of HPC clusters we propose a tool, named EECluster, for managing the energy-efficient allocation of the cluster resources, that works with both OGE/SGE and PBS/TORQUE Resource Management Systems (RMS) and whose decision-making mechanism is tuned automatically in a machine learning approach. Experimental studies have been made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects to evaluate the results obtained with the adoption of this too

EECluster: An Energy-Efficient Tool for managing HPC Clusters

High Performance Computing clusters have become a very important element in research, academic and industrial communities because they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nevertheless, this high performance comes at the price of consuming large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, driving up power and cooling costs and forcing IT companies to reduce operation costs. To reduce the high energy consumptions of HPC clusters we propose a tool, named EECluster, for managing the energy-efficient allocation of the cluster resources, that works with both OGE/SGE and PBS/TORQUE Resource Management Systems (RMS) and whose decision-making mechanism is tuned automatically in a machine learning approach. Experimental studies have been made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects to evaluate the results obtained with the adoption of this tool

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

A distributed platform for the volunteer execution of workflows on a local area network

Thesis submitted in fulfilment of the requirements for the Degree of Master of Science in Computer ScienceAlbatroz Engineering has developed a framework for over-head power lines inspection data acquisition and analysis, which includes hardware and software. The framework’s software components include inspection data analysis and reporting tools, commonly known as PLMI2 application/platform. In PLMI2, the analysis of over-head power line maintenance inspection data consists of a sequence of Automatic Tasks (ATs) interleaved with Manual Tasks (MTs). An AT consists of a set of algorithms that receives as input one or more datasets, processes them and returns new datasets. In turn, an MT enables human supervisors (also known as lines inspection operators) to correct, improve and validate the results of ATs. ATs run faster than MTs and in the overall work cycle, ATs take less than 10% of total processing time, but still take a few minutes. There is data flow dependency among tasks, which can be modelled with a workflow and even if MTs are omitted from this workflow, it is possible to carry the sequence of ATs, postponing MTs. In fact, if the computing cost and waiting time are negligible, it may be advantageous to run ATs earlier in the workflow, prior to validation. To address this opportunity, Albatroz Engineering has invested in a new procedure to stream the data through all ATs fully unattended. Considering these scenarios, it could be useful to have a system capable of detecting available workstations at a given instant and subsequently distribute the ATs to them. In this way, operators could schedule the execution of future ATs for a given inspection data, while they are performing MTs of another. The requirements of the system to implement fall within the field Volunteer Computing Systems and we will address some of the challenges posed by these kinds of systems, namely the hosts volatility and failures. Volunteer Computing is a type of distributed computing which exploits idle CPU cycles from computing resources donated by volunteers and connected through the Internet/Intranet to compute large-scale simulations. This thesis proposes and designs a new distributed task scheduling system in the context of Volunteer Computing Systems, able to schedule the ATs of PLMI2 and exploit idle CPU cycles from workstations within the company’s local area network (LAN) to accelerate the data analysis, being aware of data flow interdependencies. To evaluate the proposed system, a prototype has been implemented, and the simulations results have shown that it is scalable and supports fault-tolerance of tasks execution, by employing the rescheduling mechanism

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

International audienceThis study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. In this study, we pro-pose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments. The main design and implementation decisions for both alternatives are described and validated through experiments, demonstrating that our approaches provide high reliability when executing on pervasive environments. The analysis of the experiments also leads to several insights on the requirements and constraints from dynamic and volatile systems, reinforcing the importance of context-aware information and advanced fault-tolerance features to provide efficient and reliable MapReduce services on pervasive grids