A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

© 2015, Springer Science+Business Media New York. Optimizing task scheduling in a distributed heterogeneous computing environment, which is a nonlinear multi-objective NP-hard problem, plays a critical role in decreasing service response time and cost, and boosting Quality of Service (QoS). This paper, considers four conflicting objectives, namely minimizing task transfer time, task execution cost, power consumption, and task queue length, to develop a comprehensive multi-objective optimization model for task scheduling. This model reduces costs from both the customer and provider perspectives by considering execution and power cost. We evaluate our model by applying two multi-objective evolutionary algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA). To implement the proposed model, we extend the Cloudsim toolkit by using MOPSO and MOGA as its task scheduling algorithms which determine the optimal task arrangement among VMs. The simulation results show that the proposed multi-objective model finds optimal trade-off solutions amongst the four conflicting objectives, which significantly reduces the job response time and makespan. This model not only increases QoS but also decreases the cost to providers. From our experimentation results, we find that MOPSO is a faster and more accurate evolutionary algorithm than MOGA for solving such problems

Scientific Workflow Scheduling for Cloud Computing Environments

The scheduling of workflow applications consists of assigning their tasks to computer resources to fulfill a final goal such as minimizing total workflow execution time. For this reason, workflow scheduling plays a crucial role in efficiently running experiments. Workflows often have many discrete tasks and the number of different task distributions possible and consequent time required to evaluate each configuration quickly becomes prohibitively large. A proper solution to the scheduling problem requires the analysis of tasks and resources, production of an accurate environment model and, most importantly, the adaptation of optimization techniques. This study is a major step toward solving the scheduling problem by not only addressing these issues but also optimizing the runtime and reducing monetary cost, two of the most important variables. This study proposes three scheduling algorithms capable of answering key issues to solve the scheduling problem. Firstly, it unveils BaRRS, a scheduling solution that exploits parallelism and optimizes runtime and monetary cost. Secondly, it proposes GA-ETI, a scheduler capable of returning the number of resources that a given workflow requires for execution. Finally, it describes PSO-DS, a scheduler based on particle swarm optimization to efficiently schedule large workflows. To test the algorithms, five well-known benchmarks are selected that represent different scientific applications. The experiments found the novel algorithms solutions substantially improve efficiency, reducing makespan by 11% to 78%. The proposed frameworks open a path for building a complete system that encompasses the capabilities of a workflow manager, scheduler, and a cloud resource broker in order to offer scientists a single tool to run computationally intensive applications

A user-centric execution environment for <em>CineGrid</em> workloads

The abundance and heterogeneity of IT resources available, together with the ability to dynamically scale applications poses significant usability issues to users. Without understanding the performance profile of available resources users are unable to efficiently scale their applications in order to meet performance objectives. High quality media collaborations, like CineGrid, are one example of such diverse environments where users can leverage dynamic infrastructures to move and process large amounts of data. This paper describes our user-centric approach to executing high quality media processing workloads over dynamic infrastructures. Our main contribution is the CGtoolkit environment, an integrated system which aids users cope with the infrastructure complexity and large data sets specific to the digital cinema domain

A Survey of Pipelined Workflow Scheduling: Models and Algorithms

International audienceA large class of applications need to execute the same workflow on different data sets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task-, data-, pipelined-, and/or replicated-parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors or optimization goals. This paper surveys the field by summing up and structuring known results and approaches

Performance control of internet-based engineering applications.

2006/2007Grazie alle tecnologie capaci di semplificare l'integrazione tra programmi remoti ospitati da differenti organizzazioni, le comunità scientifica ed ingegneristica stanno adottando architetture orientate ai servizi per: aggregare, condividere e distribuire le loro risorse di calcolo, per gestire grandi quantità di dati e per eseguire simulazioni attraverso Internet. I Web Service, per esempio, permettono ad un'organizzazione di esporre, in Internet, le funzionalità dei loro sistemi e di renderle scopribili ed accessibili in un modo controllato. Questo progresso tecnologico può permettere nuove applicazioni anche nell'area dell'ottimizzazione di progetti. Gli attuali sistemi di ottimizzazione di progetti sono di solito confinati all'interno di una singola organizzazione o dipartimento. D'altra parte, i moderni prodotti manifatturieri sono l'assemblaggio di componenti provenienti da diverse organizzazioni. Componendo i servizi delle organizzazioni coinvolte, si può creare un workflow che descrive il modello del prodotto composto. Questo servizio composto puo a sua volta essere usato da un sistema di ottimizzazione inter-organizzazione. I compromessi progettuali che sono implicitamente incorporati per architetture locali, devono essere riconsiderati quando questi sistemi sono messi in opera su scala globale in Internet. Ad esempio: i) la qualità delle connessioni tra i nodi può variare in modo impredicibile; ii) i nodi di terze parti mantengono il pieno controllo delle loro risorse, incluso, per esempio, il diritto di diminuire le risorse in modo temporaneo ed impredicibile. Dal punto di vista del sistema come un'entità unica, si vorrebbero massimizzare le prestazioni, cioè, per esempio, il throughput inteso come numero di progetti candidati valutati per unità di tempo. Dal punto di vista delle organizzazioni partecipanti al workflow si vorrebbe, invece, minimizzare il costo associato ad ogni valutazione. Questo costo può essere un ostacolo all'adozione del paradigma distribuito, perché le organizzazioni partecipanti condividono le loro risorse (cioè CPU, connessioni, larghezza di banda e licenze software) con altre organizzazioni potenzialmente sconosciute. Minimizzare questo costo, mentre si mantengono le prestazioni fornite ai clienti ad un livello accettabile, può essere un potente fattore per incoraggiare le organizzazioni a condividere effettvivamente le proprie risorse. Lo scheduling di istanze di workflows, ovvero stabilire quando e dove eseguire un certo workflow, in un tale ambiente multi-organizzazione, multi-livello e geograficamente disperso, ha un forte impatto sulle prestazioni. Questo lavoro investiga alcuni dei problemi essenziali di prestazioni e di costo legati a questo nuovo scenario. Per risolvere i problemi inviduati, si propone un sistema di controllo dell'accesso adattativo davanti al workflow engine che limita il numero di esecuzioni concorrenti. Questa proposta può essere implementata in modo molto semplice: tratta i servizi come black-box e non richiede alcuna interazione da parte delle organizzazioni partecipanti. La tecnica è stata valutata in un ampio spettro di scenari, attraverso simulazione ad eventi discreti. I risultati sperimentali suggeriscono che questa tecnica può fornire dei significativi benefici garantendo alti livelli di throughput e bassi costi.Thanks to technologies able to simplifying the integration among remote programs hosted by different organizations, engineering and scientific communities are embodying service oriented architectures to aggregate, share and distribute their computing resources to process and manage large data sets, and to execute simulations through Internet. Web Service, for example, allow an organization to expose the functionality of its internal systems on the Internet and to make it discoverable and accessible in a controlled manner. Such a technological advance may enable novel applications also in the area of design optimization. Current design optimization systems are usually confined within the boundary of a single organization or department. Modern engineering products, on the other hand, are assembled out of components developed by several organizations. Composing services from the involved organizations, a model of the composite product can be described by an appropriate workflow. Such composite service can then be used by a inter-organizational design optimization system. The design trade-offs that have been implicitly incorporated within local environments, may have to be reconsidered when deploying these systems on a global scale on the Internet. For example: i) node-to-node links may vary their service quality in an unpredictable manner; ii) third party nodes retains full control over their resources including, e.g., the right to decrease the resource amount temporarily and unpredictably. From the point of view of the system as a whole, one would like to maximize the performance, i.e. throughput the number of candidate design evaluations performed per unit of time. From the point of view of a participant organization, however, one would like to minimize the cost associated with each evaluation. This cost can be an obstacle to the adoption of this distributed paradigm, because organizations participating in the composite service share they resources (e.g. CPU, link bandwidth and software licenses) with other, potentially unknown, organizations. Minimizing such cost while keeping performance delivered to clients at an acceptable level can be a powerful factor for encouraging organizations to indeed share their services. The scheduling of workflow instances in such a multi-organization, multi-tiered and geographically dispersed environment have strong impacts on performance. This work investigates some of the fundamental performance and cost related issues involved in such a novel scenario. We propose an adaptive admission control to be deployed at the workflow engine level that limits the number of concurrent jobs. Our proposal can be implemented very simply: it handles the service as black-boxes, and it does not require any hook from the participating organizations. We evaluated our technique in a broad range of scenarios, by means of discrete event simulation. Experimental results suggest that it can provide significant benefits guaranteeing high level of throughput and low costs.XX Ciclo197

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

ENERGY-TIME PERFORMANCE OF HETEROGENEOUS COMPUTING SYSTEMS: MODELS AND ANALYSIS

Ph.DDOCTOR OF PHILOSOPH

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more