An energy-aware scheduling approach for resource-intensive jobs using smart mobile devices as resource providers

The ever-growing adoption of smart mobile devices is a worldwide phenomenon that positions smart-phones and tablets as primary devices for communication and Internet access. In addition to this, the computing capabilities of such devices, often underutilized by their owners, are in continuous improvement. Today, smart mobile devices have multi-core CPUs, several gigabytes of RAM, and ability to communicate through several wireless networking technologies. These facts caught the attention of researchers who have proposed to leverage smart mobile devices aggregated computing capabilities for running resource intensive software. However, such idea is conditioned by key features, named singularities in the context of this thesis, that characterize resource provision with smart mobile devices.These are the ability of devices to change location (user mobility), the shared or non-dedicated nature of resources provided (lack of ownership) and the limited operation time given by the finite energy source (exhaustible resources).Existing proposals materializing this idea differ in the singularities combinations they target and the way they address each singularity, which make them suitable for distinct goals and resource exploitation opportunities. The latter are represented by real life situations where resources provided by groups of smart mobile devices can be exploited, which in turn are characterized by a social context and a networking support used to link and coordinate devices. The behavior of people in a given social context configure a special availability level of resources, while the underlying networking support imposes restrictionson how information flows, computational tasks are distributed and results are collected. The latter constitutes one fundamental difference of proposals mainly because each networking support ?i.e., ad-hoc and infrastructure based? has its own application scenarios. Aside from the singularities addressed and the networking support utilized, the weakest point of most of the proposals is their practical applicability. The performance achieved heavily relies on the accuracy with which task information, including execution time and/or energy required for execution, is provided to feed the resource allocator.The expanded usage of wireless communication infrastructure in public and private buildings, e.g., shoppings, work offices, university campuses and so on, constitutes a networking support that can be naturally re-utilized for leveraging smart mobile devices computational capabilities. In this context, this thesisproposal aims to contribute with an easy-to-implement  scheduling approach for running CPU-bound applications on a cluster of smart mobile devices. The approach is aware of the finite nature of smart mobile devices energy, and it does not depend on tasks information to operate. By contrast, it allocatescomputational resources to incoming tasks using a node ranking-based strategy. The ranking weights nodes combining static and dynamic parameters, including benchmark results, battery level, number of queued tasks, among others. This node ranking-based task assignment, or first allocation phase, is complemented with a re-balancing phase using job stealing techniques. The second allocation phase is an aid to the unbalanced load provoked as consequence of the non-dedicated nature of smart mobile devices CPU usage, i.e., the effect of the owner interaction, tasks heterogeneity, and lack of up-to-dateand accurate information of remaining energy estimations. The evaluation of the scheduling approach is through an in-vitro simulation. A novel simulator which exploits energy consumption profiles of real smart mobile devices, as well as, fluctuating CPU usage built upon empirical models, derived from real users interaction data, is another major contribution. Tests that validate the simulation tool are provided and the approach is evaluated in scenarios varying the composition of nodes, tasks and nodes characteristics including different tasks arrival rates, tasks requirements and different levels of nodes resource utilization.Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Simple Energy Aware Scheduler: An Empirical Evaluation

Mobile devices have evolved from single purpose devices, such as mobile phone, into general purpose multi-core computers with considerable unused capabilities. Therefore, several researchers have considered harnessing the power of these battery-powered devices for distributed computing. Despite their ever-growing capabilities, using battery as power source for mobile devices represents a major challenge for applying traditional distributed computing techniques. Particularly, researchers aimed at using mobile devices as resources for executing computationally intensive task. Different job scheduling algorithms were proposed with this aim, but many of them require information that is unavailable or difficult to obtain in real-life environments, such as how much energy would require a job to be finished. In this context, Simple Energy Aware Scheduler (SEAS) is a scheduling technique for computational intensive Mobile Grids that only require easily accessible information. It was proposed in 2010 and it has been the base for a range of research work. Despite being described as easily implementable in real-life scenarios, SEAS and other SEAS-improvements works have always been evaluated using simulations. In this work, we present a distributed computing platform for mobile devices that support SEAS and empirical evaluation of the SEAS scheduler. This evaluation followed the methodology of the original SEAS evaluation, in which Random and Round Robin schedulers were used as baselines. Although the original evaluation was performed by simulation using notebooks profile instead of smartphones and tablets, results confirms that SEAS outperforms the baseline schedulers.Fil: Pérez Campos, Ana Bella. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; ArgentinaFil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Personal mobile grids with a honeybee inspired resource scheduler

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The overall aim of the thesis has been to introduce Personal Mobile Grids (PMGrids) as a novel paradigm in grid computing that scales grid infrastructures to mobile devices and extends grid entities to individual personal users. In this thesis, architectural designs as well as simulation models for PM-Grids are developed. The core of any grid system is its resource scheduler. However, virtually all current conventional grid schedulers do not address the non-clairvoyant scheduling problem, where job information is not available before the end of execution. Therefore, this thesis proposes a honeybee inspired resource scheduling heuristic for PM-Grids (HoPe) incorporating a radical approach to grid resource scheduling to tackle this problem. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are initiated. Among the other main contributions are a comprehensive taxonomy of grid systems as well as a detailed analysis of the honeybee colony and its nectar acquisition process (NAP), from the resource scheduling perspective, which have not been presented in any previous work, to the best of our knowledge. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Comparisons with optimal values and worst bounds are conducted to gain a clear insight into HoPe behaviour, in terms of stability, throughput, turnaround time and speedup, under different running conditions of number of jobs and grid scales. Experimental results demonstrate the superiority of HoPe performance where it has successfully maintained optimum stability and throughput in more than 95% of the experiments, with HoPe achieving three times better than the OSH under extremely heavy loads. Regarding the turnaround time and speedup, HoPe has effectively achieved less than 50% of the turnaround time incurred by the OSH, while doubling its speedup in more than 60% of the experiments. These results indicate the potential of both PM-Grids and HoPe in realising futuristic grid visions. Therefore considering the deployment of PM-Grids in real life scenarios and the utilisation of HoPe in other parallel processing and high throughput computing systems are recommended

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems