Context-aware task scheduling in distributed computing systems

These days, the popularity of technologies such as machine learning, augmented reality, and big data analytics is growing dramatically. This leads to a higher demand of computational power not only for IT professionals but also for ordinary device users who benefit from new applications. At the same time, the computational performance of end-user devices increases to meet the demands of these resource-hungry applications. As a result, there is a coexistence of a huge demand of computational power on the one side and a large pool of computational resources on the other side. Bringing these two sides together is the idea of computational resource sharing systems which allow applications to forward computationally intensive workload to remote resources. This technique is often used in cloud computing where customers can rent computational power. However, we argue that not only cloud resources can be used as offloading targets. Rather, idle CPU cycles from end-user administered devices at the edge of the network can be spontaneously leveraged as well. Edge devices, however, are not only heterogeneous in their hardware and software capabilities, they also do not provide any guarantees in terms of reliability or performance. Does it mean that either the applications that require further guarantees or the unpredictable resources need to be excluded from such a sharing system? In this thesis, we propose a solution to this problem by introducing the Tasklet system, our approach for a computational resource sharing system. The Tasklet system supports computation offloading to arbitrary types of devices, including stable cloud instances as well as unpredictable end-user owned edge resources. Therefore, the Tasklet system is structured into multiple layers. The lowest layer is a best-effort resource sharing system which provides lightweight task scheduling and execution. Here, best-effort means that in case of a failure, the task execution is dropped and that tasks are allocated to resources randomly. To provide execution guarantees such as a reliable or timely execution, we add a Quality of Computation (QoC) layer on top of the best-effort execution layer. The QoC layer enforces the guarantees for applications by using a context-aware task scheduler which monitors the available resources in the computing environment and performs the matchmaking between resources and tasks based on the current state of the system. As edge resources are controlled by individuals, we consider the fact that these users need to be able to decide with whom they want to share their resources and for which price. Thus, we add a social layer on top of the system that allows users to establish friendship connections which can then be leveraged for social-aware task allocation and accounting of shared computation

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

Computation offloading for fast and energy-efficient edge computing

In recent years, the demand for computing power has increased considerably due to the popularity of applications that involve computationally intensive tasks such as machine learning or computer vision. At the same time, users increasingly run such applications on smartphones or wearables, which have limited computational power. The research community has proposed computation offloading to meet the demand for computing power. Resource-constrained devices offload workload to remote resource providers. These providers perform the computations and return the results via the network. Computation offloading has two major benefits. First, it accelerates the execution of computationally intensive tasks and therefore reduces waiting times. Second, it decreases the energy consumption of the offloading device, which is especially attractive for devices that run on battery. After years in which cloud servers were the primary resource providers, computation offloading in edge computing systems is currently gaining popularity. Edge-based systems leverage end-user devices such as smartphones, laptops, or desktop PCs instead of cloud servers as computational resource providers. Computation offloading in such environments leads to lower latencies, better utilization of end-user devices, and lower costs in comparison to traditional cloud computing. In this thesis, we present a computation offloading approach for fast and energy-efficient edge computing. We build upon the Tasklet system – a middleware-based computation offloading system. The Tasklet system allows devices to offload heterogeneous tasks to heterogeneous providers. We address three challenges of computation offloading in the edge. First, many applications are data-intensive, which necessitates a time-consuming transfer of input data ahead of a remote execution. To overcome this challenge, we introduce DataVinci – an approach that proactively places input data on suitable devices to accelerate task execution. DataVinci additionally offers task placement strategies that exploit data locality. Second, modern applications are often user-facing and responsive. They require sub-second execution of computationally intensive tasks to ensure proper user experience. We design the decentralized scheduling approach DecArt for such applications. Third, deciding whether a local or remote execution of an upcoming task will consume less energy is non-trivial. This decision is particularly challenging as task complexity and result data size vary across executions, even if the source code is similar. We introduce the energy-aware scheduling approach Voltaire, which uses machine learning and device-specific energy profiles for making precise offloading decisions. We integrate DataVinci, DecArt, and Voltaire into the Tasklet system and evaluate the benefits in extensive experiments

GPU devices for safety-critical systems: a survey

Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical systems such as autonomous driving systems. However, the integration of complex, parallel, and computationally demanding software functions with different safety-criticality levels on GPU devices with shared hardware resources contributes to several safety certification challenges. This survey categorizes and provides an overview of research contributions that address GPU devices’ random hardware failures, systematic failures, and independence of execution.This work has been partially supported by the European Research Council with Horizon 2020 (grant agreements No. 772773 and 871465), the Spanish Ministry of Science and Innovation under grant PID2019-107255GB, the HiPEAC Network of Excellence and the Basque Government under grant KK-2019-00035. The Spanish Ministry of Economy and Competitiveness has also partially supported Leonidas Kosmidis with a Juan de la Cierva Incorporación postdoctoral fellowship (FJCI-2020- 045931-I).Peer ReviewedPostprint (author's final draft

Elastic computation placement in edge-based environments

Today, technologies such as machine learning, virtual reality, and the Internet of Things are integrated in end-user applications more frequently. These technologies demand high computational capabilities. Especially mobile devices have limited resources in terms of execution performance and battery life. The offloading paradigm provides a solution to this problem and transfers computationally intensive parts of applications to more powerful resources, such as servers or cloud infrastructure. Recently, a new computation paradigm arose which exploits the huge amount of end-user devices in the modern computing landscape - called edge computing. These devices encompass smartphones, tablets, microcontrollers, and PCs. In edge computing, devices cooperate with each other while avoiding cloud infrastructure. Due to the proximity among the participating devices, the communication latencies for offloading are reduced. However, edge computing brings new challenges in form of device fluctuation, unreliability, and heterogeneity, which negatively affect the resource elasticity. As a solution, this thesis proposes a computation placement framework that provides an abstraction for computation and resource elasticity in edge-based environments. The design is middleware-based, encompasses heterogeneous platforms, and supports easy integration of existing applications. It is composed of two parts: the Tasklet system and the edge support layer. The Tasklet system is a flexible framework for computation placement on heterogeneous resources. It introduces closed units of computation that can be tailored to generic applications. The edge support layer handles the characteristics of edge resources. It copes with fluctuation and unreliability by applying reactive and proactive task migration. Furthermore, the performance heterogeneity and the consequent bottlenecks are handled by two edge-specific task partitioning approaches. As a proof of concept, the thesis presents a fully-fledged prototype of the design, which is evaluated comprehensively in a real-world testbed. The evaluation shows that the design is able to substantially improve the resource elasticity in edge-based environments

Modular software architecture for flexible reservation mechanisms on heterogeneous resources

Management, allocation and scheduling of heterogeneous resources for complex distributed real-time applications is a chal- lenging problem. Timing constraints of applications may be fulfilled by a proper use of real-time scheduling policies, admission control and enforcement of timing constraints. However, it is not easy to design basic infrastructure services that allow for an easy access to the allocation of multiple heterogeneous resources in a distributed environment. In this paper, we present a middleware for providing distributed soft real-time applications with a uniform API for reserving heterogeneous resources with real-time scheduling capabilities in a distributed environment. The architecture relies on standard POSIX OS facilities, such as time management and standard TCP/IP networking services, and it is designed around CORBA, in order to facilitate modularity, flexibility and portability of the applications using it. However, real-time scheduling is supported by proper extensions at the kernel-level, plugged within the framework by means of dedicated resource managers. Our current implementation on Linux supports reservation of CPU, disk and network bandwidth. However, additional resource managers supporting alternative real-time schedulers for these resources, as well as additional types of resources, may be easily added. We present experimental results gathered on both synthetic applications and a real multimedia video streaming case study, showing advantages deriving from the use of the proposed middleware. Finally, overhead figures are reported, showing sustainability of the approach for a wide class of complex, distributed, soft real-time applications