[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Data Mining Practical Machine Learning Tools and Techniques

http://cloud.pubs.dbs.uni-leipzig.de/sites/cloud.pubs.dbs.uni-leipzig.de/files/Brandic2008CloudcomputingandemergingITplatformsVisionhypeand.pdf

Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd.

https://beloglazov.info/papers/2010-cloudsim-spe.pdf

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Edge computing is an emerging technology that aims to include latency-sensitive and data-intensive applications such as mobile or IoT services, into the cloud ecosystem by placing computational resources at the edge of the network. Close proximity to producers and consumers of data brings significant benefits in latency and bandwidth. However, edge resources are, by definition, limited in comparison to cloud counterparts, thus, a trade-off exists between deploying a service closest to its users and avoiding resource overload. We propose a score-based edge service scheduling algorithm that evaluates both network and computational capabilities of edge nodes and outputs the maximum scoring mapping between services and resources. Our extensive simulation based on a live video streaming service, demonstrates significant improvements in both network delay and service time. Additionally, we compare edge computing technology with the state-of-the-art cloud computing and content delivery network solutions within the context of latency-sensitive and data-intensive applications. Our results show that edge computing enhanced with suggested scheduling algorithm is a viable solution for achieving high quality of service and responsivity in deploying such applications.

Scheduling Latency-Sensitive Applications in Edge Computing

Edge computing services are exposed to infrastructural failures due to geographical dispersion, ad hoc deployment, and rudimentary support systems. Two unique characteristics of the edge computing paradigm necessitate a novel failure resilience approach. First, edge servers, contrary to cloud counterparts with reliable data center networks, are typically connected via ad hoc networks. Thus, link failures need more attention to ensure truly resilient services. Second, network delay is a critical factor for the deployment of edge computing services. This restricts replication decisions to geographical proximity and necessitates joint consideration of delay and resilience. In this article, we propose a novel machine learning based mechanism that evaluates the failure resilience of a service deployed redundantly on the edge infrastructure. Our approach learns the spatiotemporal dependencies between edge server failures and combines them with the topological information to incorporate link failures. Ultimately, we infer the probability that a certain set of servers fails or disconnects concurrently during service runtime. Furthermore, we introduce Dependency- and Topology-aware Failure Resilience (DTFR), a two-stage scheduler that minimizes either failure probability or redundancy cost, while maintaining low network delay. Extensive evaluation with various real-world failure traces and workload configurations demonstrate superior performance in terms of availability, number of failures, network delay, and cost with respect to the state-of-the-art schedulers.

/pdf/learning-spatiotemporal-failure-dependencies-for-resilient-52z7ork4r8.pdf

Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services

While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectu ...

/pdf/using-meta-heuristics-and-machine-learning-for-software-1mfgs1votf.pdf

Using meta-heuristics and machine learning for software optimization of parallel computing systems : a systematic literature review

Edge computing paradigm is prone to failures as it trades reliability against other quality of service properties such as low latency and geographical prevalence. Therefore, software services that run on edge infrastructure must rely on failure resilience techniques for uninterrupted delivery. Unique combination of hardware, software, and network characteristics of edge services is not addressed by existing techniques that are designed or tailored for cloud services. In this work, we propose a novel method for evaluating the resilience of replicated edge services, which exploits failure dependencies between edge servers to forecast probability of service interruption. This is done by analyzing historical failure logs of individual servers, modeling temporal dependencies as a dynamic Bayesian network, and inferring the probability that certain number of servers fail concurrently. Furthermore, we propose two replica scheduling algorithms that optimize different criteria in resilient service deployment, namely failure probability and cost of redundancy.

Dependency Mining for Service Resilience at the Edge

Grid workflow applications are emerging as one of the most interesting programming models for the Grid. In this paper we present a novel approach for graphically modeling and describing Grid workflow applications based on the Unified Modeling Language (UML). Our approach provides a graphic representation of Grid applications based on a widely accepted standard (UML) that is more amenable than pure textual-oriented specifications (such as XML). We describe some of the most important elements for modeling control flow, data flow, synchronization, notification, and constraints. We also introduce new features that have not been included by other Grid workflow specification languages which includes broadcast and parallel loops. Our UML-based graphical editor Teuta provides the corresponding tool support. We demonstrate our approach by describing a UML-based Grid workflow model for an advanced 3D medical image reconstruction application.

Ivona Brandic

Papers

Scheduling Latency-Sensitive Applications in Edge Computing

Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services

Using meta-heuristics and machine learning for software optimization of parallel computing systems : a systematic literature review

Dependency Mining for Service Resilience at the Edge

Towards an UML Based Graphical Representation of Grid Workflow Applications