ترغب بنشر مسار تعليمي؟ اضغط هنا

Context-Aware Service Utilisation in the Clouds and Energy Conservation

98   0   0.0 ( 0 )
 نشر من قبل Richard McClatchey
 تاريخ النشر 2012
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Ubiquitous computing environments are characterised by smart, interconnected artefacts embedded in our physical world that are projected to provide useful services to human inhabitants unobtrusively. Mobile devices are becoming the primary tools of human interaction with these embedded artefacts and utilisation of services available in smart computing environments such as clouds. Advancements in capabilities of mobile devices allow a number of user and environment related context consumers to be hosted on these devices. Without a coordinating component, these context consumers and providers are a potential burden on device resources; specifically the effect of uncoordinated computation and communication with cloud-enabled services can negatively impact the battery life. Therefore energy conservation is a major concern in realising the collaboration and utilisation of mobile device based context-aware applications and cloud based services. This paper presents the concept of a context-brokering component to aid in coordination and communication of context information between mobile devices and services deployed in a cloud infrastructure. A prototype context broker is experimentally analysed for effects on energy conservation when accessing and coordinating with cloud services on a smart device, with results signifying reduction in energy consumption.



قيم البحث

اقرأ أيضاً

As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an exten sion of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the users dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service (QoS). Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this paper, we model service scheduling in MEC scenarios and propose a delay-aware and mobility-aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8% to 20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours.
Context information has emerged as an important resource to enable autonomy and flexibility of pervasive applications. The widespread use of context information necessitates efficient wide-area lookup services. In this paper, we present the design an d implementation of a peer-to-peer context lookup system to support contextaware applications over multiple smart spaces. Our system provides a distributed repository for context storage, and a semantic peer-to-peer network for context lookup. Collaborative context-aware applications that utilize different context information in multiple smart spaces can be easily built by invoking a pull or push service provided by our system. We outline the design and implementation of our system, and validate our system through the development of cross-domain applications
Energy is now a first-class design constraint along with performance in all computing settings. Energy predictive modelling based on performance monitoring counts (PMCs) is the leading method used for prediction of energy consumption during an applic ation execution. We use a model-theoretic approach to formulate the assumed properties of existing models in a mathematical form. We extend the formalism by adding properties, heretofore unconsidered, that account for a limited form of energy conservation law. The extended formalism defines our theory of energy of computing. By applying the basic practical implications of the theory, we improve the prediction accuracy of state-of-the-art energy models from 31% to 18%. We also demonstrate that use of state-of-the-art measurement tools for energy optimisation may lead to significant losses of energy (ranging from 56% to 65% for applications used in experiments) since they do not take into account the energy conservation properties.
Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data, and reducing the ingress bandwidth demand beyond the edge. However, fo r a given DNN, identifying the optimal partition configuration for distributing the DNN that maximizes performance is a significant challenge. This is because the combination of potential target hardware resources that maximizes performance and the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for user-defined objectives/constraints for partitioning. This paper presents Scission, a tool for automated benchmarking of DNNs on a given set of target device, edge and cloud resources for determining optimal partitions that maximize DNN performance. The decision-making approach is context-aware by capitalizing on hardware capabilities of the target resources, their locality, the characteristics of DNN layers, and the network condition. Experimental studies are carried out on 18 DNNs. The decisions made by Scission cannot be manually made by a human given the complexity and the number of dimensions affecting the search space. The benchmarking overheads of Scission allow for responding to operational changes periodically rather than in real-time. Scission is available for public download at https://github.com/qub-blesson/Scission.
Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics. While runtime prediction models can be used to initially select appropriate cluster resources given target runtimes, the actual runtime performa nce of dataflow jobs depends on several factors and varies over time. Yet, in many situations, dynamic scaling can be used to meet formulated runtime targets despite significant performance variance. This paper presents Enel, a novel dynamic scaling approach that uses message propagation on an attributed graph to model dataflow jobs and, thus, allows for deriving effective rescaling decisions. For this, Enel incorporates descriptive properties that capture the respective execution context, considers statistics from individual dataflow tasks, and propagates predictions through the job graph to eventually find an optimized new scale-out. Our evaluation of Enel with four iterative Spark jobs shows that our approach is able to identify effective rescaling actions, reacting for instance to node failures, and can be reused across different execution contexts.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا