Both the edge and the cloud can provide computing services for mobile devices to enhance their performance. The edge can reduce the conveying delay by providing local computing services while the cloud can support enormous computing requirements. Their cooperation can improve the utilization of computing resources and ensure the QoS, and thus is critical to edge-cloud computing business models. This paper proposes an efficient framework for mobile edge-cloud computing networks, which enables the edge and the cloud to share their computing resources in the form of wholesale and buyback. To optimize the computing resource sharing process, we formulate the computing resource management problems for the edge servers to manage their wholesale and buyback scheme and the cloud to determine the wholesale price and its local computing resources. Then, we solve these problems from two perspectives: i) social welfare maximization and ii) profit maximization for the edge and the cloud. For i), we have proved the concavity of the social welfare and proposed an optimal cloud computing resource management to maximize the social welfare. For ii), since it is difficult to directly prove the convexity of the primal problem, we first proved the concavity of the wholesaled computing resources with respect to the wholesale price and designed an optimal pricing and cloud computing resource management to maximize their profits. Numerical evaluations show that the total profit can be maximized by social welfare maximization while the respective profits can be maximized by the optimal pricing and cloud computing resource management.

Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

In bio-medical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing Data from many different sources can be collected into massive data sets via localized sensor networks Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment The application of bio-medical image fusion for big-data computing has strong development momentum, big-data bio-medical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for big-data bio-medical image of WSN is proposed in this paper In this method, the three high-frequency coefficients in wavelet domain of bio-medical image are pre-processed This pre-processing strategy can reduce the redundant ratio of big-data bio-medical image Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm Finally, based on multi-scale edge of bio-medical image, it can be fused and reconstructed Experimental results indicate the novel method is effective and very useful for transmission of big-data bio-medical image, which can solve the problem of data redundancy, more energy cost and low quality

/pdf/novel-fusion-computing-method-for-bio-medical-image-of-wsn-4kj76ng6gq.pdf

Novel fusion computing method for bio-medical image of WSN based on spherical coordinate

Context: Scheduling in cloud is complicated as a result of multi-tenancy. Diverse tenants have different requirements, including service functions, response time, QoS and throughput. Diverse tenants require different scheduling capabilities, resource consumption and competition. Multi-tenancy scheduling approaches have been developed for different service models, such as Software as a Service (SaaS), Platform as a service (PaaS), Infrastructure as a Service (IaaS), and Database as a Service (DBaaS). Objective: In this paper, we survey the current landscape of multi-tenancy scheduling, laying out the challenges and complexity of software engineering where multi-tenancy issues are involved. This study emphasises scheduling policies, cloud provisioning and deployment with regards to multi-tenancy issues. We conduct a systematic literature review of research studies related to multi-tenancy scheduling approaches on cloud platforms determine the primary scheduling approaches currently used and the challenges for addressing key multi-tenancy scheduling issues. Method: We adopted a systematic literature review method to search and review many major journal and conference papers on four major online electronic databases, which address our four predefined research questions. Defining inclusion and exclusion criteria was the initial step before extracting data from the selected papers and deriving answers addressing our enquiries. Results: Finally, 53 papers were selected, of which 62 approaches were identified. Most of these methods are developed without cloud layers’ limitation (43.40%) and on SaaS, most of scheduling approaches are oriented to framework design (43.75%). Conclusion: The results have demonstrated most of multi-tenancy scheduling solutions can work at any delivery layer. With the difference of tenants’ requirements and functionalities, the choice of cloud service delivery models is changed. Based on our study, designing a multi-tenancy scheduling framework should consider the following 3 factors: computing, QoS and storage resource. One of the potential research foci of multi-tenancy scheduling approaches is on GPU scheduling.

A systematic review of scheduling approaches on multi-tenancy cloud platforms

In recent years, cloud computing can be considered an emerging technology that can share resources with users. Because cloud computing is on-demand, efficient use of resources such as memory, processors, bandwidth, etc., is a big challenge. Despite the advantages of cloud computing, sometimes it is not a proper choice due to its delay in responding appropriately to existing requests, which led to the need for another technology called fog computing. Fog computing reduces traffic and time lags by expanding cloud services to the network and closer to users. It can schedule resources with higher efficiency and utilize them to impact the user's experience dramatically. This paper aims to survey some studies that have been done in the field of scheduling in fog/cloud computing environments. The focus of this survey is on published studies between 2015 and 2021 in journals or conferences. We selected 71 studies in a systematic literature review (SLR) from four major scientific databases based on their relation to our paper. We classified these studies into five categories based on their traced parameters and their focus area. This classification comprises 1—performance 2—energy efficiency, 3—resource utilization, 4—performance and energy efficiency, and 5—performance and resource utilization simultaneously. 42.3% of the studies focused on performance, 9.9% on energy efficiency, 7.0% on resource utilization, 21.1% on both performance and energy efficiency, and 19.7% on both performance and resource utilization. Finally, we present challenges and open issues in the resource scheduling methods in fog/cloud computing environments.

Resource scheduling methods in cloud and fog computing environments: a systematic literature review

Virtualization is one of the key enabling technologies for Cloud computing. Although it facilitates improved utilization of resources, virtualization can lead to performance degradation due to the sharing of physical resources like CPU, memory, network interfaces, disk controllers, etc. Multi-tenancy can cause highly unpredictable performance for concurrent I/O applications running inside virtual machines that share local disk storage in Cloud. Disk I/O requests in a typical Cloud setup may have varied requirements in terms of latency and throughput as they arise from a range of heterogeneous applications having diverse performance goals. This necessitates providing differential performance services to different I/O applications. In this paper, we present PriDyn, a novel scheduling framework which is designed to consider I/O performance metrics of applications such as acceptable latency and convert them to an appropriate priority value for disk access based on the current system state. This framework aims to provide differentiatedI/O service to various applications and ensures predictable performance for critical applications in multi-tenant Cloud environment. We demonstrate through experimental validations on real world I/O traces that this framework achieves appreciable enhancements in I/O performance, indicating that this approach is a promising step towards enabling QoS guarantees on Cloud storage.

PriDyn: Enabling Differentiated I/O Services in Cloud Using Dynamic Priorities

Knowledge bases are typically incomplete, meaning that they are missing information that we would expect to be there. Recent years have seen two main approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as “If two people are married, they most likely live in the same city”. These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of neuro-symbolic works that combine embeddings and rule mining approaches for fact prediction.

Combining Embeddings and Rules for Fact Prediction

As machine learning techniques are being increasingly employed for text processing tasks, the need for training data has become a major bottleneck for their application. Manual generation of large scale training datasets tailored to each task is a time consuming and expensive process, which necessitates their automated generation. In this work, we turn our attention towards creation of training datasets for named entity recognition (NER) in the context of the cultural heritage domain. NER plays an important role in many natural language processing systems. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as digitized art archives, the recognition of fine-grained entity types such as titles of artworks is of high importance. Current state of the art tools are unable to adequately identify artwork titles due to unavailability of relevant training datasets. We analyse the particular difficulties presented by this domain and motivate the need for quality annotations to train machine learning models for identification of artwork titles. We present a framework with heuristic based approach to create high-quality training data by leveraging existing cultural heritage resources from knowledge bases such as Wikidata. Experimental evaluation shows significant improvement over the baseline for NER performance for artwork titles when models are trained on the dataset generated using our framework.

/pdf/generation-of-training-data-for-named-entity-recognition-of-dw00y39g.pdf

Generation of training data for named entity recognition of artworks

Cloud computing facilitates flexible on-demand provisioning of IT resources over the Internet. It has proven to be very advantageous for a wide range of industries in the emerging markets by allowing them to match the infrastructure capabilities of their already established competitors. However, Cloud computing also poses several unique challenges in terms of resource allocation and scheduling. In particular, I/O bound applications have been mostly neglected in the research efforts in the area. Simulation tools play a key role in the evaluation of new resource management policies by providing an affordable and replicable testing environment. In this paper, we propose a novel simulation framework PriDynSim for priority based I/O policies, which manage the available resources to ensure adequate performance in the presence of deadline constraints. In a case study, we demonstrate how PriDynSim can be used to evaluate a resource management policy, which guarantees Quality of Service (QoS) for I/O bound applications running in a typical Cloud environment.

PriDynSim a Simulator for Dynamic Priority Based I/O Scheduling for Cloud Applications

Combining Embeddings and Rules for Fact Prediction (Invited Paper)

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