Service Instances Research Articles

Open-data-driven embeddable quality management services for map-based web applications

ABSTRACTVarious map-centered web services facilitate citizens’ lives. Web-map applications exist for many years already. Due to simplification and improvement of technologies supporting WebGIS, map-based services become more popular and important nowadays. Data quality assurance for such services is a significant challenge. Since many of such applications intensively use open data, approaches focused on open solutions are required. This work proposes a data-quality concept, which is based on intrinsic and comparable approaches. OpenStreetMap (OSM) allows intrinsic data evaluation. Moreover, it is used as a reference dataset for quality assessment of public-sector-information Open Data layers. Equidistant point (EDP)-based statistics enables to filter out low-quality Open Data features. A data-type model carries out the inventory of OSM data. The comparison of raster web-map tile file sizes and calculation of a simplified data quality indicator make it possible to specify acceptable data quality levels. Embeddable instances of quality assurance web services incorporate data features with acceptable quality. This work provides all required software and data for the deployment of such services under liberal licenses. Concrete instructions allow users to adopt the proposed solutions for their platforms. Some generic use cases illustrate the advantages of the introduced shared web services.

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Driven by the increasing popularity of the microservice architecture, we see an increase in services with unknown demand pattern located in the edge network. Pre-deployed instances of such services would be idle most of the time, which is economically infeasible. Also, the finite storage capacity limits the amount of deployed instances we can offer. Instead, we present an on-demand deployment scheme using the Docker platform. In Docker, service images consist of layers, each layer adding specific functionality. This allows different services to reuse layers, avoiding cluttering the storages with redundant replicas. We propose a layer placement method which allows users to connect to a server, retrieve all necessary layers -possibly from multiple locations- and deploy an instance of the requested service within the desired response time. We search for the best layer placement which maximizes the satisfied demand given the storage and delay constraints. We developed an iterative optimization heuristic which is less exhaustive by dividing the global problem in smaller subproblems. Our simulation results show that our heuristic is able to solve the problem with less system resources. Last, we present interesting use-cases to use this approach in real-life scenarios.

User incentive model and its optimization scheme in user-participatory fog computing environment

Although fog computing is recognized as an alternative computing model to cloud computing for IoT, it is not yet widely used. The replacement of network equipment is inevitable to implement fog computing; however, the entity in charge of replacement, requiring high cost, is unclear, and also the entity in charge of operating of the infrastructure is unclear. To solve these feasibility problems, we propose an incentive-based, user-participatory fog computing architecture. In terms of inducement of user participation on the proposed architecture, first, users are classified into four categories according to their tendencies and conditions, and the types of incentives, which are paid as a compensation for participation, the payment standard, and the operation model are presented in detail. From the perspective of fog service instance deployment, the instances should be deployed to reasonably minimize the incentives paid to the participating users of the proposed architecture, which is directly linked to maximizing the profitability of the infrastructure operator, while maintaining the performance. The optimization problem for the instance placement to achieve above design goal is formulated with a mixed-integer nonlinear programming, and then linearized. The proposed instance placement scheme is compared with several schemes through simulations based on actual service workload and device power consumption.

Automated Network Service Scaling in NFV: Concepts, Mechanisms and Scaling Workflow

Next-generation systems are anticipated to be digital platforms supporting innovative services with rapidly changing traffic patterns. To cope with this dynamicity in a cost-efficient manner, operators need advanced service management capabilities such as those provided by NFV. NFV enables operators to scale network services with higher granularity and agility than today. For this end, automation is key. In search of this automation, the European Telecommunications Standards Institute (ETSI) has defined a reference NFV framework that make use of model-driven templates called Network Service Descriptors (NSDs) to operate network services through their lifecycle. For the scaling operation, an NSD defines a discrete set of instantiation levels among which a network service instance can be resized throughout its lifecycle. Thus, the design of these levels is key for ensuring an effective scaling. In this article, we provide an overview of the automation of the network service scaling operation in NFV, addressing the options and boundaries introduced by ETSI normative specifications. We start by providing a description of the NSD structure, focusing on how instantiation levels are constructed. For illustrative purposes, we propose an NSD for a representative NS. This NSD includes different instantiation levels that enable different ways to automatically scale this NS. Then, we show the different scaling procedures the NFV framework has available, and how it may automate their triggering. Finally, we propose an ETSI-compliant workflow to describe in detail a representative scaling procedure. This workflow clarifies the interactions and information exchanges between the functional blocks in the NFV framework when performing the scaling operation.

Effects of Reducing VMs Management Times on Elastic Applications

Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers (VMs) dynamically, adapting the application to the workload. Scaling-out and scaling-in operations are managed by an auto-scaler module, which can be reactive (adapting the number of VMs to the current workload) or proactive (adapting to the expected future workload). The cloud infrastructure provides a management interface to create (deploy) and destroy (shutdown) server instances, operations that require some time to complete. In this work we evaluate to what extent the reduction of the time required by VM management operations, namely deployment and shutdown, impacts the performance of applications and the behavior of reactive and proactive auto-scaling policies. After establishing several ideal boundaries on the use of resources, we carry out a set of experiments that show how short management times drastically reduce the use of resources, while allowing the application to operate within the required performance bounds.

A server consolidation method with integrated deep learning predictor in local storage based clouds

SummaryServer consolidation is one of the critical techniques for energy‐efficiency in cloud data centers. As it is often assumed that cloud service instances (eg, Amazon EC2 instances) utilize the shared storage only. In recent years, however, cloud service providers have been providing local storage for cloud users, since local storage can offer a better performance with identified price. However, these cloud instances usually contain much more data than shared storage cloud instances. Thus, in such local storage based cloud center, the migration cost can be really high and is in dire need of an efficient resource pre‐allocation. If we can predict the resource demand in advance, the migration oscillation will be reduced to minify the migration cost. We have found that there are some related work about server consolidation based on forecasting. Unfortunately, their latest work did not consider the background of “local storage” as we mentioned above. At the same time, some research about local storage did not involve the prediction strategy, which plays a significant part in server consolidation. To address this issue, this paper proposes Losari, a consolidation method, which takes numeric forecasting and local storage architecture into consideration. Losari consolidates servers on the basis of the resource demand predicted value using a statistical learning method. We model the workload from real cloud production environment as a time series. Taking deep learning as a frame of reference, multiple deep belief networks integrated with ARIMA model was trained to study the feature of historical workload. The experimental results have showed that its average predicted error is only 10.7% in the short term, which is much lower than the most common model based on threshold (19.8%) on the same dataset. What is more, the results show that Losari not only simulates the true sequences in high accuracy but also scales the compute resource well, which demonstrated the validity of this integrated deep learning model.

Optimal Allocation of QoS and Web Services in Cloud Computing

Cloud computing is a great model of demand and supply in information communication and services. It represents a complex infrastructure and provides a dynamic, distributed, heterogeneous and autonomous platform for solving problems in business, science and technology. This paper proposes a cloud computing environment that supports dynamic application service composition model. We develop a Quality of Service (QoS) based framework for effective web services allocation. In computing, the service consumer is projected to provide the QoS requirements as part of service discovery query. The cloud as marketplace for trading instances of web services can be bought or leased by web applications. We found that using dynamic decision-making management approach relaying on dynamic portfolio allocation model mainly used in finance, one can achieve the purpose of saving resources by reducing costs of quality of services and eliminating risks related to different services simultaneously.

SmoothOperator

With the ever growing popularity of cloud computing and web services, Internet companies are in need of increased computing capacity to serve the demand. However, power has become a major limiting factor prohibiting the growth in industry: it is often the case that no more servers can be added to datacenters without surpassing the capacity of the existing power infrastructure. In this work, we first investigate the power utilization in Facebook datacenters. We observe that the combination of provisioning for peak power usage, highly fluctuating traffic, and multi-level power delivery infrastructure leads to significant power budget fragmentation problem and inefficiently low power utilization. To address this issue, our insight is that heterogeneity of power consumption patterns among different services provides opportunities to re-shape the power profile of each power node by re-distributing services. By grouping services with asynchronous peak times under the same power node, we can reduce the peak power of each node and thus creating more power head-rooms to allow more servers hosted, achieving higher throughput. Based on this insight, we develop a workload-aware service placement framework to systematically spread the service instances with synchronous power patterns evenly under the power supply tree, greatly reducing the peak power draw at power nodes. We then leverage dynamic power profile reshaping to maximally utilize the headroom unlocked by our placement framework. Our experiments based on real production workload and power traces show that we are able to host up to 13% more machines in production, without changing the underlying power infrastructure. Utilizing the unleashed power headroom with dynamic reshaping, we achieve up to an estimated total of 15% and 11% throughput improvement for latency-critical service and batch service respectively at the same time, with up to 44% of energy slack reduction.

An ontology-based collaborative business service selection: contributing to automatic building of collaborative business process

With worldwide inter-enterprise collaboration and interoperability background, automatic collaborative business process deduction is a crucial researching subject. We have designed a methodology of deducing collaborative process by only collecting collaborative objectives and partners’ business services. The two key problems are (i) selecting corresponding business services for a set of collaborative objectives and (ii) ordering business services with serializations and parallelization. This paper aims to present a solution of business service selection and the following business process extraction. In order to solve the problem, we have defined a collaborative ontology, which contains numerous instances of business services and processes from the MIT process handbook. The collaborative ontology contains essential concepts in collaborative situations and process-deducing rules and algorithms. We provide a brief illustration of implementation within a SaaS toolkit called Mediator Modeling 2ool.

Genetic algorithm-based cost minimization pricing model for on-demand IaaS cloud service

Cloud computing has emerged as the most effective distributed computing paradigm. It has grabbed the attention of many organizations owing to its business prospects and significant features like agility and flexibility. Dynamic scalability of cloud services and availability of number of homogenous cloud service providers in market make it difficult for the provider to fix the prices of cloud services, especially on-demand Infrastructure-as-a-Service cloud service instances. It is quite problematic for provider to map the dynamics of prices with the variation in service requirement and satisfying the users’ quality of service requirement simultaneously. At the same time, it is very essential for the provider to determine the lower bound price of the services beyond which he could not afford the provisioning of the services. This paper presents dynamic demand-based pricing model for on-demand IaaS cloud service instances that will assist the provider to dynamically determine the price of provisioning the cloud services by considering the provider’s and users’ utility concurrently. Genetic algorithm is applied for the optimized evaluation users’ request parameters and provider’s computation capacity that will minimize the cost of execution. Experimental results demonstrate that price evaluation is more efficient and users’ utility increases considerably using the proposed framework in comparison with the existing utility-based pricing model.

Utility-Centric Networking: Balancing Transit Costs With Quality of Experience

This paper is focused on techniques for maximizing utility across all users within a total network transit cost budget. We present a new method for selecting between replicated servers distributed over the Internet. First, we introduce a novel utility framework that factors in quality of service metrics. Then we design an optimization algorithm, solvable in polynomial time, to allocate user requests to servers based on utility while satisfying network transit cost constraints, mapping service names to service instance locators. We then describe an efficient, low overhead distributed model which only requires knowledge of a fraction of the data required by the global optimization formulation. Next, a load-balancing variant of the algorithm is explored that substantially reduces blocking caused by congested servers. Extensive simulations show that our method is scalable and leads to higher user utility compared with mapping user requests to the closest service replica, while meeting network traffic cost constraints. We discuss several options for real-world deployment that require no changes to end-systems based on either the use of SDN controllers or extensions to the current DNS system.

Mobile Edge Computing

A wireless network device can receive, at a traffic director in a kernel space, a data packet from a client device and determine whether the data packet is intended for an application cloud server operating in a cloud environment. The wireless network device can provide, based on determining that the data packet is intended for the application cloud server, the data packet to an application server instance executing on the wireless network device. The application server instance can be implemented in a virtualized software container in a user space, and can be configured to perform one or more operations associated with the application cloud server. The wireless network device can receive, at the traffic director and from the application server instance, a result of the application server instance performing the one or more operations on the data packet, and transmit the result to the application cloud server.

User-Participatory Fog Computing Architecture and Its Management Schemes for Improving Feasibility

The evolution of computing and networking technologies has opened the era of cloud computing, and the advent of the Internet of Things (IoT) paradigm has been questioning its limitations. Owing to advances in computer networks, cloud computing is improving, and the most promising technology is fog computing. Although fog computing is recognized as the most appropriate computing model for the IoT, it has not yet been widely used, and the major reasons are as follows. The replacement of the firmware and hardware of network equipment is inevitable; however, the operator in charge of carrying out this expensive task is unclear, and even if the operator is selected, the reason may be not rational. In addition, although fog computing is based on collaboration between several infrastructure operators and service providers, it is not clear who operates and manages the infrastructure. Furthermore, there is still a resource allocation problem for a fog service instance. In this paper, we propose a user participatory fog computing architecture and its management schemes to address the above problems related to its feasibility. In the proposed architecture, fog service instance placement optimization is performed based on service usage of participating users, which is formulated into a mixed-integer non-linear programming problem and then linearized. The proposed architecture and the fog service placement method are evaluated based on simulation, taking into account actual parameters of the IoT services and devices.

On the Feasibility of Using Current Data Centre Infrastructure for Latency-sensitive Applications

It has been claimed that the deployment of fog and edge computing infrastructure is a necessity to make high-performance cloud-based applications a possibility. However, there are a large number of middle-ground latency-sensitive applications such as online gaming, interactive photo editing and multimedia conferencing that require servers deployed closer to users than in globally centralised clouds but do not necessarily need the extreme low-latency provided by a new infrastructure of micro data centres located at the network edge, e.g., in base stations and ISP Points of Presence. In this paper we analyse a snapshot of today's data centres and the distribution of users around the globe and conclude that existing infrastructure provides a sufficiently distributed platform for middle-ground applications requiring a response time of $20\hbox{-}200$ 20 - 200 ms. However, while placement and selection of edge servers for extreme low-latency applications is a relatively straightforward matter of choosing the closest, providing a high quality of experience for middle-ground latency applications that use the more widespread distribution of today's data centres, as we advocate in this paper, raises new management challenges to develop algorithms for optimising the placement of and the per-request selection between replicated service instances.

DS‐Harmonizer: A Harmonization Service on Spatiotemporal Data Stream in Edge Computing Environment

Abundant sensors in various types are widely used in modern cities to comprehend the current situations in real time. The raw data in open conditions is always in low quality and is hard to employ directly due to its imperfect or missing records. Traditional data preprocessing methods focus on the offline historical data and remain a dilemma between the efficiency and the overhead. In this paper, a data harmonization service DS-Harmonizer is proposed on spatiotemporal data stream in the edge computing environment. Through the online cleaning and complementing steps of the hierarchical service instances, the records’ validity and continuity can be guaranteed in an efficient way. On the simulated data in a practical project, the service shows high performance, low latency, and acceptable precision in extensive conditions.

Per-service security SLAs for cloud security management: model and implementation

In the cloud computing context, Service Level Agreements (SLAs) 'tailored' to specific Cloud Service Customers (CSCs) seem to be still an utopia, and things are even worse as regards the security terms to be guaranteed. In fact, existing cloud SLAs focus only on a few service terms, and Cloud Service Providers (CSPs) mainly provide uniform guarantees for all offered services and for all customers, regardless of any particular service characteristics or of customer-specific needs. This paper presents a framework that enables the adoption of a per-service SLA model, supporting the automatic implementation of cloud security SLAs tailored to the needs of each customer for specific service instances. In particular, the process and the software architecture for per-service SLA implementation are shown. A case study application, related to the provisioning of a secure web container service, is presented and discussed, to demonstrate the feasibility and effectiveness of the proposed solution.

Per-service security SLAs for cloud security management: model and implementation

In the cloud computing context, Service Level Agreements (SLAs) 'tailored' to specific Cloud Service Customers (CSCs) seem to be still an utopia, and things are even worse as regards the security terms to be guaranteed. In fact, existing cloud SLAs focus only on a few service terms, and Cloud Service Providers (CSPs) mainly provide uniform guarantees for all offered services and for all customers, regardless of any particular service characteristics or of customer-specific needs. This paper presents a framework that enables the adoption of a per-service SLA model, supporting the automatic implementation of cloud security SLAs tailored to the needs of each customer for specific service instances. In particular, the process and the software architecture for per-service SLA implementation are shown. A case study application, related to the provisioning of a secure web container service, is presented and discussed, to demonstrate the feasibility and effectiveness of the proposed solution.

Role and Importance of IoT in the Smart City and E-Governance

In the recent years, developments growth have been rising in utilizing of recent technologies; Smart cities have been furnished with various electronic devices concentrated on the Internet of Things (IoT) to be more smarter than before. The Internet of Things is empowered by various emerging technologies such as smart cameras, sensors, wireless communication devices etc. Smart cities are huge systems connected with countless sub-systems, and these systems are depending on the electricity to move the human, things, and data to share information. Without electric power and Internet of Things, the smart city cannot be achieved and services cannot be provided. This paper aims to provide a comprehensive concept of IoT, the smart city as well as the relationship of Smart Cities to IoT. Moreover, an extensive review of the relationship of IoT to the smart city discussed with few instances of services provided by the government using IoT technologies for the citizens. Therefore the objects and applications which come under the IoT technology that can be used to meet the objective of having a smart city. In this paper, a thorough literature on the concept of IoT with Smart City and a brief comparison of it in terms of services, challenges, and issues are discussed. Also the discussion on the issues faced by service providers with regards to technologies used to equip the citizens with up to date services.

Recipe for Success: localism and bounded rationality in lobbying for radiation therapy services in North West Tasmania

Objective: Describes where bounded rationality and localism are evident in the debate over the introduction of radiation therapy services in North West Tasmania and how this affected the delivery of the message fromeach side.
 Design: Semi-structured interviews with stakeholders and patients/family over an eight month period in 2016 are contrasted with viewpoints identified via document analysis.Setting: North West Tasmania.
 Main Outcome Measures: The mechanisms for policy change and the policy beliefs of each side are examined with the intention of understanding how bounded rationality and a sense of localism can combine to effectpolicy change.
 Results: In the instance of radiation therapy services in North West Tasmania, a policy debate was originally waged between medical professionals and policy makers opposed to a local service on one side and a handful of policy actors advocating for a local service on the other. Those in favour of a local radiation therapy service harnessed a sense of localism to project the perception of widespread community support for the proposal and secured funding commitments during the 2010 Federal Election campaign.
 Conclusions: There is evidence of bounded rationality from both the stakeholder and patient groups, as well as a strong sentiment of localism expressed by patients and community advocates. Through understanding this particular case, health service managers can determine how to better time and target messages to the general public and to policy makers during periods of proposed changes to health services.

<italic>Stemflow:</italic> Software-Defined Inter-Datacenter Overlay as a Service

Modern Internet applications are typically hosted in the public cloud, with multiple server instances running within geographically distributed datacenters. Thanks to the abundantly available bandwidth on wide-area links that interconnect these datacenters, it is conceivable that bandwidth-intensive applications may improve their performance by relaying their traffic through such an inter-datacenter network . However, there does not yet exist a cloud service that provides a turn-key solution to tap into such available bandwidth resources conveniently. In this paper, we design and implement Stemflow , a new system framework that provides Inter-Datacenter Overlay as a Service based on the software-defined networking principle. It offers an attractive foundation that helps an Internet application to transparently improve its scalability and performance by using inter-datacenter networks for its traffic. With Stemflow , all deployed server instances will construct an overlay atop an inter-datacenter network, and the routing decisions to relay application traffic are made by a centralized controller. The algorithms needed to make these decisions are customized to meet the needs of individual applications, and are cached within the data plane. We motivate and describe the design decisions, and present an extensive experimental evaluation in public cloud infrastructures, using two example applications as our case studies.