Hasil untuk "Recreation leadership. Administration of recreation services"

Hadil Ben Amor, Niruthiha Selvanayagam, Manel Abdellatif et al.

Machine Learning (ML) cloud services, offered by leading providers such as Amazon, Google, and Microsoft, enable the integration of ML components into software systems without building models from scratch. However, the rapid adoption of ML services, coupled with the growing complexity of business requirements, has led to widespread misuses, compromising the quality, maintainability, and evolution of ML service-based systems. Though prior research has studied patterns and antipatterns in service-based and ML-based systems separately, automatic detection of ML service misuses remains a challenge. In this paper, we propose MLmisFinder, an automatic approach to detect ML service misuses in software systems, aiming to identify instances of improper use of ML services to help developers properly integrate ML components in ML service-based systems. We propose a metamodel that captures the data needed to detect misuses in ML service-based systems and apply a set of rule-based detection algorithms for seven misuse types. We evaluated MLmisFinder on 107 software systems collected from open-source GitHub repositories and compared it with a state-of-the-art baseline. Our results show that MLmisFinder effectively detects ML service misuses, achieving an average precision of 96.7\% and recall of 97\%, outperforming the state-of-the-art baseline. MLmisFinder also scaled efficiently to detect misuses across 817 ML service-based systems and revealed that such misuses are widespread, especially in areas such as data drift monitoring and schema validation.

Nikola Pascher, Jochen Wulf

Successful smart services require seamless integration into existing corporate systems and an interdisciplinary approach that aligns the development of both business models and technical architectures. Multi-disciplinarity and cocreating with customers add a layer of complexity but are essential collaboration schemes for validating the value proposition of smart services and building longterm customer loyalty. This paper explores these challenges and distills the design principles for the architectures of technical smart service systems, based on empirical data from architecture projects in two manufacturing companies. These principles contribute to the sparse academic literature on this topic and help practitioners navigate several design trade-offs commonly arising in smart service projects.

Sorawit Manatura, Thanawat Chanikaphon, Chantana Chantrapornchai et al.

Service liquidity across edge-to-cloud or multi-cloud will serve as the cornerstone of the next generation of cloud computing systems (Cloud 2.0). Provided that cloud-based services are predominantly containerized, an efficient and robust live container migration solution is required to accomplish service liquidity. In a nod to this growing requirement, in this research, we leverage FastFreeze, a popular platform for process checkpoint/restore within a container, and promote it to be a robust solution for end-to-end live migration of containerized services. In particular, we develop a new platform, called FastMig that proactively controls the checkpoint/restore operations of FastFreeze, thereby, allowing for robust live migration of containerized services via standard HTTP interfaces. The proposed platform introduces post-checkpointing and pre-restoration operations to enhance migration robustness. Notably, the pre-restoration operation includes containerized service startup options, enabling warm restoration and reducing the migration downtime. In addition, we develop a method to make FastFreeze robust against failures that commonly happen during the migration and even during the normal operation of a containerized service. Experimental results under real-world settings show that the migration downtime of a containerized service can be reduced by 30X compared to the situation where the original FastFreeze was deployed for the migration. Moreover, we demonstrate that FastMig and warm restoration method together can significantly mitigate the container startup overhead. Importantly, these improvements are achieved without any significant performance reduction and only incurs a small resource usage overhead, compared to the bare (\ie non-FastFreeze) containerized services.

Małgorzata Łazuka, Andreea Anghel, Thomas Parnell

As Large Language Models (LLMs) are rapidly growing in popularity, LLM inference services must be able to serve requests from thousands of users while satisfying performance requirements. The performance of an LLM inference service is largely determined by the hardware onto which it is deployed, but understanding of which hardware will deliver on performance requirements remains challenging. In this work we present LLM-Pilot - a first-of-its-kind system for characterizing and predicting performance of LLM inference services. LLM-Pilot performs benchmarking of LLM inference services, under a realistic workload, across a variety of GPUs, and optimizes the service configuration for each considered GPU to maximize performance. Finally, using this characterization data, LLM-Pilot learns a predictive model, which can be used to recommend the most cost-effective hardware for a previously unseen LLM. Compared to existing methods, LLM-Pilot can deliver on performance requirements 33% more frequently, whilst reducing costs by 60% on average.

Marco Arazzi, Serena Nicolazzo, Antonino Nocera

The novel Internet of Things (IoT) paradigm is composed of a growing number of heterogeneous smart objects and services that are transforming architectures and applications, increasing systems' complexity, and the need for reliability and autonomy. In this context, both smart objects and services are often provided by third parties which do not give full transparency regarding the security and privacy of the features offered. Although machine-based Service Level Agreements (SLA) have been recently leveraged to establish and share policies in Cloud-based scenarios, and also in the IoT context, the issue of making end users aware of the overall system security levels and the fulfillment of their privacy requirements through the provision of the requested service remains a challenging task. To tackle this problem, we propose a complete framework that defines suitable levels of privacy and security requirements in the acquisition of services in IoT, according to the user needs. Through the use of a Reinforcement Learning based solution, a user agent, inside the environment, is trained to choose the best smart objects granting access to the target services. Moreover, the solution is designed to guarantee deadline requirements and user security and privacy needs. Finally, to evaluate the correctness and the performance of the proposed approach we illustrate an extensive experimental analysis.

Xianhao Chen, Yiqin Deng, Haichuan Ding et al.

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.

Tianyi Yang, Baitong Li, Jiacheng Shen et al.

Interactions between cloud services result in service dependencies. Evaluating and managing the cascading impacts caused by service dependencies is critical to the reliability of cloud systems. This paper summarizes the dependency types in cloud systems and demonstrates the design of the Dependency Management System (DMS), a platform for managing the service dependencies in the production cloud system. DMS features full-lifecycle support for service reliability (i.e., initial service deployment, service upgrade, proactive architectural optimization, and reactive failure mitigation) and refined characterization of the intensity of dependencies.

Umair Sadiq, Mohan Kumar, Andrea Passarella et al.

Pervasive networks formed by users' mobile devices have the potential to exploit a rich set of distributed service components that can be composed to provide each user with a multitude of application level services. However, in many challenging scenarios, opportunistic networking techniques are required to enable communication as devices suffer from intermittent connectivity, disconnections and partitions. This poses novel challenges to service composition techniques. While several works have discussed middleware and architectures for service composition in well-connected wired networks and in stable MANET environments, the underlying mechanism for selecting and forwarding service requests in the significantly challenging networking environment of opportunistic networks has not been entirely addressed. The problem comprises three stages: i) selecting an appropriate service sequence set out of available services to obtain the required application level service; ii) routing results of a previous stage in the composition to the next one through a multi-hop opportunistic path; and iii) routing final service outcomes back to the requester. The proposed algorithm derives efficiency and effectiveness by taking into account the estimated load at service providers and expected time to opportunistically route information between devices. Based on this information the algorithm estimates the best composition to obtain a required service. It is shown that using only local knowledge collected in a distributed manner, performance close to a real-time centralized system can be achieved. Applicability and performance guarantee of the service composition algorithm in a range of mobility characteristics are established through extensive simulations on real/synthetic traces.

Raphael Watschinger, Michal Merta, Günther Of et al.

We present a novel approach to the parallelization of the parabolic fast multipole method for a space-time boundary element method for the heat equation. We exploit the special temporal structure of the involved operators to provide an efficient distributed parallelization with respect to time and with a one-directional communication pattern. On top, we apply a task-based shared memory parallelization and SIMD vectorization. In the numerical tests we observe high efficiencies of our parallelization approach.

Jorge Martín-Pérez, Koteswararao Kondepu, Danny De Vleeschauwer et al.

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as, remote driving, cooperative awareness, and hazard warning) will face an ever changing and dynamic environment. Traffic flows on the roads is a critical condition for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By knowing future events (such as, traffic jams), vehicular services can be dimensioned in an on-demand fashion in order to minimize Service Level Agreements (SLAs) violations, thus reducing the chances of car accidents. This research departs from an evaluation of traditional time-series techniques with recent Machine Learning (ML)-based solutions to forecast traffic flows in the roads of Torino (Italy). Given the accuracy of the selected forecasting techniques, a forecast-based scaling algorithm is proposed and evaluated over a set of dimensioning experiments of three distinct vehicular services with strict latency requirements. Results show that the proposed scaling algorithm enables resource savings of up to a 5% at the cost of incurring in an increase of less than 0.4% of latency violations.

Andrei Netedu, Sabin C. Buraga, Paul Diac et al.

Automatic Web service composition is a research direction aimed to improve the process of aggregating multiple Web services to create some new, specific functionality. The use of semantics is required as the proper semantic model with annotation standards is enabling the automation of reasoning required to solve non-trivial cases. Most previous models are limited in describing service parameters as concepts of a simple hierarchy. Our proposed method is increasing the expressiveness at the parameter level, using concept properties that define attributes expressed by name and type. Concept properties are inherited. The paper also describes how parameters are matched to create, in an automatic manner, valid compositions. Additionally, the composition algorithm is practically used on descriptions of Web services implemented by REST APIs expressed by OpenAPI specifications. Our proposal uses knowledge models (ontologies) to enhance these OpenAPI constructs with JSON-LD semantic annotations in order to obtain better compositions for involved services. We also propose an adjusted composition algorithm that extends the semantic knowledge defined by our model.

Chen Wang, Hui Ma, Gang Chen et al.

Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-optimized functional quality (i.e., Quality of Semantic Matchmaking) or non-functional quality (i.e., Quality of Service) or both due to the complexity of this problem. With a significant increase in service composition requests, many composition requests have similar input and output requirements but may vary due to different preferences from different user segments. This problem is often treated as a multi-objective service composition so as to cope with different preferences from different user segments simultaneously. Without taking a multi-objective approach that gives rise to a solution selection challenge, we perceive multiple similar service composition requests as jointly forming an evolutionary multi-tasking problem in this work. We propose an effective permutation-based evolutionary multi-tasking approach that can simultaneously generate a set of solutions, with one for each service request. We also introduce a neighborhood structure over multiple tasks to allow newly evolved solutions to be evaluated on related tasks. Our proposed method can perform better at the cost of only a fraction of time, compared to one state-of-art single-tasking EC-based method. We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.

Shahin Tasoujian, Saeed Salavati, Karolos Grigoriadis et al.

Mathematical modeling and real-time dynamics identification of the mean arterial blood pressure (MAP) response of a patient to vasoactive drug infusion can provide a reliable tool for automated drug administration and therefore, reduce the emergency costs and significantly benefit the patient's MAP regulation in an intensive care unit. To this end, a dynamic first-order linear parameter-varying (LPV) model with varying parameters and varying input delay is considered to capture the MAP response dynamics. Such a model effectively addresses the complexity and the intra- and inter-patient variability of the physiological response. We discretize the model and augment the state vector with model parameters as unknown states of the system and a Bayesian-based multiple-model square root cubature Kalman filtering (MMSRCKF) approach is utilized to estimate the model time-varying parameters. Since, unlike the other model parameters, the input delay cannot be captured by a random-walk process, a multiple-model module with a posterior probability estimation is implemented to provide the delay identification. Validation results confirm the effectiveness of the proposed identification algorithm both in simulation scenarios and also using animal experiment data.

Craig Wright, Antoaneta Serguieva

This chapter contributes to evolving the versatility and complexity of blockchain-enabled services through extending the functionality of blockchain-enforced smart contracts. The contributions include: (i) a method for automated management of contracts with hierarchical conditionality structures through an hierarchy of intelligent agents and the use of hierarchical cryptographic key-pairs; (ii) a method for efficient and secure matching and transfer of smart- contract underlyings (entities) among disparate smart contracts/subcontracts; (iii) a method for producing an hierarchy of common secrets to facilitate hierarchical communication channels of increased security in the context of smart contracts/subcontracts/underlyings; and (iv) a method for building secure and optimized repositories through distributed hash tables in the context of contracts/ subcontracts/underlyings. These methods help providing services that allow both narrower and worldwide reach and distribution of resources. The longevity of the blockchain technology is achieved through continuous innovation. Blockchain-enabled services are potentially an efficient, secure, automated, and cost-effective alternative or complement to current service infrastructures in a range of domains (legal, medical, financial, government, IoT).

Sophea Chhun, Néjib Moalla, Yacine Ouzrout

This research aims to provide the possibility to the business analysts to be able to know whether their design business processes are feasible or not. In order to solve this problem, we proposed a model called BPMNSemAuto that makes use of the existing services stored in the service registry UDDI (Universal Description Discovery and Integration). From the data extracted from the UDDI, the WSDL files and the tracking data of service execution on the server, a Web Service Ontology (WSOnto) is generated to store all the existing services. The BPMNSemAuto model takes an input of business process design specifications, and it generates an executable business process as an output. It provides an interface for business analysts to specify the description of each service task of the design business process. For each service task, the business analysts specify the task objective (keywords), inputs, outputs and weights of the Quality of Service (QoS) properties. From the design business process with the service task specifications, a Business Process Ontology (BPOnto) is generated. A service selection algorithm performs the mapping between the instances of the WSOnto and the BPOnto to obtain possible mappings between these two ontologies. The obtained mappings help the model to acquire web services to execute the desired service tasks. Moreover, the consistency checking of the inputs of the proposed model is performed before executing the service selection algorithm. WordNet is used to solve the synonym problems and at the same time a keyword extraction method is presented in this paper.

Lianghai Ji, Bin Han, Man Liu et al.

Massive Machine Type Communication (mMTC) to serve billions of IoT devices is considered to open a potential new market for the next generation cellular network. Legacy cellular networks cannot meet the requirements of emerging mMTC applications, since they were designed for human-driven services. In order to provide supports for mMTC services, current research and standardization work focus on the improvement and adaptation of legacy networks. However, these solutions face challenges to enhance the service availability and improve the battery life of mMTC devices simultaneously. In this article, we propose to exploit a network controlled sidelink communication scheme to enable cellular network with better support for mMTC services. Moreover, a context-aware algorithm is applied to ensure the efficiency of the proposed scheme and multiple context information of devices are taken into account. Correspondingly, signaling schemes are also designed and illustrated in this work to facilitate the proposed technology. The signaling schemes enable the network to collect required context information with light signaling effort and thus network can derive a smart configuration for both the sidelink and cellular link. In order to demonstrate the improvements brought by our scheme, a system-level simulator is implemented and numerical results show that our scheme can simultaneously enhance both the service availability and battery life of sensors.

George Grispos, William Bradley Glisson, Tim Storer

Cloud storage services such as Dropbox, Box and SugarSync have been embraced by both individuals and organizations. This creates an environment that is potentially conducive to security breaches and malicious activities. The investigation of these cloud environments presents new challenges for the digital forensics community. It is anticipated that smartphone devices will retain data from these storage services. Hence, this research presents a preliminary investigation into the residual artifacts created on an iOS and Android device that has accessed a cloud storage service. The contribution of this paper is twofold. First, it provides an initial assessment on the extent to which cloud storage data is stored on these client-side devices. This view acts as a proxy for data stored in the cloud. Secondly, it provides documentation on the artifacts that could be useful in a digital forensics investigation of cloud services.

Paul Harrison, Guy Rixon

The Universal Worker Service (UWS) pattern defines how to manage asynchronous execution of jobs on a service. Any application of the pattern defines a family of related services with a common service contract. Possible uses of the pattern are also described.

Maurice H. ter Beek, Stefania Gnesi, Mercy N. Njima

PL for SOA proposes, formally, a software engineering methodology, development techniques and support tools for the provision of service product lines. We propose rigorous modeling techniques for the specification and verification of formal notations and languages for service computing with inclinations of variability. Through these cutting-edge technologies, increased levels of flexibility and adaptivity can be achieved. This will involve developing semantics of variability over behavioural models of services. Such tools will assist organizations to plan, optimize and control the quality of software service provision, both at design and at run time by making it possible to develop flexible and cost-effective software systems that support high levels of reuse. We tackle this challenge from two levels. We use feature modeling from product line engineering and, from a services point of view, the orchestration language Orc. We introduce the Smart Grid as the service product line to apply the techniques to.

Bo Sang, Jianfeng Zhan, Guanhua Tian

Previous work shows request tracing systems help understand and debug the performance problems of multi-tier services. However, for large-scale data centers, more than hundreds of thousands of service instances provide online service at the same time. Previous work such as white-box or black box tracing systems will produce large amount of log data, which would be correlated into large quantities of causal paths for performance debugging. In this paper, we propose an innovative algorithm to eliminate valueless logs of multitiers services. Our experiment shows our method filters 84% valueless causal paths and is promising to be used in large-scale data centers.