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Sports racing is attracting billions of audiences each year. It is powered and transformed by the latest data analysis technologies, from race car design, driving skill improvements to audience engagement on social media. However, most of the data processing are off-line and retrospective analysis. The emerging real-time data analysis from the Internet of Things (IoT) result in fast data streams generated from distributed sensors. Applying advanced Machine Learning/Artificial Intelligence over such data streams to discover new information, predict future insights and make control decision is a crucial process. In this paper, we start by articulating racing car big data characteristics and present time-critical anomaly detection of the racing cars with the real-time sensors of cars and the tracks from actual racing events. We build a scalable system infrastructure based on neuro-morphic Hierarchical Temporal Memory Algorithm (HTM) algorithm and Storm stream processing engine. By courtesy of historical Indy500 racing logs, evaluation experiments on this prototype system demonstrate good performance in terms of anomaly detection accuracy and service level objective (SLO) of latency for a real-world streaming application.

Resource under-utilization is prevalent in data centers as a result of substantial stochastic workload variations and resource provision guarantee for the requirements to provide high-level service availability. To avoid resource under-utilization, recently, the Economy cloud computing service class (Economy class in short) is proposed that sells allocated but unused (i.e., reclaimed) physical resources with long-term service level objectives (SLOs). Though the Economy class would lead to revenue benefit, how much of the reclaimed resources should be used as the Economy class (i.e., capacity planning) and how to set its price and SLAs to maximize revenue benefit have not been studied. To address this problem, in this paper, we propose a multi-level SLA for Economy class, which has multiple levels of SLOs and penalty. We then present a rigorous theoretical analysis on the capacity planning and pricing of the new Economy class with an objective to maximize the revenue of the cloud computing service provider. Theoretical results are validated using resource utilization data derived from a publicly available Google cluster workload trace dataset.

We are witnessing the emergence of elastic web services which are hosted in public cloud infrastructures. For reasons of cost-effectiveness, it is crucial for the elasticity of these web services to match the dynamically-evolving user demand. Traditional approaches employ clusters of virtual machines (VMs) to dynamically scale resources based on application demand. However, they still face challenges such as higher cost due to over-provisioning or incur service level objective (SLO) violations due to under-provisioning. Motivated by this observation, we propose Spock, a new scalable and elastic control system that exploits both VMs and serverless functions to reduce cost and ensure SLO for elastic web services. We show that under two different scaling policies, Spock reduces SLO violations of queries by up to 74\% when compared to VM-based resource procurement schemes. Further, Spock yields significant cost savings, by up to 33\% compared to traditional approaches which use only VMs.

Fog computing has the potential to be an energy-efficient alternative to cloud computing for guaranteeing latency requirements of Latency-critical (LC) IoT services. However, even in fog computing low energy-efficiency of homogeneous multi-core server processors can be a major contributor to energy wastage. Recent studies have shown that Heterogeneous Multi-core Processors (HMPs) can improve energy efficiency of servers by adapting to dynamic load changes of LC-services. However, proposed approaches optimize energy only at a single server level. In our work, we demonstrate that optimization at the cluster-level across many HMP-servers can offer much greater energy savings through optimal work distribution across the HMP-servers while still guaranteeing the Service Level Objectives (SLO) of LC-services. In this paper, we present Greeniac, a cluster-level task manager that employs Reinforcement Learning to identify optimal configurations at the server- A nd cluster-levels for different workloads. We develop a server-level service scheduler and a cluster-level load balancing module to assign services and distribute tasks across HMP servers based on the learned configurations. In addition to meeting the required SLO targets, Greeniac achieves up to 28% energy saving compared to best-case cluster scheduling techniques with local HMP-aware scheduling on a 4-server fog cluster, with potentially larger savings in a larger cluster.

Service level objectives (SLOs) stipulate performance goals for cloud applications, microservices, and infrastructure. SLOs are widely used, in part, because system managers can tailor goals to their products, companies, and workloads. Systems research intended to support strong SLOs should target realistic performance goals used by system managers in the field. Evaluations conducted with uncommon SLO goals may not translate to real systems. Some textbooks discuss the structure of SLOs but (1) they only sketch SLO goals and (2) they use outdated examples. We mined real SLOs published on the web, extracted their goals and characterized them. Many web documents discuss SLOs loosely but few provide details and reflect real settings. Systematic literature review (SLR) prunes results and reduces bias by (1) modeling expected SLO structure and (2) detecting and removing outliers. We collected 75 SLOs where response time, query percentile and reporting period were specified. We used these SLOs to confirm and refute common perceptions. For example, we found few SLOs with response time guarantees below 10 ms for 90% or more queries. This reality bolsters perceptions that single digit SLOs face fundamental research challenges.

With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented.

Cloud customers want strong, understandable promises (Service Level Objectives, or SLOs) that their applications will run reliably and with adequate performance, but cloud providers don't want to offer them, because they are technically hard to meet in the face of arbitrary customer behavior and the hidden interactions brought about by statistical multiplexing of shared resources. Existing cloud SLOs are more concerned with defending against corner cases than defining normal behavior. This and other tensions make SLOs surprisingly hard to define. We show that this problem shares some similarities with the challenges of applying statistics to make decisions based on sampled data. We argue that a mutually beneficial set of Service Level Expectations (SLEs) and Customer Behavior Expectations (CBEs) ameliorates many of the problems of today's SLOs by explicitly sharing risk between customer and service provider.

The diversity of cloud offerings motivated the proposition of cloud modelling languages (CMLs) to abstract complexities related to selection of cloud services. However, current CMLs lack the support for modelling service level objectives (SLOs) that are required for the customer applications. Consequently, we propose an application- and provider-independent SLO modelling language (SLO-ML) to enable customers to specify the required SLOs. We also sketch the architecture to realise SLO-ML.

Microservices enable a fine-grained control over the cloud applications that they constitute and thus became widely-used in the industry. Each microservice implements its own functionality and communicates with other microservices through language- and platform-agnostic API. The resources usage of microservices varies depending on the implemented functionality and the workload. Continuously increasing load or a sudden load spike may yield a violation of a service level objective (SLO). To characterize the behavior of a microservice application which is appropriate for the user, we define a MicroService Capacity (MSC) as a maximal rate of requests that can be served without violating SLO.The paper addresses the challenge of identifying MSC individually for each microservice. Finding individual capacities of microservices ensures the flexibility of the capacity planning for an application. This challenge is addressed by sandboxing a microservice and building its performance model. This approach was implemented in a tool Terminus. The tool estimates the capacity of a microservice on different deployment configurations by conducting a limited set of load tests followed by fitting an appropriate regression model to the acquired performance data. The evaluation of the microservice performance models on microservices of four different applications shown relatively accurate predictions with mean absolute percentage error (MAPE) less than 10%.The results of the proposed performance modeling for individual microservices are deemed as a major input for the microservice application performance modeling.

With increase in cellular-enabled IoT devices having diverse traffic characteristics and service level objectives (SLOs), handling the control traffic in a scalable and resource-efficient manner in the cellular packet core network is critical. The traditional monolithic design of the cellular core adopted by service-providers is inflexible with respect to the diverse requirements and bursty loads of IoT devices, specifically for properties such as elasticity, customizability, and scalability. To address this key challenge, we focus on the most critical control plane component of the cellular packet core network, the Mobility Management Entity (MME). We present MMLite, a functionally decomposed and stateless MME design wherein individual control procedures are implemented as microservices and states are decoupled from their processing, thus enabling elasticity and fault tolerance. For SLO compliance, we develop a multi-level load balancing approach based on skewed consistent hashing to efficiently distribute incoming connections. We evaluate the performance benefits of MMLite over existing approaches with respect to scaling, fault tolerance, SLO compliance and resource efficiency.

In a Cloud context, Solid State Drive (SSD) became a must-have technology. This technology is too expensive to replace Hard Disk Drive (HDD), both are used in Hybrid Storage Systems (HSS). When it comes to storing data, placement strategies are employed to find the best storage class to use (SSD or HDD). While for many applications, those strategies need to be I/O performance driven, in a Cloud context, they must be cost driven: minimize the cost of data placement while satisfying Service Level Objectives. This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a genetic algorithm based approach (G-COPS) and an ad-hoc heuristic approach (H-COPS) based on incremental optimizations. Both algorithms were tested for small and large instance problems. While G-COPS proved to be closer to the optimal solution in case of small instance problems, H-COPS showed a better scalability as it approached the exact solution even for large instance problems (by 10% on average). Both performed better than state-of-the-art solutions as they reduced the overall cost by more than 40%. In addition, H-COPS showed small execution times which makes it a good candidate for a runtime use. Moreover, H-COPS drastically limits the over-provisioning of resources.

Reducing delay and latency in the cloud computing environment is a challenge for the present research community. This study performed a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Smart Grid (SG). To meet the consumers’ demand and optimize cloud services to achieve service level objectives is of great importance. The fog is introduced to enhance the efficiency of the cloud and to fulfill the consumer requests at the edge of the network. When the requests of Smart Societies (SSs) are huge on fog, the increased demand for real-time response is becoming a challenge for the SG. In this study, Particle Swarm Optimization is implemented and compared with the proposed techniques: Improved PSO with Lewy Walk (IPSOLW). These load balancing algorithms are compared on the basis of Closest Data Center (CDC) and Optimize Response Time (ORT). These proposed algorithms handle the load of SS on fog. The proposed IPSOLW handles more requests because of LW, the requests are directly allocated to best DC.

Consolidating multiple servers into a physical machine is now commonplace in cloud infrastructures. The virtualized systems often arrange virtual disks of multiple virtual machines (VMs) on the same underlying storage device while striving to guarantee the performance service level objective (SLO) for each VM. Unfortunately, sync operations called by a VM make it hard to satisfy the performance SLO by disturbing I/O activities of other VMs. We reveal that the disk cache flush command is a root cause of this problem and present a novel VM-aware flush mechanism, called vFLUSH, which supports the VM-based persistency control of the disk cache flush command. Our evaluation shows that vFLUSH reduces the average latency of disk cache flush commands by up to 52.0% and improves the overall I/O performance by up to 59.6% on real workloads.

Inspired by the potential power of random scheduling at data centers, a novel approach for combining arbitrary dispatching policy and tail-latency prediction in heterogeneous fork-join network environments is proposed. Tail prediction is of practical importance in commercial data centers, where the need for sharing resources between many applications is desired at most, to ensure client satisfaction with guaranteed service level objectives (SLOs). Lots of research works in parallel scheduling were presented using event-based simulations, but none of them were able to implant dynamic variation of tasks numbers and maintain the determined load region using a precise, and reliable approach. In this paper, we propose extensive case studies for the presented prediction model in heterogeneous black-box using model-driven simulations. Experimental results show that by using random scheduling algorithm accompanied with inserted effects of different requests fan-out, tail latency can be predicted and stay consistent with relative errors of 10% at high load regions.

Part 5: Distributed Applications; International audience; Distributed control applications are considered where the control functions are implemented by software of a centralized controller and where system state information and control commands are communicated through a shared communication infrastructure. Through the shared communication infrastructure the control functionalities of the individual control loops may influence each other and can have an affect on Service Level Objectives (SLO) to be guaranteed for each individual control application. In this contribution a novel approach is suggested for an enhanced CSMA/CD local area network infrastructure where the application control layer is directly put on an enhanced Media Access Control (MAC) layer of the communication infrastructure to minimize control delays. A comprehensive model is derived for the resulting network with all competing applications and protocol functions for safe communications. The performance of the MAC layer request is analyzed exactly by a stochastic phase TC representing its aggregated behavior. The delay performance of the individual control applications is analyzed by means of a queuing model of type GI/G/1 where GI represents the total control request arrival process and G a virtual service time TC from which the aggregated network delay parameters are derived. The distributed network control system (NCS) model with the shared infrastructure is analyzed exactly by methods of Control Theory by computational algorithms and MATLAB Simulink tool support.

Dynamic Voltage and Frequency Scaling (DVFS) is a method to save energy consumption of electronic devices and to protect them against overheating by automatic sensing and adaptation of their energy consumption. This can be accomplished either on the program instruction level for electronic devices or on the task or job level for server clusters. This paper models DVFS on the job level and through which Service Levels Objectives can be guaranteed with respect to prescribed mean or quantiles of service delays according to given Service Level Agreements (SLA) between user and service provider. The two parameters V (voltage) and f (frequency) cannot be changed independently of each other; typically only several combinations of V and f values are implemented in hardware for several power states. In this paper a novel analysis of operating DVFS is suggested for Server Clusters of Cloud Data Centers (CDC) under prescribed bounds of service level objectives which are defined by SLAs. The method is based on the theory of queuing models of the type GI/G/n for a server cluster to establish a relationship between SLA parameters and the power consumption and is performed for the example of the Intel Pentium M Processor with Enhanced SpeedStep Power Management. As result of this method precise bounds are provided for the load ranges of service request rates $\lambda$ for each power mode which guarantee minimum power consumption dependent on given SLA values and job arrival and service statistics. As the instantaneous load in a CDC can be highly volatile the current load level is usually monitored by periodic sensing which may result in a rather high frequency of DVFS range changes and corresponding overhead. For that reason an automated smoothing method is suggested which reduces the frequency of DVFS range changes significantly. This method is based on a Finite State Machine (FSM) with hysteresis levels.

In the Internet of Things (IoT), billions of physical devices, distributed over a large geographic area, provide a near real-time state of the world. These devices’ capabilities can be abstracted as IoT services and delivered to users in a demand-driven way. In such a dynamic large-scale environment, a service provider who supports a service level agreement (SLA) can have a comprehensive competitive edge in terms of service quality management, service customization, optimized resource allocation, and trustworthiness. However, there is no consistent way of drafting an SLA with respect to describing heterogeneous IoT services, which obstructs automatic service selection, SLA negotiation, and SLA monitoring. In this paper, we propose an ontology, WIoT-SLA, to achieve semantic interoperability. We combine IoT service properties with two prominent web service SLA specifications: WS-Agreement and WSLA, to take advantage of their complementary features. This ontology is used to formalize the SLAs and SLA negotiation offers, which further facilitates the service selection and automatic SLA negotiation. It can also be used by a monitoring engine to detect SLA violations by providing the semantics of service level objectives (SLOs) and quality metrics. To evaluate our work, a prototype is implemented to demonstrate its feasibility and efficiency.

The performance of the same type of cloud resources, such as virtual machines (VMs), varies over time mainly due to hardware heterogeneity, resource contention among co-located VMs, and virtualization overhead. The performance variation can be significant, introducing challenges to learn workload-specific resource provisioning policies to automatically scale the cloud-hosted applications to maintain the desired response time. Moreover, auto-scaling multi-tier applications using minimal resources is even more challenging because bottlenecks may occur on multiple tiers concurrently. In this paper, we address the problem of using performance varying VMs for gracefully auto-scaling a multi-tier application using minimal resources to handle dynamically increasing workloads and satisfy the response time requirements. The proposed system uses a supervised learning method to identify the appropriate resources provisioning for multi-tier applications based on the prediction of the application response time and the request arrival rate. The supervised learning method learns a state transition configuration map which encodes a resource allocation states invariant to the underlying VMs performance variations. This configuration map helps to use performance varying resources in predictive autoscaling method. Our experimental evaluation using a real-world multi-tier web application hosted on a public cloud shows an improved application performance with minimal resources compared to conventional predictive auto-scaling methods. IEEE Scopus

Agile/XP (Extreme Programming) software teams are expected to follow a number of specific practices in each iteration, such as estimating the effort ("points") required to complete user stories, properly using branches and pull requests to coordinate merging multiple contributors’ code, having frequent "standups" to keep all team members in sync, and conducting retrospectives to identify areas of improvement for future iterations. We combine two observations in developing a methodology and tools to help teams monitor their performance on these practices. On the one hand, many Agile practices are increasingly supported by web-based tools whose "data exhaust" can provide insight into how closely the teams are following the practices. On the other hand, some of the practices can be expressed in terms similar to those developed for expressing service level objectives (SLO) in software as a service; as an example, a typical SLO for an interactive Web site might be "over any 5-minute window, 99% of requests to the main page must be delivered within 200ms" and, analogously, a potential Team Practice (TP) for an Agile/XP team might be "over any 2-week iteration, 75% of stories should be ’1-point’ stories". Following this similarity, we adapt a system originally developed for monitoring and visualizing service level agreement (SLA) compliance to monitor selected TPs for Agile/XP software teams. Specifically, the system consumes and analyzes the data exhaust from widely-used tools such as GitHub and Pivotal Tracker and provides team(s) and coach(es) a "dashboard" summarizing the teams’ adherence to various practices. As a qualitative initial investigation of its usefulness, we deployed it to twenty student teams in a four-sprint software engineering project course.We find an improvement of the adherence to team practice and a positive students’ self-evaluations of their team practices when using the tool, compared to previous experiences using an Agile/XP methodology. The demo video is located at https://youtu.be/A4xwJMEQh9c and a landing page with a live demo at https://isa-group.github.io/2019- 05-eagle-demo/. Ministerio de Economía y Competitividad TIN2015-70560-R Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C21

With the growing complexity of microservice applications and proliferation of containers, scaling of cloud applications became challenging. Containers enabled the adaptation of the application capacity to the changing workload on the finer level of granularity than it was possible only with virtual machines. The common way to automate the adaptation of a cloud application is via autoscaling. Autoscaling is provided both on the level of virtual machines and containers. Its accuracy on dynamic workloads suffers significantly from the reactive nature of the available autoscaling solutions. The aim of the paper is to explore potential improvements of autoscaling by designing and evaluating several predictive-based autoscaling policies. These policies are naive (used as a baseline), best resource pair, only-Delta-load, always-resize, resize when beneficial. The scaling policies were implemented in Scaling Policy Derivation Tool (SPDT). SPDT takes the long-term forecast of the workload and the capacity model of microservices as input to produce the sequence of scaling actions scheduled for the execution in future with the aims to meet the service level objectives and minimize the costs. Policies implemented in SPDT were evaluated for three microservice applications and several workload patterns. The tests demonstrate that the combination of horizontal and vertical scaling enables more flexibility and reduces costs. Schedule derivation according to some policies might be compute-intensive, therefore careful consideration of the optimization objective (e.g. cost minimization or timeliness of the scaling policy) is required from the user of SPDT.

This chapter aims to theoretically examine effective surveillance management (ESM) during service encounters within the servicescape and provide a conceptual framework for the study of this topic in a service management perspective. It analyses antecedents, dimensions and effects of ESM. This study especially proposes as antecedents both improving customer service experience along with meeting customers' need for security and implementing a surveillance service-oriented strategy that includes secure and safe servicescape design, deterrent communication, and trained and motivated security staff. This chapter suggests also that the dimensions of ESM (customer-physical service environment encounters, customer-technological surveillance systems encounters, and customer-security staff encounters) contribute to enhancing service quality, experience quality, and staff productivity. The integration of these dimensions, antecedents, and effects create a theoretically grounded framework that can serve as a starting point for future studies about this topic in the field of service management.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The question of how to specify, provide and measure service quality for network end-users has been of utmost interest for service and network infrastructure providers and their clients as well. The Service Level Agreement (SLA) is a beneficial tool in formalizing the interrelationships resulting from a negotiation among all participating actors with the target of achieving a common comprehension concerning delivery of services, its priorities, quality, responsibilities, and other relevant parameters. A horizontal SLA is an agreement between two service-providers existing at the same architectural layer (as for example two Internet Protocol (IP) or two Optical Transport Network (OTN) domains). A vertical SLA is an agreement between two individual providers at two different architectural layers (for instance, between an optical network and the core MPLS network). A service has to be defined without ambiguity utilizing Service Level Specifications (SLS) and three information types must be described: i) The QoX metrics as well as their corresponding thresholds; ii) A method of service performance measurement; iii) Service schedule. In this work we present preliminary simulation results that enable the development of a generic methodology for SLA modeling and establishment that will lead to a win-win situation for all involved actors. As an example, we put special attention in the benefits obtained by Optical Networks operators.

In recent years, increasing attention has been given to the possibility of guaranteeing Service Level Objectives (SLOs) to users about their applications, either regarding performance or power consumption. SLO can be implemented for parallel applications since they can provide many control knobs (e.g., the number of threads to use, the clock frequency of the cores, etc.) to tune the performance and power consumption of the application. Different from most of the existing approaches, we target sequential stream processing applications by proposing a solution based on C++ annotations. The user specifies which parts of the code to parallelize and what type of requirements should be enforced on that part of the code. Our solution first automatically parallelizes the annotated code and then applies self-adaptation approaches at run-time to enforce the user-expressed objectives. We ran experiments on different real-world applications, showing its simplicity and effectiveness.

In cloud computing, heterogeneous resources located in different data centers are used to provide pay-per-use performance. Resource provisioning encounters problems due to uncertainty and dispersion of resources in cloud computing environments. According to our research, there are various techniques, but the existing methods and frameworks cannot handle the behaviors of these applications, environment, and resources. The cloud provider wants a framework to execute the task for achieving cost-effective delivery and reliable cloud services. Cloud workload's essential requirement is Quality of Service (QoS) based on adequate resource provisioning as per cloud workload needs. The resource discovery and best paring of workload-resource as per the cloud user's QoS need for workload requirements is an optimization problem. The resource provisioning is appropriate and can be acceptable if offered as per QoS needs in a controlled way. A mutual contract of Service Level Agreement (SLA) is signed between Cloud Service Provider (CSP) and cloud users, which determines the Service Level Objectives (SLO). The SLA contains the QoS parameters and notifies the dependent SLA's recent status. The user will get Quality of Service (QoS), based on prediction, where the resource would manage efficiently and lead to perform loads and deliver cost-effective and reliable cloud services. This paper presents a prediction-based resource management technique called Predictive Cloud Computing Systems (PCCSs). Focus on the self-healing-based prediction that handles unexpected failures and self-configuration-based prediction of resources and applications. Predictive Cloud Computing Systems (PCCSs) performance evaluated in the cloud simulator. The simulation results reveal that Predictive Cloud Computing Systems (PCCSs) achieve better results than existing techniques as execution time, cost-effective, resource conflict, SLA breach while delivering reliable services. [JJCIT 2021; 7(2.000): 206-222]

Distributed Permutation Flowshop Scheduling Problem (DPFSP) has become a research hotspot in recent years. However, as a service level objective, the Total Weighted Earliness and Tardiness (TWET) has not been addressed so far. Due to the importance of the service level objective in modern industry, we deal with the minimization of the TWET for the DPFSP with due windows . An Iterated Greedy (IG) algorithm, namely IG with Idle Time insertion Evaluation (IG I T E ), is proposed. In the algorithm, an adapted NEH heuristic with five rules based on the unit earliness weight and unit tardiness weight, the due date, and the smallest slack on the last machine is used to generate an initial solution. Destruction procedure with a dynamic size is provided to enhance the exploration capability of the algorithm. Idle time insertion method is utilized to make the completion time of jobs within the due windows or as close to the due windows as possible. A large number of experiments show that the presented algorithm performs significantly better than the five competing algorithms adapted in the literature. The performance analysis shows that the IG I T E is the most appropriate for the DPFSP with due windows among the tested algorithms.

The service industries have experienced dramatic changes in service delivery format because of fast-paced innovation in information technology. Hybrid services involve service deliveries through conventional and technology-enabled environments, each of which is complementary and non-interchangeable. However, limited research has examined consumer behavior in response to a series of relevant service episodes in different channels at different points in time. In other words, how customers perceive service quality at each service encounter and how the cumulative effect of these perceptions influences customers’ overall service value assessment and satisfaction remain unclear. This study addresses this research gap by investigating the sequential influence of e-service quality on in-person service quality in a hybrid service format in which interpersonal and computer-mediated services coexist but at different points in time. Empirical analysis shows that e-service quality directly and positively affec...

Purpose The purpose of this paper is to examine how practices influence service systems. Design/methodology/approach Data across three service contexts (crafts, healthcare and fitness) were collected through depth interviews and netnographic analysis, and analyzed with a two-study multi-method approach focusing first on the micro- (individual) level and then on the macro- (network) level of service systems. Study 1 focused on a micro-level analysis using qualitative techniques (Spiggle, 1994). Study 2 focused on a macro-level analysis using partial least squares regression. Findings The results illustrate how practices can change service systems. This occurs when a nuanced practice (i.e. a practice style) orders and roots a service system in a specific form of value creation. The findings reveal four practice styles: individual-extant, social-extant, individual-modified and social-modified practice styles. These practice styles shift in response to event triggers and change service systems. These event triggers are: service beneficiary enhancement, service beneficiary failure, service provider failure and social change. Thus, the findings show that practices – when shifting in response to event triggers – change service systems. This transpires in the understudied meta-layer of a service system. Practical implications The study identifies four practice styles that can serve as the basis for segmentation and service design. Originality/value Service systems are dynamic and ever changing. This study explores how service systems change by proposing a practice approach to service systems.

Purpose The purpose of this paper is to identify service design strategies to improve outcome-oriented services by enhancing consumers’ emotional experience, while overcoming customer variability. Design/methodology/approach An abductive, multiple-case study involves 12 service firms from diverse online and offline service sectors. Findings Overall, six service design strategies represent two overarching themes: customer empowerment can involve design for typical customers, visibility, and community building, while customer accommodation can involve design for personas, invisibility, and relationship building. Using these strategies helps set the stage for a service to offer an emotional experience. Research limitations/implications The study offers a first step toward combining investigations of service experience and user experience. Further research can strengthen these links. Practical implications The six design strategies described using examples from case research offer managerial recommendations. In particular, these strategies can help service managers address the customer-induced variability inherent in services. Originality/value Extant studies of experience staging have focused on particular sectors such as hospitality and leisure; this study contributes by investigating outcome-focused services and identifying strategies to create unique experiences that offset variability. It also represents a rare effort to combine research from service management and interaction design, shedding light on the link between service experience and user experience.

PurposeThe purpose of this paper is to discuss the rationale and functioning of the partnership-based brokerage model as a vehicle of service integration with special reference to its support for information intermediation, learning and service market creation.Design/methodology/approachThe theoretical framework is built on the tension between New Public Management (NPM) and post-NPM thinking, which frames the analysis of the brokerage model for elderly care services in the city of Tampere, Finland. The empirical data are derived from interviews, evaluation reports and existing case descriptions.FindingsIn the Kotitori model, the broker enhances the capacity building of the city government and the cost-effectiveness of its service provision, provides added value through improved information processes and handles matters relating to subcontracting and the facilitation of the service provider network. The model as a whole reflects the hybridisation of public administration. Even if Kotitori contains many NPM-inspired elements, they are complemented by features derived from New Public Governance and the neo-Weberian local state. The most neglected aspect of post-NPM thinking in the design of Kotitori is citizen centredness.Originality/valueThis paper broadens the perspective on the role of brokers in public service provision and highlights the multi-dimensionality of the brokerage function. It also shows how such partnership-based brokerage model reflects various aspects of both NPM and post-NPM paradigm.

Cloud Computing has emerged as the prime IT computing model for an on-demand access using a pool of shared resources with least IT support. Cloud computing is starting to replace the legacy office IT infrastructure and helpdesk support system. Corporate and home users alike are turning into cloud service consumers in a huge way and moving their data and work to the cloud. Therefore, the CSA between the cloud service consumers and cloud service providers has critical significance that can guarantee the highest-level service quality and delivery. The current CSA fall short on the service delivery commitments with no common terminology or standard followed industry wide by the cloud service providers. Comparing agreements from multiple cloud service providers continues to be an issue. This paper provides a pragmatic approach for Cloud Service Agreements, comparing the current process with the proposed parameters and the new framework for CSA to determine the role of various elements and terms in the decision-making process for cloud service agreements for SaaS, PaaS, IaaS and STaaS.

In tactical missions, seamless and effective communication is crucial for mission success. For future deployed networks, the user experience can be improved with proper resource allocation mechanisms, taking into consideration quality of service characteristics of the traffic. Such a resource allocation method requires knowledge of the key performance indicators of the communication path. In this article, we present a framework for the key performance indicator estimation service for deployed heterogeneous networks. Our proposal utilizes several different approaches for measuring key performance indicators. Several mechanisms are used to improve the accuracy and reliability of the service. We also discuss the lessons learned during the key performance indicator estimation service implementation and the validation process.

Summary The purpose of this study was to determine the relationship between bus service satisfaction and the transport mode of choice among university students in Qatar. The degree of bus service satisfaction was collected directly from questionnaire surveys, in which university students were asked questions in relation to their satisfaction with the bus service they used and their transport mode of choice. These questions were categorized into three factors according to confirmatory factor analysis: service at bus stops, service of busses, and service of drivers. Furthermore, the students were asked which mode of transport they used given the choice between public and private transport. This study presents a structural equation model to determine how much bus service satisfaction affects people's decisions about their transport mode. The results from the analysis showed that three key factors—namely, service at bus stops, service of busses, and service of bus drivers—were strongly correlated to the mode of choice. In particular, the bus stop was strongly associated with ease of use, shade, cleanliness, safety, and crowdedness level, while the bus itself influenced reliability, travel time, and frequency. Complying with traffic laws and the driver's attitude were also important contributors to the level of bus service satisfaction. Ultimately, this study will be beneficial for policy/decision-makers. It will allow them to determine what needs to be accomplished to encourage people to use public transportation. Copyright © 2016 John Wiley & Sons, Ltd.

Current service selection is unable to perform proactively. When a service provider overloads, the services list is ever-lengthening, which leads to backlog and failure of service composition. To compensate for this deficiency, this paper improves the proactive service selection. In this strategy, the service provider analyses a time series of services received to forecast the backlog and consign services to the others through a negotiation process. The least squares support vector learning is used to predict a random list of services, and an acquaintance model (AM) makes a consigner allocate backlog services to other providers with high degree of relationship. The backlog of services by forecasting is entrusted to the provider who can implement the same service, and negotiation between the providers with the same role would allow generation of a new service selection solution before a fault occurs. Experiments showed that the least squares support vector machine (LS-SVM) algorithm was more accurate in predicting a services list and a negotiation mechanism using AM decreased communication time effectively, which improved the success rate of service selection and reduced the execution time of service composition.

The cloud service market has seen a tremendous growth in recent years. Consequently, more and more service providers have entered this dynamic market, turning it into one of the most competitive markets in modern IT industry. With the market becoming mature, providers that focused on growth strategies in the past, however, need to shift their attention to profitability now. Based on a qualitative study with 14 cloud service experts, the authors explore the relationship between cloud service provider profitability and value facilitation, i.e., the capability to build up resources in advance of future customer engagements. The authors' results indicate a positive relationship between cloud service profitability and value facilitation, which gives valuable insights for both researcher and practitioners and how to design cloud service offerings.

During the process of enterprise management, those chemical enterprises usually adopt outsourcing, as they hand over the logistical work to the third party. Here, we call the third part the “logistics service provider”. In this paper, Chemical Enterprise A is set as an example to study the application of the optimizing selection for the third party logistics service provider, as well as its improvement of aggregate cost method. This paper is aimed at theory, practicality and operability, and hoping to choose the provider which can guarantee the cost is smallest by introducing the original provider model. Among all the aspects, this paper is persuasive. Meanwhile, due to the instability of the relevant data which are collected, the factors impacting upon the total cost are uncertain, thus their credibility is affected. Later, according to the actual demand of the logistics service provider as well as the Benefit-Cost Ratio in aggregate cost method, the qualitative method and the quantitative method are combined with each other, and then the cost can be reduced through Analytic Hierarchy Process. Moreover, the factors which are irrelevant to cost selection are reasonably transformed into the cost adjustment proportion, and an improved provider selection model is established. At last, by introducing the specific case of the third party logistics service provider of Chemical Enterprise A, the effectiveness in practical application of improving the provider selection model can be verified.

In recent years, with the development of cloud computing, more and more service providers appear, making the cloud computing market more open and competitive. However, it is difficult for service consumers to choose the most suitable service provider for their own, especially for the service providers to provide the best service in the negotiated price, while the service providers and the service consumers themselves can't automatically negotiate, thus in this paper, we propose a cloud service broker framework based on dynamic game theory for bilateral SLA negotiation in cloud environment, aiming for successful negotiation among participators. This paper also proposes a Nash equilibrium point and a satisfaction degree difference algorithm, aiming to find the optimal value of SLA attributes (price and bandwidth) at the minimum satisfaction degree difference among the participators. Experimental results show that the service consumers and the service providers are able to achieve the same satisfaction degree on price and bandwidth.

Purpose Service recovery is very important to the insurance industry; it helps to maintain clients, it is a crucial competitive advantage for business survival and it adds value for the organization’s continued future. The purpose of this paper is to investigate the factors influencing service recovery performance (SRP) of customer service employees in the life insurance industry from three dimensions; organizational (customer service orientation and top management commitment), human resource management (rewards, training, teamwork and empowerment) and personal (affective organizational commitment, role ambiguity, role conflict and emotional exhaustion). This study also investigated job satisfaction and the intent to resign. Design/methodology/approach Data were gathered through self-administered questionnaires from 350 customer service staff employed by life insurance companies in the Northern region of Peninsular Malaysia by using a convenience sampling technique. Data were analyzed using multiple regressions. Findings The findings indicated that customer service orientation, training, empowerment, affective organizational commitment, role stressors and emotional exhaustion influenced staff’s SRP. The findings also showed that SRP influenced job satisfaction and intention to resign. Practical implications The research advances understanding of the influence of organizational, personal and human resource management factors on SRP and result constructs, namely, turnover intentions and job satisfaction. The researchers in Malaysia can use this model for future research in a service sector fields such as banking, retailing and hospitality to replicate and compare this finding. For practitioners especially the managers in insurance services providers can take actions and formulate proper strategies for customer service employees to deliver high level of performance in order to satisfied customer and continue stay in the organizations. Originality/value Very little attention has been given to examine the impact of human resource, personal and organizational factors on SRP and the influence of SRP on result constructs, namely, job satisfaction and intention to resign in the life insurances area. Furthermore applying equity theory especially in the SRP area was not given fully attention.

Purpose The purpose of this paper is to investigate consumers’ responses to unsolicited cross-selling of supplementary paid-for services made during delivery of a core service, and the contextual and personal variables moderating those responses. Design/methodology/approach Three formal experiments test the effect on participants’ responses of the perceived relevance of the supplementary service to the core service, personal psychological reactance, in the case of a high-relevance supplementary service, and self-monitoring, in scenarios in which a low-relevance supplementary service is proposed either in public or privately. Findings The experiments found that participants’ satisfaction ratings were reduced in response to cross-selling of a supplementary service that was of low relevance to the core service, and that satisfaction ratings if it was perceived to be of high relevance compared were not reduced despite the unsolicited attempt at cross-selling. However, the non-negative response to a high-relevance offer was limited to participants with a lower tendency to reactance. Furthermore, a high predisposition to self-monitoring evoked more positive judgments if a low-relevance supplementary service was proposed in public rather than privately. That of low self-monitors was no different in either case. Originality/value This paper examines the trade-off faced by a service provider between customer satisfaction and extra revenue from supplementary services, and explores conditions under which a provider can propose unsolicited supplementary services without offending customers.

Purpose The purpose of this paper is to explore influencing factors that affect the effectiveness of service recovery strategies using social network from operations management perspective. Specifically, the authors study the relationships between social media agent responses to customer complaints, customer emotion changes and customer satisfaction. Furthermore, the authors investigate the roles of recovery speed and failure severity in the service recovery process using social network platform. Design/methodology/approach The results are based on 347 mini cases drawn from the Twitter accounts of a sample of airlines. Grounded theory approach is used to conduct qualitative analysis using NVivo 9, a qualitative data analysis program. A conceptual framework was developed, then tested using χ2 analysis. Findings Agent responses that do not require customers to take further initiatives for problem solving have positive effects on customer emotion alleviation and satisfaction. In contrast, responses that provide further directions poses negative effect on service recovery outcomes. There is a strong positive linkage between customer emotion change and customer satisfaction. Surprisingly, the direct effect of recovery speed on customer emotion and satisfaction is not supported by the data. Rather, it plays a moderating role in affecting the relationship between agent responses and customer satisfaction. The qualitative data further reveals the pivotal role of failure severity, one of key service failure attributes. Research limitations/implications The authors study service businesses’ recovery strategies using social media. A conceptual framework is developed to link agent responses, customer emotion changes and customer satisfaction from the lens of service providers, using an operations-oriented approach. Finding on recovery speed and failure severity reveal that these variables play different roles when service recovery is operated on social media platform as compared to traditional channels. Additionally, relying on tweets as data sources has constrained us from assessing other long-term service recovery outcomes such as loyalty, repurchase intent and word of mouth. The drawback is resulted from the limited information conveyed through tweets, which tends to be short and brief. The study focusses on the airline industry, which limits the generalizability of the findings to other service industries. Practical implications The authors highlight the value and potential of service recovery strategies using social network and provide insights for recovery operations where agent responses should be focussing on real time problem solving. The findings support the benefits of empowering social network agents for service recovery operations. Improving recovery speed should be less of a priority as it serves as a qualifier when service recovery is operated via social network. Given the pivotal role of failure severity, it is critical for social network agents to stand in the shoes of the complaining customers, making imminent assessment of the actual failure severity and taking action accordingly in real time. In the meantime, effective communication through social network may help to lower perceived magnitude of failure by customers, which in turn enhance the effectiveness of other service recovery efforts. Originality/value This study is the first attempt to investigate the service recovery process using social media from an operations-oriented perspective. The results supports the potentials of employing service recovery strategies using social media.

Purpose The purpose of this paper is to explore the complexities of how external service provider employees culturally fit within client workplaces. Design/methodology/approach It uses the facilities management industry to apply this research concept and is conducted using a qualitative multi-method design. A combination of semi-structured interviews and focus groups are the qualitative methods used to collect the data. Findings The major findings of this study are that organisational socialisation can be successful in socialising external service provider staff into the client workplace despite a current lack of awareness of its use. This study has developed a framework that can be used to achieve this integration if client organisations and service providers work together to socialise external service provider staff. Originality/value The findings of this study will appeal to anyone working within the service industry who manages the interface between external service provider staff and the client work space.

This article reports on a study that examined service quality and recovery among South African airlines. Service quality is pivotal in the airline industry as service failures could negatively affect operations. The authors created the acronym “AOSA” which stands for “airlines owned by South Africa” for the purposes of anonymity and confidentiality in order to protect the airlines’ identity. A quantitative research approach was used with a cross-sectional analysis (sample survey) conducted with passengers of South African-owned airlines. The questionnaire was designed using a Likert scale tool, adapted to the SERVQUAL model. A non-probability convenient sampling method was used to collect primary data from 684 passengers at O.R.Tambo International Airport in Johannesburg and King Shaka International Airport in Durban. The key findings were that: (1) significant statistical gaps exist between passengers’ expectations and perceptions of AOSA’s service quality and that unsatisfactory service quality is antecedent to service failure. (2) Unsatisfactory service quality is tantamount to service failure in the provision of services by AOSA. (3) A significant positive correlation exists between service quality and the dimensional variables of tangibility, reliability, responsiveness, assurance, and empathy of AOSA. In conclusion, AOSA service quality is unsatisfactory, and management should take steps to empower and train staff in service recovery techniques in other to avoid service failures.

This paper provides a detailed examination of a quality of service indicator utilised by the Scottish Government to assess the opinions of passengers towards bus transport. The quality of service indicator takes the form of an 11 item opinion scale which covers an array of service aspects. Factor analysis is employed to identify latent constructs which are present within this scale. Three latent constructs associated with attitudes towards perceived quality of bus service are identified, covering convenience, cabin environment and ease of use issues. These latent constructs are further explored through an appreciation of how attitudes towards perceived quality of bus service vary across socio-economic cohorts and the degree to which these attitudes can be of use in explaining variation in perceived satisfaction with the bus service. Results of the analysis suggest that attitudes regarding quality of bus service vary significantly across passenger groups, with females having a tendency to exhibit relatively negative opinions regarding the quality of the cabin environment with a similar finding observed in the case of passengers who are looking after the home and family. In addition, perceived convenience of the bus service appears to have a significant positive explanatory power over perceived satisfaction with the bus service, suggesting that improvements to service frequency, availability, reliability and stability will likely increase perceived satisfaction amongst existing passengers. At a more general level, this paper demonstrates the level of additional knowledge which can be attained through a more detailed analysis of existing transport policy data.

With advance of cloud computing technologies, there have been more diverse and heterogeneous workloads running on cloud datacenters. As more and more workloads run on the datacenters, the contention for the limited shared resources may increase, which can make the management of the resources difficult, often leading to low resource utilization. For effective resource management, the management software for the datacenters should be redesigned and used in a software-defined way to dynamically allocate "right" resources to workloads based on different characteristics of workloads so that they can decrease the cost of their operation while meeting the service level objectives such as satisfying the latency requirement. However, recent datacenter resource management frameworks do not operate in such software-defined ways, thus leading to not only the waste of resources, but also the performance degradation. To address this problem, we have designed and developed a workload-aware resource management framework for software-defined compute. The framework consists mainly of the workload profiler and workload-aware schedulers. To demonstrate the effectiveness of the framework, we have prototyped the schedulers that minimize the interferences on the shared computing and memory resources. We have compared them with the existing schedulers in the OpenStack and VMWare vSphere testbeds, and evaluated its effectiveness in high contention scenarios. Our experimental study suggests that the use of our proposed approach can lead to up to 100 % improvements in throughput and up to 95 % reductions in tail latency for latency critical workloads compared to the existing ones.

We study the provision of collaborative services under online reviews, where the service outcome depends on the effort of both the service provider and the client. The provider decides not only her own effort but also the client's, at least to some extent. The client gives the review based on his net utility upon service completion. We develop a signal‐jamming model in which the provider's inherent capability or type is unobservable, and the market infers the provider type through observable signals such as the service outcome, the client review, or both. We show that compared to the benchmark case when the service outcome is observed as a signal, the client review generally leads to less effort of both the provider and the client. The review hence tends to sacrifice the service effectiveness in favor of the efficiency of the client's effort input. Nevertheless, when clients incorporate private information about the provider type into their reviews, service providers are better motivated to devote effort. Interestingly, we find the provider's effort choices may be either strategic complements or substitutes. With a reasonable level of informativeness, online reviews could lead to favorable performance in service effectiveness, client effort efficiency, and provider type distinguishability. Surprisingly, we demonstrate that when both the review and the outcome are available, the provider may lack sufficient incentive to devote effort, resulting in inferior distinguishability of provider types. It thus illustrates that richer information may not necessarily generate favorable strategic outcomes.

Citizens of different countries are now experiencing a newly invented service delivery channel to receive service from government portals. This study addresses whether a service delivery channel, based on mobile phones and provided through a short messaging service can be included in public administration to meet citizen requirements. The study further examines what factors are important to develop citizen perceptions of high value and effectiveness of this new service delivery channel. It also attempts to identify if culture has any effect on developing a positive attitude toward this service delivery channel. This study, through an empirical investigation among citizens in three countries - the USA, India, and Bangladesh, has identified that citizens are quite satisfied with this new service delivery channel use by the public service domain. The success of this delivery channel depends on the issue of segmentation. If public service providers can effectively segment the market based on time, location, and requirements, and can deliver the preferred message to concerned users with relevant and information that is easy to access and process, citizens will regard this service delivery channel as effective and satisfactory, and as competent as its private counterparts. Users are satisfied with mobile SMS to receive service from government portals.The key to success in delivering service to citizens is to appropriately segment the market.There are several factors to consider as key to effective segmentation for target population.The design and strategies for initiating this service delivery channel are culture bound.A generalized design might not be successful for all countries.

A system dynamics simulation model shows the interdependence of strategy and performance in a typical service company. Computed with the model, decision scenarios trace the firm's sinister service quality dynamics to the inauspicious blunder of pulling on internal policy levers too hard. The resulting dysfunctional behavior jolts the entire service organization, including its customers, defectors and profit per customer metric. As a method for modeling service organizations, system dynamics provides the integrated process required for comprehending self-inflicted problems in services. Along with insights toward effective and productive service quality design, management and performance, the model's simulation results also unveil the morphology of a likely topology, showing how service customers might aptly tidy their perceptions of good and poor service quality. These results urgently call for service quality research, aimed at effective and productive, customer-centric service quality design and management that entail high-quality service production, delivery and consumption processes.

The purpose of this study is to clarify the best practice of marketing strategy in Japanese accounting cloud services. The authors examine platform development and ecosystem formation of an accounting cloud service in the second stage. As the results, the authors obtain three findings. First, the best accounting cloud service has been driving a spiral model of marketing strategy consistently. Second, customer groups of the cloud service, which are connected by chain composition with path dependence, constitute multiplex markets structure. Associated service groups, which are also connected chain composition, constitute multi-dimensions service structure. Third, the service provider has started new Open & Close strategy that involves formulating closure of network structure of associated services. These strategies realized its sustainable growth. Therefore, cloud service providers not only adopt Open & Close strategy, but also build up a spiral model of marketing strategy to formulate two chain compositions with path dependence for customer groups and associated service groups.