A warm welcome to the 2024 IEEE World Congress on Services (SERVICES). With Professor Zhi Jin and Professor Michael Sheng serving as the Congress General Chairs, I trust everyone will have a rewarding experience participating in the IEEE Computer Society's flagship annual event in services computing, whether attending on-site or remotely.

A warm welcome to the 2024 IEEE World Congress on Services (SERVICES). With Professor Zhi Jin and Professor Michael Sheng serving as the Congress General Chairs, I trust everyone will have a rewarding experience participating in the IEEE Computer Society's flagship annual event in services computing, whether attending on-site or remotely.

In an enterprise-grade hybrid-multicloud computing environment, capability-providing as-a-service endpoints (or aaS-endpoints) can be deployed across diverse computing platforms, e.g., public clouds and on-prem enterprise private clouds. To ensure a seamless, unified, and enterprise-compliant acquisition of the capabilities by client applications, the presence of a cross-cloud API mediation service is crucial. However, as the number and heterogeneity of aaS-endpoints increase, delivering the API mediation service at scale becomes increasingly costly. This paper presents a robust approach to API service mediation in enterprise-grade hybrid-multicloud computing environments. It tackles the challenges, offering a distributed architecture comprising dynamically composed managed microservices, microservice zones, intelligent endpoint selection, and adaptive statistical learning (aiming to exploit localities in performance history of aaS-endpoint invocations and to facilitate adding or removing active aaS-endpoints). The successful reference implementation and$24\mathrm{x}7\mathrm{x}365$delivery in real-world settings of the approach validate its efficacy as a practical solution for API service mediation.

Mobile applications can leverage high-quality deep learning models such as convolutional neural networks and deep neural networks to provide high-performance cognitive services. Prior work on deep learning models-based mobile applications in a cloud-edge computing environment focuses on performing lightweight data pre-processing tasks on edge servers for cloud-hosted cognitive servers. These approaches have two major limitations. First, it is uneasy for the mobile applications to assure satisfactory user experience in terms of network communication delay, because the intermediary edge servers are used only to pre-process data (e.g., images and videos) and the cloud servers are used to complete the tasks. Second, these approaches assume the pre-trained deep learning models deployed on cloud servers are static, and will not attempt to automatically upgrade in a context-aware manner. In this article, we propose a cloud-edge collaboration framework that facilitates delivering cognitive services with long-lasting, fast response, and high accuracy properties. We fist deploy a shallow model (i.e., EdgeCNN) on the edge server and a deep model (i.e., CloudCNN) on the cloud server. EdgeCNN can provide durable and rapid response cognitive services, because edge servers not only provide computing resources for mobile applications, but also close to users. Then, we enable CloudCNN to assist in training EdgeCNN to improve the performance of the latter. Thus, EdgeCNN also provides high-accuracy cognitive services. Furthermore, because users may continue to upload data to edge servers in real-world scenarios, we propose to use the ongoing assistance of CloudCNN to further improve the accuracy of the shallow model. Experimental results show that EdgeCNN can reduce the average response time of cognitive services by up to 55.08 percent and improve accuracy by up to 26.70 percent.

With Moore's law progressively running out of steam, heterogeneous computing architectures have been powering the top supercomputers in the world for many years and are now finding broader adoption across the industry. The trend towards sustainable computing also requires domain-specific heterogeneous hardware architectures, which promise further gains in energy efficiency. At the same time, today's high performance computing applications have evolved from monolithic simulations in a single domain to multidisciplinary complex workflows. In this paper, we explore how these trends affect system design decisions and what this means for future computing system architectures.

As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.

The complexity of building, deploying, and managing cross-organizational enterprise computing services with self-service, security, and quality assurances has been increasing exponentially in the era of hybrid multiclouds. AI-accelerated material discovery capabilities, for example, are desirable for enterprise application users to consume through business API services with assurance of satisfactory nonfunctional properties, e.g., enterprise-compliant self-service management of sharable sensitive data and machine learning capabilities at Internet scale. This paper presents a composable microservices based approach to creating and continuously improving enterprise computing services. Moreover, it elaborates on several key architecture design decisions for Navarch, a composable enterprise microservices fabric that facilitates consuming, managing, and composing enterprise API services. Under service management model of individual administration, every Navarch microservice is a managed composable API service that can be provided by an internal organization, an enterprise partner, or a public service provider. This paper also illustrates a Navarch-enabled systematic and efficient approach to transforming an AI-accelerated material discovery tool into secure, scalable, and composable enterprise microservices. Performance of the microservices can be continuously improved by exploiting advanced heterogeneous microservice hosting infrastructures. Factual comparative performance analyses are provided before the paper concludes with future work.

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Composing several API-defined services into one composite service per user requirements has become an important service creation approach in the cloud-enabled API economy. Various service selection approaches in support of service composition on demand have been proposed. They usually assume that networking resources are over-provisioned and their usage needs not be considered when making quality-aware service composition decisions. In practice, these approaches often lead to wasteful network resource consumption and impractical end-to-end QoS optimality for cloud-based services. This paper proposes a network-aware cloud service composition approach, named NetMIP, with comparative experimental evaluations for the clouds that adopt the widely deployed fat-tree network topology. By formalizing the service composition goal as a multi-objective constraint optimization problem, we have validated the proposed approach can be used to effectively reduce network resource consumption and deliver QoS optimality while satisfying the end-to-end QoS constraints for the candidate composite services in the cloud. The comparative experimental evaluations are done via a credible cloud infrastructure simulation system, named WebCloudSim. Extensive evaluation results show that NetMIP outperforms several representative cloud service composition approaches in terms of network resource consumption, QoS optimality, and computation time under various service selection workloads and fat-tree network topology settings.

Financial services provide complex financial intermediation to match sources and uses of USD $262T of funds globally, generating an annual revenue of USD $5T. Retail Banking (35%), Corporate and Commercial Banking (30%), Wealth, Asset Management and Investment Banking (18%), Payments (14%), and Financial Market Infrastructure (3%) constitute the five major financial services in terms of the share of this revenue. This paper presents an industry viewpoint of the financial services challenges with a call for action to the R&D community through broad finance industry goals to advance smart financial services to meet the economic, societal, and individual needs.

The papers in this special section focus on data centric big data services. As an overwhelming amount of data is generated at a faster rate every day from all sources, and applications such as cloud services, the Internet of Things (IoT), social network services and intelligent terminals, it has become more urgent than ever to design, deploy and provision services more wisely so that the provisioned services could support effective acquisition, storage, transformation, process, management and utilization of such data. Manipulating and getting the most out of the Big Data can bring unprecedented value and new opportunities that are critical to business success. Services should be ideally provisioned in a way that speeds up data processing, scales up with data volume, and improves the adaptability and extensibility over data diversity and uncertainties, and finally turns low-level data into actionable knowledge towards better understanding and manipulation of the Big Data. Datacentric big service is an inevitable evolution of services with the emergence of big data in the last decade.

Advances in mobile Internet technology have enabled the clients of Web services to be able to keep their service sessions alive while they are on the move. Since the services consumed by a mobile client may be different over time due to client location changes, a multi-dimensional spatiotemporal model is necessary for analyzing the service consumption relations. Moreover, competitive Web service recommenders for the mobile clients must be able to predict unknown quality-of-service (QoS) values well by taking into account the target client's service requesting time and location, e.g., performing the prediction via a set of multi-dimensional QoS measures. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient Web service recommendation for mobile clients via tensor decomposition and reconstruction optimization algorithms. In light of the unavailability of measured multi-dimensional QoS datasets in the public domain, this paper also presents a transformational approach to creating a credible multi-dimensional QoS dataset from a measured taxi usage dataset which contains high dimensional time and space information. Comparative experimental evaluation results show that the proposed QoS prediction approach can result in much better accuracy in recommending Web services than several other representative ones.

Cloud-based application systems are rapidly deployed worldwide in production use via virtualization and services computing technologies. The scaling demands for these application capabilities to the cloud providers, compound with differentiated requirements on the quality of services, have brought severe technical challenges. This special section focuses on the techniques of virtualization and services for cloud-based application systems, mainly including multi-scale resource management and sharing, elastic scheduling and allocation of computing and network resources, monitoring and diagnosis for cloud-based services, and cloud-based mobile systems. The articles of this special section illustrate recent advances in virtualization and services provisioning for cloud-based application systems. We expect that this special section will provide an integrated view of the state-of-the-art techniques, identify new challenges as well as opportunities, and promote collaboration among researchers in this field. We received 34 submissions and we finally accepted 4 articles. The acceptance rate is as low as 11.8 percent.

Presents corrections to author affiliation information in the paper, “Guest Editor’s Introduction: Special Section on Virtualization and Services for Cloud-Based Application Systems,” (Zhange, Y. et al), IEEE Trans. Serv. Comput., vol. 12, no. 1, pp. 88–90, Jan./Feb. 2019.

Mobile Edge Computing (MEC) is designed to extend the edge of the cloud network to decrease latency and network congestion, which would significantly improve the quality of experience (QoE) of adaptive streaming service for mobile users. This paper proposes QCSS, a QoE-aware control plane for adaptive streaming service over MEC infrastructures. QCSS aims to assure high QoE delivery of online streaming service to mobile users. The design of QCSS features: 1) a timeslot system with a look-ahead window for calculating cost of edge node switch and video quality adaption (to balance network load and reduce latency); 2) conducting service adaption via a set of cooperative action components running on client devices, edge nodes, and center nodes (to ensure a smooth viewing experience); 3) constructing a flexible QoE model and extending the scope and meaning of user-perceived experience. The effectiveness of QCSS has been validated via three real datasets. The validation results show that the proposed QCSS can improve QoE performance and network load performance for adaptive streaming service over MEC Infrastructures.

It is challenging to conduct quality of experience (QoE) evaluations of web-based streaming video services effectively and efficiently. Aiming to overcome this challenge, we have created QoECenter, a web-based visual platform that innovatively facilitates comprehensive QoE evaluations of the streaming video services. QoECenter offers a holistic approach to conducting the QoE evaluations via an integrated set of technologies for source video classification, QoS realization of video encoding and network transmission, and context-aware user experience data gathering and analysis. From a QoECenter consumer's viewpoint, three kinds of data are required for an end-to-end streaming video QoE evaluation: video source level data, system process level data, and end user level data. QoECenter provides visual interfaces for parameter setting and data acquisition for each data level, and supports both objective and subjective datadriven QoE analyses. A QoECenter consumer can easily conduct comparative QoE evaluations like running easy-to-use visual applications. The effectiveness and efficiency design objectives of QoECenter have been validated by various real experiments.

Most state-of-the-art service selection approaches assume user preferences can be provided by the target user with sufficient precision and ignore historical service usage data for all users. It is desirable for ordinary users to possess a new service selection approach that can recommend satisfactory services to them even when their service selection preferences are specified imprecisely in terms of vagueness, inaccuracy, and incompleteness. This paper proposes a novel service selection approach that resolves the imprecise characteristics of user preferences and can recommend satisfactory services for users with varying cognitive levels in terms of service experience. The proposed service selection approach is comprised of four major tasks: 1) employ user-friendly linguistic variables to collect apparent user preferences (AUP) and convert the linguistic variables to standardized fuzzy weights as AUP weights; 2) evaluate all users' respective cognitive levels for the target service type and obtain the cognitive level threshold for that type of services; 3) adjust the AUP weights based on the calculated cognitive levels and the threshold, and supplement the potential user preferences weights; and 4) prioritize candidate services per a user satisfaction maximization objective. In-depth comparative experimental evaluations were performed using two real-world datasets. The results show that our service selection model outperforms three other representative ones and could provide a stable and reliable selection of services for the users with low service cognitive levels.

With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.

Fast deployment of mobile Internet makes Web services often consumed under a multi-dimensional spatiotemporal model, wherein a specific service client could keep active while its location is changing. Recommending Web services for such clients must be able to predict unknown QoS values with the target client's service requesting time and location taken into account, e.g., Performing the prediction via a set of measured multi-dimensional QoS data. Most QoS prediction methods focus on the QoS characteristics for one specific dimension, e.g., Time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient service recommendation for Web service based mobile clients via tensor decomposition and reconstruction optimization algorithms. Comparative experimental evaluation results show that the proposed QoS prediction approach could result in much better accuracy in recommending Web services than several other representative ones.

The popularity of cloud service spurs the increasing demands of virtual resources to the service vendors. Along with the promising business opportunities, it also brings new technique challenges such as effective capacity planning and instant cloud resource provisioning. In this paper, we describe our research efforts on improving the service quality for the capacity planning and instant cloud resource provisioning problem. We first formulate both of the two problems as a generic cost-sensitive prediction problem. Then, considering the highly dynamic environment of cloud, we propose an asymmetric and heterogeneous measure to quantify the prediction error. Finally, we design an ensemble prediction mechanism by combining the prediction power of a set of prediction techniques based on the proposed measure. To evaluate the effectiveness of our proposed solution, we design and implement an integrated prototype system to help improve the service quality of the cloud. Our system considers many practical situations of the cloud system, and is able to dynamically adapt to the changing environment. A series of experiments on the IBM Smart Cloud Enterprise (SCE) trace data demonstrate that our method can significantly improve the service quality by reducing the resource provisioning time while maintaining a low cloud overhead.

Cloud computing as a service promises many business benefits. The cost to pay is that it also faces many technique challenges. One of the challenges is to effectively manage cloud capacity in response to the increased demand changes in clouds, as computing customers now can provision and de-provision virtual machines more frequently. This paper studies cloud capacity prediction as a response to the challenge. We propose an integrated solution for intelligent cloud capacity estimation. In this solution, a novel measure is introduced to quantify and guide the prediction process. Then an ensemble method is utilized to predict the future provisioning/de-provisioning demands respectively. The cloud capacity is estimated using the active virtual machines and the future provisioning/de-provisioning demands altogether. Our proposed solution is simple and with low computational cost. The experiments on the IBM Smart Cloud Enterprise trace data shows our solution is effective.

Economic analysis on the financial benefits and risks is crucial for deciding whether an IT transformation should take place. While the financial analysis techniques for general investment is well known, and there have been case studies for many IT types of IT transformations, there is no good methodology that an IT professional can follow and readily conduct return on investment or total cost analysis. This paper aims to fill this void and proposes an economic analysis methodology that is applicable to all IT transformations.

The promise of cloud computing is to provide computing resources instantly whenever they are needed. The state-of-art virtual machine (VM) provisioning technology can provision a VM in tens of minutes. This latency is unacceptable for jobs that need to scale out during computation. To truly enable on-the-fly scaling, new VM needs to be ready in seconds upon request. In this paper, We present an online temporal data mining system called ASAP, to model and predict the cloud VM demands. ASAP aims to extract high level characteristics from VM provisioning request stream and notify the provisioning system to prepare VMs in advance. For quantification issue, we propose Cloud Prediction Cost to encodes the cost and constraints of the cloud and guide the training of prediction algorithms. Moreover, we utilize a two-level ensemble method to capture the characteristics of the high transient demands time series. Experimental results using historical data from an IBM cloud in operation demonstrate that ASAP significantly improves the cloud service quality and provides possibility for on-the-fly provisioning.

Effective management of Web Services systems relies on accurate understanding of end-to-end transaction flows, which may change over time as the service composition evolves. This work takes a data mining approach to automatically recovering end-to-end transaction flows from (potentially obscure) monitoring events produced by monitoring tools. We classify the caller-callee relationships among monitoring events into three categories(identity, direct-invoke, and cascaded-invoke), and propose unsupervised learning algorithms to generate rules for each type of relationship. The key idea is to leverage the temporal information available in the monitoring data and extract patterns that have statistical significance. By piecing together the caller-callee relationships a teach step along the invocation path, we can recover the end-to-end flow for every executed transaction. Experiments demonstrate that our algorithms outperform human experts in terms of solution quality, scale well with the data size, and are robust against noises in monitoring data.

We study dependable group communication for large-scale and delay-sensitive mission critical applications. The goal is to design a protocol that imposes low loads on bottleneck network links and provides both stable throughput and fast delivery of multicast messages even in the presence of frequent node and link failures. To this end, we propose our GoCast protocol. GoCast builds a resilient overlay network that is proximity aware and has balanced node degrees. Multicast messages propagate rapidly through an efficient tree embedded in the overlay. In the background, nodes exchange message summaries (gossips) with their overlay neighbors and pick up missing messages due to disruptions in the tree-based multicast. Our simulation based on real Internet data shows that, compared with a traditional gossip-based multicast protocol, GoCast can reduce the delivery delay of multicast messages by a factor of 8.9 when no node fails or a factor of 2.3 when 20% nodes fail.

A service level agreement (SLA) is a service contract that includes the evaluation criteria for agreed service quality standards. Since agreeable specifications on the evaluation criteria cannot be limited in practice, competitive SLA management products must be extensible in terms of their support for contract-specific SLA compliance evaluations. While the need of running and managing those software products as services increases, we have found that developing a good solution for configuring them as per contractual terms is a challenging task. This paper presents the Fresco framework, which facilitates configuring extensible SLA management systems using Web services. An XML-based specification of SLA management related data called SCOL will also be presented to show how the framework supports contract-specific SLA terms and contract-specific extensions of the deployed SLA management software. The paper furthermore shows how the Fresco system uses a template-based approach to communicate with other Web services applications with support for various input and output formats. Our experience with implementing the Fresco framework for a leading commercial SLA management software product demonstrates that the framework facilitates the creation of effective and efficient solutions for configuring extensible SLA management systems.

The routing tables of Distributed Hash Tables (DHTs) can vary from size O(1) to O(n). Currently, what is lacking is an analytic framework to suggest the optimal routing table size for a given workload. This paper (1) compares DHTs with O(1) to O(n) routing tables and identifies some good design points; and (2) proposes protocols to realize the potential of those good design points.We use total traffic as the uniform metric to compare heterogeneous DHTs and emphasize the balance between maintenance cost and lookup cost. Assuming a node on average processes 1,000 or more lookups during its entire lifetime, our analysis shows that large routing tables actually lead to both low traffic and low lookup hops. These good design points translate into one-hop routing for systems of medium size and two-hop routing for large systems.Existing one-hop or two-hop protocols are based on a hierarchy. We instead demonstrate that it is possible to achieve completely decentralized one-hop or two-hop routing, i.e., without giving up being peer-to-peer. We propose 1h-Calot for one-hop routing and 2h-Calot for two-hop routing. Assuming a moderate lookup rate, compared with DHTs that use O(log n) routing tables, 1h-Calot and 2h-Calot save traffic by up to 70% while resolving lookups in one or two hops as opposed to O(log n) hops.

Ubiquitous IP telephony has become a feasible Internet service, and it is expected to meet the quality standards of traditional telephone services. The work presents a distributed voice-over-IP (VoIP) conferencing system called Venus that is implemented as a composable application-level service overlay network. Compared to the traditional centralized approach, Venus achieves better scalability and resource utilization by efficiently aggregating resources across distributed voice mixers. Moreover, Venus provides multi-constrained quality-of-service (QoS) provisioning by establishing each conferencing session based on multiple QoS constraints (e.g., delay, loss rate) and resource requirements (e.g., bandwidth, audio channels). Venus provides a failure resilient VoIP conferencing service by leveraging the fast failure recovery capability of the application-level service overlay network. Large-scale simulation results illustrate the efficiency of the Venus system.

Many value-added and content delivery services are being offered via service level agreements (SLAs). These services can be interconnected to form a service overlay network (SON) over the Internet. Service composition in SON has emerged as a cost-effective approach to quickly creating new services. Previous research has addressed the reliability, adaptability, and compatibility issues for composed services. However little has been done to manage generic quality-of-service (QoS) provisioning for composed services, based on the SLA contracts of individual services. In this paper we present QUEST a QoS assUred composEable Service infrasTructure, to address the problem. QUEST framework provides: (1) initial service composition, which can compose a qualified service path under multiple QoS constraints (e.g., response time, availability). If multiple qualified service paths exist, QUEST chooses the best one according to the load balancing metric; and (2) dynamic service composition, which can dynamically recompose the service path to quickly recover from service outages and QoS violations. Different from the previous work, QUEST can simultaneously achieve QoS assurances and good load balancing in SON.

It is imperative for a competitive e-business service provider to be positioned to manage the execution of its service level agreement (SLA) contracts in business terms (e.g., minimizing financial penalties for service-level violations, maximizing service-level measurement based customer satisfaction metrics). This paper briefly describes the design rationale of an integrated set of business-oriented service level management (SLM) technologies under development in the SAM project at IBM TJ Watson Research Center. The e-business SLA execution manager SAM, (1) enables the provider to deploy an effective means of capturing and managing contractual SLA data as well as provider-facing non-contractual SLM data; (2) assists service personnel to prioritize the processing of action-demanding quality management alerts as per the provider's SLM objectives; and (3) automates the prioritization and execution management Of approved SLM processes on behalf of the provider, including assigning SLM tasks to service personnel.

This paper presents a mechanism that facilitates and enhances the use of independently administered remote network servers in the presence of server interface heterogeneity. The mechanism is designed under the client-service model, which extends the client-server model with an abstraction of service to decouple abstract server capabilities from concrete server interface specifics such as server interface binding protocols and the interface operation invocation protocols. The mechanism selects servers, accommodates server interface heterogeneity, and handles server access failures as per the abstract server capabilities desired by the client. It could return the identity of the server used for each service access invocation to facilitate billing, refining service specifications, and reporting server-specific errors. This paper also illustrates a C library interface to this mechanism, and describes a language veneer over the C programming language demonstrating how a typed procedural language could be extended by a few language constructs to support the mechanism under the client-service model. In this language, server capabilities are referenced by abstract data type (ADT) objects, and are accessed by invoking the objects' interface operations using a call-by-value-result paradigm.>

Three of the most important issues in exploiting network servers concern how to specify services so that service-server bindings can be changed dynamically without disturbing clients, how to make clients resilient to network or server failure, and how to accommodate server protocol heterogeneity to provide a single system view to the clients. A service acquisition mechanism is presented for solving these issues. This mechanism is designed under a client/service model in which the abstraction of service is a first-class entity. The components of the mechanism are discussed.>