The proliferation of smart devices has paved the way for the development of intelligent applications across various sectors. While Cloud Computing offers virtually limitless resources for deploying applications and handling data generated by these devices, emerging applications impose higher performance demands that surpass the capabilities of traditional Cloud Computing paradigms. This paper introduces the Edge Continuum Orchestrator (ECO), a novel framework aimed at addressing the deployment and management challenges of serverless applications across the Cloud Continuum to meet these critical application demands such as ultra-low latency, high transmission bandwidth and low energy consumption. Through the architectural design and real-world deployment of the ECO, this work investigates the performance impacts of Serverless applications deployed to a Edge-Cloud Continuum and the flexibility of ECO to accommodate these heterogeneous topologies.

Machine Learning (ML) and Artificial Intelligence (AI) can harness the immense amount of operational data from clouds to services, to social and communication networks. In the era of data science and connected devices of all varieties, Intelligence have found ways to improve operations and management of next generation networks, systems, and services. Further research is therefore needed to understand and improve the potential and suitability of ML/AI in the context of network, system, and service operations and management. This will provide deeper understanding and better decision making based on largely collected and available operational and management data. It will also present opportunities for improving ML/AI algorithms on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in control and management systems. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advance ML/AI algorithms to drive management decisions in open and highly programmable networks, clouds, and data centers.

Multi-containerized applications are becoming more common. Modern mobile Internet-of-Things applications, e.g., augmented reality and online gaming, have increased the need for ultra-low and real-time application responses. Edge computing is a solution for decreasing response times by relocating services closer to users. Migrating multiple services in this environment is unavoidable, given user mobility and load balancing. We utilize the MiGrror migration method, to propose a novel strategy for increasing bandwidth for short critical periods instead of maintaining increased bandwidth throughout the entire migration process. Our novel strategy drastically reduces downtime and migration time while enhancing bandwidth utilization by freeing up additional bandwidth during migration, which increases bandwidth availability for other services. The findings indicate that, even without using additional bandwidth, adjusting the timing of a bandwidth increase/decrease for services improves performance. The results are comparable to increasing the bandwidth for the entire migration duration with equivalent bandwidth consumption. Additionally, this study demonstrates that the proposed strategy is more sustainable than when using the original bandwidth, which results in less data transfer.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

The success and quality of this journal depends on the dedication and expertise of a large number of reviewers. On behalf of the Editorial Board, I would like to thank them explicitly for their excellent work during the non-trivial year 2021. Their substantial and constructive reviews have proven to be very valuable to the authors and are highly appreciated.I am particularly pleased to report that three reviewers for IEEE Transactions on Network and Service Management (TNSM),Viktoria Fodor, KTH Royal Institute of Technology, SwedenHani Sami, Concordia University, CanadaKaiping Xue, University of Science and Technology of China, China have received the IEEE Transactions Outstanding Reviewer Award 2021.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

Artificial Intelligence (AI) has the potential to leverage the immense amount of operational data of clouds, services, and social and communication networks. As a concrete example, AI techniques have been adopted by telcom operators to develop virtual assistants based on advances in natural language processing (NLP) for interaction with customers and machine learning (ML) to enhance the customer experience by improving customer flow. Machine learning has also been applied to finding fraud patterns which enables operators to focus on dealing with the activity as opposed to the previous focus on detecting fraud.

Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using machine learning, artificial intelligence and data analytics to improve operations and management of information technology services, systems and networks.

Designing survivable communication networks to achieve carrier-grade five-nines reliability is of paramount importance for the network operators. This article addresses service reliability and its related aspects such as nodal reachability, network connectivity, and edge-disjoint routing in both traditional networks and software defined networks (SDNs). The proposed roadmap is based on two phases: Fundamental analytical phase and performance evaluation phase. In the first phase, a graph operator is defined to analyze the characteristics of the reliability metric and its associated reachability feature. This phase will focus on both the macro- and micro-level properties of reliability. In the second phase, we exploit the analysis in the former phase to get an insight into the performance evaluation of traditional and SDN-based networks against the reliability metric, and then calculate the statistical significance of the mean difference of their reliability values. Reliability under edge-disjoint paths to avoid resource competition is also investigated. Various types of topologies are utilized to test the service reliability of both architecture designs. Extensive simulation results show that SDN-based networks have comparable performance to its legacy counterpart against the operational reliability metric. Our findings not only shed light on enhancing reliability using edge-disjoint paths under link failure scenarios but also expected to benefit the operators to achieve their service level objectives while migrating from legacy to SDN-based platform.

Introducing resiliency in a legacy power grid is one of the core mandates of smart grid architecture. Resiliency in power grid enables electrical distribution companies to maintain their services to the customers in the event of a grid failure. Its main advantages appear during the fault event: balance the power load and overcome the fault state by minimizing its impact. Although researchers have proposed many schemes for post-failure events to isolate faults (under certain conditions), yet there is no overall framework to evaluate the resiliency of the existing power grid and to improve it if required to recover from fault incidents successfully. In this paper, we propose a two-phase resiliency framework for the smart power grid. The first phase aims to assess the resiliency of a power grid. This phase provides important information regarding the weaknesses and the strengths of a power grid. Phase two will process the input of phase one to increase resiliency by employing redundant grid's elements such as power lines and poles to back up/strengthen the vulnerable components. This preventive framework increases the overall grid resiliency and enables providing an efficient self-recovery policy upon any possible power failure. To achieve this goal, we utilize graph-theoretic metrics, mainly distortion metric, to implement our proposed framework. The proposed framework is implemented and its effectiveness with link failure events is demonstrated through three IEEE bus systems: 14, 30, and 118.

Software Defined Networking (SDN) is an emerging paradigm in networking and of high interest to the mobile network operators due to its potential benefit in improving network performance. The idea of integrating SDN in mobile network architecture, such as in LTE-evolved packet core (EPC), has already been proposed by many researchers. The SDN-based EPC network offers better Quality of Service (QoS) which is critical to the successful delivery of real-time multimedia applications in the mobile operator networks. In this paper, we provide state-of-the-art work to evaluate and analyze the QoS metrics in real-time services. We conduct a comprehensive comparison between the performance of EPC and SDN-based EPC regarding latency, jitter, and packet loss. We inject active probe packets in the networks to collect statistical data about the metrics. Various levels of workload are applied to test the robustness and stability of both EPC and SDN-based EPC designs. Our results show that SDN-based EPC significantly outperforms its legacy counterpart in all three QoS metrics. To the best of our knowledge, our study is the first comparative analysis that evaluates QoS in EPC and SDN-based EPC for real-time applications.

Cloud and network analytics can harness the immense stream of operational data from clouds and networks, and can perform analytics processing to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using statistical analysis, Artificial Intelligence (AI) and machine learning to improve operations and management of IT systems and networks.

Domain Name System (DNS) is a crucial component of current IP-based networks as it is the standard mechanism for name to IP resolution. However, due to its lack of data integrity and origin authentication processes, it is vulnerable to a variety of attacks. One such attack is Typosquatting. Detecting this attack is particularly important as it can be a threat to corporate secrets and can be used to steal information or commit fraud. In this paper, a machine learning-based approach is proposed to tackle the typosquatting vulnerability. To that end, exploratory data analytics is first used to better understand the trends observed in eight domain name-based extracted features. Furthermore, a majority voting-based ensemble learning classifier built using five classification algorithms is proposed that can detect suspicious domains with high accuracy. Moreover, the observed trends are validated by studying the same features in an unlabeled dataset using K-means clustering algorithm and through applying the developed ensemble learning classifier. Results show that legitimate domains have a smaller domain name length and fewer unique characters. Moreover, the developed ensemble learning classifier performs better in terms of accuracy, precision, and F-score. Furthermore, it is shown that similar trends are observed when clustering is used. However, the number of domains identified as potentially suspicious is high. Hence, the ensemble learning classifier is applied with results showing that the number of domains identified as potentially suspicious is reduced by almost a factor of five while still maintaining the same trends in terms of features' statistics.

Cloud computing offers opportunities for organizations to reduce IT costs by using the computation and storage of a remote provider. Despite the benefits offered by the cloud computing paradigm, organizations are still wary of delegating their computation and storage to a cloud service provider due to trust concerns. The trust issues with the cloud can be addressed by a combinatiog of regulatory frameworks and supporting technologies. However, there appears to be little existing work that includes the application of software updates in trust assessment. Not applying software updates that fix security vulnerabilities can lead to serious problems. For example, the Canada Revenue Agency failed to update OpenSSL on their servers resulting in a temporary shutdown of tax filing. To address this issue we have developed a framework based on remote attestation and integrity measurements to address this problem. We showed that this could be effective in ensuring that software updates are done and our initial results suggests little run-time overhead.

To meet the increasing mobile data services demand, several solutions have been proposed such as wireless resource virtualization and device-to-device (D2D) communication. Virtualization allows for more efficient utilization of the spectrum, reduces expenditures, and can support higher peak rates. D2D communication can achieve higher data rates due to the proximity of devices while controlling the interference it causes to cellular communication. However, the increase in data rate multimedia demand has led to an increase in global energy consumption. Thus, it is crucial to employ more energy-aware schemes as this would provide both environmental and financial gains for service providers. In this paper, we extend our work in [1] by formulating the problem of power-aware wireless resource virtualization with D2D communication underlaying the LTE network. Since the problem is a mixed integer non-linear programming problem (MINLP), it is divided into four smaller linear programs, each of which is solved to optimality. Two lower complexity heuristic algorithms to solve the power allocation problems are introduced. Results show significant savings at both eNodeB and D2D devices.

Cloud and network analytics can harness the immense stream of operational data from clouds and networks, and can perform analytics processing to improve reliability, configuration, performance, and security management. In particular, we see a growing trend towards using statistical analysis and machine learning to improve operations and management of IT systems and networks.

Managing single-VM applications in data centres is a well-studied problem, managing multi-VM applications is not. Multi-VM applications usually have, in addition to their basic resource requirements, a special set of requirements that the data centre provider has to satisfy as well. We refer to these requirements as placement constraints. We propose two management strategies designed to manage multi-VM applications with placement constraints in data centres. These management strategies aim to increase data centre's resource utilization (and thus reduce power consumption) while at the same time minimizing SLA violations. One of the management strategies enforces placement constraints at all times, while the other allows for a temporary violation of placement constraints. The two management strategies are evaluated through simulation in multiple scenarios. Results suggest that both strategies are able to achieve high levels of infrastructure utilization and SLA achievement, while satisfying applications' placement constraints, and that temporarily violating constraints does not provide any advantage and it does add cost.

While MANETs can provide seamless networking that is fast and easy to deploy, the lack of a stable topology, no centralized control, and the use of wireless links with limited bandwidth make MANETs highly vulnerable to attacks. The autonomy of individual nodes must be balanced with the need to ensure that nodes contribute to the network function as a whole. Trust-based systems are often proposed in this situation, but the mechanisms by which trust are built is still an active area of research. In this paper we present a policy-based trust management framework that uses the observed behaviour of neighbouring MANET nodes to identify and react to attacks. The system uses policy rules to evaluate observed behaviour of neighbouring nodes and reputation values shared by peers. The calculated trust values are then sent to neighbours. The calculation of trust based on a combination of flexible policy rules and distributed evaluation is a novel approach and a significant contribution of the paper. A simulation-based evaluation of our framework based on a wormhole attack shows that the scheme is both scalable and robust.

Mobile devices have limited resources including short battery life, storage capacity and processor performance. This limits the applications that can run on it. Mobile applications can be partitioned so that some of the application runs on a cloud. This works well for applications with relatively little data to be transferred and that do not have a high level of interactiveness with the user. Challenges with applications that have large amounts of data to be transferred and have a high level interactiveness is the high latency incurred by the wired network and packet loss of the wireless network. A cloudlet is a resource-rich computer or cluster of computers that is connected to the Internet and is available for use by nearby mobile devices. A mobile application can be partitioned so that part of it runs on the cloudlet. In this paper, we present the MC-Skynet framework which introduces fine-grained offloading approach and support for runtime and dynamic partitioning of an application. This is different from previous approaches, in that MC-Skynet not only provides dynamic partitioning and offloading, but is also adaptive to the changes of the state of a cloudlet by introducing a cloudlet mesh network and self-learningg decision making module to estimate the offloading cost.

Increasingly, applications are moving into the cloud, which is actually supported by large-scale data centres on the ground. These data centres are complex systems to manage and centralized solutions might not be able to meet the required scale nor make an efficient use of their networks. In this paper, we propose a hierarchical approach to dynamic resource management in data centres, where we leverage the topology of the data centre network to design the management hierarchy. We define a set of aggregate metrics at various levels in the hierarchy to convey system state information to higher management levels, and define managers' responsibilities and interactions. We evaluate our proposed approach through simulation. Experiments show that this management approach greatly reduces the flow of management data across the data centre network, thus reducing network overhead.

Mobile devices are now the most convenient and ubiquitous interface for accessing information services. The advanced sensing capabilities of these devices enable them to effectively observe, analyze and understand the context of the current environment and thus are used as service providers. However, the resource constraints of mobile devices limit the type and scale of functionality that can be offered by mobile applications/services. Mobile cloud promises to bridge the gap between limited resources and increasing demands of future mobile applications through an elastic resource provisioning model. Mobile ubiquitous cloud enables resource-constrained mobile devices to offload some or all of their processing and storage requirements to the cloud infrastructure, on the go. This convergence between cloud computing technologies and the convenience of mobile devices offers improved performance of mobile systems and superior user experience. The topic of ubiquitous mobile cloud draws on inspirational ideas from many diverse domains including cloud computing, wireless technologies, context-aware computing, mobile commerce, location-based services, and vehicular networks.

Computing today is increasingly moving into large-scale virtualized data centres, offering computing resources in the form of virtual machines (VMs) on a pay-per-usage basis. In order to minimize costs, VMs should be consolidated on as few physical machines (PMs) as possible, switching idle PMs into a power saving mode. It may be necessary to dynamically allocate and reallocate VMs to PMs in order to meet highly dynamic VM resource requirements. The problem of assigning VMs to PMs is known to be NP-Hard. Most solutions focus on a centralized approach, with a single management node making allocation decisions periodically. This approach suffers from poor scalability and the existence of a single point of failure. We present a fully distributed approach to dynamic VM management, and evaluate our approach using a simulation tool. Results indicate that the distributed approach can achieve similar performance to the centralized solution, while eliminating the single point of failure and reducing the network bandwidth required for management.

Computing is increasingly moving into large-scale data centres, providing resources on-demand for clients on a pay-per-usage basis. One form of such on-demand computing is an Infrastructure as a Service (IaaS) Cloud, which provides low level access to virtualized resources. Developing and evaluating data centre management techniques for such large-scale data centres presents a significant challenge. As such, most work turns to simulation tools as the test environment. We present a number of extensions and additional features to an existing simulation tool, DCSim. Our improvements to DCSim include work on the core of the simulator, improved event, communication and management mechanisms, and a more complete model of the structure of a data centre. We also present improved simulation configuration tools and output, including a unique visualization tool. We evaluate the usefulness of the simulator through a demonstration of its use in comparing dynamic VM management techniques.

Node faults may be frequent in a mobile ad hoc network (MANET). Most work related to fault detection and localization for MANETS essentially use changes in topology to identify faults. Most of this work does not distinguish between node movement that results in a functioning node moving out of range of all other nodes versus a node that crashes as the result of a radio transmitter malfunctioning or a battery no longer operating. This paper introduces a novel protocol that makes this distinction. Nodes forward their neighbourhood observations to a cluster head. The cluster head uses this to determine a list of suspected crashed nodes and sends this list to all the other nodes to filter out those nodes that have moved out of range. The cross-layer implementation of the protocol is effective in distinguishing between crashed nodes and nodes that have moved out of range.

Trust plays an important role in decision making especially in cases of uncertainty and incomplete information. There are many challenges to trust calculation. In this paper, we identify the challenges and offer a middleware solution. Our solution consists of a SCOUT middleware and a SCOUT client (Trust Calculator) that can be used by an application to facilitate trust calculation. As validation, a scenario is implemented. The scenario consists of service selection based on trust calculated by our proposed solution.

In a desktop grid model, the job (computational task) is submitted for execution in the resource only when the resource is idle. There is no guarantee that the job which has started to execute in a resource will complete its execution without any disruption from user activity (such as a keyboard stroke or mouse move) if the desktop machines are used for other purposes. This problem becomes more challenging in a Peer-to-Peer (P2P) model for a desktop grid where there is no central server that decides to allocate a job to a particular resource. This paper describes a P2P desktop grid framework that utilizes resource availability prediction. We improve the functionality of the system by submitting the jobs on machines that have a higher probability of being available at a given time. We benchmark our framework and provide an analysis of our results.

Policy-based network management (PBNM) in mobile ad-hoc networks (MANETs) requires additional reliable and efficient mechanisms over PBNM in wired networks. Thus, it is important that the management system in MANETs should cluster the moving nodes and manage their movements in an effective manner. In the paper, a scalable framework is proposed for the policy-based management in ad hoc networks in which we use k-hop clustering with extended COPS-PR. And, we discuss methods for policy enforcement points (PEPs) to discover autonomously the policy decision point (PDP) and set the management area in the framework. Also, three regions are suggested to effectively maintain PDP/PEP clusters in the PBNM system. Finally, we discuss the results achieved through simulations.

This paper presents a distributed file-system for the present day medium-sized network. Existing servers and workstations pool their unused storage resources to form a communal share. Erasure codes provide fault tolerance and eliminate the need for replication. Middleware libraries facilitate object routing on an overlay network.

A policy can be defined as any type of formal behavioural guide that is input to the system. Examples of policies that can be found in data center management systems include SLA, traditional event-triggered rules and business objectives. Specified policies are mapped to executable elements in the system. Usually each policy is mapped separately. This work shows how a set of policy can be considered as a whole in the context of the management of a data center.

WS-Policy 4MASC is an XML language for specification of policies for run-time Web service management (monitoring and control) activities executed by the manageable and adaptable services compositions (MASC) middleware. Among its original contributions are specification of diverse business values (benefits or costs, tangible or intangible, agreed or possible, absolute or relative) and specification of various control strategies maximizing different business values (e.g., only agreed intangible benefits). To facilitate development of Web service systems that can be managed with WS-Policy 4MASC and the MASC middleware and to improve alignment between run-time management activities and design-time models, we developed novel UML profiles for WS-Policy 4MASC. Their original contributions are in improved support for: a) specification of run-time management activities and business values within design-time models, b) automatic creation of run-time management policies from design- time models, c) feedback of run-time management information values into analysis of design-time models. We validated our solutions on detailed examples.

The motivation of the work is based on the static use of reference point values in dynamic feedback control systems that are based on control theoretic techniques. Reference point values are threshold values that values of attributes characterizing system behavior can be compared to. The result of the comparison is used to adjust the tuning parameter of the system being controlled. If the entity being controlled is a server then the best choice for the reference value may depend on many factors such as the required processing times, the machine speed that the server is executing on, etc. This paper illustrates an approach to automatically change the reference value for different server hardware. Comparisons are made between the approach proposed in this paper and other dynamic feedback control approaches. Results show that the proposed approach outperforms the other dynamic feedback control approaches

Policy-based network management (PBNM) in the mobile ad-hoc networks (MANETs) requires additional reliable and efficient mechanism over PBNM in wired networks. Thus, it is important that the management system in MANETs should cluster the moving nodes and manage their movements in an effective manner. In this paper, we propose a mechanism for the policy-based management in ad hoc networks in which we discuss methods to discover the policy decision point (PDP), set the management area, and manage the movements of nodes in the PBNM system. Moreover, COPS-PR is extended for the mechanism. Finally, we discuss the results through simulations

The goal of distributed systems management is to provide reliable, secure and efficient utilization of the network, processors and devices that comprise those systems. The management system makes use of management agents to collect events and data from managed objects while policies provide information on how to modify the behaviour of a management system. Systems, as well as policies governing the behaviour of the system and its constituents, change dynamically. This paper explores the services needed to efficiently identify and deploy management entities and be able to respond to a change in the system being managed or in changes to the policies being applied. We show how these services can be built on existing management services found in commercial management systems

A data center is defined as a set of computing resources that is owned by an organization and shared among multiple applications from different client organizations. Applications often have non-functional run time requirements for different classes of users. These are referred to as service level objectives (SLO) and are considered part of a service level agreement (SLA). Allocating resources to an application should be dynamic and based on specific run-time conditions. It should be possible to change these conditions without recoding. This paper describes a framework, a prototype based on this framework and an application of the prototype that dynamically allocates resources and allows the application environment to adapt in the case of having insufficient resources.

The management of Extensible Markup Language (XML) Web services executing in mobile and/or embedded environments poses new challenges that result from the limited availability of computing resources and from the need for characteristic management activities, such as handling context sensitivity. This paper briefly describes management requirements for and design of our management infrastructure for monitoring of mobile/embedded Web services. Its architecture is based on extending a SOAP engine with management modules, an approach adopted by several existing tools for monitoring non-mobile/non-embedded Web services. Apart for modules for management of non-mobile/non-embedded Web services, our infrastructure contains additional support for handling context-sensitivity, disruptions in quality of service (QoS), and intermittent connectivity. We have been implementing the proof-of-concept prototype of this infrastructure as an extension of the Web Service Offerings Infrastructure (WSOI).

This paper considers the use of feedback to improve the performance of computing systems that offer differentiated services. The motivation of the work is based on the increasing demand on application servers. It is not always sufficient to buy high-performance software for the server. Multiple servers may be needed. To guarantee that QoS requirements are satisfied, it is possible to statically assign resources for a specific class. This often results in underutilization of resources. This paper describes a novel technique that is based on control theory principles applied to a server cluster that provides differentiated service. The paper shows that feedback can be used to adjust the number of client requests concurrently being processed based on dynamic information such as CPU utilization. The paper also compares the use of the proposed technique with a dynamic non control-theoretic approach that is not based on control theory principles. Results show a dramatic increase in the number of served users using the control theory principles compared with a non control-theoretic approach during the same experiment duration. The improvement provided by the proposed technique exceeded 20%.

Context-aware applications are able to use context, which refers to information about the surrounding environment, to provide relevant information and/or services to the user. A context-aware application may need to make use of existing services (e.g., a print service). There may be several possible choices of services. The context-aware application should be able to discover and select a service that considers context (e.g., current user location). Existing architectures and protocols for service discovery, however, are not suitable for doing so. Contextual information, by its very nature, is dynamic, reflecting the current state and conditions of the application, its user, or its operating environment. Existing architectures and protocols for service discovery, however, tend to assume the world is static, with attributes describing services offered never changing. If attributes are allowed to change, the approaches do not provide the architectural mechanisms required to update them; dynamic attributes with no means of updating are static for all intents and purposes. To support context-aware service discovery and selection, a better approach is required. This paper discusses one possible approach that is based on existing techniques.

This paper describes a location positioning system called Herecast. Herecast addresses many of the current weaknesses found in current location positioning systems. This paper describes the Herecast architecture and how it has been used to develop several location-aware applications

Operational profiles are a quantification of usage patterns for a software application. These profiles are used to measure software reliability by testing the software in a manner that represents actual use. The current definition of an operational profile states that it is the set of operations available in the application, and the operations probabilities of occurrence in customer usage scenarios. This definition is too limited. In most industrial applications, focusing on operations alone does not offer adequate representation of the use of software. The limited definition of operational profiles can restrict their applicability and hence software reliability analysis for many software development organizations. This paper describes a formal and practical extension of the current definition of operational profiles to increase their applicability.

Quality of service (QoS) requirements refer to non-functional, run-time requirements such as performance. Different applications have different QoS requirements and hence different resource needs. Differentiated services (DiffServ) from the IETF allows the network to treat IP packets differently based on a service class. The paper describes a policy-based QoS management system that allows for a change in the treatment of packets from a specific flow using DiffServ. The decision to change is based on a number of things, including the monitoring of the application's behavior (referred to as application feedback). Corrective actions include changing the service class and/or target bandwidth. The paper provides a description of the policy-based QoS management system used and experimental results.

It is often difficult to ensure that every application in a computing environment receives the level of quality of service required by their users. In such cases, the demand for computing resources to do so simply exceeds the limited supply that is available. To ensure that at least some user applications meet quality of service requirements, service differentiation is one approach growing in popularity. In this approach, preferential service is given to selected applications, while others deemed less important suffer in comparison. Most work in this area bases service differentiation decisions on static information about the applications, such as the name and type of application, the owner of the application, execution time, and the host on which the application was executed. In this paper, we discuss a new approach to service differentiation that takes into consideration dynamic application usage information in service differentiation decisions. In doing so, we can make better or fairer service differentiation decisions that allow more users to enjoy higher levels of quality of service. This is accomplished by ensuring preferential service is given to applications whose users can actually benefit from the improved service, as opposed to those applications that are essentially ignored or in a state that renders them unusable.

There is; an emergence of Internet applications that have real-time requirements. These applications require IP to support guaranteed capacity, higher priority and lower packet loss rate. To address this, the Internet Engineering Task Force (IETF) is developing a set of protocols and standards for Integrated Services on the Internet. Using RSVP and policies to manage the allocation of network resources in order to provide different levels of service is a topic of great interest to service providers. Currently, it is not possible to dynamically reallocate resources during an application's session. This paper discusses how policies in conjunction with new service class can provide a more enhanced network resource allocation by allowing for this dynamic reallocation.

This paper discusses issues related to managing a Web sever that supports differentiated QoS. An architecture and a prototype based on that architecture are presented.

With the increased availability and complexity of distributed systems comes a greater need for solutions to assist in the management of distributed system components. We address three main aspects of management: 1) the need to efficiently utilize resources in the system, 2) the need to satisfy user and management requirements, as well as the requirements on resource usage, and 3) the need for dynamic solutions, i.e., that can be reconfigured due to changes in requirements, resource availability and management policies. Despite the significant contributions made towards the development of management tools that monitor and control distributed systems, little has been done to address these points. In this paper we describe a model that captures the points above. The need for reconfiguration, our implementation and its performance are also described.

A report is made on an experience of teaching a senior-year course on software maintenance, centred around a maintenance project. The main triumphs and pitfalls are recounted, and recommendations are made on project selection and general course conduct.

With the increased availability and complexity of distributed systems comes a greater need for solutions to assist in the management of distributed system components. Despite the significant contributions made towards the development of management tools that monitor and control distributed systems, little has been done to address issues such as the cost of management and how it can adapt to the dynamic changes in user requirements as well as system resources. We present an adaptive model in which an initial optimal configuration of management agents is determined according to a set of user/system requirements. These agents can later be dynamically reconfigured to adapt to changes in resource availability and user/system constraints, with minimal effect on the behaviour of the managed system components. Algorithm, prototype, and experimental results are presented.

There is relatively little information on the process that should be taken to derive a software architecture. We propose a process for the development of software architectures for large systems and describe our experiences in the development of an architecture for distributed systems that helped in defining this process.

Optimism is a powerful technique for avoiding latency by increasing concurrency. Optimistically assuming the results of one computation allows other computations to execute in parallel, even when they depend on the assumed result. Optimistic techniques can particularly benefit distributed systems because of the critical impact of communications latency. This paper reviews HOPE: our model of optimistic programming, and describes how optimism can enhance distributed program performance by avoiding remote communications delay. We then present the wait-free algorithm used to implement HOPE in a distributed environment.

Management policies can be used to specify requirements about the desired behaviour of distributed systems. Violations of policies (faults) can then be detected, isolated, located and corrected using a policy-driven fault management system. Other work in this area to date has focused on network-level faults. We believe that in a distributed system it is more appropriate to focus on faults at the application level. Furthermore, this work has been largely domain-specific-a generic, structured approach to this problem is needed. Our work has focused on policy-driven fault management in distributed systems at the application level. In this paper, we define a generic architecture for policy-driven fault management and present a prototype system based on this architecture. We also discuss experience to date using and experimenting with our prototype system.

Optimism is a powerful technique for avoiding latency by increasing concurrency. By optimistically assuming the results of some computation, other computations can be executed in parallel, even when they depend on the assumed result. Optimistic techniques can be particularly beneficial to parallel and distributed systems because of the critical impact of inter-node communications latency. This paper describes how optimism can be used to enhance the performance of distributed programs by avoiding remote communications delay. We then present a new programming model that automates many of the difficulties of using optimistic techniques in a general programming environment, and describe a prototype implementation. Finally, we present performance measurements showing how optimism improved the performance of a test application in this environment.

Article Formal semantics for expressing optimism: the meaning of HOPE Share on Authors: Crispin Cowan Department of Computer Science and Engineering, Oregon Graduate Institute, P.O. Box 91000 Portland, OR Department of Computer Science and Engineering, Oregon Graduate Institute, P.O. Box 91000 Portland, ORView Profile , Hanan Lutfiyya Computer Science Department, Middlesex College, University of Western Ontario, London, Ontario N6A 5B7 Computer Science Department, Middlesex College, University of Western Ontario, London, Ontario N6A 5B7View Profile Authors Info & Claims PODC '95: Proceedings of the fourteenth annual ACM symposium on Principles of distributed computingAugust 1995 Pages 164–173https://doi.org/10.1145/224964.224983Online:20 August 1995Publication History 4citation206DownloadsMetricsTotal Citations4Total Downloads206Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

One approach for providing fault tolerance is through examining the behavior and properties of the application and deriving executable assertions that detect faults. This paper focuses on transforming the assertions of a verification proof of a program to executable assertions. These executable assertions may be embedded in the program to create a fault-tolerant program. It is also shown how the natural redundancy of the program variables can be used to reduce the number of executable assertions needed. While this approach has been applied to the sequential programming environment, the distributed programming environment presents special challenges. The authors discuss the application of concurrent programming axiomatic proof systems to generate executable assertions in a distributed environment using distributed branch and bound as a model problem.>