Dynamic Service Provisioning Research Articles

Pathways of development of dynamic capabilities for servitization transformation: A longitudinal multi-case study

Servitization is a transformation process requiring manufacturers to develop dynamic capabilities to support the change process and overcome emerging challenges over time. In this paper, we study pathways of development of dynamic capabilities for servitization transformation (the sequence of the development of capabilities and how they work together over time) and how they relate to servitization transformation outcomes. We do so based on six longitudinal in-depth case studies of manufacturing firms which, having departed from similar servitization maturity starting points, followed different capability development pathways in their transformation processes and achieved different outcomes. We found that successful pathways of development of capabilities for servitization transformation are associated with (1) developing (first-order) dynamic service provision capabilities sequentially, following a specific order over time and (2) developing (second-order) dynamic reconfiguring capabilities to overcome challenges and sustain the development of service provision capabilities and the transformation process. Our study contributes to the literature by providing an in-depth understanding of how the pathways of development of dynamic capabilities over time influence the outcomes of the servitization transformation process. It is one of the first studies to unveil in detail mechanisms by which different reconfiguring and service provision capabilities work together over time to facilitate the servitization transformation process.

Egalitarian Transient Service Composition in Crowdsourced IoT Environment

The Crowdsourced IoT Service (CIS) market is inherently different from other service markets, e.g., web services and cloud. The CIS market is dominated by transient services as both consumers and providers are dynamic in space and time. Consumer requests are usually long-term and demand continuity in service provision. We propose a novel egalitarian transient service composition framework from the CIS market perspective. We apply a Dynamic Bayesian Network to model the dynamic service provision behavior of the providers. The proposed framework transforms the composition of transient services into a multi-objective temporal optimization, i.e., providing continuous services to the maximum number of consumers, and minimizing the consumers' cost of service usages over a long-term period. We incorporate a Pareto-based genetic algorithm to enable the fair distribution of services among the consumers. Experimental results prove the efficiency of the proposed approach in terms of continuous availability of service as well as fair distribution among consumers.

Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm

The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

Reducing blocking probability and QoT violation in dynamic elastic optical networks via load-aware margin selection

In this paper, we study the effect of load-aware margin selection on the performance of elastic optical networks (EONs) in dynamic service provisioning. First, a mixed-integer linear programming (MILP) formulation is proposed as a benchmark for dynamic routing, modulation level, spectrum, and power assignment (RMSPA). In this formulation, nonlinear interferences (NLIs) in optical fibers and the noise of amplifiers are considered. In addition, a margin which is calculated based on the network load is considered to estimate the additional noise which may degrade the signal quality of transmission (QoT) during its life time. The objective of this formulation is to minimize the spectrum usage and impairments of/from the signal which is optimized by choosing the optimum channel among a number of pre-calculated channels based on the QoT constraints. In order to reduce the RMSPA run time, heuristic algorithms are also proposed. The algorithms employ K-least weighted paths for routing. The defined weights are distance, the amount of traffic load, and a mixture scheme. Furthermore, the algorithms try to minimize the spectrum usage by choosing the highest possible modulation level while considering a specific criterion. The assumed criteria are: i) choosing the lowest possible power spectral density (PSD) in a discrete set, ii) selecting the optimum power among the set, iii) obtaining maximum SNR. We simulated the proposed algorithms and the MILP, and compared their performances on NSFNET and US Backbone topologies. The results reveal that the constrained best SNR scheme has the best performance among the heuristics and its performance is close to MILP, especially in higher loads. Moreover, results show that considering the links congestion leads to blocking reduction. We also simulated the first-fit based algorithms without considering NLIs which assign the modulation based on: i) considering only amplifier noise and self-channel interference (SCI), ii) experimental distance thresholds, and iii) overestimating the NLI. The results imply that the proposed MILP and heuristic algorithms outperform the other algorithms in terms of the summation of outage (probability of QoT violation) and blocking probability.

Dynamic service provisioning of hybrid advanced and immediate requests in SDM-EONs

Hybrid immediate request (IR) and advanced reservation (AR) is an inevitable traffic model for future SDM-EONs to accommodate uncertainty and abundance of services. Different from the traditional IR handling instant services like chat messaging, AR can balance the traffic load by postponing allocation to delay-tolerant requests such as cloud computing, e-science, IPTV etc. Although IR/AR mixed traffics are essential in future SDM-EONs, several problems arise especially when solving dynamic RSCA problems. Most importantly, inter-core crosstalk (XT) and resource fairness need to be considered to satisfy high-quality transmission and to avoid unnecessary resource occupation of formerly arriving AR requests.We propose a dynamic HTA-RSCA algorithm that combines time recycling and reuse mechanisms in the time domain with routing, spectrum and core assignment (RSCA) in the spatial-spectrum domain. Specially, pretreatment techniques include super-channel (Sp-Ch) order pre-definition and time-spectrum partition. These appropriate operations are ready for efficient resource searching and requests assignment. Taking transmission quality into consideration, XT-avoid principle is involved in Sp-Ch order pre-definition and dynamic time domain mechanisms to realize the fair and flexible provisioning. XT-awareness is included in the final allocation step to rank available resources options. Simulation results confirm that the dynamic HTA-RSCA can reduce network blocking probability (BP) than comparison algorithms. To the best of our knowledge, this is the first attempt applying time mechanisms to address dynamic provisioning of hybrid IR and AR requests in SDM-EONs.

Guest Editorial: Artificial Intelligence Empowered Future Connection

Recently, artificial intelligence (AI) has seen great success in solving problems from various domains. It is envisioned that AI and Machine Learning (ML) will be an important technology for the successful development of future networks, especially in the dynamic service provisioning, adaptive traffic control, and security issues. Compared to meticulously manually designed strategies, AI&ML provides a generalized model and uniform learning method without prespecified processes for various network scenarios. Until now, limited research efforts have been done in Al-ernpowered future communications networks.

Elastic Metro Ethernet and Elastic Cloud Services

Lifecycle Services Orchestration (LSO) architecture for Metro Ethernet Services was introduced by MEF. In the LSO architecture, the ALLEGRO interface was defined for dynamic service control between customer and Service Provider while the INTERLUDE interface was defined between Orchestrators of two Operators to initiate a technical operation or dynamic service control. In order to address dynamic control of services supported by networks of multiple operators over ALLEGRO and INTERLUDE interfaces, Elastic E-Line Service was introduced by the author in MEF. In addition, Cloud Services Architecture was introduced as an architectural framework for virtual services by the author. In this paper, we describe LSO architecture and Elastic E-Line Service concept, and interactions between various LSO entities for dynamic service provisioning. After that, we describe Cloud Services Architecture and dynamic modifications of Elastic Cloud Services over ALLEGRO and INTERLUDE interfaces, for the first time.

Energy-Efficient Stable and Balanced Task Scheduling in Data Centers

It is well known that load balancing in data centres can lead to unnecessary energy usage if all servers are kept active. Using dynamic server provisioning, the number of servers that serve requests can be reduced by turning off idle servers and thereby saving energy. However, such a scheme, usually increases the risk of instability of server queues. In this work, we analyze the trade-off between energy usage and stability of servers in a data center when we balance the load by dispatching arriving jobs. We propose algorithms to solve a stability and energy objective stochastic optimization problem with a high degree of flexibility to handle the trade-off between these two objectives. We consider variable size jobs to apply load balancing on selected active servers and find that the optimal solution is an NP-hard problem. We therefore develop two computationally efficient greedy and randomized approximation schemes to achieve the trade-off between these objectives. We investigate the performance of our proposed algorithms in minimizing the risk of queue length growth as well as the number of active servers needed to serve jobs, and compare it with several metrics in heterogeneous load scenarios.

Reliable Optical Networks With ODTN: Resiliency and Fail-Over in Data and Control Planes

This article reports a technical demonstration showing the use of the ONOS SDN controller for disaggregated transport networks, summarizing the latest developments and results within the Open Networking Foundation (ONF) Open Disaggregated Transport Networks (ODTN) project. The demonstration mainly covers the dynamic provisioning of data connectivity services and advanced automatic failure recovery, both at the control and data plane levels. For the provisioning, we demonstrate the usage of open and standard protocols and interfaces. For the recovery part, we first demonstrate, covering the control plane, how ONOS can behave as a logically centralized controller while using multiple coordinated instances for robustness. We show how the different devices remain under control even in the event of a failure of one of such instances, relying on the ATOMIX framework and the dynamic real-time negotiation of device mastership. For the data plane, we demonstrate the capabilities of the controller to perform automatic restoration of optical services.

Heuristic Access Points Grouping for Mobility Driven User-Centric Ultra Dense Networks

In ultra-dense network (UDN) scenarios the big number of users and dense placement is comparable to those of the Access Points (APs). In such UDNs new approaches for mobility management are necessary to ensure a reliable service provision, seamless connectivity and high unit area throughput. Dynamic access point grouping for user service provision is considered to be a core function of the UDN with user centric wireless access. In this paper, a heuristic approach with low computational complexity for AP grouping for mobility driven user-centric UDN is proposed, based on two general metrics reflecting the user density and distribution, user requirements and available resources in the APs. This allows the AP grouping process and user association to one or a group of APs to be represented as a dynamic service provision system and its performance as a function of user allocation, user mobility and user service requests to be more effective.

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

Generic input template for cloud simulators: A case study of CloudSim

SummaryCloud computing and its service models, such as Platform as a Service (PaaS), have changed the way that computing resources are allocated to Information and Communications Technology enterprises and users. Although multiple cloud providers support dynamic service provisioning, it is necessary to facilitate the management of the cloud infrastructure and applications in order to allow the continuous refinement of cloud models. Therefore, issues are raised regarding the cloud orchestration, including the flexible portability and interoperability of cloud applications among multiple cloud providers. Having said that, there is a need for a standardized design and management of the cloud use cases (during the creation of scenarios, application's deployment, and patching) to ensure efficient applications' migration between different providers. This paper proposes an artifact, GITS, a generic input template for CloudSim and other cloud simulators. GITS can be provided by PaaS offering to manage the creation, monitoring, administration, and patching of infrastructure and applications in the cloud. GITS defines the cloud schema that can be used with conforming cloud models and independent cloud providers; thus, portability and interoperability can be enabled in PaaS cloud models. GITS focuses on the architecture‐based modeling for cloud infrastructure and application not only in terms of computational resources but also in terms of high availability properties associated with infrastructure and applications. The main objective of the GITS template is to provide the cloud user with a modular, simple, readable, and reusable model that still supports the essential components and provide them with the ability to control the applications' execution, deployment, and other management needs in addition to the allocation environment. This paper describes GITS usage, specifically as an input template for CloudSim.

A review and future directions of SOA-based software architecture modeling approaches for System of Systems

Software architecture is a software system’s earliest set of design decisions that are critical for the quality of the system desired by the stakeholders. The architecture makes it easier to reason about and manage change during different phases of complex software life cycle. The modeling of software architecture for System of Systems (SoS) is a challenging task because of a system’s complexity arising from an integration of heterogeneous, distributed, managerially and operationally independent systems collaborating to achieve global missions. SoS is essentially dynamic and evolutionary by design requiring suitable architectural patterns to deal with runtime volatility. Service-oriented architecture offers several architectural features to these complex systems; these include, interoperability, loose coupling, abstraction and the provision of dynamic services based on standard interfaces and protocols. There is some research work available that provides critical analysis of current software architecture modeling approaches for SoS. However, none of them outlines the important characteristics of SoS or provides detailed analysis of current service-oriented architecture modeling approaches to model those characteristics. This article addresses this research gap and provides a taxonomy of software architecture modeling approaches, comparing and contrasting them using criteria critical for realization of SoS. Additionally, research gaps are identified, and future directions are outlined for building software architecture for SoS to model and reason about architecture quality in a more efficient way in service-oriented paradigm.

Towards complex dynamic fog network orchestration using embedded neural switch

ABSTRACT Cloud data centers used for High Performance Computing (HPC) with volatile Internet of Things (IoT) devices absolutely requires zero-speed switching/low latency, normalized throughput, network stability with near zero packet drops during heavy traffic workload. Deadlock traffic condition found in baseline Fog-nodes results when there is no dynamic provisioning of services at Fog layer. In this case, Quality of Service (QoS) metrics of Service Level Agreements (SLA) are violated. Motivated by these concerns, this paper proposes Smart Hierarchical Network (SHN) as a reliable Fog dynamic design structure based on Software Defined Artificial Neural Network (SD-ANN). It features congestion-aware neural switch model with embedded predictive receding horizon for intelligent congestion management. The goal is to exploit the locally optimized Fog neural switch connections and maximize the overall QoS while satisfying the enormous traffic workload requirements. A sampled real-world trace-file workload from Galaxy backbone, Nigeria, is compared with SHN for Fog service provisioning. It is shown that with receding horizon, the ANN-based model ideally offers 100% throughput R value. Under the established training scenarios, the ANN switch offers the lowest mean square error while yielding acceptable QoS metrics. The result is significant for scalable networks supporting massive computational workloads.

New kid on the block

Network management currently undergoes massive changes towards realizing more flexible management of complex networks. Recent efforts include slicing data plane resources by network (link) virtualization and applying operating system design principles to Software Defined Networking to rethink network management. Driven by network operators, network management principles are currently envisioned to be even further improved by virtualizing network (middlebox) functions. The resulting Network Functions Virtualization (NFV) paradigm abstracts network functions from dedicated hardware to virtual machines running on commodity hardware. This change in the design of carrier networks is inspired by the success of virtualization in the server market. By deploying NFV, network operators envision to achieve benefits similar to the server market and elastic cloud services, e.g., flexible and dynamic service provisioning, increased resource utilization, improved energy efficiency, and ultimately decreased operational costs. Despite these efforts, the ability of NFV to satisfy performance demands is often questioned. Tackling these challenges opens a set of research questions that felt short in the current discussion and are of particular relevance to the SIGCOMM community. In this position paper, we therefore provide an overview on the current state-of-the-art and open research questions.

Controllable Multicast for Adaptive Scalable Video Streaming in Software-Defined Networks

Scalable video coding is a promising technique to enable flexible video transmission for heterogeneous terminals and varying channel throughput. However, it is challenging to perform in-network adaptation in conventional networks because the network nodes are uncontrollable and transparent for media streaming applications. Software-defined networking (SDN) is an attractive network technology that supports the applications to collaborate with network nodes to achieve intelligent and dynamic service provisioning. Against this changing network landscape, we redesign the scalable multimedia multicast streaming by exploiting the complete network knowledge of the SDN controller to enable intelligent scalable video transmission. The proposed scalable multimedia multicast streaming framework is capable of in-network identifying, processing, and manipulating the media streams. In order to achieve the in-network adaptation, we apply equivalent bandwidth theory to estimate the affordable video layers that a link may accommodate, and apply finite-state machine to implement adaptive enhancement layer switching for multicast paths. In contrast to IP multicast, the proposed method is a controllable multicast scheme, which provides admission control in a multicast context, in-network adaptation, and supporting heterogeneous devices having different display capability. We further implement a prototype for illustrating the success of the proposed solution. The experimental results are also presented to show the effectiveness of the proposed equivalent bandwidth based adaptive enhancement layer switching algorithm.

Dynamic Service Provisioning and Selection for Satisfying Cloud Applications and Cloud Providers in Hybrid Cloud

The paper presents a hybrid cloud service provisioning and selection optimization scheme, and proposes a hybrid cloud model which consists of hybrid cloud users, private cloud and public cloud. This scheme aims to effectively provide cloud service and allocate cloud resources, such that the system utility can be maximized subject to public cloud resource constraints and hybrid cloud users constraints. The paper makes use of a utility-driven approach to solve interaction among private cloud user, hybrid cloud service provider and public cloud provider in hybrid cloud environment. The paper presents hybrid cloud service provisioning and selection algorithm in hybrid cloud. The hybrid cloud market consists of hybrid cloud user agent, hybrid cloud service agent and hybrid cloud agent, which represent the interests of different roles. The experiments are designed to compare the performance of proposed algorithm with the other related work.

Extending Demand Response to Tenants in Cloud Data Centers via Non-Intrusive Workload Flexibility Pricing

Participating in demand response programs is a promising tool for reducing energy costs in data centers by modulating energy consumption. Toward this end, data centers can employ a rich set of resource management knobs, such as workload shifting and dynamic server provisioning. Nonetheless, these knobs may not be readily available in a cloud data center (CDC) that serves cloud tenants/users, because workloads in CDCs are managed by tenants themselves who are typically charged based on a usage-based or flat-rate pricing and often have no incentive to cooperate with the CDC operator for demand response and cost saving. Toward breaking such “split incentive” hurdle, a few recent studies have tried market-based mechanisms, such as dynamic pricing, inside CDCs. However, such mechanisms often rely on complex designs that are hard to implement and difficult to cope with by tenants. To address this limitation, we propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing for cloud resources unchanged for a long period. While it charges tenants based on a usage-based pricing (UP) as used by today's major cloud operators, it rewards tenants proportionally based on the time length that tenants set as deadlines for completing their workloads. This new mechanism is called UP with monetary reward (UPMR). We demonstrate the effectiveness of UPMR both analytically and empirically, showing: 1) UPMR can effectively reduce the CDC's peak power consumption and energy cost without decreasing the CDC's profit and 2) UPMR outperforms the state-of-the-art approaches that are used by today's CDC operators to charge their tenants in terms of the profit gained by the CDC.

Fog Computing as a Support for 5G Network

5G will enable new future Internet of Services (IoSs) paradigms such as Anything as a Service (AaaS), where devices, terminals, machines, also smart things and robots will become innovative tools that will produce and will use applications, services and data. However, 5G will have to support huge mobile traffic volumes, and will also have to deal with the rapid increase of new and complex applications and services. On the other hand, Fog Computing, which extends Cloud Computing to the edge of the network, with its service orchestration mechanisms offers virtually unlimited dynamic resources for computation, storage and service provision, that will overcome the constraints of smart mobile devices. 5G in the fog computing environment will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates Fog Computing as a support mechanism for 5G Network in terms of latency, throughput, and energy efficiency.

Service-actuated multi-channel operation for vehicular communications

We present a novel approach for multi-channel operation (MCO) in vehicular communication systems, which allows for efficient utilization of the available bandwidth by asynchronous channel switching and enables dynamic service provisioning and usage by means of service advertisements. The proposed solution – Service-Actuated Multi-Channel Operation (SAMCO) – provides a logic that controls the prioritization of services and the timing of channel switching. It takes into account users preferences to decide on the consumption of a particular service if several concurrent services are available. SAMCO employs a novel channel load estimation scheme that, in addition to measuring the load on the channel at the physical layer, exploits the information contained in service advertisements. We perform simulations and use platooning as an example of a service with particularly stringent requirements to show that SAMCO can support service prioritization, continuity of service for high-priority services, and graceful degradation for low-priority services. Furthermore, by limiting the admission to services in high load scenarios, we show that SAMCO effectively controls the channel load and thereby complements congestion control mechanisms. Finally, we discuss the extensions needed in currently standardized solutions to implement SAMCO.