Digitalization and Innovation

Developments in digital technology offer new opportunities to design new products and services. However, creating such digitalized products and services often creates new problems and challenges to firms that are trying to innovate. In this essay, we analyze the impact of digitalization of products and services on innovations. In particular, we argue that digitalization of products will lead to an emergence of new layered product architecture. The layered architecture is characterized by its generative design rules that connect loosely coupled heterogeneous layers. It is pregnant with the potential of unbounded innovations. The new product architecture will require organizations to adopt a new organizing logic of innovation that we dubbed as doubly distributed innovation network. Based on this analysis, we propose five key issues that future researchers need to explore.innovation, innovation, product architecture, design rules

Industrial districts as organizational environments: resources, networks and structures

The paper combines economic and sociological perspectives on organizations in order to gain a better understanding of the forces shaping the structures of industrial districts (IDs) and the organizations of which they are constituted. To effect the combination , the resource based view (RBV) and resource dependency theory are combined to explain the evolution of different industry structures. The paper thus extends work by Toms and Filatotchev by spatializing consideration of resource distribution and resource dependence. The paper has important implications for conventional interpretations in the fields of business and organizational history and for the main areas of theory hitherto considered separately, particularly the Chandlerian model of corporate hierarchy as contrasted with the alternative of clusters of small firms coordinated by networks

Industrial districts as organizational environments: resources, networks and structures

The paper combines economic and sociological perspectives on organizations in order to gain a better understanding of the forces shaping the structures of industrial districts (IDs) and the organizations of which they are constituted. To effect the combination , the resource based view (RBV) and resource dependency theory are combined to explain the evolution of different industry structures. The paper thus extends work by Toms and Filatotchev by spatializing consideration of resource distribution and resource dependence. The paper has important implications for conventional interpretations in the fields of business and organizational history and for the main areas of theory hitherto considered separately, particularly the Chandlerian model of corporate hierarchy as contrasted with the alternative of clusters of small firms coordinated by networks.clustering; dynamics; resource-based views; resource dependency

Traffic-Driven Energy Efficient Operational Mechanisms in Cellular Access Networks

Recent explosive growth in mobile data traffic is increasing energy consumption in cellular networks at an incredible rate. Moreover, as a direct result of the conventional static network provisioning approach, a significant amount of electrical energy is being wasted in the existing networks. Therefore, in recent time, the issue of designing energy efficient cellular networks has drawn significant attention, which is also the foremost motivation behind this research. The proposed research is particularly focused on the design of self-organizing type traffic-sensitive dynamic network reconfiguring mechanisms for energy efficiency in cellular systems. Under the proposed techniques, radio access networks (RANs) are adaptively reconfigured using less equipment leading to reduced energy utilization. Several energy efficient cellular network frameworks by employing inter-base station (BS) cooperation in RANs are proposed. Under these frameworks, based on the instantaneous traffic demand, BSs are dynamically switched between active and sleep modes by redistributing traffic among them and thus, energy savings is achieved. The focus is then extended to exploiting the availability of multiple cellular networks for extracting energy savings through inter-RAN cooperation. Mathematical models for both of these single-RAN and multi-RAN cooperation mechanisms are also formulated. An alternative energy saving technique using dynamic sectorization (DS) under which some of the sectors in the underutilized BSs are turned into sleep mode is also proposed. Algorithms for both the distributed and the centralized implementations are developed. Finally, a two-dimensional energy efficient network provisioning mechanism is proposed by jointly applying both the DS and the dynamic BS switching. Extensive simulations are carried out, which demonstrate the capability of the proposed mechanisms in substantially enhancing the energy efficiency of cellular networks

Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters

Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes the appearance of a new spectrum of services with sophisticated workload and resource management requirements. Also, data centers are growing by addition of various type of hardware to accommodate the ever-increasing requests of users. Nowadays a large percentage of cloud resources are executing data-intensive applications which need continuously changing workload fluctuations and specific resource management. To this end, cluster computing frameworks are shifting towards distributed resource management for better scalability and faster decision making. Such systems benefit from the parallelization of control and are resilient to failures. Throughout this thesis we investigate algorithms, protocols and techniques to address these challenges in large-scale data centers. We introduce a distributed resource management framework which consolidates virtual machine to as few servers as possible to reduce the energy consumption of data center and hence decrease the cost of cloud providers. This framework can characterize the workload of virtual machines and hence handle trade-off energy consumption and Service Level Agreement (SLA) of customers efficiently. The algorithm is highly scalable and requires low maintenance cost with dynamic workloads and it tries to minimize virtual machines migration costs. We also introduce a scalable and distributed probe-based scheduling algorithm for Big data analytics frameworks. This algorithm can efficiently address the problem job heterogeneity in workloads that has appeared after increasing the level of parallelism in jobs. The algorithm is massively scalable and can reduce significantly average job completion times in comparison with the-state of-the-art. Finally, we propose a probabilistic fault-tolerance technique as part of the scheduling algorithm