Monitoring the Complexity of IT Architectures: Design Principles and an IT Artifact

Monitoring the complexity of a firm’s IT architecture is imperative to ensure a stable and flexible platform foundation for competing in the era of digital business strategy. However, IT architects lack IT support for dealing with this important problem. We engaged with five companies in a significant design science research (DSR) program and drew on the heuristic theorizing framework both to solve this problem through evolving IT artifacts and to accumulate nascent design knowledge. We base the design knowledge development on a conceptual framework involving three essential concepts for understanding and solving this problem: structural complexity, dynamic complexity, and problem-solving complexity. Drawing on this foundation, we address the research question: How can IT support be provided for reducing the problem-solving complexity of monitoring the structural and dynamic complexity of IT architectures in the context of a digital business strategy? To answer this question, we present a set of design principles that we derived from our iterative process of IT artifact construction and evaluation activities with five companies. Our nascent design knowledge contributes to the research on IT architecture management in the context of digital business strategy. In addition, we also contribute to the understanding of how, through the use and illustration of the heuristic theorizing framework, design knowledge can be accumulated systematically on the basis of generalization from IT artifact construction and evaluation outcomes generated across multiple contexts and companies

Data processing in high-performance computing systems

The paper integrates the results of a large group of authors working in different areas that are important in the scope of big data, including but not limited to: overview of the basic solutions for the development of data centers; storage and processing; decomposition of a problem into sub-problems of lower complexity (such as applying divide and conquer algorithms); models and methods allowing broad parallelism to be realized; alternative techniques for potential acceleration; programming languages; and practical applications

Neural Packet Classification

Packet classification is a fundamental problem in computer networking. This problem exposes a hard tradeoff between the computation and state complexity, which makes it particularly challenging. To navigate this tradeoff, existing solutions rely on complex hand-tuned heuristics, which are brittle and hard to optimize. In this paper, we propose a deep reinforcement learning (RL) approach to solve the packet classification problem. There are several characteristics that make this problem a good fit for Deep RL. First, many of the existing solutions are iteratively building a decision tree by splitting nodes in the tree. Second, the effects of these actions (e.g., splitting nodes) can only be evaluated once we are done with building the tree. These two characteristics are naturally captured by the ability of RL to take actions that have sparse and delayed rewards. Third, it is computationally efficient to generate data traces and evaluate decision trees, which alleviate the notoriously high sample complexity problem of Deep RL algorithms. Our solution, NeuroCuts, uses succinct representations to encode state and action space, and efficiently explore candidate decision trees to optimize for a global objective. It produces compact decision trees optimized for a specific set of rules and a given performance metric, such as classification time, memory footprint, or a combination of the two. Evaluation on ClassBench shows that NeuroCuts outperforms existing hand-crafted algorithms in classification time by 18% at the median, and reduces both time and memory footprint by up to 3x

Cross-layer Peer-to-Peer Computing in Mobile Ad Hoc Networks

The future information society is expected to rely heavily on wireless technology. Mobile access to the Internet is steadily gaining ground, and could easily end up exceeding the number of connections from the fixed infrastructure. Picking just one example, ad hoc networking is a new paradigm of wireless communication for mobile devices. Initially, ad hoc networking targeted at military applications as well as stretching the access to the Internet beyond one wireless hop. As a matter of fact, it is now expected to be employed in a variety of civilian applications. For this reason, the issue of how to make these systems working efficiently keeps the ad hoc research community active on topics ranging from wireless technologies to networking and application systems. In contrast to traditional wire-line and wireless networks, ad hoc networks are expected to operate in an environment in which some or all the nodes are mobile, and might suddenly disappear from, or show up in, the network. The lack of any centralized point, leads to the necessity of distributing application services and responsibilities to all available nodes in the network, making the task of developing and deploying application a hard task, and highlighting the necessity of suitable middleware platforms. This thesis studies the properties and performance of peer-to-peer overlay management algorithms, employing them as communication layers in data sharing oriented middleware platforms. The work primarily develops from the observation that efficient overlays have to be aware of the physical network topology, in order to reduce (or avoid) negative impacts of application layer traffic on the network functioning. We argue that cross-layer cooperation between overlay management algorithms and the underlying layer-3 status and protocols, represents a viable alternative to engineer effective decentralized communication layers, or eventually re-engineer existing ones to foster the interconnection of ad hoc networks with Internet infrastructures. The presented approach is twofold. Firstly, we present an innovative network stack component that supports, at an OS level, the realization of cross-layer protocol interactions. Secondly, we exploit cross-layering to optimize overlay management algorithms in unstructured, structured, and publish/subscribe platforms