User subscription-based resource management for Desktop-as-a-Service platforms

The Desktop-as-a-Service (DaaS) idiom consists of utilizing a cloud or other server infrastructure to host the user's desktop environment as a virtual desktop. Typical for cloud and DaaS services is the pay-as-you-go pricing model in combination with the availability of multiple subscription types to accommodate the needs of the users. However, optimal cost-efficient allocation of the virtual desktops to the infrastructure proves to be a combinatorial NP-hard problem, for which a heuristic is presented in the current article. We present a cost model for the DaaS service, from which a revenue of different configurations of virtual desktops to the servers can be derived. In this cost model, both subscription fee and penalties for degraded service are recorded, that are described in service-level agreements (SLAs) between the service provider and the users, and make realistic assumptions that different subscription types result in particular SLA contracts. The heuristic proposed states that for a given user base for which the virtual desktops (VDs) must be hosted, the VDs should be spread evenly over the infrastructure. Experiments through discrete event simulation show that this heuristic yields an approximation within 1 % of the theoretically achievable revenue

TVA: A Requirements Driven, Machine-Learning Approach for Addressing Tactic Volatility in Self-Adaptive Systems

From self-driving cars to self-adaptive websites, the world is increasingly becoming more reliant on autonomous systems. Similar to many other domains, the system\u27s behavior is often determined by its requirements. For example, a self-adaptive web service is likely to have some maximum value that response time should not surpass. To maintain this requirement, the system uses tactics, which may include activating additional computing resources. In real-world environments, tactics will frequently experience volatility, known as tactic volatility. This can include unstable time required to execute the tactic or frequent fluctuations in the cost to execute the tactic. Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics have static attributes. To address the limitations in current processes, we propose a Tactic Volatility Aware (TVA) solution. Our approach focuses on providing a volatility aware solution that enables the system to properly maintain requirements. Specifically, TVA utilizes a Autoregressive Integrated Moving Average Model (ARIMA) to estimate potential future values for requirements, while also using a Multiple Regression Analysis (MRA) model to make predictions of tactic latency and tactic cost at runtime. This enables the system to both better estimate the true behavior of its tactics and it allows the system to properly maintain its requirements. Using data containing real-world volatility, we demonstrate the effectiveness of using TVA with both statistical analysis methods and self-adaptive experiments. In this work, we demonstrate (I) The negative impact of not accounting for tactic volatility (II) The benefits of a ARIMA-modeling approach in monitoring system requirements (III) The effectiveness of MRA in predicting tactic volatility (IV) The overall benefits of TVA to the self-adaptive process. This work also presents the first known publicly available dataset of real-world tactic volatility in terms of both cost and latency

Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

Diffusion of network innovation : implications for adoption of internet services

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 51-53).by Mark S. Shuster.B.S.M.Eng

Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility

When self-adaptive systems encounter changes within their surrounding environments, they enact tactics to perform necessary adaptations. For example, a self-adaptive cloud-based system may have a tactic that initiates additional computing resources when response time thresholds are surpassed, or there may be a tactic to activate a specific security measure when an intrusion is detected. In real-world environments, these tactics frequently experience tactic volatility which is variable behavior during the execution of the tactic. Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics do not experience volatility. This limitation creates uncertainty in the decision-making process and may adversely impact the system's ability to effectively and efficiently adapt. Additionally, many processes do not properly account for volatility that may effect the system's Service Level Agreement (SLA). This can limit the system's ability to act proactively, especially when utilizing tactics that contain latency. To address the challenge of sufficiently accounting for tactic volatility, we propose a Tactic Volatility Aware (TVA) solution. Using Multiple Regression Analysis (MRA), TVA enables self-adaptive systems to accurately estimate the cost and time required to execute tactics. TVA also utilizes Autoregressive Integrated Moving Average (ARIMA) for time series forecasting, allowing the system to proactively maintain specifications