Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

Toward Customizable Multi-tenant SaaS Applications

abstract: Nowadays, Computing is so pervasive that it has become indeed the 5th utility (after water, electricity, gas, telephony) as Leonard Kleinrock once envisioned. Evolved from utility computing, cloud computing has emerged as a computing infrastructure that enables rapid delivery of computing resources as a utility in a dynamically scalable, virtualized manner. However, the current industrial cloud computing implementations promote segregation among different cloud providers, which leads to user lockdown because of prohibitive migration cost. On the other hand, Service-Orented Computing (SOC) including service-oriented architecture (SOA) and Web Services (WS) promote standardization and openness with its enabling standards and communication protocols. This thesis proposes a Service-Oriented Cloud Computing Architecture by combining the best attributes of the two paradigms to promote an open, interoperable environment for cloud computing development. Mutil-tenancy SaaS applicantions built on top of SOCCA have more flexibility and are not locked down by a certain platform. Tenants residing on a multi-tenant application appear to be the sole owner of the application and not aware of the existence of others. A multi-tenant SaaS application accommodates each tenant’s unique requirements by allowing tenant-level customization. A complex SaaS application that supports hundreds, even thousands of tenants could have hundreds of customization points with each of them providing multiple options, and this could result in a huge number of ways to customize the application. This dissertation also proposes innovative customization approaches, which studies similar tenants’ customization choices and each individual users behaviors, then provides guided semi-automated customization process for the future tenants. A semi-automated customization process could enable tenants to quickly implement the customization that best suits their business needs.Dissertation/ThesisDoctoral Dissertation Computer Science 201

BRAHMA : an intelligent framework for automated scaling of streaming and deadline-critical workflows

The prevalent use of multi-component, multi-tenant models for building novel Software-as-a-Service (SaaS) applications has resulted in wide-spread research on automatic scaling of the resultant complex application workflows. In this paper, we propose a holistic solution to Automatic Workflow Scaling under the combined presence of Streaming and Deadline-critical workflows, called AWS-SD. To solve the AWS-SD problem, we propose a framework BRAHMA, that learns workflow behavior to build a knowledge-base and leverages this info to perform intelligent automated scaling decisions. We propose and evaluate different resource provisioning algorithms through CloudSim. Our results on time-varying workloads show that the proposed algorithms are effective and produce good cost-quality trade-offs while preventing deadline violations. Empirically, the proposed hybrid algorithm combining learning and monitoring, is able to restrict deadline violations to a small fraction (3-5%), while only suffering a marginal increase in average cost per component of 1-2% over our baseline naive algorithm, which provides the least costly provisioning but suffers from a large number (35-45%) of deadline violations

SensorCloud: Towards the Interdisciplinary Development of a Trustworthy Platform for Globally Interconnected Sensors and Actuators

Although Cloud Computing promises to lower IT costs and increase users' productivity in everyday life, the unattractive aspect of this new technology is that the user no longer owns all the devices which process personal data. To lower scepticism, the project SensorCloud investigates techniques to understand and compensate these adoption barriers in a scenario consisting of cloud applications that utilize sensors and actuators placed in private places. This work provides an interdisciplinary overview of the social and technical core research challenges for the trustworthy integration of sensor and actuator devices with the Cloud Computing paradigm. Most importantly, these challenges include i) ease of development, ii) security and privacy, and iii) social dimensions of a cloud-based system which integrates into private life. When these challenges are tackled in the development of future cloud systems, the attractiveness of new use cases in a sensor-enabled world will considerably be increased for users who currently do not trust the Cloud.Comment: 14 pages, 3 figures, published as technical report of the Department of Computer Science of RWTH Aachen Universit

Beyond Co-Location: Clustering the Social Economy

There is an absence of a coherent body of knowledge that allows one to understand the strategies and motivations that have resulted in the clustering approach. This project has three objectives: 1. Provide a literature review on the application of cluster models both within traditional economic sectors as well as within the social economy. 2. Identify exemplary cases (within BC and Alberta, across North America as well as abroad) in the form of short, descriptive case profiles. 3. Develop a case study framework to use in subsequent phases to understand organizational best practices and outcomes. Based on an analysis of 14 exemplary cases four key patterns emerge: 1. Most centres are found in urban centres, located primarily in downtown cores or business districts. 2. There is a strong connection to heritage preservation and restoration. 3. There is a range of community animation and tenant engagement initiatives aimed at collaboration, innovation and learning. 4. Some of the newest models include for-profit companies, social enterprises and social entrepreneurs as well as non-profit organizations.This is the final report of BALTA research project D4 - Sustainable Infrastructure for the Social Economy: Cluster-Based Social Enterprise Models. Political and economic restructuring over the past 30 years has had a profound impact on the social economy, particularly for non-profit organizations. In the wake of state withdrawal of services and significant funding cutbacks, many non-profit organizations have taken on greater responsibility for addressing social needs and environmental concerns within communities with increasingly limited resources. Many non-profits are partnering with social enterprises in common spaces to share resources that is resulting in a reduction of overhead costs and creating greater efficiencies, often referred to as co-location. Social economy organizations are moving beyond co-location by adopting a clustering approach that has resulted in dynamic centres of social change and innovation. This research project examined the literature on clustering and a number of case studies.BC-Alberta Social Economy Research Alliance (BALTA) ; Social Sciences and Humanities Research Council of Canada (SSHRC