Optimal Placement Algorithms for Virtual Machines

Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers

A Semantic-enabled Framework For Future Internet Of Things Applications

While the challenge of connecting Internet of Things (IoT) devices at the lowest layer has been widely studied, integrating and interoperating huge amounts of sensed data of heterogeneous IoT devices is becoming increasingly important because of the possibility of consuming such data in supporting many potential novel IoT applications. A common approach to processing and consuming IoT data is a centralized paradigm: sensor data is sent over the network to a comparatively powerful central server or a cloud service, where all processing takes place. However, this approach has some limitations as it requires devices to interact directly with a cloud which is not cost effective. First, it has high demands on the device's storage and computational capabilities. Second, as devices grow rapidly in a deployment area, sending all the data to a centralized cloud server requires high network bandwidth. Moreover, this often creates data privacy concerns as all raw data will be sent to a centralized place. To address the above limitations for building future Internet of Things applications, we present an early design of a novel framework that combines Internet of Things, Semantic Web, and Big Data concepts. We not only present the core components to build an IoT system, but also list existing alternatives with their merits. This framework aims to incorporate open standards to address the potential challenges in building future IoT applications. Therefore, our discussion revolves around open standards to build the framework, rather than proprietary standards

The Role of Models in Self-adaptive and Self-healing Systems

Self-healing and self-adaptive systems dynamically react on changes in the environment. They enable software systems to adjust to new conditions and work optimally even in unstable environments. However, such systems have to cope with an ever increasing complexity and size of software systems. In order to handle such systems, models are an efficient means for analysis, control, and documentation. Furthermore, hierarchically structured models can make self-healing and self-adaptation manageable. In this report, we discuss several questions that address the role of models in self-healing and self-adaptive systems. We outline today\u27s challenges and present different viewpoints on the application and benefit of models

An academic perspective on globalization in the software industry

In this paper, the author has presented the challenges in global software development and then identified the gaps in the academic curriculum that should be addressed in order to train software engineers to be cognizant of current issues in executing large, global software projects. We have also outlined a practically oriented academic course that will stress on the skills needed to actually execute global projects. Over multiple offerings of such a course we hope to abstract out the actual issues faced and present suitable solutions as learnings.© IEE