Lamred : location-aware and privacy preserving multi-layer resource discovery for IoT

The resources in the Internet of Things (IoT) network are distributed among different parts of the network. Considering huge number of IoT resources, the task of discovering them is challenging. While registering them in a centralized server such as a cloud data center is one possible solution, but due to billions of IoT resources and their limited computation power, the centralized approach leads to some efficiency and security issues. In this paper we proposed a location aware and decentralized multi layer model of resource discovery (LaMRD) in IoT. It allows a resource to be registered publicly or privately, and to be discovered in a decentralized scheme in the IoT network. LaMRD is based on structured peer-to-peer (p2p) scheme and follows the general system trend of fog computing. Our proposed model utilizes Distributed Hash Table (DHT) technology to create a p2p scheme of communication among fog nodes. The resources are registered in LaMRD based on their locations which results in a low added overhead in the registration and discovery processes. LaMRD generates a single overlay and it can be generated without specific organizing entity or location based devices. LaMRD guarantees some important security properties and it showed a lower latency comparing to the cloud based and decentralized resource discovery

Static Web content distribution and request routing in a P2P overlay

The significance of collaboration over the Internet has become a corner-stone of modern computing, as the essence of information processing and content management has shifted to networked and Webbased systems. As a result, the effective and reliable access to networked resources has become a critical commodity in any modern infrastructure. In order to cope with the limitations introduced by the traditional client-server networking model, most of the popular Web-based services have employed separate Content Delivery Networks (CDN) to distribute the server-side resource consumption. Since the Web applications are often latency-critical, the CDNs are additionally being adopted for optimizing the content delivery latencies perceived by the Web clients. Because of the prevalent connection model, the Web content delivery has grown to a notable industry. The rapid growth in the amount of mobile devices further contributes to the amount of resources required from the originating server, as the content is also accessible on the go. While the Web has become one of the most utilized sources of information and digital content, the openness of the Internet is simultaneously being reduced by organizations and governments preventing access to any undesired resources. The access to information may be regulated or altered to suit any political interests or organizational benefits, thus conflicting with the initial design principle of an unrestricted and independent information network. This thesis contributes to the development of more efficient and open Internet by combining a feasibility study and a preliminary design of a peer-to-peer based Web content distribution and request routing mechanism. The suggested design addresses both the challenges related to effectiveness of current client-server networking model and the openness of information distributed over the Internet. Based on the properties of existing peer-to-peer implementations, the suggested overlay design is intended to provide low-latency access to any Web content without sacrificing the end-user privacy. The overlay is additionally designed to increase the cost of censorship by forcing a successful blockade to isolate the censored network from the rest of the Internet

Performance Evaluation of a Cluster-Based Service Discovery Protocol for Heterogeneous Wireless Sensor Networks

Abstract—This paper evaluates the performance in terms of resource consumption of a service discovery protocol proposed for heterogeneous Wireless Sensor Networks (WSNs). The protocol is based on a clustering structure, which facilitates the construction of a distributed directory. Nodes with higher capabilities are chosen to be clusterheads. We measure the energy and memory costs depending on the capabilities and the roles of the nodes in the clustering structure, both during the maintenance of the structure and discovery of services. We show that the service discovery protocol succeeds in alleviating the resource-lean sensor nodes of heavy tasks, while delegating more consuming duties to the more powerful nodes

Resource discovery for distributed computing systems: A comprehensive survey

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category

Self-management for large-scale distributed systems

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management. In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control

A read-only distributed hash table

Ph.DDOCTOR OF PHILOSOPH