Decision Strategies for a P2P Computing System

Peer-to-Peer (P2P) computing (also called ‘public-resource computing’) is an effective approach to perform computation of large tasks. Currently used P2P computing systems (e.g., BOINC) are most often centrally managed, i.e., the final result of computations is created at a central node using partial results – what may be not efficient in the case when numerous participants are willing to download the final result. In this paper, we propose a novel approach to P2P computing systems. We assume that results can be delivered to all peers in a distributed way using three types of network flows: unicast, Peer-to-Peer and anycast. We describe our concept of the system architecture with a special focus on the decision strategies developed for system participants. Using our discrete realtime simulator we evaluate the proposed strategies in various scenarios and present a comprehensive analysis of obtained results. According to obtained results, the Peer-to-Peer flow provides lower operational cost of the computing system compared to unicast and anycast flows. Moreover, an appropriate selection of decision strategy can significantly reduce the operational cost

Resilient virtual topologies in optical networks and clouds

Optical networks play a crucial role in the development of Internet by providing a high speed infrastructure to cope with the rapid expansion of high bandwidth demand applications such as video, HDTV, teleconferencing, cloud computing, and so on. Network virtualization has been proposed as a key enabler for the next generation networks and the future Internet because it allows diversification the underlying architecture of Internet and lets multiple heterogeneous network architectures coexist. Physical network failures often come from natural disasters or human errors, and thus cannot be fully avoided. Today, with the increase of network traffic and the popularity of virtualization and cloud computing, due to the sharing nature of network virtualization, one single failure in the underlying physical network can affect thousands of customers and cost millions of dollars in revenue. Providing resilience for virtual network topology over optical network infrastructure thus becomes of prime importance. This thesis focuses on resilient virtual topologies in optical networks and cloud computing. We aim at finding more scalable models to solve the problem of designing survivable logical topologies for more realistic and meaningful network instances while meeting the requirements on bandwidth, security, as well as other quality of service such as recovery time. To address the scalability issue, we present a model based on a column generation decomposition. We apply the cutset theorem with a decomposition framework and lazy constraints. We are able to solve for much larger network instances than the ones in literature. We extend the model to address the survivability problem in the context of optical networks where the characteristics of optical networks such as lightpaths and wavelength continuity and traffic grooming are taken into account. We analyze and compare the bandwidth requirement between the two main approaches in providing resiliency for logical topologies. In the first approach, called optical protection, the resilient mechanism is provided by the optical layer. In the second one, called logical restoration, the resilient mechanism is done at the virtual layer. Next, we extend the survivability problem into the context of cloud computing where the major complexity arises from the anycast principle. We are able to solve the problem for much larger network instances than in the previous studies. Moreover, our model is more comprehensive that takes into account other QoS criteria, such that recovery time and delay requirement

An exact algorithm for design of content delivery networks in MPLS environment, Journal of Telecommunications and Information Technology, 2004, nr 2

Content delivery network (CDN) is an efficient and inexpensive method to improve Internet service quality. In this paper we formulate an optimisation problem of replica location in a CDN using MPLS techniques. A novelty, comparing to previous work on this subject, is modelling the network flow as connection-oriented and introduction of capacity constraint on network links to the problem. Since the considered optimisation problem is NP-complete, we propose and discuss exact algorithm based on the branch-and-cut and branch-and-bound methods. We present results of numerical experiments showing comparison of branch-and-cut and branch-and-bound methods

Optical Grid Network Dimensioning, Provisioning, and Job Scheduling

An optical grid network reliably provides high speed communications. It consists of grid resources (e.g., computing and data servers) and huge-data paths that are connected to geographically dispersed resources and users. One of the important issues is dimensioning optical grid networks, i.e., to determine the link bandwidth utilization and amount of server resources, and finding the location of servers. Another issue is the provisioning of the job requests (maximization of services) on the capacitated networks, also referred to as Grade of Service (GoS). Additionally, job scheduling on the servers has also an important impact on the utilization of computing and network resources. Dimensioning optical grid network is based on Anycast Routing and Wavelength Assignment (ACRWA) with the objective of minimizing (min-ACRWA) the resources. The objective of GoS is maximizing the number of job requests (max-ACRWA) under the limited resources. Given that users of such optical grid networks in general do not care about the exact physical locations of the server resources, a degree of freedom arises in choosing for each of their requests the most appropriate server location. We will exploit this anycast routing principle -- i.e., the source of the traffic is given, but the destination can be chosen rather freely. To provide resilience, traffic may be relocated to alternate destinations in case of network/server failures. This thesis investigates dimensioning optical grids networks and task scheduling. In the first part, we present the link capacity dimensioning through scalable exact Integer Linear Programming (ILP) optimization models (min-ACRWA) with survivability. These models take step by step transition from the classical RWA (fixed destination) to anycast routing principle including shared path protection scheme. In the second part, we present scalable optimization models for maximizing the IT services (max-ACRWA) subject to survivability mechanism under limited link transport capacities. We also propose the link capacity formulations based on the distance from the servers and the traffic data set. In the third part, we jointly investigate the link dimensioning and the location of servers in an optical grid, where the anycast routing principle is applied for resiliency under different levels of protection schemes. We propose three different decomposition schemes for joint optimization of link dimensioning and finding the location of servers. In the last part of this research, we propose the exact task scheduling ILP formulations for optical grids (data centers). These formulations can also be used in advance reservation systems to allocate the grid resources. The purpose of this study is to design efficient tools for planning and management of the optical grid networks

Random Approach to Optimization of Overlay Public-Resource Computing Systems

The growing need for computationally demanding systems triggers the development of various network-oriented computing systems organized in a distributed manner. In this work we concentrate on one kind of such systems, i.e. public-resource computing systems. The considered system works on the top of an overlay network and uses personal computers and other relatively simple electronic equipment instead of supercomputers. We assume that two kinds of network flows are used to distribute the data in the public-resource computing systems: unicast and peer-to-peer. We formulate an optimization model of the system. After that we propose random algorithms that optimize jointly the allocation of computational tasks and the distribution of the output data. To evaluate the algorithms we run numerical experiments and present results showing the comparison of the random approach against optimal solutions provided by the CPLEX solver

Saturation routing for asynchronous transfer mode (ATM) networks

The main objective of this thesis is to show that saturation routing, often in the past considered inefficient, can in fact be a viable approach to use in many important applications and services over an Asynchronous Transfer Mode (ATM) network. For other applications and services, a hybrid approach (one that partially uses saturation routing) is presented. First, the minimum effects of saturation routing are demonstrated by showing that the ratio, defined as f, of routing overhead cells over information cells is small even for large networks. Second, modeling and simulation and M/D/l queuing analysis techniques are used to show that the overall effect on performance when using saturation routing is not significant over ATM networks. Then saturation routing ATM implementation is also provided, with important extensions to services such as multicast routing. After an analytical comparison, in terms of routing overhead, is made between Saturation Routing and the currently proposed Private Network-Network Interface (PNNI) procedure for ATM routing made by the ATM forum. This comparison is made for networks of different sizes (343node and 2401 -node networks) and different number of hierarchical levels (3 and 4 levels of hierarchy). The results show that the higher the number of levels of hierarchy and the farthest (in terms of hierarchical levels) the source and the destination nodes are from each other, the more advantageous saturation routing becomes. Finally, a set of measures of performance for use by saturation routing (or any routing algorithm), as metrics for routing path selection, is proposed. Among these measures, an innovative new measure of performance derived for measuring quality of service provided to Constant Bit Rate (CBR) users (e.g., such as voice and video users) called the Burst Voice Arrival Lag (BVAL) is described and derived

IP and ATM integration: A New paradigm in multi-service internetworking

ATM is a widespread technology adopted by many to support advanced data communication, in particular efficient Internet services provision. The expected challenges of multimedia communication together with the increasing massive utilization of IP-based applications urgently require redesign of networking solutions in terms of both new functionalities and enhanced performance. However, the networking context is affected by so many changes, and to some extent chaotic growth, that any approach based on a structured and complex top-down architecture is unlikely to be applicable. Instead, an approach based on finding out the best match between realistic service requirements and the pragmatic, intelligent use of technical opportunities made available by the product market seems more appropriate. By following this approach, innovations and improvements can be introduced at different times, not necessarily complying with each other according to a coherent overall design. With the aim of pursuing feasible innovations in the different networking aspects, we look at both IP and ATM internetworking in order to investigating a few of the most crucial topics/ issues related to the IP and ATM integration perspective. This research would also address various means of internetworking the Internet Protocol (IP) and Asynchronous Transfer Mode (ATM) with an objective of identifying the best possible means of delivering Quality of Service (QoS) requirements for multi-service applications, exploiting the meritorious features that IP and ATM have to offer. Although IP and ATM often have been viewed as competitors, their complementary strengths and limitations from a natural alliance that combines the best aspects of both the technologies. For instance, one limitation of ATM networks has been the relatively large gap between the speed of the network paths and the control operations needed to configure those data paths to meet changing user needs. IP\u27s greatest strength, on the other hand, is the inherent flexibility and its capacity to adapt rapidly to changing conditions. These complementary strengths and limitations make it natural to combine IP with ATM to obtain the best that each has to offer. Over time many models and architectures have evolved for IP/ATM internetworking and they have impacted the fundamental thinking in internetworking IP and ATM. These technologies, architectures, models and implementations will be reviewed in greater detail in addressing possible issues in integrating these architectures s in a multi-service, enterprise network. The objective being to make recommendations as to the best means of interworking the two in exploiting the salient features of one another to provide a faster, reliable, scalable, robust, QoS aware network in the most economical manner. How IP will be carried over ATM when a commercial worldwide ATM network is deployed is not addressed and the details of such a network still remain in a state of flux to specify anything concrete. Our research findings culminated with a strong recommendation that the best model to adopt, in light of the impending integrated service requirements of future multi-service environments, is an ATM core with IP at the edges to realize the best of both technologies in delivering QoS guarantees in a seamless manner to any node in the enterprise

Machine Learning and Big Data Methodologies for Network Traffic Monitoring

Over the past 20 years, the Internet saw an exponential grown of traffic, users, services and applications. Currently, it is estimated that the Internet is used everyday by more than 3.6 billions users, who generate 20 TB of traffic per second. Such a huge amount of data challenge network managers and analysts to understand how the network is performing, how users are accessing resources, how to properly control and manage the infrastructure, and how to detect possible threats. Along with mathematical, statistical, and set theory methodologies machine learning and big data approaches have emerged to build systems that aim at automatically extracting information from the raw data that the network monitoring infrastructures offer. In this thesis I will address different network monitoring solutions, evaluating several methodologies and scenarios. I will show how following a common workflow, it is possible to exploit mathematical, statistical, set theory, and machine learning methodologies to extract meaningful information from the raw data. Particular attention will be given to machine learning and big data methodologies such as DBSCAN, and the Apache Spark big data framework. The results show that despite being able to take advantage of mathematical, statistical, and set theory tools to characterize a problem, machine learning methodologies are very useful to discover hidden information about the raw data. Using DBSCAN clustering algorithm, I will show how to use YouLighter, an unsupervised methodology to group caches serving YouTube traffic into edge-nodes, and latter by using the notion of Pattern Dissimilarity, how to identify changes in their usage over time. By using YouLighter over 10-month long races, I will pinpoint sudden changes in the YouTube edge-nodes usage, changes that also impair the end users’ Quality of Experience. I will also apply DBSCAN in the deployment of SeLINA, a self-tuning tool implemented in the Apache Spark big data framework to autonomously extract knowledge from network traffic measurements. By using SeLINA, I will show how to automatically detect the changes of the YouTube CDN previously highlighted by YouLighter. Along with these machine learning studies, I will show how to use mathematical and set theory methodologies to investigate the browsing habits of Internauts. By using a two weeks dataset, I will show how over this period, the Internauts continue discovering new websites. Moreover, I will show that by using only DNS information to build a profile, it is hard to build a reliable profiler. Instead, by exploiting mathematical and statistical tools, I will show how to characterize Anycast-enabled CDNs (A-CDNs). I will show that A-CDNs are widely used either for stateless and stateful services. That A-CDNs are quite popular, as, more than 50% of web users contact an A-CDN every day. And that, stateful services, can benefit of A-CDNs, since their paths are very stable over time, as demonstrated by the presence of only a few anomalies in their Round Trip Time. Finally, I will conclude by showing how I used BGPStream an open-source software framework for the analysis of both historical and real-time Border Gateway Protocol (BGP) measurement data. By using BGPStream in real-time mode I will show how I detected a Multiple Origin AS (MOAS) event, and how I studies the black-holing community propagation, showing the effect of this community in the network. Then, by using BGPStream in historical mode, and the Apache Spark big data framework over 16 years of data, I will show different results such as the continuous growth of IPv4 prefixes, and the growth of MOAS events over time. All these studies have the aim of showing how monitoring is a fundamental task in different scenarios. In particular, highlighting the importance of machine learning and of big data methodologies