The distributed p-median problem in computer networks

The exponential growth of the Internet over the last decades has led to a significant evolution of the network services and applications. One of the challenges is to provide better services scalability by placing service replica in appropriate network locations. Finding the optimal solution to the facility location problem is particularly complex and is not feasible for large scale systems. Locating facilities in near-optimal locations have been extensively studied in many works and for different application domains. This work investigates one of the most notable problems in facility location, i.e. the p-median problem, which locates p facilities with a minimum overall communication cost. All previous studies on the p-median problem used a centralised approach to find the near-optimal solution. In this case the required information needs to be collected in order to apply a sequential algorithm to find a solution. The centralised approach is infeasible in large-scale networks due to the time and space complexity of the sequential algorithms as well as the large communication cost and latency to aggregate the global information. Therefore, this work investigates the p-median problem in a distributed environment. To the best of the author’s knowledge, this is the first work to study the distributed pmedian problem for large-scale computer networks. Solving the p-median problem in a fully distributed way is a challenging task due to the lack of global knowledge and of a centralised coordinator. Two new approaches for solving the p-median problem in a distributed environment are proposed in this thesis. Both are designed to be executed without any centralised collection of the data in a single node. These methods apply an iterative heuristic approach to improve a random initial solution and to converge to a final solution with a local minimum of the cost. The first approach builds a global view of the system and improves the current solution by replacing a single facility at each iteration. The second approach, is designed according to the well-known k-medoids clustering algorithm. At each iteration a local view of each cluster is generated and all facilities can be updated to optimise the solution. Both approaches were implemented within the Java-based PeerSim network simulator for investigating the performance in large-scale systems and tested against different parameters such as the size of networks, number of facilities to be placed and different initial solutions. The results have shown that the first protocol is better at addressing locations for facilities since it converges to a lower total cost of the solution than the second protocol. However, the second one is faster in optimising the solution

The distributed p-median problem in computer networks

Many distributed services in computer networks rely on a set of active facilities that are selected among a potentially large number of candidates. The active facilities then contribute and cooperate to deliver a specific service to the users of the distributed system. In this scenario graph partitioning or clustering is often adopted to determine the most efficient locations of the facilities. The identification of the optimal set of facility locations is known as the p-median problem in networks, is NP-hard and is typically solved by using heuristic methods. The goal is to select p locations among all candidate network nodes such that some cost function is minimised. A typical example of such a function is the overall communication cost to deliver the service to the users of the distributed system. Locating facilities in near-optimal locations has been extensively studied for different application domains. Most of these studies have investigated sequential algorithms and centralised approaches. However, centralised approaches are practically infeasible in large-scale and dynamic networks, where the problem is inherently distributed or because of the large communication overhead and memory requirements for gathering complete information about the network topology and the users. In this work distributed approaches to the p-median problem are investigated. Two solutions are proposed for addressing the facility locations problem in a fully distributed environment. Two different iterative heuristic approaches are applied to gradually improve a random initial solution and to converge to a final solution with a local minimum of the overall cost. While the first approach adopts a fine granularity by identifying a single change to improve the solution at each iteration, the second approach applies changes to every component of the solution at each iteration. An experimental comparative analysis based on simulations has shown that the approach with a finer granularity is able to deliver a better optimisation of the overall cost with longer convergence time. Both approaches have excellent scalability and provide an effective tool to optimise the facility locations from within the network. No prior knowledge of the system is required, no data needs to be gathered in a centralised server and the same process is used to identify and to deploy the facility locations solution in the network since the process is fully decentralised

Coordination of Mobile Mules via Facility Location Strategies

In this paper, we study the problem of wireless sensor network (WSN) maintenance using mobile entities called mules. The mules are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and fix it. The mules must constantly optimize their collective deployment to account for occupied mules. The objective is to define the optimal deployment and task allocation strategy for the mules, so that the sensors' downtime and the mules' traveling distance are minimized. Our solutions are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location. Our empirical results demonstrate how cooperation enhances the team's performance, and indicate that a combination of k-Median based deployment with closest-available task allocation provides the best results in terms of minimizing the sensors' downtime but is inefficient in terms of the mules' travel distance. A k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Modelling Clock Synchronization in the Chess gMAC WSN Protocol

We present a detailled timed automata model of the clock synchronization algorithm that is currently being used in a wireless sensor network (WSN) that has been developed by the Dutch company Chess. Using the Uppaal model checker, we establish that in certain cases a static, fully synchronized network may eventually become unsynchronized if the current algorithm is used, even in a setting with infinitesimal clock drifts