Distributed Load Balancing in Peer-to-Peer Computing

In this paper, we address the load balancing problem in the context of peer-to-peer computing environments. The key challenge to employ peer-to-peer networks for distributed computing is to exploit the heterogeneous processing capability of the participating hosts as well as the diverse network conditions. The contribution of our work is twofold. First, we model the load balance problem as an optimization problem with the objective of minimizing the system response time. This modeling considers not only the current loading of hosts, but also the fluctuation of network delay, which completely captures the characteristics of the P2P systems. Second, we propose a gradient projection algorithm to solve the optimization problem, which is fully distributed and easy for implementation. Simulation results demonstrate that our scheme has satisfied performance in terms of convergence, response time and load distribution

Towards a Framework for DHT Distributed Computing

Distributed Hash Tables (DHTs) are protocols and frameworks used by peer-to-peer (P2P) systems. They are used as the organizational backbone for many P2P file-sharing systems due to their scalability, fault-tolerance, and load-balancing properties. These same properties are highly desirable in a distributed computing environment, especially one that wants to use heterogeneous components. We show that DHTs can be used not only as the framework to build a P2P file-sharing service, but as a P2P distributed computing platform. We propose creating a P2P distributed computing framework using distributed hash tables, based on our prototype system ChordReduce. This framework would make it simple and efficient for developers to create their own distributed computing applications. Unlike Hadoop and similar MapReduce frameworks, our framework can be used both in both the context of a datacenter or as part of a P2P computing platform. This opens up new possibilities for building platforms to distributed computing problems. One advantage our system will have is an autonomous load-balancing mechanism. Nodes will be able to independently acquire work from other nodes in the network, rather than sitting idle. More powerful nodes in the network will be able use the mechanism to acquire more work, exploiting the heterogeneity of the network. By utilizing the load-balancing algorithm, a datacenter could easily leverage additional P2P resources at runtime on an as needed basis. Our framework will allow MapReduce-like or distributed machine learning platforms to be easily deployed in a greater variety of contexts

Overlay-Centric Load Balancing: Applications to UTS and B&B

International audienceTo deal with dynamic load balancing in large scale distributed systems, we propose to organize computing resources following a logical peer-to-peer overlay and to distribute the load according to the so-defined overlay. We use a tree as a logical structure connecting distributed nodes and we balance the load according to the size of induced subtrees. We conduct extensive experiments involving up to 1000 computing cores and provide a throughout analysis of different properties of our generic approach for two different applications, namely, the standard Unbalanced Tree Search and the more challenging parallel Branch-and-Bound algorithm. Substantial improvements are reported in comparison with the classical random work stealing and two finely tuned application specific strategies taken from the literature

Software-defined networking for ubiquitous healthcare service delivery

The growth of the mobile, portable devices and the server-to-server communication through cloud computing increase the network traffic. The dependence of the ubiquitous healthcare service delivery on the network connectivity creates failures that may interrupt or delay the treatment plan with adverse effects in patients’ quality of life even leading to mortality. In the present work, we propose the incorporation of Software Defined Networking (SDN) features in the healthcare domain in order to provide the appropriate bandwidth and guarantee the accurate real time medical data transmission independently of the connectivity of the ISP provider. The SDN controller monitors the network traffic and specifies how traffic should be routed providing load balancing, lower delays and better performance. Finally, the proposed healthcare architecture addresses the SDN scalability challenge by incorporating the logically centralized control plane using multiple distributed controllers. A 2-tier hierarchical overlay is formed among SDN controllers following the principles of peer-to-peer networking

A critical analysis of simulators in grid

In parallel and distributed computing environment such as "The Grid", anticipating the behavior of the resources and tasks based on certain scheduling algorithm is a great challenging.Thus, studying and improving these types of environments becomes very difficult. Out of this, the developers have spent remarkable efforts to come up with simulators which facilitate the studies in this domain.In addition, these simulators have a significant role in enhancing and proposing many scheduling algorithms, and this in turn has reflected efficiently on the Grid.In this paper, we will present some of these tools, which are: GridSim for large scales distributed computing and parallel environment, Alea for tackling dynamic scheduling problems, Sim-G-Batch grid simulator for simulating the security and energy concept and Balls simulator for evaluation peer-to-peer with integrated load balancing algorithm.Furthermore, this paper aims to guide and assist the researcher to choose the proper tool that can fit the studied research area, by providing functionality analysis for the reviewed simulators

Communication Patterns for Randomized Algorithms

Examples of large scale networks include the Internet, peer-to-peer networks, parallel computing systems, cloud computing systems, sensor networks, and social networks. Efficient dissemination of information in large networks such as these is a funda- mental problem. In many scenarios the gathering of information by a centralised controller can be impractical. When designing and analysing distributed algorithms we must consider the limitations imposed by the heterogeneity of devices in the networks. Devices may have limited computational ability or space. This makes randomised algorithms attractive solutions. Randomised algorithms can often be simpler and easier to implement than their deterministic counterparts. This thesis analyses the effect of communication patterns on the performance of distributed randomised algorithms. We study randomized algorithms with application to three different areas. Firstly, we study a generalization of the balls-into-bins game. Balls into bins games have been used to analyse randomised load balancing. Under the Greedy[d] allocation scheme each ball queries the load of d random bins and is then allocated to the least loaded of them. We consider an infinite, parallel setting where expectedly λn balls are allocated in parallel according to the Greedy[d] allocation scheme in to n bins and subsequently each non-empty bin removes a ball. Our results show that for d = 1,2, the Greedy[d] allocation scheme is self-stabilizing and that in any round the maximum system load for high arrival rates is exponentially smaller for d = 2 compared to d = 1 (w.h.p). Secondly, we introduce protocols that solve the plurality consensus problem on arbitrary graphs for arbitrarily small bias. Typically, protocols depend heavily on the employed communication mechanism. Our protocols are based on an interest- ing relationship between plurality consensus and distributed load balancing. This relationship allows us to design protocols that are both time and space efficient and generalize the state of the art for a large range of problem parameters. Finally, we investigate the effect of restricting the communication of the classical PULL algorithm for randomised rumour spreading. Rumour spreading (broadcast) is a fundamental task in distributed computing. Under the classical PULL algo- rithm, a node with the rumour that receives multiple requests is able to respond to all of them in a given round. Our model restricts nodes such that they can re- spond to at most one request per round. Our results show that the restricted PULL algorithm is optimal for several graph classes such as complete graphs, expanders, random graphs and several Cayley graphs

PARALLEL COMPUTING WITH P2P DESKTOP GRIDS

Tightly-coupled parallel computing is an important tool for problem solving. Structured peer-to-peer network overlays are failure-tolerant and have a low admin- istrative burden. This work seeks to unite the two. First, I present a completely decentralized algorithm for parallel job scheduling and load balancing in distributed peer-to-peer environments. This algorithm is useful for meta-scheduling across known clusters and scheduling on desktop grids. To accomplish this, I build on previous work to route jobs to appropriate resources then use the new algorithm to start parallel jobs and balance load across the grid. I also discuss what constitutes useful clusterings for this algorithm as well as inherent scaling limitations. Ultimately, I show that my algorithm performs comparably to one using centralized load balancing with global up-to-date information. The principal contribution of this work is that the parallel job scheduling is completely decentralized, which is not featured in previous work, and enables reliable ad hoc sharing of distributed resources to run parallel computations. Second, I show how clusters of computers can be found dynamically by using an existing latency prediction technique coupled with a new refinement algorithm. Several latency prediction techniques are compared experimentally. One, based on a tree metric space embedding, is found to be superior to the others. Nevertheless, I show that it is not quite accurate enough. To solve this problem, I present a refinement algorithm for producing quality clusters while still maintaining bounds for the amount of information any given node must store about other nodes. I show that clusters derived this way have scheduler performance comparable to those chosen statically with global knowledge. Lastly, I discuss previously undiscovered under-specifications in the Content Addressable Network (CAN) structured peer to peer system. In high-churn situ- ations, the CAN allows stale information and changes to the overlay structure to create routing problems. I show solutions to these two problems, as well as discuss other issues that may also disrupt a CAN