Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

Decentralized load balancing for highly irregular search problems

In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.

Dynamic load balancing in parallel KD-tree k-means

One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.

Load balancing solution and evaluation of F5 content switch equipment

The Thesis focused on hardware based Load balancing solution of web traffic through a load balancer F5 content switch. In this project, the implemented scenario for distributing HTTPtraffic load is based on different CPU usages (processing speed) of multiple member servers.Two widely used load balancing algorithms Round Robin (RR) and Ratio model (weighted Round Robin) are implemented through F5 load balancer. For evaluating the performance of F5 content switch, some experimental tests has been taken on implemented scenarios using RR and Ratio model load balancing algorithms. The performance is examined in terms of throughput (bits/sec) and Response time of member servers in a load balancing pool. From these experiments we have observed that Ratio Model load balancing algorithm is most suitable in the environment of load balancing servers with different CPU usages as it allows assigning the weight according to CPU usage both in static and dynamic load balancing of servers.

A load balancing algorithm for web based server grids

Load balance is a critical issue in distributed systems, such as server grids. In this paper, we propose a Balanced Load Queue (BLQ) model, which combines the queuing theory and hydro-dynamic theory, to model load balance in server grids. Base on the BLQ model, we claim that if the system is in the state of global fairness, then the performance of the whole system is the best. We propose a load balanced algorithm based on the model: the algorithm tries its best to keep the system in the global fairness status using job deviation. We present three strategies: best node, best neighbour, and random selection, for job deviation. A number of experiments are conducted for the comparison of the three strategies, and the results show that the best neighbour strategy is the best among the proposed strategies. Furthermore, the proposed algorithm with best neighbour strategy is better than the traditional round robin algorithm in term of processing delay, and the proposed algorithm needs very limited system information and is robust.

An MPI implementation supported by process migration and load balancing

This report describes an implementation of MPI-1 on the GENESIS cluster operating system and compares this implementation to a UNIX based MPI implementation. The changes that were made to the implementation are compared between the two, and the advantages of porting to GENESIS are detailed. This report demonstrates how GENESIS load balancing supported by process migration improves the execution performance of an MPI application. The significance of this report is in demonstrating how these services can enhance parallel programming tools to improve performance and how future parallel programming tool design could take advantage of these services.

Load balancing based concurrent execution of NAS parallel benchmarks with BYTE sequential benchmarks on a non-dedicated cluster

Dedicated clusters are becoming commonly used for high performance parallel processing. Computers of a non-dedicated cluster are often idle or lightly loaded. These under utilised computers can be employed to execute parallel applications. Thus, they have to be shared by parallel and sequential applications, which could lead to the improvement of their execution performance. There is a lack of experimental study showing the behaviour and performance of executing parallel and sequential applications concurrently on a non-dedicated cluster. We present the result of an experimental study into load balancing of a mixture of parallel and sequential applications on a non-dedicated cluster.

A load-balancing spare capacity reallocation approach in service-rich SONET metro mesh networks

The next-generation SONET metro network is evolving into a service-rich infrastructure. At the edge of such a network, multi-service provisioning platforms (MSPPs) provide efficient data mapping enabled by Generic Framing Procedure (GFP) and Virtual Concatenation (VC). The core of the network tends to be a meshed architecture equipped with Multi-Service Switches (MSSs). In the context of these emerging technologies, we propose a load-balancing spare capacity reallocation approach to improve network utilization in the next-generation SONET metro networks. Using our approach, carriers can postpone network upgrades, resulting in increased revenue with reduced capital expenditures (CAPEX). For the first time, we consider the spare capacity reallocation problem from a capacity upgrade and network planning perspective. Our approach can operate in the context of shared-path protection (with backup multiplexing) because it reallocates spare capacity without disrupting working services. Unlike previous spare capacity reallocation approaches which aim at minimizing total spare capacity, our load-balancing approach minimizes the network load vector (NLV), which is a novel metric that reflects the network load distribution. Because NLV takes into consideration both uniform and non-uniform link capacity distribution, our approach can benefit both uniform and non-uniform networks. We develop a greedy loadbalancing spare capacity reallocation (GLB-SCR) heuristic algorithm to implement this approach. Our experimental results show that GLB-SCR outperforms a previously proposed algorithm (SSR) in terms of established connection capacity and total network capacity in both uniform and non-uniform networks.

A Load-Balancing Shared-Protection-Path Reconfiguration Approach in WDM Wavelength-Routed Networks

Wavelength-routed networks (WRN) are very promising candidates for next-generation Internet and telecommunication backbones. In such a network, optical-layer protection is of paramount importance due to the risk of losing large amounts of data under a failure. To protect the network against this risk, service providers usually provide a pair of risk-independent working and protection paths for each optical connection. However, the investment made for the optical-layer protection increases network cost. To reduce the capital expenditure, service providers need to efficiently utilize their network resources. Among all the existing approaches, shared-path protection has proven to be practical and cost-efficient [1]. In shared-path protection, several protection paths can share a wavelength on a fiber link if their working paths are risk-independent. In real-world networks, provisioning is usually implemented without the knowledge of future network resource utilization status. As the network changes with the addition and deletion of connections, the network utilization will become sub-optimal. Reconfiguration, which is referred to as the method of re-provisioning the existing connections, is an attractive solution to fill in the gap between the current network utilization and its optimal value [2]. In this paper, we propose a new shared-protection-path reconfiguration approach. Unlike some of previous reconfiguration approaches that alter the working paths, our approach only changes protection paths, and hence does not interfere with the ongoing services on the working paths, and is therefore risk-free. Previous studies have verified the benefits arising from the reconfiguration of existing connections [2] [3] [4]. Most of them are aimed at minimizing the total used wavelength-links or ports. However, this objective does not directly relate to cost saving because minimizing the total network resource consumption does not necessarily maximize the capability of accommodating future connections. As a result, service providers may still need to pay for early network upgrades. Alternatively, our proposed shared-protection-path reconfiguration approach is based on a load-balancing objective, which minimizes the network load distribution vector (LDV, see Section 2). This new objective is designed to postpone network upgrades, thus bringing extra cost savings to service providers. In other words, by using the new objective, service providers can establish as many connections as possible before network upgrades, resulting in increased revenue. We develop a heuristic load-balancing (LB) reconfiguration approach based on this new objective and compare its performance with an approach previously introduced in [2] and [4], whose objective is minimizing the total network resource consumption.

Tecniche Cloud Avanzate per Adaptive Hadoop Load Balancing tramite Sahara

Ogni giorno vengono generati grandi moli di dati attraverso sorgenti diverse. Questi dati, chiamati Big Data, sono attualmente oggetto di forte interesse nel settore IT (Information Technology). I processi digitalizzati, le interazioni sui social media, i sensori ed i sistemi mobili, che utilizziamo quotidianamente, sono solo un piccolo sottoinsieme di tutte le fonti che contribuiscono alla produzione di questi dati. Per poter analizzare ed estrarre informazioni da questi grandi volumi di dati, tante sono le tecnologie che sono state sviluppate. Molte di queste sfruttano approcci distribuiti e paralleli. Una delle tecnologie che ha avuto maggior successo nel processamento dei Big Data, e Apache Hadoop. Il Cloud Computing, in particolare le soluzioni che seguono il modello IaaS (Infrastructure as a Service), forniscono un valido strumento all'approvvigionamento di risorse in maniera semplice e veloce. Per questo motivo, in questa proposta, viene utilizzato OpenStack come piattaforma IaaS. Grazie all'integrazione delle tecnologie OpenStack e Hadoop, attraverso Sahara, si riesce a sfruttare le potenzialita offerte da un ambiente cloud per migliorare le prestazioni dell'elaborazione distribuita e parallela. Lo scopo di questo lavoro e ottenere una miglior distribuzione delle risorse utilizzate nel sistema cloud con obiettivi di load balancing. Per raggiungere questi obiettivi, si sono rese necessarie modifiche sia al framework Hadoop che al progetto Sahara.

Load Balancing in LTE Mobile Networks with Information-Centric Networking

Mobile networks usage rapidly increased over the years, with great consequences in terms of performance requirements. In this paper, we propose mechanisms to use Information-Centric Networking to perform load balancing in mobile networks, providing content delivery over multiple radio technologies at the same time and thus efficiently using resources and improving the overall performance of content transfer. Meaningful results were obtained by comparing content transfer over single radio links with typical strategies to content transfer over multiple radio links with Information-Centric Networking load balancing. Results demonstrate that Information-Centric Networking load balancing increases the performance and efficiency of 3GPP Long Term Evolution mobile networks while greatly improving the network perceived quality for end users.

A multi-resource load balancing algorithm for cloud cache systems

With the advent of cloud computing model, distributed caches have become the cornerstone for building scalable applications. Popular systems like Facebook [1] or Twitter use Memcached [5], a highly scalable distributed object cache, to speed up applications by avoiding database accesses. Distributed object caches assign objects to cache instances based on a hashing function, and objects are not moved from a cache instance to another unless more instances are added to the cache and objects are redistributed. This may lead to situations where some cache instances are overloaded when some of the objects they store are frequently accessed, while other cache instances are less frequently used. In this paper we propose a multi-resource load balancing algorithm for distributed cache systems. The algorithm aims at balancing both CPU and Memory resources among cache instances by redistributing stored data. Considering the possible conflict of balancing multiple resources at the same time, we give CPU and Memory resources weighted priorities based on the runtime load distributions. A scarcer resource is given a higher weight than a less scarce resource when load balancing. The system imbalance degree is evaluated based on monitoring information, and the utility load of a node, a unit for resource consumption. Besides, since continuous rebalance of the system may affect the QoS of applications utilizing the cache system, our data selection policy ensures that each data migration minimizes the system imbalance degree and hence, the total reconfiguration cost can be minimized. An extensive simulation is conducted to compare our policy with other policies. Our policy shows a significant improvement in time efficiency and decrease in reconfiguration cost.

Cluster scheduling and load balancing via TCP options

This paper describes an experiment in designing, implementing and testing a Transport layer cluster scheduling and dispatching architecture. The motivation for the experiment was the hypothesis that a Transport layer clustering solution may offer advantantages over the existing industry-standard Network layer and Data Link Layer approaches. The critical success factors initially established to guide and evaluate the experiment were reduced dispatcher work load, reduced dispatcher internal state memory requirements, distributed denial of service resilience, and cluster software design simplicity. The functional design stage of the experiment produced a Transport layer strategy for scheduling and load balancing based on the specification of two new TCP options. Implementation required the introduction of the newly specified TCP options into the Linux (2.4) kernel. The implementation produced an extended Linux Socket API to facilitate user-process access to the additional TCP capability. The testing stage of the experiment confirmed the operational efficiency of the solution.

A study of adaptive load balancing algorithms for distributed systems

With the advent of distributed computer systems with a largely transparent user interface, new questions have arisen regarding the management of such an environment by an operating system. One fertile area of research is that of load balancing, which attempts to improve system performance by redistributing the workload submitted to the system by the users. Early work in this field concentrated on static placement of computational objects to improve performance, given prior knowledge of process behaviour. More recently this has evolved into studying dynamic load balancing with process migration, thus allowing the system to adapt to varying loads. In this thesis, we describe a simulated system which facilitates experimentation with various load balancing algorithms. The system runs under UNIX and provides functions for user processes to communicate through software ports; processes reside on simulated homogeneous processors, connected by a user-specified topology, and a mechanism is included to allow migration of a process from one processor to another. We present the results of a study of adaptive load balancing algorithms, conducted using the aforementioned simulated system, under varying conditions; these results show the relative merits of different approaches to the load balancing problem, and we analyse the trade-offs between them. Following from this study, we present further novel modifications to suggested algorithms, and show their effects on system performance.