scholarly journals Power-aware load balancing of large scale MPI applications

2009 ◽  
Author(s):  
Maja Etinski ◽  
Julita Corbalan ◽  
Jesus Labarta ◽  
Mateo Valero ◽  
Alex Veidenbaum
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Mpi Applications

Routing Metric Based on Node Degree for Load-Balancing in Large-Scale Networks

2011 ◽  
Author(s):  
Hitomi Tamura ◽  
Masato Uchida ◽  
Masato Tsuru ◽  
Jun'ichi Shimada ◽  
Takeshi Ikenaga ◽  
...  
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Node Degree ◽  
Routing Metric ◽  
Large Scale Networks

scholarly journals PeerFlow: Secure Load Balancing in Tor

2017 ◽  
Vol 2017 (2) ◽  
pp. 74-94 ◽  
Author(s):  
Aaron Johnson ◽  
Rob Jansen ◽  
Nicholas Hopper ◽  
Aaron Segal ◽  
Paul Syverson
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Large Scale ◽  
Scanning System ◽  
Large Scale Network ◽  
Improve Accuracy ◽  
Scale Network ◽  
Network Simulations ◽  
Improved Design ◽  
Speed And Accuracy

Abstract We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidth-scanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.

Periodic hierarchical load balancing for large supercomputers

2011 ◽  
Vol 25 (4) ◽  
pp. 371-385 ◽  
Author(s):  
Gengbin Zheng ◽  
Abhinav Bhatelé ◽  
Esteban Meneses ◽  
Laxmikant V. Kalé
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Parallel Machines ◽  
National Laboratory ◽  
Argonne National Laboratory ◽  
Parallel Applications ◽  
Scientific Application ◽  
Computing Center ◽  
Blue Gene ◽  
Advanced Computing

Large parallel machines with hundreds of thousands of processors are becoming more prevalent. Ensuring good load balance is critical for scaling certain classes of parallel applications on even thousands of processors. Centralized load balancing algorithms suffer from scalability problems, especially on machines with a relatively small amount of memory. Fully distributed load balancing algorithms, on the other hand, tend to take longer to arrive at good solutions. In this paper, we present an automatic dynamic hierarchical load balancing method that overcomes the scalability challenges of centralized schemes and longer running times of traditional distributed schemes. Our solution overcomes these issues by creating multiple levels of load balancing domains which form a tree. This hierarchical method is demonstrated within a measurement-based load balancing framework in Charm++. We discuss techniques to deal with scalability challenges of load balancing at very large scale. We present performance data of the hierarchical load balancing method on up to 16,384 cores of Ranger (at the Texas Advanced Computing Center) and 65,536 cores of Intrepid (the Blue Gene/P at Argonne National Laboratory) for a synthetic benchmark. We also demonstrate the successful deployment of the method in a scientific application, NAMD, with results on Intrepid.

Load Balancing to Increase the Consistency of Replicas in Data Grids

2012 ◽  
pp. 42-57
Author(s):  
Ghalem Belalem ◽  
Naima Belayachi ◽  
Radjaa Behidji ◽  
Belabbes Yagoubi
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Data Grids ◽  
Distributed Resources ◽  
Data Set ◽  
Large Scale Systems ◽  
Consistency Management ◽  
Geographically Distributed ◽  
Replication Technique ◽  
Improve Service Quality

Data grids are current solutions to the needs of large scale systems and provide a set of different geographically distributed resources. Their goal is to offer an important capacity of parallel calculation, ensure a data effective and rapid access, improve the availability, and tolerate the breakdowns. In such systems, however, these advantages are possible only by using the replication technique. The use of this technique raises the problem of maintaining consistency of replicas of the same data set. In order to guarantee replica set reliability, it is necessary to have high coherence. This fact, however, penalizes performance. In this paper, the authors propose studying balancing influence on replica quality. For this reason, a service of hybrid consistency management is developed, which combines the pessimistic and optimistic approaches and is extended by a load balancing service to improve service quality. This service is articulated on a hierarchical model with two levels.

Peer-to-Peer Service Discovery for Grid Computing

2012 ◽  
pp. 232-259
Author(s):  
Eddy Caron ◽  
Frédéric Desprez ◽  
Franck Petit ◽  
Cédric Tedeschi
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Service Discovery ◽  
Large Scale ◽  
Dynamic Networks ◽  
Peer To Peer ◽  
Indexing System ◽  
Key Points ◽  
Computing Platforms ◽  
Traditional Approaches

Within distributed computing platforms, some computing abilities (or services) are offered to clients. To build dynamic applications using such services as basic blocks, a critical prerequisite is to discover those services. Traditional approaches to the service discovery problem have historically relied upon centralized solutions, unable to scale well in large unreliable platforms. In this chapter, we will first give an overview of the state of the art of service discovery solutions based on peer-to-peer (P2P) technologies that allow such a functionality to remain efficient at large scale. We then focus on one of these approaches: the Distributed Lexicographic Placement Table (DLPT) architecture, that provide particular mechanisms for load balancing and fault-tolerance. This solution centers around three key points. First, it calls upon an indexing system structured as a prefix tree, allowing multi-attribute range queries. Second, it allows the mapping of such structures onto heterogeneous and dynamic networks and proposes some load balancing heuristics for it. Third, as our target platform is dynamic and unreliable, we describe its powerful fault-tolerance mechanisms, based on self-stabilization. Finally, we present the software prototype of this architecture and its early experiments.

scholarly journals Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4415 ◽  
Author(s):  
Taewoon Kim ◽  
Chanjun Chun ◽  
Wooyeol Choi
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Mobile Internet ◽  
Base Stations ◽  
Traffic Load ◽  
User Association ◽  
Network Simulator ◽  
Traffic Loads ◽  
Association Strategy ◽  
Radio Access

In networking systems such as cloud radio access networks (C-RAN) where users receive the connection and data service from short-range, light-weight base stations (BSs), users’ mobility has a significant impact on their association with BSs. Although communicating with the closest BS may yield the most desirable channel conditions, such strategy can lead to certain BSs being over-populated while leaving remaining BSs under-utilized. In addition, mobile users may encounter frequent handovers, which imposes a non-negligible burden on BSs and users. To reduce the handover overhead while balancing the traffic loads between BSs, we propose an optimal user association strategy for a large-scale mobile Internet of Things (IoT) network operating on C-RAN. We begin with formulating an optimal user association scheme focusing only on the task of load balancing. Thereafter, we revise the formulation such that the number of handovers is minimized while keeping BSs well-balanced in terms of the traffic load. To evaluate the performance of the proposed scheme, we implement a discrete-time network simulator. The evaluation results show that the proposed optimal user association strategy can significantly reduce the number of handovers, while outperforming conventional association schemes in terms of load balancing.

scholarly journals ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

2017 ◽  
Vol 10 (13) ◽  
pp. 162
Author(s):  
Amey Rivankar ◽  
Anusooya G
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Energy Efficient ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

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