Distributed Load Balancing Scheme Research Articles

An improved load-balancing mechanism based on deadline failure recovery on GridSim

Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called "Enhanced GridSim with Load balancing based on Deadline Failure Recovery" (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.

Distributed Proportional Fair Load Balancing in Heterogenous Systems

We consider the problem of distributed load balancing in heterogenous parallel server systems, where the service rate achieved by a user at a server depends on both the user and the server. Such heterogeneity typically arises in wireless networks (e.g., servers may represent frequency bands, and the service rate of a user varies across bands). We assume that each server equally shares in time its capacity among users allocated to it. Users initially attach to an arbitrary server, but at random instants of time, they probe the load at a new server and migrate there if this improves their service rate. The dynamics under this distributed load balancing scheme, referred to as Random Local Search (RLS), may be interpreted as those generated by strategic players updating their strategy in a load balancing game. In closed systems, where the user population is fixed, we show that this game has pure Nash Equilibriums (NEs), and that these equilibriums get close to a Proportionally Fair (PF) allocation of users to servers when the user population grows large. We provide an anytime upper bound of the gap between the allocation under RLS and the PF allocation. In open systems, where users randomly enter the system and leave upon service completion, we establish that the RLS algorithm stabilizes the system whenever this it at all possible under centralized load balancing schemes, i.e., it is throughput-optimal. The proof of this result relies on a novel Lyapounov analysis that captures the dynamics due to both users' migration and their arrivals and departures. To our knowledge, the RLS algorithm constitutes the first fully distributed and throughput-optimal load balancing scheme in heterogenous parallel server systems. We extend our analysis to various scenarios, e.g. to cases where users can be simultaneously served by several servers. Finally we illustrate through numerical experiments the efficiency of the RLS algorithm.

An enhanced load balancing mechanism based on deadline control on GridSim

A Grid is a network of computational resources that may potentially span many continents. Load balancing in a Grid is a hot research issue which affects every aspect of the Grid, including service selection and task execution. Thus, it is necessary and significant to solve the load balancing problem in a Grid. In this paper, we propose a dynamic, distributed load balancing scheme for a Grid which provides deadline control for tasks. In our scenario, first, resources check their state and make a request to the Grid Broker according to the change of load state. Then, the Grid Broker assigns Gridlets between resources and scheduling for load balancing under the deadline request. We apply our load balancing strategy into a popular Grid simulation platform GridSim. Experimental results prove that our proposed load balancing mechanism can (1) reduce the makespan, (2) improve the finished rate of the Gridlet, and (3) reduce the resubmitted time.

Enhanced GridSim architecture with load balancing

Grid is a network of computational resources that may potentially span many continents. Maximization of the resource utilization hinges on the implementation of an efficient load balancing scheme, which provides (i) minimization of idle time, (ii) minimization of overloading, and (iii) minimization of control overhead. In this paper, we propose a dynamic and distributed load balancing scheme for grid networks. The distributed nature of the proposed scheme not only reduces the communication overhead of grid resources but also cuts down the idle time of the resources during the process of load balancing. We apply the proposed load balancing approach on Enhanced GridSim in order to gauge the effectiveness in terms of communication overhead and response time reduction. We show that significant savings are delivered by the proposed technique compared to other approaches such as centralized load balancing and no load balancing.

Parallel architectures for processing high speed network signaling protocols

We study the effectiveness of different parallel architectures for achieving the high throughputs and low latencies needed in processing signaling protocols for high speed networks. A key performance issue is the trade off between the load balancing gains and the call record management overhead. Arranging processors in large groups potentially yields higher load balancing gains but also incurs higher overhead in maintaining consistency among the replicated copies of the call records. We study this tradeoff and its impact on the design of protocol processing systems for two generic classes of parallel architectures, namely, shared memory and distributed memory architectures. In shared memory architectures, maintaining a common message queue in the shared memory can provide the maximal load balancing gains. We show, however, in order to optimize performance it is necessary to organize the processors in small groups since large groups result in higher call record management overhead. In distributed memory architectures with each processor maintaining its own message queue there is no inherent provision for load balancing. Based on a detailed simulation analysis we show that organizing the processors into small groups and using a simple distributed load balancing scheme yields modest performance gains even after call record management overheads are taken into account. We find that the common message queue architecture outperforms the distributed architecture in terms of lower response time due to its improved load balancing capability. Finally, we do a fault-tolerance analysis with respect to the call-record data structure. Using a simple failure recovery model of the processors and the local memory, we show that in the case of shared memory architecture, the availability is also optimized when processors are organized in small groups. This is because when comparing architectures the higher call record management overhead incurred for larger group sizes must be accounted for as system unavailability.