Satish Penmatsa

Game-theoretic static load balancing for distributed systems

In this paper, we present a game theoretic approach to solve the static load balancing problem for single-class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network. The objective of our approach is to provide fairness to all the jobs (in a single-class system) and the users of the jobs (in a multi-user system). To provide fairness to all the jobs in the system, we use a cooperative game to model the load balancing problem. Our solution is based on the Nash Bargaining Solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. To provide fairness to all the users in the system, the load balancing problem is formulated as a non-cooperative game among the users who try to minimize the expected response time of their own jobs. We use the concept of Nash equilibrium as the solution of our non-cooperative game and derive a distributed algorithm for computing it. Our schemes are compared with other existing schemes using simulations with various system loads and configurations. We show that our schemes perform near the system optimal schemes and are superior to the other schemes in terms of fairness.

Dynamic Multi-User Load Balancing in Distributed Systems

In this paper, we review two existing static load balancing schemes based on M/M/1 queues. We then use these schemes to propose two dynamic load balancing schemes for multi-user (multi-class) jobs in heterogeneous distributed systems. These two dynamic load balancing schemes differ in their objective. One tries to minimize the expected response time of the entire system while the other tries to minimize the expected response time of the individual users. The performance of the dynamic schemes is compared with that of the static schemes using simulations with various loads and parameters. The results show that, at low communication overheads, the dynamic schemes show superior performance over the static schemes. But as the overheads increase, the dynamic schemes (as expected) yield similar performance to that of the static schemes.

Job allocation schemes in computational grids based on cost optimization

In this paper we propose two price-based job allocation schemes for computational grids. A grid system tries to solve problems submitted by various grid users by allocating the jobs to the computing resources governed by different resource owners. The prices charged by these owners are obtained based on a pricing model using a bargaining game theory framework. These prices are then used for job allocation. We present the grid system model and formulate the two schemes as a constraint minimization problem and as a non-cooperative game respectively. The objective of these schemes is to minimize the cost for the grid users. We present algorithms to compute the optimal load (job) fractions to allocate jobs to the computers. Finally, the two schemes are compared under simulations with various system loads and configurations and conclusions are drawn.

Price-based user-optimal job allocation scheme for grid systems

In this paper, we propose a price-based user-optimal job allocation scheme for grid systems whose nodes are connected by a communication network. The job allocation problem is formulated as a noncooperative game among the users who try to minimize the expected cost of their own jobs. We use the concept of Nash equilibrium as the solution of our noncooperative game and derive a distributed algorithm for computing it. The prices that the grid users has to pay for using the computing resources owned by different resource owners are obtained using a pricing model based on a game theory framework. Finally, our scheme is compared with a system-optimal job allocation scheme under simulations with various system loads and configurations and conclusions are drawn

Cooperative load balancing for a network of heterogeneous computers

In this paper, we present a game theoretic approach to solve the static load balancing problem in a distributed system which consists of heterogeneous computers connected by a single channel communication network. We use a cooperative game to model the load balancing problem. Our solution is based on the Nash bargaining solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. Our scheme is compared with that of other existing schemes under simulations with various system loads and configurations. We show that the solution of our scheme is near optimal and is superior to the other schemes in terms of fairness.

Distributed loop-scheduling schemes for heterogeneous computer systems

Distributed computing systems are a viable and less expensive alternative to parallel computers. However, a serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. Some distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been designed in the past. In this work we study self‐scheduling schemes for parallel loops with independent iterations which have been applied to multiprocessor systems in the past. We extend one important scheme of this type to a distributed version suitable for heterogeneous distributed systems. We implement our new scheme on a network of computers and make performance comparisons with other existing schemes. Copyright

Implementation of Distributed Loop Scheduling Schemes on the TeraGrid

Grid computing can be used for high performance computations. However, a serious difficulty in concurrent programming of such heterogeneous systems is how to deal with scheduling and load balancing of such systems which may consist of heterogeneous computers on different sites. Distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been proposed and analyzed in the past. In this article, we implement the previous schemes in MPICH-G2 and MPIg on the TeraGrid. We present performance results for three loop scheduling schemes on single and multi-site TeraGrid clusters.

Spectrum Load Balancing for Medium Access in Cognitive Radio Systems

In this work, the problem of spectrum allocation in cognitive radios is shown to be similar to the load balancing problem in distributed computer systems. Spectrum load balancing (SLB) algorithm based on the non-cooperative load balancing problem in computers is proposed, and is applied to a cognitive radio system. The capability of SLB to support QoS in the presence of other competing cognitive networks is evaluated via simulations and compared with the existing spectrum load smoothing (SLS) algorithm. SLB is more efficient than SLS and it provides a Nash equilibrium.

Cost minimization in utility computing systems

Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system‐optimal and user‐optimal price‐based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system‐optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user‐optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system‐optimal scheme is formulated as a constraint minimization problem, and the user‐optimal scheme is formulated as a non‐cooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non‐cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations. Copyright

Scalable loop self-scheduling schemes for heterogeneous clusters

Heterogeneous cluster systems (e.g., a LAN of computers) can be used for concurrent processing for some applications. However, a serious difficulty in concurrent programming of a heterogeneous system is how to deal with scheduling and load balancing of such a system that may consist of heterogeneous computers. Distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been proposed and analysed in the past. Here, we implement the previous schemes in MPI. We present an extension of these schemes implemented in a hierarchical Master Slave architecture and include experimental results and comparisons.