Results 1  10
of
33
TIGHT ANALYSES OF TWO LOCAL LOAD BALANCING ALGORITHMS
 SIAM J. COMPUT.
, 1999
"... This paper presents an analysis of the following load balancing algorithm. At each step, each node in a network examines the number of tokens at each of its neighbors and sends a token to each neighbor with at least 2d + 1 fewer tokens, where d is the maximum degree of any node in the network. We ..."
Abstract

Cited by 49 (5 self)
 Add to MetaCart
This paper presents an analysis of the following load balancing algorithm. At each step, each node in a network examines the number of tokens at each of its neighbors and sends a token to each neighbor with at least 2d + 1 fewer tokens, where d is the maximum degree of any node in the network. We show that within O(∆/α) steps, the algorithm reduces the maximum difference in tokens between any two nodes to at most O((d 2 log n)/α), where ∆ is the global imbalance in tokens (i.e., the maximum difference between the number of tokens at any node initially and the average number of tokens), n is the number of nodes in the network, and α is the edge expansion of the network. The time bound is tight in the sense that for any graph with edge expansion α, and for any value ∆, there exists an initial distribution of tokens with imbalance ∆ for which the time to reduce the imbalance to even ∆/2 is at least Ω(∆/α). The bound on the final imbalance is tight in the sense that there exists a class of networks that can be locally balanced everywhere (i.e., the maximum difference in tokens between any two neighbors is at most 2d), while the global imbalance remains Ω((d 2 log n)/α). Furthermore, we show that upon reaching a state with a global imbalance of O((d 2 log n)/α), the time for this algorithm to locally balance the network can be as large as Ω(n 1/2). We extend our analysis to a variant of this algorithm for dynamic and asynchronous
The Generalized Dimension Exchange Method for Load Balancing in kary ncubes and Variants
, 1995
"... The Generalized Dimension Exchange (GDE) method is a fully distributed load balancing method that operates in a relaxation fashion for multicomputers with a direct communication network. It is parameterized by an exchange parameter that governs the splitting of load between a pair of directly conne ..."
Abstract

Cited by 44 (9 self)
 Add to MetaCart
The Generalized Dimension Exchange (GDE) method is a fully distributed load balancing method that operates in a relaxation fashion for multicomputers with a direct communication network. It is parameterized by an exchange parameter that governs the splitting of load between a pair of directly connected processors during load balancing. An optimal would lead to the fastest convergence of the balancing process. Previous work has resulted in the optimal for the binary ncubes. In this paper, we derive the optimal 's for the kary ncube network and its variantsthe ring, the torus, the chain, and the mesh. We establish the relationships between the optimal convergence rates of the method when applied to these structures, and conclude that the GDE method favors high dimensional kary ncubes. We also reveal the superiority of the GDE method to another relaxationbased method, the diffusion method. We further show through statistical simulations that the optimal 's do speed up the GDE...
Local Divergence of Markov Chains and the Analysis of Iterative LoadBalancing Schemes
 IN PROCEEDINGS OF THE 39TH IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS ’98
, 1998
"... We develop a general technique for the quantitative analysis of iterative distributed load balancing schemes. We illustrate the technique by studying two simple, intuitively appealing models that are prevalent in the literature: the diffusive paradigm, and periodic balancing circuits (or the dimensi ..."
Abstract

Cited by 42 (0 self)
 Add to MetaCart
We develop a general technique for the quantitative analysis of iterative distributed load balancing schemes. We illustrate the technique by studying two simple, intuitively appealing models that are prevalent in the literature: the diffusive paradigm, and periodic balancing circuits (or the dimension exchange paradigm). It is well known that such load balancing schemes can be roughly modeled by Markov chains, but also that this approximation can be quite inaccurate. Our main contribution is an effective way of characterizing the deviation between the actual loads and the distribution generated by a related Markov chain, in terms of a natural quantity which we call the local divergence. We apply this technique to obtain bounds on the number of rounds required to achieve coarse balancing in general networks, cycles and meshes in these models. For balancing circuits, we also present bounds for the stronger requirement of perfect balancing, or counting.
Analysis of The Generalized Dimension Exchange Method for Dynamic Load Balancing
 Journal of Parallel and Distributed Computing
, 1992
"... The dimension exchange method is a distributed load balancing method for pointtopoint networks. We add a parameter, called the exchange parameter, to the method to control the splitting of load between a pair of directly connected processors, and call this parameterized version the generalized di ..."
Abstract

Cited by 42 (7 self)
 Add to MetaCart
The dimension exchange method is a distributed load balancing method for pointtopoint networks. We add a parameter, called the exchange parameter, to the method to control the splitting of load between a pair of directly connected processors, and call this parameterized version the generalized dimension exchange (GDE) method. The rationale for the introduction of this parameter is that splitting the workload into equal halves does not necessarily lead to an optimal result (in terms of the convergence rate) for certain structures. We carry out an analysis of this new method, emphasizing on its termination aspects and potential efficiency. Given a specific structure, one needs to determine a value to use for the exchange parameter that would lead to an optimal result. To this end, we first derive a sufficient and necessary condition for the termination of the method. We then show that equal splitting, proposed originally by others as a heuristic strategy, indeed yields optimal efficie...
Iterative Dynamic Load Balancing in Multicomputers
 Journal of Operational Research Society
, 1994
"... Dynamic load balancing in multicomputers can improve the utilization of processors and the efficiency of parallel computations through migrating workload across processors at runtime. We present a survey and critique of dynamic load balancing strategies that are iterative: workload migration is car ..."
Abstract

Cited by 21 (3 self)
 Add to MetaCart
Dynamic load balancing in multicomputers can improve the utilization of processors and the efficiency of parallel computations through migrating workload across processors at runtime. We present a survey and critique of dynamic load balancing strategies that are iterative: workload migration is carried out through transferring processes across nearest neighbor processors. Iterative strategies have become prominent in recent years because of the increasing popularity of pointtopoint interconnection networks for multicomputers. Key words: dynamic load balancing, multicomputers, optimization, queueing theory, scheduling. INTRODUCTION Multicomputers are highly concurrent systems that are composed of many autonomous processors connected by a communication network 1;2 . To improve the utilization of the processors, parallel computations in multicomputers require that processes be distributed to processors in such a way that the computational load is evenly spread among the processors...
On Runtime Parallel Scheduling for Processor Load Balancing
 IEEE Trans. Parallel and Distributed Systems
, 1997
"... Parallel scheduling is a new approach for load balancing. In parallel scheduling, all processors cooperate to schedule work. Parallel scheduling is able to accurately balance the load by using global load information at compiletime or runtime. It provides highquality load balancing. This paper pre ..."
Abstract

Cited by 21 (0 self)
 Add to MetaCart
Parallel scheduling is a new approach for load balancing. In parallel scheduling, all processors cooperate to schedule work. Parallel scheduling is able to accurately balance the load by using global load information at compiletime or runtime. It provides highquality load balancing. This paper presents an overview of the parallel scheduling technique. Scheduling algorithms for tree, hypercube, and mesh networks are presented. These algorithms can fully balance the load and maximize locality 1. Introduction Static scheduling balances the workload before runtime and can be applied to problems with a predictable structure, which are called static problems. Dynamic scheduling performs scheduling activities concurrently at runtime, which applies to problems with an unpredictable structure, which are called dynamic problems. Static scheduling utilizes the knowledge of problem characteristics to reach a wellbalanced load [1, 2, 3, 4]. However, it is not able to balance the load for dynami...
Nearest Neighbor Algorithms for Load Balancing in Parallel Computers
, 1995
"... With nearest neighbor load balancing algorithms, a processor makes balancing decisions based on localized workload information and manages workload migrations within its neighborhood. This paper compares a couple of fairly wellknown nearest neighbor algorithms, the dimensionexchange (DE, for shor ..."
Abstract

Cited by 19 (2 self)
 Add to MetaCart
With nearest neighbor load balancing algorithms, a processor makes balancing decisions based on localized workload information and manages workload migrations within its neighborhood. This paper compares a couple of fairly wellknown nearest neighbor algorithms, the dimensionexchange (DE, for short) and the diffusion (DF, for short) methods and their several variantsthe average dimensionexchange (ADE), the optimallytuned dimensionexchange (ODE), the local average diffusion (ADF) and the optimallytuned diffusion (ODF). The measures of interest are their efficiency in driving any initial workload distribution to a uniform distribution and their ability in controlling the growth of the variance among the processors' workloads. The comparison is made with respect to both oneport and allport communication architectures and in consideration of various implementation strategies including synchronous/asynchronous invocation policies and static/dynamic random workload behaviors. It t...
An Adversarial Model for Distributed Dynamic Load Balancing
, 1998
"... We study the problem of balancing the load on processors of an arbitrary network. If jobs arrive or depart during the process of load balancing, we have the dynamic load balancing problem; otherwise, we have the static load balancing problem. While static load balancing on arbitrary and special netw ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
We study the problem of balancing the load on processors of an arbitrary network. If jobs arrive or depart during the process of load balancing, we have the dynamic load balancing problem; otherwise, we have the static load balancing problem. While static load balancing on arbitrary and special networks has been well studied, very little is known about dynamic load balancing. The difficulty lies in modeling the arrivals and departures of jobs in a clean manner. In this paper, we initiate the study of dynamic load balancing by modeling job traffic using an adversary. Our main result is that a simple, local control distributed load balancing algorithm maintains the load of the network within a stable level against this powerful adversary. Our results hold for different models of traffic patterns and processor communication. 1 Introduction An important problem in a distributed system is to balance the total workload among the various processors of the underlying system. Such load balan...
Performance comparison of dynamic loadbalancing strategies for distributed computing
 In: Proceedings of the Thirtysecond Annual Hawaii International Conference on System Sciences
, 1999
"... Abstract * The DASUD (Diffusion Algorithm Searching Unbalanced Domains) algorithm belongs to the nearestneighbours class and operates in a diffusion scheme where a processor balances its load with all its neighbours. DASUD detects unbalanced domains and performs local exchange of load between proces ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
Abstract * The DASUD (Diffusion Algorithm Searching Unbalanced Domains) algorithm belongs to the nearestneighbours class and operates in a diffusion scheme where a processor balances its load with all its neighbours. DASUD detects unbalanced domains and performs local exchange of load between processors to achieve global balancing. The DASUD algorithm has been evaluated by comparison with another wellknown strategy, namely, the SID (Sender Initiated Diffusion) algorithm across a range of networks topologies including ring, torus and hypercube where the number of processors varies from 8 to 128. From the experiments we have observed that DASUD outperforms the other strategy as it provides the best tradeoff between the balance degree obtained at the final state and the number of iterations required to reach such state. DASUD is able to coerce any initial load distribution into a highly balanced global state and also exhibits good scalability properties.
DimensionExchange Algorithms for Load Balancing on Trees
 Procs. of 9th Int. Colloquium on Structural Information and Communication Complexity
, 2002
"... This paper considers dimensionexchange algorithms for load balancing on trees with finitelydivisible loads (token distribution). We present improved analysis of an existing protocol, and in particular, establish a logarithmic upper bound on the discrepancy of the final distribution. Our second con ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
This paper considers dimensionexchange algorithms for load balancing on trees with finitelydivisible loads (token distribution). We present improved analysis of an existing protocol, and in particular, establish a logarithmic upper bound on the discrepancy of the final distribution. Our second contribution is a new algorithm, which assuming each node has knowledge of the total number of nodes, determines a perfectly balanced distribution.