A process is an operating system abstraction representing an instance of a running computer program. Process migration is the act of transferring a process between two machines during its execution. Several implementations

Abstract-Optimal load allocation for load sharing a divisible job over processors interconnected in either a bus or a tree network is considered. The processors are either equipped with front-end processors or not so equipped. Closed form solutions for the minimum finish time and the optimal data allocation for each processor are obtained. The performance of large symmetric tree networks is examined by aggregating the component links and processors into a single equivalent processor. This allows an easy examination of large tree networks. In addition, it becomes possible to find a closed form solution for the optimal amount of data that is to be assigned to each processor in the tree network in order to achieve the minimum finish time. Index Terms- Load sharing, load balancing, divisible job, multiprocessors. I.

In a distributed real-time system, uneven task arrivals temporarily overload some nodes while leaving others idle or underloaded. Consequently, some tasks may miss their deadlines even if the overall system has the capacity to meet the deadlines of all tasks. An effective load sharing (LS) scheme is proposed as a solution to this problem. Upon arrival of a task at a node, the node determines whether or not the node can complete the task in time under the minimum--laxity--first--served policy. If the task cannot be guaranteed or if guarantees of some other tasks are to be violated due to the addition of this task to the existing schedule, the node looks up the list of loss--minimizing decisions , and determines the best node among a set of nodes in its physical proximity, called its buddy set , to which the task(s) may be transferred. This list of decisions is periodically updated using Bayesian decision analysis and prior/posterior state distributions. These probability distributions a...

We consider the traffic allocation problem: arriving customers have to be assigned to one of a group of servers. The aim is to optimize system performance measures, such as mean waiting time of a customer or total number of customers in the system, under a given static allocation policy. Two static policies are considered: probabilistic assignment and allocation according to a fixed pattern. For these two policies general properties as well as optimization aspects are discussed. 1991 Mathematics Subject Classification: 60K25, 68M20. Keywords & Phrases: static load balancing, probabilistic allocation, pattern allocation, waiting times minimization, MAP/G/1 queue. Note: The first author was supported by NFI. Part of the second author's research has been supported by the European Grant BRA-QMIPS of CEC DG XIII. 1 Introduction In a distributed computer system, tasks generated by a group of users can be distributed over a number of available processors. This contrasts with syste...

Traditionally, allocation of data in distributed database management systems has been determined by o�-line analysis and optimization. This technique works well for static database access patterns, but is often inadequate for frequently changing workloads. In this paper we address how to dynamically reallocate data for partionable distributed databases with changing access patterns. Rather than complicated and expensive optimization algorithms, a simple heuristic is presented and shown, via an implementation study, to improve system throughput by 30 � in a local area network based system. Based on arti�cial wide area network delays, we show that dynamic reallocation can improve system throughput by a factor of two and a half for wide area networks. We also show that individual site load must be taken into consideration when reallocating data, and provide a simple policy that incorporates load in the reallocation decision.

We investigate optimal load balancing strategies for a multi-class multi-server processor-sharing system with a Poisson input stream, heterogeneous service rates, and a server-dependent holding cost per unit time. Specifically, we study (i) the centralized setting in which a dispatcher routes incoming jobs based on their service time requirements so as to minimize the weighted mean sojourn time in the system; and (ii) the decentralized, distributed non-cooperative setting in which each job, aware of its service time, selects a server with the objective of minimizing its weighted mean sojourn time in the system. For the decentralized setting we show the existence of a potential function, which allows us to transform the non-cooperative game into a standard convex optimization problem. For the two aforementioned settings, we characterize the set of optimal routing policies and obtain a closed form expression for the load on each server under any such policy. Furthermore, we show the existence of an optimal policy that routes a job independently of its service time requirement. We also show that the set of servers used in the decentralized setting is a subset of set of servers used in the centralized setting. Finally, we compare the performance perceived by jobs in the two settings by studying the so-called Price of Anarchy (PoA), that is, the ratio between the decentralized and the optimal centralized solutions. When the holding cost per unit time is the same for all servers, it is known that the PoA is upper bounded by the number of servers in the system. Interestingly, we show that the PoA for our system can be unbounded. In particular this indicates that in our system, the performance of selfish routing can be extremely inefficient. keywords: Load balancing, M/G/1 processor-sharing queues, server farms, potential game, Price of Anarchy. 1

This paper presents a performance evaluation approach to compare different distributed load balancing schemes on a unified basis. This approach is an integration of simulation, statistical and analytical models, and takes into account the fundamental system parameters that can possibly affect the performance. We show that all the sender-initiated distributed load balancing strategies can be modeled by a central server open queuing network. Furthermore, these load balancing strategies can be characterized by only two queuing parameters – the average execution queue length and the probability that a newly arrived task is executed locally or migrated to another node. To capture the relation between these queuing parameters and various system parameters, a statistical analysis has been carried out on the empirical data obtained through simulation. The analytical queuing model is then used to predict the response time of a system with any combination of system parameters. Experimental results are obtained for six different load balancing strategies. The proposed model provides performance results in a straightforward manner and can be beneficial to the system designers in assessing the system under varying conditions.

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contract No. DABT63-91-C-0028. The content of the information does not necessarily re ect the position or the policy of the Government and no o cial endorsement should be inferred. This paper presents a simple load balancing algorithm and its probabilistic analysis. Unlike most of the previous load balancing algorithms, this algorithm maintains locality. We show that the cost of this load balancing algorithm is small for practical situations and discuss some interesting applications for data remapping.

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