The multiple server location problem

In this paper, we introduce the Multiple Server location problem. A given number of servers are to be located at nodes of a network. Demand for these servers is generated at each node, and a subset of nodes need to be selected for locating one or more servers in each. There is no limit on the number of servers that can be established at each node. Each customer at a node selects the closest server (with demand divided equally when the closest distance is measured to more than one node). The objective is to minimize the sum of the travel time and the average time spent at the server, for all customers. The problem is formulated and analysed. Results using heuristic solution procedures: descent, simulated annealing, tabu search and a genetic algorithm are reported. The problem turns out to be a very difficult combinatorial problem when the total demand is very close to the total capacity of the servers.     

Open queueing networks in discrete time-some limit theorems

A finite number of nodes, each with a single server and infinite buffers, is considered in discrete time. The service may be FIFO and the service times are constant. The external arrivals and the routing decision variables form a general stationary sequence. Stability of the system is proved under these assumptions. Extension to multiple servers at a node and general stationary distributions holds. If the external input is i.i.d. and the routing is Markovian then stochastic ordering, continuity of stationary distributions, rates of convergence, a functional CLT and a functional LIL and various other limit theorems for the queue length process are also proved. Generalizations to multiple servers at nodes, customers with priority, multiple customer classes, general service length and Markov modulated external arrival cases are discussed.     

Emergency path restoration problems

We consider the problem of optimally scheduling the restoration of edges of a transportation network destroyed/damaged by a disaster. The restoration is performed by service units (servers) which have fixed restoration speeds. If several servers work simultaneously at the same point of the network, their collective restoration speed is the sum of their individual restoration speeds. The servers are initially located at some nodes. Each server can travel within the already restored part of the network with infinite speed, that is, at any time can immediately relocate to another point of the same connected component of the already restored part of the network. It is required to minimize a scheduling objective that can be expressed as the maximum or the sum of nondecreasing functions of the recovery times of the nodes, where the recovery time of a node is the time when the node is reached for the first time by a server. We present polynomial-time algorithms on path networks for problems with fixed initial locations of the servers. For problems with flexible locations that should also be optimized, we present polynomial-time algorithms for the case of equal restoration speeds of the servers, and prove that the problems are strongly NP-hard if the restoration speeds of the servers can be different.     

The multiple server center location problem

In this paper, we introduce the multiple server center location problem. p servers are to be located at nodes of a network. Demand for services of these servers is located at each node, and a subset of nodes are to be chosen to locate one or more servers in each. Each customer selects the closest server. The objective is to minimize the maximum time spent by any customer, including travel time and waiting time at the server sites. The problem is formulated and analyzed. Results for heuristic solution approaches are reported. Paper was partially supported by a College of Business Administration, California State University San Marcos summer grant of the first author. Paper was partially supported by an NSERC grant of the second author.     

The BMAP/PH/N retrial queue with Markovian flow of breakdowns

We consider a multi-server retrial queue with the Batch Markovian Arrival Process (BMAP). The servers are identical and independent of each other. The service time distribution of a customer by a server is of the phase (PH) type. If a group of primary calls meets idle servers the primary calls occupy the corresponding number of servers. If the number of idle servers is insufficient the rest of calls go to the orbit of unlimited size and repeat their attempts to get service after exponential amount of time independently of each other. Busy servers are subject to breakdowns and repairs. The common flow of breakdowns is the MAP. An event of this flow causes a failure of any busy server with equal probability. When a server fails the repair period starts immediately. This period has PH type distribution and does not depend on the repair time of other broken-down servers and the service time of customers occupying the working servers. A customer whose service was interrupted goes to the orbit with some probability and leaves the system with the supplementary probability. We derive the ergodicity condition and calculate the stationary distribution and the main performance characteristics of the system. Illustrative numerical examples are presented.     

Choosing among heterogeneous server clouds

This paper considers a model of interest in cloud computing applications. We consider a multiserver system consisting of N heterogeneous servers. The servers are categorized into M(\(\ll N\)) different types according to their service capabilities. Jobs having specific resource requirements arrive at the system according to a Poisson process with rate \(N \lambda \). Upon each arrival, a small number of servers are sampled uniformly at random from each server type. The job is then routed to the sampled server with maximum vacancy per server capacity. If a job cannot obtain the required amount of resources from the server to which it is assigned, then the job is discarded. We analyze the system in the limit as \(N \rightarrow \infty \). This gives rise to a mean field, which we show has a unique fixed point and is globally attractive. Furthermore, as \(N\rightarrow \infty \), the servers behave independently. The stationary tail probabilities of server occupancies are obtained from the stationary solution of the mean field. Numerical results suggest that the proposed scheme significantly reduces the average blocking probability compared to static schemes that probabilistically route jobs to servers in proportion to the number of servers of each type. Moreover, the reduction in blocking holds even for systems at high load. For the limiting system in statistical equilibrium, our simulation results indicate that the occupancy distribution is insensitive to the holding time distribution and only depends on its mean.     

A tight bound on the number of mobile servers to guarantee transferability among dominating configurations

In this paper, we propose a new framework to provide continuous services to users by a collection of mobile servers distributed over an interconnection network. We model those mobile servers as a subset of nodes, and assume that a user can receive the service if at least one adjacent node (including itself) plays the role of a server; i.e., we assume that the service could not be routed via the interconnection network. The main results obtained in this paper are summarized as follows: For the class of trees consisting of n nodes, ⌊n/2⌋ mobile servers are sometimes necessary and always sufficient to realize continuous services by the mobile servers, and for the class of Hamiltonian graphs with n nodes, ⌈(n+1)/3⌉ mobile servers are sometimes necessary and always sufficient.     

Features of some discrete-time cyclic queueing networks

A class of discrete-time closed cyclic networks is analyzed, where queues at each node have ample waiting room and have independent geometric service times with possibly unequal means. If each node has a single server or if there are sufficiently many parallel servers at each node to accommodate all jobs, equilibrium vectors of product form are obtained. For some other cases, equilibrium vectors of product form need not exist. For the single-server model, a normalization constant is computed and used to determine the queue-length distribution at a node.     

Dynamic scheduling for minimum delay in tandem and parallel constrained queueing models

The optimal scheduling problem in two queueing models arising in multihop radio networks with scheduled link activation is investigated. A tandem radio network is considered. Each node receives exogenous arriving packets which are stored in its unlimited capacity buffer. Links adjacent to the same node cannot transmit simultaneously because of radio interference constraints. The problem of link activation scheduling for minimum delay is studied for two different traffic types. In the first type all packets have a common destination that is one end-node of the tandem. In this case the system is modeled by a tandem queueing network with dependent servers. The server scheduling policy that minimizes the delay is obtained. In the second type of traffic, the destination of each packet is an immediate neighbor of the node at which the packet enters the network. In this case the system corresponds to a set of parallel queues with dependent servers. It is shown that the optimal policy activates the servers so that the maximum number of packets are served at each slot.     

Waiting time in a multi-server cutoff-priority gueue, and its application to an urban ambulance service

We consider a priority queue in steady state with N servers, two classes of customers, and a cutoff service discipline. Low priority arrivals are "cut off" (refused immediate service) and placed in a queue whenever N1 or more servers are busy, in order to keep N-N1 servers free for high priority arrivals. A Poisson arrival process for each class, and a common exponential service rate, are assumed. Two models are considered: one where high priority customers queue for service and one where they are lost if all servers are busy at an arrival epoch. Results are obtained for the probability of n servers busy, the expected low priority waiting time, and (in the case where high priority customers do not queue) the complete low priority waiting time distribution. The results are applied to determine the number of ambulances required in an urban fleet which serves both emergency calls and low priority patients transfers.     

Analysis of GI/M/n/n queueing system with ordered entry and no waiting line

This paper deals with a multi-server, finite-capacity queuing system with recurrent input and no waiting line. The interarrival times are arbitrarily distributed whereas service times are exponentially distributed. Moreover, the servers are heterogeneous and independent of each other. Arriving customers choose the server with the lowest index number among the empty servers. When all servers are busy at a time of an arrival, that arrival must leave the system without being served. The semi-Markov process method is used to describe this model and embedded Markov chain of the process is obtained. Furthermore, the Laplace–Stieltjes transform of the distribution of interoverflow times is derived which is the main objective of the paper. Finally, it is offered a new formulation for the loss probability which provides more efficient and rapid calculation is proposed.     

Performance analysis of a constrained resource sharing system

We consider a queueing system where the servers are arranged in a circle, and each arriving customer requires a pair of resources that is shared by its server with the respective neighbors on either side. If either resource is being used, the customer is denied service. Customers arrive at each server according to independent Poisson processes, and lengths of service times at each server have an exponential distribution. We derive a closed-form formula for the expected fraction of busy servers at any time in terms of the number of servers and the utilization factor (defined as the arrival rate times the mean service-time duration). This allows us to evaluate system performance when these parameters are varied, and to determine whether denying service to arrivals at alternate servers improves performance. We relate the system to Dijkstra's dining philosophers problem, which is an abstraction for resource sharing in an operating system. This revised version was published online in June 2006 with corrections to the Cover Date.     

Optimal assignment of servers to tasks when collaboration is inefficient

Consider a Markovian system of two stations in tandem with finite intermediate buffer and two servers. The servers are heterogeneous, flexible, and more efficient when they work on their own than when they collaborate. We determine how the servers should be assigned dynamically to the stations with the goal of maximizing the system throughput. We show that the optimal policy depends on whether or not one server is dominant (i.e., faster at both stations) and on the magnitude of the efficiency loss of collaborating servers. In particular, if one server is dominant then he must divide his time between the two stations, and we identify the threshold policy the dominant server should use; otherwise each server should focus on the station where he is the faster server. In all cases, servers only collaborate to avoid idleness when the first station is blocked or the second station is starved, and we determine when collaboration is preferable to idleness as a function of the efficiency loss of collaborating servers.     

Multiserver queue with addressed retrials

This paper presents a multiserver retrial queueing system with servers kept apart, thereby rendering it impossible for one to know the status (idle/busy) of the others. Customers proceeding to one channel will have to go to orbit if the server in it is busy and retry after some time to some channel, not necessarily the one already tried. Each orbital customer, independently of others, chooses the server randomly according to some specified probability distribution. Further this distribution is identical for all customers. We assume that the same ‘orbit’ is used by all retrial customers, between repeated attempts, to access the servers. We derive the system state probability distribution under Poisson arrival process of external customers, exponentially distributed service times and linear retrial rates to access the servers. Several system state characteristics are obtained and numerical illustrations provided. AMS subject classification: Primary 60K25 60K20     

Pooling in tandem queueing networks with non-collaborative servers

This paper considers pooling several adjacent stations in a tandem network of single-server stations with finite buffers. When stations are pooled, we assume that the tasks at those stations are pooled but the servers are not. More specifically, each server at the pooled station picks a job from the incoming buffer of the pooled station and conducts all tasks required for that job at the pooled station before that job is placed in the outgoing buffer. For such a system, we provide sufficient conditions on the buffer capacities and service times under which pooling increases the system throughput by means of sample-path comparisons. Our numerical results suggest that pooling in a tandem line generally improves the system throughput—substantially in many cases. Finally, our analytical and numerical results suggest that pooling servers in addition to tasks results in even larger throughput when service rates are additive and the two systems have the same total number of storage spaces.     

Analysis of M/M/S queues with 1-limited service

In this paper, a multiple server queue, in which each server takes a vacation after serving one customer is studied. The arrival process is Poisson, service times are exponentially distributed and the duration of a vacation follows a phase distribution of order 2. Servers returning from vacation immediately take another vacation if no customers are waiting. A matrix geometric method is used to find the steady state joint probability of number of customers in the system and busy servers, and the mean and the second moment of number of customers and mean waiting time for this model. This queuing model can be used for the analysis of different kinds of communication networks, such as multi-slotted networks, multiple token rings, multiple server polling systems and mobile communication systems.     

Round robin scheduling of heterogeneous parallel servers in heavy traffic

We consider scheduling for heterogeneous server systems, where tasks arrive according to a Poisson process, with their processing requirements following a discrete distribution with finite support. For a system with a dispatcher and several heterogeneous servers, we propose an optimized multi-layered round robin routing policy followed by shortest remaining processing time scheduling at each server. Using a heavy traffic approximation, we show that the proposed policy performs as well as the optimal scheduling policy for a heterogeneous servers system with a single queue (no routing) in heavy traffic. Additional simulation results suggest that such policies will be effective in more general settings.     

Heavy traffic resource pooling in parallel‐server systems

We consider a queueing system with r non‐identical servers working in parallel, exogenous arrivals into m different job classes, and linear holding costs for each class. Each arrival requires a single service, which may be provided by any of several different servers in our general formulation; the service time distribution depends on both the job class being processed and the server selected. The system manager seeks to minimize holding costs by dynamically scheduling waiting jobs onto available servers. A linear program involving only first‐moment data (average arrival rates and mean service times) is used to define heavy traffic for a system of this form, and also to articulate a condition of overlapping server capabilities which leads to resource pooling in the heavy traffic limit. Assuming that the latter condition holds, we rescale time and state space in standard fashion, then identify a Brownian control problem that is the formal heavy traffic limit of our rescaled scheduling problem. Because of the assumed overlap in server capabilities, the limiting Brownian control problem is effectively one‐dimensional, and it admits a pathwise optimal solution. That is, in the limiting Brownian control problem the multiple servers of our original model merge to form a single pool of service capacity, and there exists a dynamic control policy which minimizes cumulative cost incurred up to any time t with probability one. Interpreted in our original problem context, the Brownian solution suggests the following: virtually all backlogged work should be held in one particular job class, and all servers can and should be productively employed except when the total backlog is small. It is conjectured that such ideal system behavior can be approached using a family of relatively simple scheduling policies related to the cμ rule. This revised version was published online in June 2006 with corrections to the Cover Date.     

Limit laws for local counters in random binary search trees

Limit laws for several quantities in random binary search trees that are related to the local shape of a tree around each node can be obtained very simply by applying central limit theorems for w-dependent random variables. Examples include: the number of leaves (L_n), the number of nodes with k descendants (k fixed), the number of nodes with no left child, the number of nodes with k left descendants. Some of these results can also be obtained via the theory of urn models, but the present method seems easier to apply.