1,914 research outputs found
Routing and Staffing when Servers are Strategic
Traditionally, research focusing on the design of routing and staffing
policies for service systems has modeled servers as having fixed (possibly
heterogeneous) service rates. However, service systems are generally staffed by
people. Furthermore, people respond to workload incentives; that is, how hard a
person works can depend both on how much work there is, and how the work is
divided between the people responsible for it. In a service system, the routing
and staffing policies control such workload incentives; and so the rate servers
work will be impacted by the system's routing and staffing policies. This
observation has consequences when modeling service system performance, and our
objective is to investigate those consequences.
We do this in the context of the M/M/N queue, which is the canonical model
for large service systems. First, we present a model for "strategic" servers
that choose their service rate in order to maximize a trade-off between an
"effort cost", which captures the idea that servers exert more effort when
working at a faster rate, and a "value of idleness", which assumes that servers
value having idle time. Next, we characterize the symmetric Nash equilibrium
service rate under any routing policy that routes based on the server idle
time. We find that the system must operate in a quality-driven regime, in which
servers have idle time, in order for an equilibrium to exist, which implies
that the staffing must have a first-order term that strictly exceeds that of
the common square-root staffing policy. Then, within the class of policies that
admit an equilibrium, we (asymptotically) solve the problem of minimizing the
total cost, when there are linear staffing costs and linear waiting costs.
Finally, we end by exploring the question of whether routing policies that are
based on the service rate, instead of the server idle time, can improve system
performance.Comment: First submitted for journal publication in 2014; accepted for
publication in Operations Research in 2016. Presented in select conferences
throughout 201
Architecture for Mobile Heterogeneous Multi Domain Networks
Multi domain networks can be used in several scenarios including military, enterprize networks, emergency networks and many other cases. In such networks, each domain might be under its own administration. Therefore, the cooperation among domains is conditioned by individual domain policies regarding sharing information, such as network topology, connectivity, mobility, security, various service availability and so on. We propose a new architecture for Heterogeneous Multi Domain (HMD) networks, in which one the operations are subject to specific domain policies. We propose a hierarchical architecture, with an infrastructure of gateways at highest-control level that enables policy based interconnection, mobility and other services among domains. Gateways are responsible for translation among different communication protocols, including routing, signalling, and security. Besides the architecture, we discuss in more details the mobility and adaptive capacity of services in HMD. We discuss the HMD scalability and other advantages compared to existing architectural and mobility solutions. Furthermore, we analyze the dynamic availability at the control level of the hierarchy
Routing and staffing when servers are strategic
Traditionally, research focusing on the design of routing and staffing policies for service systems has modeled servers as having fixed (possibly heterogeneous) service rates. However, service systems are generally staffed by people. Furthermore, people respond to workload incentives; that is, how hard a person works can depend both on how much work there is, and how the work is divided between the people responsible for it. In a service system, the routing and staffing policies control such workload incentives; and so the rate servers work will be impacted by these policies. This observation has consequences when modeling service system performance, and our objective in this paper is to investigate those consequences.
We do this in the context of the M/M/N queue, which is the canonical model for large service systems. First, we present a model for "strategic" servers that choose their service rate, in which there is a trade-off between an "effort cost" and a "value of idleness": faster service rates require more exertion of effort, but also lead to more idle time. Next, we characterize the symmetric Nash equilibrium service rate under any routing policy that routes based on the server idle time (such as the Longest Idle Server First policy). This allows us to (asymptotically) solve the problem of minimizing the total cost, when there are linear staffing costs and linear waiting costs. We find that an asymptotically optimal staffing policy staffs strictly more than the common square-root staffing policy. Finally, we end by exploring the question of whether routing policies that are based on the service rate, instead of the server idle time, can improve system performance
Analysis of join-the-shortest-queue routing for web server farms
Join the Shortest Queue (JSQ) is a popular routing policy for server farms. However, until now all analysis of JSQ has been limited to First-Come-First-Serve (FCFS) server farms, whereas it is known that web server farms are better modeled as Processor Sharing (PS) server farms. We provide the first approximate analysis of JSQ in the PS server farm model for general job-size distributions, obtaining the distribution of queue length at each queue. To do this, we approximate the queue length of each queue in the server farm by a one-dimensional Markov chain, in a novel fashion. We also discover some interesting insensitivity properties of PS server farms with JSQ routing, and discuss the near-optimality of JSQ
Optimal Rate-Matrix Pruning For Large-Scale Heterogeneous Systems
We present an analysis of large-scale load balancing systems, where the
processing time distribution of tasks depends on both the task and server
types. Our study focuses on the asymptotic regime, where the number of servers
and task types tend to infinity in proportion. In heterogeneous environments,
commonly used load balancing policies such as Join Fastest Idle Queue and Join
Fastest Shortest Queue exhibit poor performance and even shrink the stability
region. Interestingly, prior to this work, finding a scalable policy with a
provable performance guarantee in this setup remained an open question.
To address this gap, we propose and analyze two asymptotically delay-optimal
dynamic load balancing policies. The first policy efficiently reserves the
processing capacity of each server for ``good" tasks and routes tasks using the
vanilla Join Idle Queue policy. The second policy, called the speed-priority
policy, significantly increases the likelihood of assigning tasks to the
respective ``good" servers capable of processing them at high speeds. By
leveraging a framework inspired by the graphon literature and employing the
mean-field method and stochastic coupling arguments, we demonstrate that both
policies achieve asymptotic zero queuing. Specifically, as the system scales,
the probability of a typical task being assigned to an idle server approaches
1
- …