Optimal Flow Aggregation

Current IP routers are stateless: they forward individual packets based on the destination address contained in the packet header, but maintain no information about the application or flow to which a packet belongs. This stateless service model works well for best effort datagram delivery, but is grossly inadequate for applications that require quality of service guarantees, such as audio, video, or IP telephony. Maintaining state for each flow is expensive because the number of concurrent flows at a router can be in the hundreds of thousands. Thus, stateful solutions such as Intserv (integrated services) have not been adopted for their lack of scalability. Motivated by this dilemma, we formulate and solve the flow aggregation problem, where we give an efficient algorithm for computing the smallest set of aggregated flows that encode the forwarding state of individual flows. Our hope is that such aggregation of state information might increase the viability of Intserv-type protocols

The Life-Cycle Permanent-Income Model and Consumer Durables

This paper presents an extension of the life-cycle permanent-income model of consumption to the case of a durable good whose purchase involves lumpy trans- actions costs. Where individual behavior is concerned, the implications of the model are different in some respects from those of standard consumption theory. Specifically, rather than choose an optimal path for the service flow from durables, the optimizing consumer will choose an optimal range and try to keep his service flow inside that range. The dynamics implied by this behavior is different from that of the stock adjustment model. Properties of aggregate durables consumption are derived by explicit aggregation. In particular, it is shown that expenditures on durables display very large short-run elasticity to changes in permanent income. Empirical tests of the sort suggested by Hall (1978) generally produce results that are in line with the predictions of the theory.

A Test Between Unemployment Theories Using Matching Data

This paper tests whether aggregate matching is consistent with unemployment being mainly due to search frictions or due to job queues. Using U.K. data and correcting for temporal aggregation bias, estimates of the random matching function are consistent with previous work in this field, but random matching is formally rejected by the data. The data instead support 'stock-flow' matching. Estimates find that around 40 per cent of newly unemployed workers match quickly - they are interpreted as being on the short-side of their skill markets. The remaining workers match slowly, their re-employment rates depending statistically on the inflow of new vacancies and not on the vacancy stock. Having failed to match with existing vacancies, these workers wait for the arrival of new job vacancies. The results have important policy implications, particularly with reference to the design of optimal unemployment insurance programs.Matching, Unemployment, Temporal aggregation

Hierarchical planning in a single stage system

Single stage systems with high set-up times and high utilization levels occur in flow process industries. In this paper a two-tiered hierarchical model is developed for such a system. At the top level, the optimal value of the control parameters is determined, while the operational scheduling function is performed at the bottom level. The conceptual aggregation approach used in this model is compared to the aggregation approach used in classical hierarchical approaches

Towards a Queueing-Based Framework for In-Network Function Computation

We seek to develop network algorithms for function computation in sensor networks. Specifically, we want dynamic joint aggregation, routing, and scheduling algorithms that have analytically provable performance benefits due to in-network computation as compared to simple data forwarding. To this end, we define a class of functions, the Fully-Multiplexible functions, which includes several functions such as parity, MAX, and k th -order statistics. For such functions we exactly characterize the maximum achievable refresh rate of the network in terms of an underlying graph primitive, the min-mincut. In acyclic wireline networks, we show that the maximum refresh rate is achievable by a simple algorithm that is dynamic, distributed, and only dependent on local information. In the case of wireless networks, we provide a MaxWeight-like algorithm with dynamic flow splitting, which is shown to be throughput-optimal

A virtual power plant model for time-driven power flow calculations

This paper presents the implementation of a custom-made virtual power plant model in OpenDSS. The goal is to develop a model adequate for time-driven power flow calculations in distribution systems. The virtual power plant is modeled as the aggregation of renewable generation and energy storage connected to the distribution system through an inverter. The implemented operation mode allows the virtual power plant to act as a single dispatchable generation unit. The case studies presented in the paper demonstrate that the model behaves according to the specified control algorithm and show how it can be incorporated into the solution scheme of a general parallel genetic algorithm in order to obtain the optimal day-ahead dispatch. Simulation results exhibit a clear benefit from the deployment of a virtual power plant when compared to distributed generation based only on renewable intermittent generation.Peer ReviewedPostprint (published version

Computation-Aware Data Aggregation

Data aggregation is a fundamental primitive in distributed computing wherein a network computes a function of every nodes\u27 input. However, while compute time is non-negligible in modern systems, standard models of distributed computing do not take compute time into account. Rather, most distributed models of computation only explicitly consider communication time. In this paper, we introduce a model of distributed computation that considers both computation and communication so as to give a theoretical treatment of data aggregation. We study both the structure of and how to compute the fastest data aggregation schedule in this model. As our first result, we give a polynomial-time algorithm that computes the optimal schedule when the input network is a complete graph. Moreover, since one may want to aggregate data over a pre-existing network, we also study data aggregation scheduling on arbitrary graphs. We demonstrate that this problem on arbitrary graphs is hard to approximate within a multiplicative 1.5 factor. Finally, we give an O(log n ? log(OPT/t_m))-approximation algorithm for this problem on arbitrary graphs, where n is the number of nodes and OPT is the length of the optimal schedule