Optimal Scheduling in a Queue with Differentiated Impatient Users

We consider a M/M/1 queue in which the average reward for servicing a job is an exponentially decaying function of the job’s sojourn time. The maximum reward and mean service times of a job are i.i.d. and chosen from arbitrary distributions. The scheduler is assumed to know the maximum reward, service rate, and age of each job. We prove that the scheduling policy that maximizes average reward serves the customer with the highest product of potential reward and service rate

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation through which the predictability of user behavior is exploited to balance the wireless traffic over time, and hence, significantly reduce the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which the smart wireless devices are assumed to predict the arrival of new requests and submit them to the network T time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain} to establish that the decay rate of the outage event probabilities increases with the prediction duration T. This model is then generalized to incorporate the effect of the randomness in the prediction look-ahead time T. Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by the primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed lights on multicasting with predictable demands and show that the proactive multicast networks can achieve a significantly higher diversity gain that scales super-linearly with T. Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive (non-causal) wireless networking framework

Minimal-variance distributed scheduling under strict demands and deadlines

Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, variability in service capacity often incurs operational and infrastructure costs. In this abstract, we characterize an optimal distributed algorithm that minimizes service capacity variability when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes service capacity variance subject to strict demand and deadline requirements under stationary Poisson arrivals. Moreover, we show how close the performance of the optimal distributed algorithm is to that of the optimal centralized algorithm by deriving a competitive-ratio-like bound