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Resource allocation in large-scale multi-server systems
textThe focus of this dissertation is the task of resource allocation in multi- server systems arising from two applications – multi-channel wireless com- munication networks and large-scale content delivery networks. The unifying theme behind all the problems studied in this dissertation is the large-scale nature of the underlying networks, which necessitate the design of algorithms which are simple/greedy and therefore scalable, and yet, have good perfor- mance guarantees. For the multi-channel multi-hop wireless communication networks we consider, the goal is to design scalable routing and scheduling policies which stabilize the system and perform well from a queue-length and end-to-end delay perspective. We first focus on relay assisted downlink networks where it is well understood that the BackPressure algorithm is stabilizing, but, its delay performance can be poor. We propose an alternative algorithm - an iterative MaxWeight algorithm and show that it stabilizes the system and outperforms the BackPressure algorithm. Next, we focus on wireless networks which serve mobile users via a wide-area base-station and multiple densely deployed short- range access nodes (e.g., small cells). We show that traditional algorithms that forward each packet at most once, either to a single access node or a mobile user, do not have good delay performance and propose an algorithm (a distributed scheduler - DIST) and show that it can stabilize the system and performs well from a queue-length/delay perspective. In content delivery networks, each arriving job can only be served by servers storing the requested content piece. Motivated by this, we consider two settings. In the first setting, each job, on arrival, reveals a deadline and a subset of servers that can serve it and the goal is to maximize the fraction of jobs that are served before their deadlines. We propose an online load balanc- ing algorithm which uses correlated randomness and prove its optimality. In the second setting, we study content placement in a content delivery network where a large number of servers, serve a correspondingly large volume of con- tent requests arriving according to an unknown stochastic process. The main takeaway from our results for this setting is that separating the estimation of demands and the subsequent use of the estimations to design optimal content placement policies (learn-and-optimize approach) is suboptimal. In addition, we study two simple adaptive content replication policies and show that they outperform all learning-based static storage policies.Electrical and Computer Engineerin
A New Competitive Ratio for Network Applications with Hard Performance Guarantee
Online algorithms are used to solve the problems which need to make decisions
without future knowledge. Competitive ratio is used to evaluate the performance
of an online algorithm. This ratio is the worst-case ratio between the performance
of the online algorithm and the offline optimal algorithm. However, the competitive
ratios in many current studies are relatively low and thus cannot satisfy the
need of the customers in practical applications. To provide a better service, a practice
for service provider is to add more redundancy to the system. Thus we have
a new problem which is to quantify the relation between the amount of increased
redundancy and the system performance.
In this dissertation, to address the problem that the competitive ratio is not
satisfactory, we ask the question: How much redundancy should be increased to
fulfill certain performance guarantee? Based on this question, we will define a
new competitive ratio showing the relation between the system redundancy and
performance of online algorithm compared to offline algorithm. We will study
three applications in network applications. We propose online algorithms to solve
the problems and study the competitive ratio. To evaluate the performances, we
further study the optimal online algorithms and some other commonly used algorithms
as comparison.
We first study the application of online scheduling for delay-constrained mobile
offloading. WiFi offloading, where mobile users opportunistically obtain data
through WiFi rather than through cellular networks, is a promising technique to greatly improve spectrum efficiency and reduce cellular network congestion. We
consider a system where the service provider deploys multiple WiFi hotspots to
offload mobile traffic with unpredictable mobile users’ movements. Then we study
online job allocation with hard allocation ratio requirement. We consider that jobs
of various types arrive in some unpredictable pattern and the system is required to
allocate a certain ratio of jobs. We then aim to find the minimum capacity needed
to meet a given allocation ratio requirement. Third, we study online routing in
multi-hop network with end-to-end deadline. We propose reliable online algorithms
to schedule packets with unpredictable arriving information and stringent
end-to-end deadline in the network