Cloud-based computing infrastructure provides an efficient means to support real-time processing workloads, e.g., virtualized base station processing, and collaborative video conferencing. This paper addresses resource allocation for a computing system with multiple resources supporting heterogeneous soft real-time applications subject to Quality of Service (QoS) constraints on failures to meet processing deadlines. We develop a general outer bound on the feasible QoS region for non-clairvoyant resource allocation policies, and an inner bound for a natural class of policies based on dynamically prioritizing applications' tasks by favoring those with the largest (QoS) deficits. This provides an avenue to study the efficiency of two natural resource allocation policies: (1) priority-based greedy task scheduling for applications with variable workloads, and (2) priority-based task selection and optimal scheduling for applications with deterministic workloads. The near-optimality of these simple policies emerges when task processing deadlines are relatively large and/or when the number of compute resources is large. Analysis and simulations show substantial resource savings for such policies over reservation-based designs.

An increasing number of real-time applications with compute and/or communication deadlines are being supported on shared infrastructure. Such applications can often tolerate occasional deadline violations without substantially impacting their Quality of Service (QoS). A fundamental problem in such systems is deciding how to allocate shared resources so as to meet applications' QoS requirements. A simple framework to address this problem is to, (1) dynamically prioritize users as a possibly complex function of their deficits (difference of achieved vs required QoS), and (2) allocate resources so to expedite users with higher priority. This paper focuses on a general class of systems using such priority-based resource allocation. We first characterize the set of feasible QoS requirements and show the optimality of max weight-like prioritization. We then consider simple weighted Largest Deficit First (w-LDF) prioritization policies, where users with higher weighted QoS deficits are given higher priority. The paper gives an inner bound for the feasible set under w-LDF policies, and, under an additional monotonicity assumption, characterizes its geometry leading to a sufficient condition for optimality. Additional insights on the efficiency ratio of w-LDF policies, the optimality of hierarchical-LDF and characterization of clustering of failures are also discussed.

Cellular systems using Coordinated Multi-Point (CoMP) transmissions leveraging clusters of spatially distributed radio antennas as Virtual Base Stations (VBSs) have the potential to realize overall throughput gains and, perhaps more importantly, can deliver substantial enhancement to poor performing “edge” users. In this paper we propose a novel framework aimed at fully exploiting the potential of such systems through dynamic radio resource clustering and user scheduling which maximize system utility. The dynamic clustering problem is modeled as a maximum weight clustering problem which is NP-hard, however, we show that by structuring the set of possible VBSs to be “2-decomposable” it can be efficiently computed. We also propose to optimize over a class of power allocation policies to radio resources, and thus VBSs, which allow dynamic user scheduling and flexible power allocations depending on instantaneous channel realizations. We use simulation to compare our approach with a state-of-the-art baseline which exploits dynamic frequency reuse and opportunistic user scheduling, but no clustering, and show edge users' throughput gains are as high as 80% without degrading the performance of others.

Although electric vehicles are attracting increasing interest from consumers and automakers, the disadvantages associated with limited range and the current scarcity of public recharge stations have played a key role in limiting their large scale adoption. In this paper, we explore how information technologies might be used to mitigate ‘range anxiety’ and further strengthen the potential of electric vehicle integration with the renewable energy generation and storage. We motivate several mobile applications/services which would improve the ownership experience of electric vehicles and flexibility for energy providers. Our work leverages a previously proposed sensor platform for collecting travel-time and energy-usage data for a road network by a community of electric car drivers. Travel-time and energy-usage on a given road segment may exhibit substantial variability due to environmental and temporal factors, e.g., congestion, road's grade, AC on/off, etc. Such variability in turn, makes it difficult to accurately predict travel-times as well as the feasible range of a car given its current energy reserves. However, by collecting statistical data using cars/mobiles as probes one can quantify such uncertainty and develop complementary algorithms to counter the anxiety and time waste associated with such uncertainty. This paper develops the necessary (routing) algorithms to support these new classes of applications/services for electric vehicles.