The vast adoption of mobile devices with cameras has greatly contributed to the proliferation of the creation and distribution of videos. For a variety of purposes, valuable information may be extracted from these videos. While the computational capability of mobile devices has greatly improved recently, video processing is still a demanding task for mobile devices. We design an on-demand video processing system, NetVision, that performs distributed video processing using deep learning across a wireless network of mobile and edge devices to answer queries while minimizing the query response time. However, the problem of minimal query response time for processing videos stored across a network is a strongly NP-hard problem. To deal with this, we design a greedy algorithm with bounded performance. To further deal with the dynamics of the transmission rate between mobile and edge devices, we design an adaptive algorithm. We built NetVision and deployed it on a small testbed. Based on the measurements of the testbed and by extensive simulations, we show that the greedy algorithm is close to the optimum and the adaptive algorithm performs better with more dynamic transmission rates. We then perform experiments on the small testbed to examine the realized system performance in both stationary networks and mobile networks.

Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes (rebuffers) and enhancing the quality of the video. A bitrate that is too high leads to frequent rebuffering, while a bitrate that is too low leads to poor video quality. Video providers segment videos into short segments and encode each segment at multiple bitrates. The video player adaptively chooses the bitrate of each segment to download, possibly choosing different bitrates for successive segments. We formulate bitrate adaptation as a utility-maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1/V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, BOLA does not require prediction of available network bandwidth. We empirically validate BOLA in a simulated network environment using a collection of network traces. We show that BOLA achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and for the evolving DASH standard for video transmission. We also implemented an updated version of BOLA that is now part of the standard reference player dash.js and is used in production by several video providers such as Akamai, BBC, CBS, and Orange.

In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations. It is challenging to make migration decisions optimally because of the uncertainty in such a dynamic cloud environment. In this paper, we formulate the service migration problem as a Markov decision process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for the uniform 1-D user mobility, while it provides a close approximation for uniform 2-D mobility with a constant additive error. We also propose a new algorithm and a numerical technique for computing the optimal solution, which is significantly faster than traditional methods based on the standard value or policy iteration. We illustrate the application of our solution in practical scenarios where many theoretical assumptions are relaxed. Our evaluations based on real-world mobility traces of San Francisco taxis show the superior performance of the proposed solution compared to baseline solutions.

Sensor networks are commonly adopted to collect a variety of measurements in indoor and outdoor settings. However, collecting such measurements from every node in the network, although providing high accuracy and resolution of the phenomena of interest, may easily cause sensors’ battery depletion. In this article, we show that measurement correlation can be successfully exploited to reduce the amount of data collected in the network without significantly sacrificing the monitoring accuracy. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and we formulate an optimization problem to select the monitors under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. We also develop statistical approaches that automatically switch between learning and estimation phases to take into account the variability occurring in real networks. Simulations carried out on real-world traces show that our approach outperforms previous solutions based on compressed sensing, and it can be successfully applied to the real application of solar irradiance prediction of photovoltaics systems.

Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost overtime, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is 0(1)-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) usermobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems.

The vast adoption of mobile devices with cameras has greatly assisted in the proliferation of the creation and distribution of videos. For a variety of purposes, valuable information may be extracted from these videos. While the computational capability of mobile devices has greatly improved recently, video processing is still a demanding task for mobile devices. Given a network consisting of mobile devices and video-clouds, mobile devices may be able to upload videos to video-clouds, which are more computationally capable for these processing tasks. However, due to networking constraints, when a video processing task is initiated through a query, most videos will not likely have been uploaded to the video-clouds, especially when the query is about a recent event. We investigate the problem of minimal query response time for processing videos stored across a network; however, this problem is a strongly NP-hard problem. To deal with this, we first propose a greedy algorithm with bounded performance. To further deal with the dynamics of the transmission rate between mobile devices and video-clouds, we propose an adaptive algorithm. To evaluate these algorithms, we built an on-demand video processing system. Based on the measurements of the system, we perform simulations to extensively evaluate the proposed algorithms. We also perform experiments on a small testbed to examine the realized system performance. Results show the performance of the greedy algorithm is close to the optimal and much better than other approaches, and the adaptive algorithm performs better with more dynamic transmission rates.

Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes and enhancing the quality of the video shown to the user. A bitrate that is too high leads to frequent video freezes (i.e., rebuffering), while a bitrate that is too low leads to poor video quality. Video providers segment the video into short chunks and encode each chunk at multiple bitrates. The video player adaptively chooses the bitrate of each chunk that is downloaded, possibly choosing different bitrates for successive chunks. While bitrate adaptation holds the key to a good quality of experience for the user, current video players use ad-hoc algorithms that are poorly understood. We formulate bitrate adaptation as a utility maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization techniques to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1/V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, our algorithm does not require any prediction of available network bandwidth. We empirically validate our algorithm in a simulated network environment using an extensive collection of network traces. We show that our algorithm achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and BOLA is part of the reference player implementation for the evolving DASH standard for video transmission.

Seamless computing and data access is enabled by the emerging technology of mobile micro-clouds (MMCs). Different from traditional centralized clouds, an MMC is typically connected directly to a wireless base-station and provides services to a small group of users, which allows users to have instantaneous access to cloud services. Due to the limited coverage area of base-stations and the dynamic nature of mobile users, network background traffic, etc., the question of where to place the services to cope with these dynamics arises. In this paper, we focus on dynamic service placement for MMCs. We consider the case where there is an underlying mechanism to predict the future costs of service hosting and migration, and the prediction error is assumed to be bounded. Our goal is to find the optimal service placement sequence which minimizes the average cost over a given time. To solve this problem, we first propose a method which solves for the optimal placement sequence for a specific look-ahead time-window, based on the predicted costs in this time-window. We show that this problem is equivalent to a shortest-path problem and propose an algorithm with polynomial time-complexity to find its solution. Then, we propose a method to find the optimal look-ahead window size, which minimizes an upper bound of the average cost. Finally, we evaluate the effectiveness of the proposed approach by simulations with realworld user-mobility traces.

Network monitoring is an essential component of network operation and, as the network size increases, it usually generates a significant overhead in large scale networks such as sensor and data center networks. In this paper, we show that measurement correlation often exhibited in real networks can be successfully exploited to reduce the network monitoring overhead. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and formulate an optimization problem to select the monitors which minimize the estimation error under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. In order to apply our framework to real-world networks, in which measurement distribution and correlation may significantly change over time, we also develop a learning based approach that automatically switches between learning and estimation phases using a change detection algorithm. Simulations carried out on two real traces from sensor networks and data centers show that our algorithms outperforms previous solutions based on compressed sensing and it is able to reduce the monitoring overhead by 50% while incurring a low estimation error. The results further demonstrate that applying the change detection algorithm reduces the estimation error up to two orders of magnitude.

We study the problem of maximizing the multicast throughput in a dense multi-channel multi-radio (MC-MR) wireless network with multiple multicast sessions. Specifically, we consider a fully connected network topology where all nodes are within transmission range of each other. In spite of its simplicity, this topology is practically important since it is encountered in several real-world settings. Further, a solution to this network can serve as a building block for more general scenarios that are otherwise intractable. For this network, we show that the problem of maximizing the uniform multicast throughput across multiple sessions is NP-hard. However, its special structure allows us to derive useful upper bounds on the achievable uniform multicast throughput. We show that an intuitive class of algorithms that maximally exploit the wireless broadcast feature can result in very poor worst case performance. Using a novel group splitting idea, we then design two polynomial time approximation algorithms that are guaranteed to achieve a constant factor of the throughput bound under arbitrary multicast group memberships. These algorithms are simple to implement and provide interesting tradeoffs between the achievable throughput and the total number of transmissions used.

We study the dynamic service migration problem in mobile edge-clouds that host cloud-based services at the network edge. This offers the benefits of reduction in network overhead and latency but requires service migrations as user locations change over time. It is challenging to make these decisions in an optimal manner because of the uncertainty in node mobility as well as possible non-linearity of the migration and transmission costs. In this paper, we formulate a sequential decision making problem for service migration using the framework of Markov Decision Process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for uniform one-dimensional mobility while it provides a close approximation for uniform two-dimensional mobility with a constant additive error term. We also propose a new algorithm and a numerical technique for computing the optimal solution which is significantly faster in computation than traditional methods based on value or policy iteration. We illustrate the effectiveness of our approach by simulation using real-world mobility traces of taxis in San Francisco.

Internet-scale Distributed Networks (IDNs) are large distributed systems that comprise hundreds of thousands of servers located around the world. IDNs consume significant amounts of energy to power their deployed server infrastructure, and nearly as much energy to cool that infrastructure. We study the potential benefits of using renewable open air cooling (OAC) in an IDN. Our results show that by using OAC, a global IDN can extract 51% cooling energy reducing during summers and a 92% reduction in the winter.

We investigate optimal resource allocation for delay-limited cooperative communication in time varying wireless networks. Motivated by real-time applications that have stringent delay constraints, we develop a dynamic cooperation strategy that makes optimal use of network resources to achieve a target outage probability (reliability) for each user subject to average power constraints. Using the technique of Lyapunov optimization, we first present a general framework to solve this problem and then derive quasi-closed form solutions for several cooperative protocols proposed in the literature. Unlike earlier works, our scheme does not require prior knowledge of the statistical description of the packet arrival, channel state, and node mobility processes and can be implemented in an online fashion.

We investigate optimal channel assignment algorithms that maximize per node throughput in dense multi-channel multi-radio (MC-MR) wireless networks. Specifically, we consider an MC-MR network where all nodes are within the transmission range of each other. This situation is encountered in many real-life settings such as students in a lecture hall, delegates attending a conference, or soldiers in a battlefield. In this scenario, we show that intelligent assignment of the available channels results in a significantly higher per node throughput. We first propose a class of channel assignment algorithms, parameterized by T (the number of transceivers per node), that can achieve Θ(1/N1/T) per node throughput using Θ(TN1-1/T) channels. In view of practical constraints on T, we then propose another algorithm that can achieve Θ((1/log2n)2) per node throughput using only two transceivers per node. Finally, we identify a fundamental relationship between the achievable per node throughput, the total number of channels used, and the network size under any strategy. Using analysis and simulations, we show that our algorithms achieve close to optimal performance at different operating points on this curve. Our work has several interesting implications on the optimal network design for dense MC-MR wireless networks.

Modern Internet-scale distributed networks have hundreds of thousands of servers deployed in hundreds of locations and networks around the world. Canonical examples of such networks are content delivery networks (called CDNs) that we study in this paper. The operating expenses of large distributed networks are increasingly driven by the cost of supplying power to their servers. Typically, CDNs procure power through long-term contracts from co-location providers and pay on the basis of the power (KWs) provisioned for them, rather than on the basis of the energy (KWHs) actually consumed. We propose the use of batteries to reduce both the required power supply and the incurred power cost of a CDN. We provide a theoretical model and an algorithmic framework for provisioning batteries to minimize the total power supply and the total power costs of a CDN. We evaluate our battery provisioning algorithms using extensive load traces derived from Akamai's CDN to empirically study the achievable benefits. We show that batteries can provide up to 14% power savings, that would increase to 22% for more power-proportional next-generation servers, and would increase even more to 35.3% for perfectly power-proportional servers. Likewise, the cost savings, inclusive of the additional battery costs, range from 13.26% to 33.8% as servers become more power-proportional. Further, much of these savings can be achieved with a small cycle rate of one full discharge/charge cycle every three days that is conducive to satisfactory battery lifetimes. In summary, we show that a CDN can utilize batteries to significantly reduce both the total supplied power and the total power costs, thereby establishing batteries as a key element in future distributed network architecture. While we use the canonical example of a CDN, our results also apply to other similar Internet-scale distributed networks.

We investigate opportunistic cooperation between secondary (femtocell) users and primary (macrocell) users in cognitive femtocell networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently with a primary user. However, it can employ cooperative relaying of primary user data in order to improve the latter's effective transmission rate. In the second model, called the Interference Model, a secondary user is allowed to transmit its data concurrently with a primary user. However, the secondary user can "cooperate" by deferring its transmissions when the primary user is busy. In both models, the secondary users must make intelligent cooperation decisions as they seek to maximize their own throughput subject to average power constraints. The decision options are different during idle and busy periods of the primary user, and the decisions in turn influence the durations of these periods according to a controllable infinite state Markov chain. Such problems can be formulated as constrained Markov decision problems, and conventional solution techniques require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using a generalized Lyapunov optimization technique, we design a novel greedy and online control algorithm that overcomes these challenges. Remarkably, this algorithm does not require any knowledge of the network arrival rates and is provably optimal.

Since the electricity bill of a data center constitutes a significant portion of its overall operational costs, reducing this has become important. We investigate cost reduction opportunities that arise by the use of uninterrupted power supply (UPS) units as energy storage devices. This represents a deviation from the usual use of these devices as mere transitional fail-over mechanisms between utility and captive sources such as diesel generators. We consider the problem of opportunistically using these devices to reduce the time average electric utility bill in a data center. Using the technique of Lyapunov optimization, we develop an online control algorithm that can optimally exploit these devices to minimize the time average cost. This algorithm operates without any knowledge of the statistics of the workload or electricity cost processes, making it attractive in the presence of workload and pricing uncertainties. An interesting feature of our algorithm is that its deviation from optimality reduces as the storage capacity is increased. Our work opens up a new area in data center power management.

We investigate Quality of Information (QoI) aware scheduling in task processing networks. Specifically, we consider the scenario where a network sequentially receives tasks from an end user, utilizes its resources to process them, and sends back its response. The utility derived by the end user from this response depends on both the accuracy and the freshness of the information. There is often a trade-off between these two attributes and we present a model that quantifies this dependence. Using dynamic programming and optimal stopping theory, we characterize the optimal scheduling policy that maximizes the time average utility delivered by the network. We show that for many scenarios of practical interest, the optimal policy has a simple threshold structure. We also propose a method to approximately compute the threshold in closed-form. This work takes a step towards incorporating application aware objectives in making optimal scheduling decisions.

We investigate two quantities of interest in a delay-tolerant mobile ad hoc network: the network capacity region and the minimum energy function. The network capacity region is defined as the set of all input rates that the network can stably support considering all possible scheduling and routing algorithms. Given any input rate vector in this region, the minimum energy function establishes the minimum time-average power required to support it. In this paper, we consider a cell-partitioned model of a delay-tolerant mobile ad hoc network with general Markovian mobility. This simple model incorporates the essential features of locality of wireless transmissions as well as node mobility and enables us to exactly compute the corresponding network capacity and minimum energy function. Furthermore, we propose simple schemes that offer performance guarantees that are arbitrarily close to these bounds at the cost of an increased delay.

We investigate optimal resource allocation and power management in virtualized data centers with time-varying workloads and heterogeneous applications. Prior work in this area uses prediction based approaches for resource provisioning. In this work, we take an alternate approach that makes use of the queueing information available in the system to make online control decisions. Specifically, we use the recently developed technique of Lyapunov Optimization to design an online admission control, routing, and resource allocation algorithm for a virtualized data center. This algorithm maximizes a joint utility of the average application throughput and energy costs of the data center. Our approach is adaptive to unpredictable changes in the workload and does not require estimation and prediction of its statistics.

We investigate optimal resource allocation for delay-limited cooperative communication in time varying wireless networks. Motivated by real-time applications that have stringent delay constraints, we develop dynamic cooperation strategies that make optimal use of network resources to achieve a target outage probability (reliability) for each user subject to average power constraints. Using the technique of Lyapunov optimization, we first present a general framework to solve this problem and then derive quasi-closed form solutions for several cooperative protocols proposed in the literature.

We develop opportunistic scheduling policies for cognitive radio networks that maximize the throughput utility of the secondary (unlicensed) users subject to maximum collision constraints with the primary (licensed) users. We consider a cognitive network with static primary users and potentially mobile secondary users. We use the technique of Lyapunov Optimization to design an online flow control, scheduling, and resource allocation algorithm that meets the desired objectives and provides explicit performance guarantees.

We develop opportunistic scheduling policies for cognitive radio networks that maximize the throughput utility of the secondary (unlicensed) users subject to maximum collision constraints with the primary (licensed) users. We consider a cognitive network with static primary users and potentially mobile secondary users. We use the technique of Lyapunov Optimization to design an online flow control, scheduling and resource allocation algorithm that meets the desired objectives and provides explicit performance guarantees.

We investigate two quantities of fundamental interest in a mobile ad-hoc network: the capacity region and the minimum energy function of the network. The capacity region is defined as the closure of the set of all input rates that the network can stably support. The minimum energy function establishes a lower bound on the amount of energy required to support a given set of input rates. We consider a specific model of the mobile ad-hoc network that enables us to exactly compute these quantities. Further, we propose schemes that offer performance guarantees that are arbitrarily close to these bounds at the cost of an increased delay. The exact nature of the associated delay tradeoff when performance is pushed towards the minimum energy bound is another fundamental characteristic of the network that is discussed in this work.

We propose a learning-based approach to efficiently and reliably route data to a mobile sink in a wireless sensor field. Specifically, we consider a mobile sink that does not know when to query or does not need to query. Furthermore, the sink moves in a certain pattern within the sensor field. Such a sink passively listens for incoming data that distant source sensors unilaterally push towards it. Unlike traditional routing mechanisms, our technique takes the time-domain explicitly into account, with each node involved making the decision "at this time what is the best way to forward the packet to the sink?". In the presented scheme, motes (nodes in the vicinity of the sink) learn its movement pattern over time and statistically characterize it as a probability distribution function. Having obtained this information at the motes, our scheme uses reinforcement learning to locate the sink efficiently at any point of time.