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.

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 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.

We study a basic resource allocation problem that arises in cloud computing environments. The physical network of the cloud is represented as a graph with vertices representing servers and edges corresponding to communication links. A workload is a set of processes with processing requirements and mutual communication requirements. The workloads arrive and depart over time, and the resource allocator must map each workload upon arrival to the physical network. We consider the objective of minimizing the congestion. We show that solving a subproblem (\sf SingleMap) about mapping a single workload to the physical graph essentially suffices for solving the general problem. In particular, an $\alpha$-approximation algorithm for \sf SingleMap gives an $O(\alpha \log nD)$ competitive algorithm for the general problem, where $n$ is the number of nodes in the physical network and $D$ is the maximum to minimum workload duration ratio. We then consider the \sf SingleMap problem for two natural classes of workloads, namely depth-$d$ trees and complete-graph workloads. For depth-$d$ trees, we give an $n^{O(d)}$ time $O(d^2 \log (nd))$-approximation algorithm based on a strong LP relaxation inspired by the Sherali--Adams hierarchy. For complete graphs, we give a polylogarithmic approximation algorithm using Räcke decompositions.

The application of complex network models to communication systems has led to several important results: nonetheless, previous research has often neglected to take into account their temporal properties, which in many real scenarios play a pivotal role. At the same time, network robustness has come extensively under scrutiny. Understanding whether networked systems can undergo structural damage and yet perform efficiently is crucial to both their protection against failures and to the design of new applications. In spite of this, it is still unclear what type of resilience we may expect in a network which continuously changes over time. In this work, we present the first attempt to define the concept of temporal network robustness: we describe a measure of network robustness for time-varying networks and we show how it performs on different classes of random models by means of analytical and numerical evaluation. Finally, we report a case study on a real-world scenario, an opportunistic vehicular system of about 500 taxicabs, highlighting the importance of time in the evaluation of robustness. Particularly, we show how static approximation can wrongly indicate high robustness of fragile networks when adopted in mobile time-varying networks, while a temporal approach captures more accurately the system performance.

Spot virtual-machine (VM) instances, such as Amazon EC2 Spot VMs, are a class of VMs that are purchased through a market mechanism of price-bids submitted by cloud users. Spot VMs can be obtained at substantially lower cost than other VM classes such as Reserved and On-demand instances, but they do not have guaranteed availability since it depends on the submitted price bids and the fluctuating spot VM price. Many applications with large computing requirements but no real-time availability constraints, such as scientific computing, financial modelling and large data analysis, can be carried out at a significantly lower cost using spot VMs. For such jobs, an important question that arises is what should the submitted price bids be so that the computation is completed within a fixed time interval while the cost is minimized. Towards this goal, we model a job as a fixed computation request with a deadline constraint and formulate the problem of designing a dynamic bidding policy that minimizes the average cost of job completion. We obtain analytical and closed-form results for the optimal strategy under a Markov spot price evolution, and then evaluate the performance of the algorithms on the actual spot price history of Amazon EC2 Spot VMs.

Amazon introduced Spot Instance Market to utilize the idle resources of Amazon Elastic Compute Cloud (EC2) more efficiently. The price of a spot instance changes dynamically according to the current supply and demand for cloud resources. Users can bid for a spot instance and the job request will be granted if the current spot price falls below the bid, whereas the job will be terminated if the spot price exceeds the bid. In this paper, we investigate the problem of designing a bidding strategy from a cloud service broker's perspective, where the cloud service broker accepts job requests from cloud users, and leverages the opportunistic yet less expensive spot instances for computation in order to maximize its own profit. In this context, we propose a profit aware dynamic bidding (PADB) algorithm, which observes the current spot price and selects the bid adaptively to maximize the time average profit of the cloud service broker. We show that our bidding strategy achieves a near-optimal solution, i.e., (1-∈) of the optimal solution to the profit maximization problem, where ∈ can be arbitrarily small. The proposed dynamic bidding algorithm is self-adaptive and requires no a priori statistical knowledge on the distribution of random job sizes from cloud users.

Recently, many research studies have explored the use of wireless fading to generate an information-theoretic shared secret key over an open wireless channel. While this line of research is now mature enough to be built into demonstrative working systems for scenarios involving a (limited) passive/eavesdropping adversary model, the case of an active (jamming) adversary has not been sufficiently studied. Under an active adversary, information-bits that need to be exchanged during the process of key setup will not only be subject to eavesdropping, but also message disruptions that could lead to a high communication cost per bit of secret key generated. Measuring efficiency of key exchange as the ratio of communication cost to the size of secret key generated, in this paper, we address the following question: Is generating a secret key by exploiting wireless fading an efficient process? We obtain analytical results that quantify the minimum number of information-bits that must be exchanged to obtain one bit of shared secret key and show that this number rapidly increases with an active adversary's signal power. Thus, through our analysis, we conclude that the effectiveness of generating a secret key from wireless fading is limited when considering active adversaries.

The application of complex network theory to communication systems has led to several important results. Nonetheless, previous research has often neglected to take into account their temporal properties, which in many real scenarios play a pivotal role. Mainly because of mobility, transmission delays or protocol design, a communication network should not be considered only as a static entity. At the same time, network robustness has come extensively under scrutiny. Understanding whether networked systems can undergo structural damage and yet perform efficiently is crucial to both their protection against failures and to the design of new applications. In spite of this, it is still unclear what type of resilience we may expect in a network that continuously changes over time. In this work we present the first attempt to define the concept of temporal network robustness: we describe a measure of network robustness for time-varying networks and we show how it performs on different classes of random models by means of analytical and numerical evaluation. Particularly, we show how static approximation can wrongly indicate high robustness of fragile networks when adopted in mobile time-varying networks, while a temporal approach captures more accurately the system performance.

The performance of many algorithms in dynamic networks depends on the properties of the underlying graph representing the network. Since such a graph is inherently time-varying, quantifying the change in its structure is important for understanding the behavior of higher-layer network algorithms. In this paper, we study change in the dynamic graph structure of mobile wireless networks that evolve over time due to node mobility. We define several graph evolution metrics and evaluate them through extensive numerical simulations under Levy Walk mobility, which has been shown previously to have similarities to human mobility patterns. Based on the mean and distribution of these metrics, we obtain important insights into the properties of the evolving graph generated by Levy Walk mobility, and then compare the results to the Random Waypoint mobility model. Finally, we discuss the effects of the rate of graph change on the performance of network applications such as data routing and flooding. Our results suggest that the proposed metrics are viable for quantitatively measuring the magnitude of change in a sequence of evolving graphs.

We study a basic resource allocation problem that arises in cloud computing environments. The physical network of the cloud is represented as a graph with vertices denoting servers and edges corresponding to communication links. A workload is a set of processes with processing requirements and mutual communication requirements. The workloads arrive and depart over time, and the resource allocator must map each workload upon arrival to the physical network. We consider the objective of minimizing the congestion.

Received power measurements at spatially distributed monitors can be usefully exploited to deduce various characteristics of active wireless transmitters. In this paper, we study the problem of “blind” estimation of a wireless node's transmit power utilizing solely received power measurements at spatially distributed monitors, without any prior knowledge about the transmitter's location or any statistical characterization of its transmit power. We first consider a deterministic setup and utilize a geometrical approach to obtain fundamental limitations on estimating the transmit power and location of an unknown wireless node. We show that a regular placement of monitors, though appealing, does not provide sufficient measurement diversity to yield a unique estimate. We then extend the setup to consider wireless fading and present a theoretical analysis of maximum likelihood (ML) estimate, which is analytically shown to be asymptotically optimal. Finally, we provide numerical results comparing the performance of the estimator through simulations and on a dataset of field measurements.

The problem of interest is the detection of transient signals in additive white Gaussian noise (AWGN) in the presence of missing signal observations (samples). Specifically, a fusion center aims at detecting the presence of transient signals by collecting measurements from individual sensors through erasure channels. Under the assumption that the fusion center can control the sampling procedure through a feedback channel, a strategy is proposed to adapt the sampling rate in response to sample missing with the goal of achieving accurate and timely decisions with the minimum communication cost measured by sampling rate. The proposed strategy is flexible in that it can be configured to suit different performance requirements. Compared with fixed-rate sampling, the proposed strategy achieves better tradeoff between Quality of Detection (QoD) and communication cost through dynamic adaptation.

We study blind estimation of transmission power of a node based on received power measurements obtained under wireless fading. Specifically, the setup consists of a set of monitors that measure the signal power received from the transmitter, and the goal is to utilize these measurements to estimate the transmission power in the absence of any prior knowledge of the transmitter's location or any statistical distribution of its power. Towards this end, we exploit spatial diversity in received-power measurements and cooperation among the multiple monitoring nodes; based on theoretical analysis we obtain the Maximum Likelihood (ML) estimate, derive fundamental geometrical insights and show that this estimate is asymptotically optimal. Finally, we provide numerical results comparing the performance of the estimators through simulations and on a data-set of field measurements.

Transmit-power estimation is an important part in power-aware designs of mobile ad-hoc networks (MANETs). In this paper, we consider the cooperation among multiple monitor-nodes to estimate the transmit power of other nodes. Utilizing a geometric approach, we characterize the theoretical performance of such cooperative monitoring schemes and propose transmit-power estimation techniques with different number of cooperating nodes. We introduce the novel concept of confidence region that provides a fundamental confidence level for the accuracy of the power estimation and enables the development of techniques for allocating network monitors. Finally, we present a simple, distributed cooperative estimation scheme for a large-scale wireless network and give illustrative simulation results to quantify its performance.

We study energy-efficient transmission of data with deadline constraints over a time-varying channel. Specifically, the system model consists of a wireless transmitter with controllable transmission rate, time-varying and stochastic channel state, and strict delay constraints on the packets in the queue. While the transmitter can control the rate, the transmission power required depends on the chosen rate and the prevailing channel condition. The objective is to obtain a rate control policy that serves the data within the deadline constraints while minimizing the total energy expenditure. Toward this end, we first introduce the canonical problem of transmitting B units of data by deadline T over a Markov fading channel, and obtain the optimal policy for it using continuous-time stochastic control theory. Using a novel cumulative curves methodology and a decomposition approach, we extend the above setup to consider extensions involving variable deadlines on the packets. Finally, utilizing the analysis we present a heuristic policy for the case of arbitrary packet arrivals to the queue with individual deadline constraints, and give illustrative simulation results for its performance.

We address multi-user scheduling over the downlink channel in wireless data systems. Specifically, we consider a time-slotted system with a single transmitter serving multiple users, where the channel condition of each user is time varying. Based on the throughput requirements, the user set is divided into two classes (i) throughput guaranteed (QoS) users, and, (ii) best effort (BE) users. For this system we obtain the optimal policy that serves the QoS users with the minimum time-slot utilization and maximizes the total fraction of time-slots allocated to the BE users. We show that the optimal policy has a simple geometric structure that can be easily visualized graphically. In the special case of Rayleigh fading, we obtain closed-form formulas that relate the achievable throughput-rate guarantee of the QoS users as a function of other system parameters, thus, providing closed-from relationships to understand the various system tradeoffs. Analytical comparison between the optimal and the random-scheduling policy shows that gains on the order of ln(N) can be achieved, where N is the number of QoS users. Finally, we present simulation results comparing the optimal policy under Rayleigh and Nakagami fading with other heuristic policies including a well known opportunistic-scheduling policy.

We consider a multi-hop wireless network with a connection-oriented traffic model and multiple transmission channels that can be spatially re-used. In such a network the blocking probability of a call that makes a channel request depends on (a) the channel assignment scheme and (b) the transmission radius of the nodes which affects the network link structure. In this work, we study these two aspects for simple wireless networks. Specifically, we develop blocking probability analysis for a wireless line and grid network and explore the tradeoff between transmission radius and blocking probability for multi-hop calls. We show that for a line network a larger transmission radius can substantially reduce the blocking probability of calls, while for a grid network with a more dense node topology using a smaller transmission radius is better. We then, investigate various channel assignment schemes and present a novel non-rearranging channel assignment algorithm for multi-hop calls in a general network. Our algorithm efficiently incorporates spatial channel re-use and significantly reduces call blocking probability when compared to other algorithms.

We consider a queueing system with controllable service rate; for example, a transmitter whose rate can be controlled by varying the transmission power. For such a system we obtain optimal data transmission policies that satisfy given quality of service (QoS) constraints and also minimize the total transmission energy expenditure. First, we consider the deterministic case of known arrivals and present a formulation based on a calculus approach using arrival and minimum departure curves. The problem is posed as a continuous time optimization and an optimal solution is obtained for general arrival curves and QoS constraints. In the latter half of the paper, we consider a stochastic arrival process (Poisson process) and a single deadline constraint. The objective is to obtain a transmission policy that minimizes the expected energy expenditure. The problem is formulated as a stochastic optimal control problem and an explicit solution is obtained with some relaxation. Finally, simulation results comparing various policies are presented.

We consider the problem of transmission scheduling of data over a wireless fading channel with hard deadline constraints. Our system consists of N users, each with a fixed amount of data that must be served by a common deadline. Given that, for each user, the channel fade state determines the throughput per unit of energy expended, our objective is to minimize the overall expected energy consumption while satisfying the deadline constraint. We consider both a linear and a strictly convex rate-power curve and obtain optimal solutions, based on dynamic programming (DP), and tractable approximate heuristics in both cases. For the special non-fading channel case with convex rate-power curve, an optimal solution is obtained based on the shortest path formulation. In the case of a linear rate-power curve, our DP solution has a nice "threshold" form; while for the convex rate-power curve we are able to obtain a heuristic algorithm with comparable performance with that of the optimal scheduling scheme.