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Conventional computing resource trading over mobile networks generally faces many challenges, e.g., excessive decision-making latency, undesired trading failures, and underutilization of dynamic resources, owing to the constraint of wireless networks. To improve resource utilization rate under dynamic network conditions, this paper introduces a novel computing resource provisioning mechanism empowered by overbooking, that allows the amount of booked resources to exceed the resource supply. Cloud-aided mobile edge networks are considered for the proposed framework, where an edge server can purchase more resources from a cloud server to offer computing services to multiple end-users with computation-intensive tasks. Specifically, the proposed mechanism relies on designing pre-signed forward trading contracts among edge and end-users, as well as between edge and cloud in advance to future practical trading; while encouraging an appropriate overbooking rate to improve resource utilization, via analyzing historical statistics associated with uncertainties such as dynamic resource supply/demand, and varying channel qualities. The contract design is formulated as a multi-objective optimization problem that aims to maximize the expected utilities of end-users, edge, and cloud, via evaluating potential risks; for which a two-phase multilateral negotiation scheme is proposed that facilitates the bargaining procedure among the three parties, to reach the final trading consensus (namely, contract terms). Experimental results demonstrate that the proposed mechanism achieves mutually beneficial utilities of three parties, while outperforming baseline methods on significant indicators such as task completion, trading failure, time efficiency, resource usage, etc., from various analytical angles. [ABSTRACT FROM AUTHOR]

The scheduling policy installed on switches of datacenters plays a significant role on congestion control. Shortest-Remaining-Processing-Time (SRPT) achieves the near-optimal average message completion time (MCT) in various scenarios, but is difficult to deploy as viewed by the industry. The reasons are two-fold: 1) many commodity switches only provide FIFO queues, and 2) the information of remaining message size is not available. Recently, the idea of emulating SRPT using only a few FIFO queues and the original message size has been coined as the approximate and deployable SRPT (ADS) design. In this paper, we provide the first theoretical study on the optimal ADS design. Specifically, we first characterize a wide range of feasible ADS scheduling policies via a unified framework, and then derive the steady-state MCT, slowdown, and impoliteness in the M/G/1 setting. Hence we formulate the optimal ADS design as a non-linear combinatorial optimization problem, which aims to minimize the average MCT given the available FIFO queues. We also take into account the proportional fairness and temporal fairness constraints based on the maximal slowdown and impoliteness, respectively. The optimal ADS design problem is NP-hard in general, and does not exhibit monotonicity or sub-modularity. We leverage its decomposable structure and devise an efficient algorithm to solve the optimal ADS policy. We carry out extensive flow-level simulations and packet-level experiments to evaluate the proposed optimal ADS design. Results show that the optimal ADS policy installed on eight FIFO queues is capable of emulating the true SRPT. [ABSTRACT FROM AUTHOR]

Chemical algorithms are statistical control algorithms described and represented as chemical reaction networks. They are analytically tractable, they reinforce a deterministic state-to-dynamics relation, they have configurable stability properties, and they are directly implemented in state space using a high-level visual representation. These properties make them attractive solutions for traffic shaping and generally the control of dynamics in computer networks. In this paper, we present a framework for deploying chemical algorithms on field programmable gate arrays. Besides substantial computational acceleration, we introduce a low-overhead approach for hardware-level programmability and re-configurability of these algorithms at runtime, and without service interruption. We believe that this is a promising approach for expanding the control-plane programmability of software defined networks (SDN), to enable programmable network dynamics. To this end, the simple high-level abstractions of chemical algorithms offer an ideal northbound interface to the hardware, aligned with other programming primitives of SDN (e.g., flow rules). [ABSTRACT FROM AUTHOR]

Network virtualization and programmability allow operators to deploy a wide range of services over a common physical infrastructure and elastically allocate cloud and network resources according to changing requirements. While the elastic reconfiguration of virtual resources enables dynamically scaling capacity in order to support service demands with minimal operational cost, reconfiguration operations make resources unavailable during a given time period and may incur additional cost. In this paper, we address the dynamic cloud network control problem under non-negligible reconfiguration delay and cost. We show that while the capacity region remains unchanged regardless of the reconfiguration delay/cost values, a reconfiguration-agnostic policy may fail to guarantee throughput-optimality and minimum cost under nonzero reconfiguration delay/cost. We then present an adaptive dynamic cloud network control policy that allows network nodes to make local flow scheduling and resource allocation decisions while controlling the frequency of reconfiguration in order to support any input rate in the capacity region and achieve arbitrarily close to minimum cost for any finite reconfiguration delay/cost values. [ABSTRACT FROM AUTHOR]

The problem of allocating jobs to appropriate servers in cloud computing is studied in this paper. We consider that the jobs of various types arrive in some unpredictable pattern and the system is required to allocate a certain ratio of jobs. In order to meet the hard allocation ratio requirement in the presence of unknown arrival patterns, one can increase the capacity of servers by expanding the size of data centers. We then aim to find the minimum capacity needed to meet a given allocation ratio requirement. We study this problem for both systems with persistent jobs, such as video streaming, and systems with dynamic jobs, such as database queries. For both systems, we propose online job allocation policies with low complexity. For systems with persistent jobs, we prove that our policies can achieve a given hard allocation ratio requirement with the least capacity. For systems with dynamic jobs, the capacity needed for our policies to achieve the hard allocation ratio requirement is close to a theoretical lower bound. Two other popular policies are studied, and we demonstrate that they need at least an order higher capacity to meet the same hard allocation ratio requirement. Simulation results demonstrate that our policies remain far superior than the other two even, when the jobs arrive according to some random process. [ABSTRACT FROM AUTHOR]

Although some excellent works have been done on networking, caching, and computing, these three important areas have traditionally been addressed separately in the literature. In this paper, we describe the recent advances in jointing networking, caching, and computing and present a novel integrated framework: software-defined networking, caching, and computing (SD-NCC). SD-NCC enables dynamic orchestration of networking, caching, and computing resources to efficiently meet the requirements of different applications and improve the end-to-end system performance. Energy consumption is considered as an important factor when performing resource placement in this paper. Specifically, we study the joint caching, computing, and bandwidth resource allocation for SD-NCC and formulate it as an optimization problem. In addition, to reduce computational complexity and signaling overhead, we propose a distributed algorithm to solve the formulated problem, based on recent advances in alternating direction method of multipliers (ADMM), in which different network nodes only need to solve their own problems without exchange of caching/computing decisions with fast convergence rate. Simulation results show the effectiveness of our proposed framework and ADMM-based algorithm with different system parameters. [ABSTRACT FROM AUTHOR]

We consider a centralized content delivery infrastructure where a large number of storage-intensive files are replicated across several collocated servers. To achieve scalable mean delays in file downloads under stochastic loads, we allow multiple servers to work together as a pooled resource to meet individual download requests. In such systems, basic questions include: How and where to replicate files? What is the impact of dynamic service allocation across request types, and whether such allocations can provide substantial gains over simpler load balancing policies? What are tradeoffs among performance, reliability and recovery costs, and energy? This paper provides a simple performance model for large systems towards addressing these basic questions. [ABSTRACT FROM AUTHOR]

The need of highly directional communications at mmWave band introduces high overhead for beam training and alignment, which also makes the medium access control (MAC) a grand challenge. However, the need of supporting highly directional multiple beams between transmitters and receivers makes the MAC design even harder. To harvest the gain of multi-beam mmWave communications, which benefits not only from the large bandwidth of mmWave spectrum but also the diversity of concurrent multi-user multi-beam transmissions, this paper studies the medium access control (MAC) layer related issues in multi-beam mmWave networks, including (1) efficient multi-beam training schemes to enable lower overhead thus faster AP association and beam alignment, (2) block-sparse mmWave channel estimation in different beam resolutions, and (3) effective concurrent radio resource allocation to facilitate better multi-user multi-beam transmissions. Simulation results demonstrate that the proposed schemes outperform existing techniques in improving the efficiency of mmWave communications thus achieving significantly higher network performances. To our best knowledge, we are the first to comprehensively consider both efficient training for beam alignment and resource scheduling in the MAC design to enable highly directional multi-user multi-beam concurrent transmissions in a mmWave network. [ABSTRACT FROM AUTHOR]

We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resource; some tasks can be executed simultaneously in batch by sharing the resource, while others requiring exclusive use of the resource. We seek an optimal resource allocation and the related scheduling policy maximizing the overall system utility. This problem, termed as the contiguous-resource batching task scheduling problem, arises in a variety of engineering fields, where communication and storage resources are potential bottlenecks and thus need to be carefully scheduled. Two motivating examples are the spectrum bonding problem in dynamic spectrum access systems and the dynamic storage allocation problem in computer systems. In this paper, we investigate both offline and online scheduling settings. We first establish the NP-hardness of the offline setting and the inapproximability of the online setting in its generic form. Given the theoretical performance limit, we then develop approximation algorithms with mathematically proven performance guarantee in terms of approximation and competitive ratios for the offline and online settings. [ABSTRACT FROM AUTHOR]

In a multi-tenant cloud, cloud vendors provide services (e.g., elastic load-balancing, virtual private networks) on service nodes for tenants. Thus, the mapping of tenants’ traffic and service nodes is an important issue in multi-tenant clouds. In practice, unreliability of service nodes and uncertainty/dynamics of tenants’ traffic are two critical challenges that affect the tenants’ QoS. However, previous works often ignore the impact of these two challenges, leading to poor system robustness when encountering system accidents. To bridge the gap, this paper studies the problem of robust service mapping in multi-tenant clouds (RSMP). Due to traffic dynamics, we take a two-step approach: service node assignment and tenant traffic scheduling. For service node assignment, we prove its NP-Hardness and analyze its problem difficulty. Then, we propose an efficient algorithm with bounded approximation factors based on randomized rounding and knapsack. For tenant traffic scheduling, we design an approximation algorithm based on fully polynomial time approximation scheme (FPTAS). The proposed algorithm achieves the approximation factor of 2+ $\epsilon $ , where $\epsilon $ is an arbitrarily small value. Both small-scale experimental results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we study learning-assisted multi-user scheduling for the wireless downlink. There have been many scheduling algorithms developed that optimize for a plethora of performance metrics; however a systematic approach across diverse performance metrics and deployment scenarios is still lacking. We address this by developing a meta-scheduler – given a diverse collection of schedulers, we develop a learning-based overlay algorithm (meta-scheduler) that selects that “best” scheduler from amongst these for each deployment scenario. More formally, we develop a multi-armed bandit (MAB) framework for meta-scheduling that assigns and adapts a score for each scheduler to maximize reward (e.g., mean delay, timely throughput etc.). The meta-scheduler is based on a variant of the Upper Confidence Bound algorithm (UCB), but adapted to interrupt the queuing dynamics at the base-station so as to filter out schedulers that might render the system unstable. We show that the algorithm has a poly-logarithmic regret in the expected reward with respect to a genie that chooses the optimal scheduler for each scenario. Finally through simulation, we show that the meta-scheduler learns the choice of the scheduler to best adapt to the deployment scenario (e.g. load conditions, performance metrics). [ABSTRACT FROM AUTHOR]

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs’ resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs’ resource demands. Most approaches in the latter category are heuristic and lack theoretical performance guarantees. In this paper, we formulate the dynamic VM management as a large-scale Markov decision process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, we show that MadVM can be implemented in a distributed system with at most two times of the optimal migration cost. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage, and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms. [ABSTRACT FROM AUTHOR]

Jobs in modern computing clusters have highly diverse processing duration and heterogeneous resource requirements. In this paper, we consider the problem of job scheduling for a computing cluster comprised of multiple servers with heterogeneous computation resources, while taking the different resource demands of the jobs into account. Our focus is to achieve a low overall job response time for the system (which is also referred to as the job flowtime) while providing fairness between small and large jobs. Since the job flowtime minimization problem under multiple (even homogeneous) servers are known to be NP-hard, we propose an approximation algorithm to tackle the original online scheduling problem by adopting the recently-proposed notion of fractional job flowtime as a surrogate objective for minimization. For the general online job arrival case with multi-dimensional resource requirements, we apply Online Convex Optimization (OCO) techniques to design the corresponding scheduling algorithm with performance guarantees. In the single-dimensional resource setting, we show that the dynamic fit of the online version of our approximate algorithm grows only sublinearly with respect to time and derive a bound for its dynamic regret when comparing to its offline counterpart. While the baseline version of our proposed scheduling algorithm assumes the possibilities of job preemption and job migration across different servers, we show that the extent of job preemption and migration can be well controlled by augmenting the objective function with the corresponding switching costs. [ABSTRACT FROM AUTHOR]

Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling schemes could cause undesired results: (1) power inefficiency: due to unbalanced traffic allocation on active routes, extra switches and links may be activated to cater to bursty traffic surges on congested routes, and (2) Quality of Service (QoS) fluctuation: because of the limited flow entry processing ability, switches may not be able to timely install/delete/update flow entries to properly route/reroute flows. In this paper, we propose AggreFlow, a dynamic flow scheduling scheme that achieves power efficiency and QoS improvement using three techniques: Flow-set Routing, Lazy Rerouting, and Adaptive Rerouting. Flow-set Routing achieves load balancing with a small number of flow entry operations by routing flows in a coarse-grained flow-set fashion. Lazy Rerouting spreads rerouting operations over a relatively long period of time, reducing the burstiness of entry operation on switches. Adaptive Rerouting selectively reroutes flow-sets to maintain load balancing. We built an NS3 based fat-tree network simulation platform to evaluate AggreFlow’s performance. The simulation results show that AggreFlow reduces power consumption by about 18%, yet achieving load balancing and improved QoS (low packet loss rate and reducing the number of processing entries for flow scheduling by 98%), compared with baseline schemes. [ABSTRACT FROM AUTHOR]

This paper addresses the slicing of radio access network resources by multiple tenants, e.g., virtual wireless operators and service providers. We consider a criterion for dynamic resource allocation amongst tenants, based on a weighted proportionally fair objective, which achieves desirable fairness/protection across the network slices of the different tenants and their associated users. Several key properties are established, including: the Pareto-optimality of user association to base stations, the fair allocation of base stations’ resources, and the gains resulting from dynamic resource sharing across slices, both in terms of utility gains and capacity savings. We then address algorithmic and practical challenges in realizing the proposed criterion. We show that the objective is NP-hard, making an exact solution impractical, and design a distributed semi-online algorithm, which meets performance guarantees in equilibrium and can be shown to quickly converge to a region around the equilibrium point. Building on this algorithm, we devise a practical approach with limited computational information and handoff overheads. We use detailed simulations to show that our approach is indeed near-optimal and provides substantial gains both to tenants (in terms of capacity savings) and end users (in terms of improved performance). [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel sealed-bid auction framework to address the problem of dynamic spectrum allocation in cognitive radio (CR) networks. We design an optimal auction mechanism that maximizes the moderator's expected utility, when the spectrum is not available with certainty. We assume that the moderator employs collaborative spectrum sensing in order to make a reliable inference about spectrum availability. Due to the presence of a collision cost whenever the moderator makes an erroneous inference, and a sensing cost at each CR, we investigate feasibility conditions that guarantee a non-negative utility at the moderator. Since the moderator fuses CRs' sensing decisions to obtain a global inference regarding spectrum availability, we propose a novel strategy-proof fusion rule that encourages the CRs to simultaneously reveal truthful sensing decisions, along with truthful valuations to the moderator. We also present tight theoretical bounds on instantaneous network throughput achieved by our auction mechanism. Numerical examples are presented to provide insights into the performance of the proposed auction under different scenarios. [ABSTRACT FROM PUBLISHER]

MapReduce jobs need to shuffle a large amount of data over the network between mapper and reducer nodes. The shuffle time accounts for a big part of the total running time of the MapReduce jobs. Therefore, optimizing the makespan of shuffle phase can greatly improve the performance of MapReduce jobs. A large fraction of production jobs in data centers are recurring with predictable characteristics, and the recurring jobs split the network into periodic busy and idle time slots, which allows us to better schedule the shuffle data in order to reduce the makespan of shuffle phase with the future predictable network status available. In this paper, we formulate the shuffle scheduling problem with the aim to minimize the makespan of MapReduce shuffle phase by leveraging the predictable periodic network status. We then propose a simple yet effective network-aware shuffle scheduling algorithm (NAS) to reduce the number of idle time slots required to transfer the shuffle data so as to reduce the shuffle makespan. We also prove that the proposed algorithm NAS is a $\frac {3}{2}$ -approximation algorithm to the shuffle scheduling problem when all the future idle time slots have the same duration. We finally conduct experiments through simulations. Experimental results demonstrate the proposed algorithm can effectively reduce the makespan of MapReduce shuffle phase and increase network utilization. [ABSTRACT FROM AUTHOR]

Wireless spectrum is a precious resource and must be allocated and used efficiently. Conventional spectrum allocations let a government agency (e.g., FCC) sell a portion of spectrum to one provider. This is not only restrictive, but also limits spectrum reuse and may lead to significant under-utilization of spectrum. In this paper, we develop a novel truthful double-auction scheme to let any resource owner (e.g., a cellular provider), who has spare spectrum at a given time period, sell to one or more providers that need additional spectrum at that time. Spectrum auctions are fundamentally different from conventional auction problems since spectrum can be reused and competition among buyers is complex due to wireless interference. Our proposal is the first double-auction design for spectrum allocation that explicitly decouples the buyer-side and seller-side auction design while achieving: 1) truthfulness; 2) individual rationality; and 3) budget-balance. To accurately capture wireless interference and support spectrum reuse, we partition the conflict graph so that buyers with strong direct and indirect interference are put into the same subgraph, and buyers with no interference or weak interference are put into separate subgraphs. Then, we compute pricing independently within each subgraph. We then develop a scheme to combine spectrum allocation results from different subgraphs and resolve potential conflicts. We further extend our approach to support local sellers whose spectrum can only be sold to buyers within certain regions, instead of all buyers. Using conflict graphs generated from real cell tower locations, we extensively evaluate our approach and demonstrate that it achieves high efficiency, revenue, and utilization. [ABSTRACT FROM PUBLISHER]

There is a growing interest in the development of in-network dispersed computing paradigms that leverage the computing capabilities of heterogeneous resources dispersed across the network for processing a massive amount of data collected at the edge of the network. We consider the problem of task scheduling for such networks, in a dynamic setting in which arriving computation jobs are modeled as chains, with nodes representing tasks, and edges representing precedence constraints among tasks. In our proposed model, motivated by significant communication costs in dispersed computing environments, the communication times are taken into account. More specifically, we consider a network where servers can serve all task types, and sending the outputs of processed tasks from one server to another server results in some communication delay. We first characterize the capacity region of the network, then propose a novel virtual queueing network encoding the state of the network. Finally, we propose a Max-Weight type scheduling policy, and considering the stochastic network in the fluid limit, we use a Lyapunov argument to show that the policy is throughput-optimal. Beyond the model of chains, we extend the scheduling problem to the model of the directed acyclic graph (DAG) which imposes a new challenge, namely logic dependency difficulty, requiring the data of processed parents tasks to be sent to the same server for processing the child task. We propose a virtual queueing network for DAG scheduling over broadcast networks, where servers always broadcast the data of processed tasks to other servers, and prove that Max-Weight policy is throughput-optimal. [ABSTRACT FROM AUTHOR]

The dominant portion of smartphone traffic is generated by apps that involve human interactivity. Particularly, when human users receive information from a server, they spend a few seconds of information processing before taking an action. The user processing time creates an idle communication period during the app session. Moreover, the generation of the future traffic depends on the service of the current query-response pair. In this paper, we aim at leveraging the properties of such interactions to reap quality-of-experience gains. Existing schedulers, both in practice and theory, are not designed in view of the aforementioned traffic characteristics. Theoretical works predominantly focus on scheduling of traffic that is either generated independently or directly controlled, but not governed by the specific dynamics caused by human interactions. Schedulers in practice, on the other hand, employ round-robin and processor-sharing methods to serve multiple ongoing sessions. We show that neither of these approaches is effective for serving apps that involve human interactivity. Instead, we show that optimal scheduling for interactive traffic is non-randomized over packets, which we call action-based, as it avoids breaking ongoing service of actions in order to align human response times with the service of other actions. Since the design of optimal action-based policy is computationally prohibitive, we develop low-complexity suboptimal action-based policies that are optimal for two ongoing sessions. Our numerical studies based on a real-data trace reveal that our proposed action-based policies can reduce total delay by 22% with respect to packet-based equal processor sharing. [ABSTRACT FROM AUTHOR]

The explosive growth of cellular traffic and its highly dynamic nature often make it increasingly expensive for a cellular service provider to provision enough cellular resources to support the peak traffic demands. In this paper, we propose iDEAL, a novel auction-based incentive framework that allows a cellular service provider to leverage resources from third-party resource owners on demand by buying capacity whenever needed through reverse auctions. iDEAL has several distinctive features: 1) iDEAL explicitly accounts for the diverse spatial coverage of different resources and can effectively foster competition among third-party resource owners in different regions, resulting in significant savings to the cellular service provider. 2) iDEAL provides revenue incentives for third-party resource owners to participate in the reverse auction and be truthful in the bidding process. 3) iDEAL is provably efficient. 4) iDEAL effectively guards against collusion. 5) iDEAL effectively copes with the dynamic nature of traffic demands. In addition, iDEAL has useful extensions that address important practical issues. Extensive evaluation based on real traces from a large US cellular service provider clearly demonstrates the effectiveness of our approach. We further demonstrate the feasibility of iDEAL using a prototype implementation. [ABSTRACT FROM PUBLISHER]

Although infrastructure as a service (IaaS) users are busy scaling up/out their cloud instances to meet the ever-increasing demands, the dynamics of their demands, as well as the coarse-grained billing options offered by leading cloud providers, have led to substantial instance underutilization in both temporal and spatial domains. This paper systematically examines an instance subletting service, where sublettable instances can be leased to others within predetermined periods when underutilized, from both theoretical and practical perspectives. The studied instance subletting service extends and complements the existing instance market of IaaS providers. We identify the unique challenges and opportunities in this new service, and design online auction mechanisms to make allocation and pricing decisions for the instances to be sublet. For static supplies of instances, our mechanism guarantees truthfulness and individual rationality with the best possible competitive ratio. We then incorporate a multi-stage discount strategy to gracefully handle dynamic supplies. Extensive trace-driven simulations show that our service achieves significant performance gains in both cost savings and social welfare. We further pinpoint the challenges in implementing such a service in the real-world system and validate our modeling assumptions through a container-based prototype implemented over Amazon EC2. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) allows unlicensed users to access the licensed spectrum opportunistically (i.e., when the spectrum is left unused by the licensed users) to enhance the spectrum utilization efficiency. In this paper, the problem of allocating resources (channels and transmission power) in multihop CR networks is modeled as a multicommodity flow problem with the dynamic link capacity resulting from dynamic resource allocation, which is in sharp contrast with existing flow-control approaches that assume fixed link capacity. Based on queue-balancing network flow control that is ideally suited for handling dynamically changing spectrum availability in CR networks, we propose a distributed scheme (installed and operational in each node) for optimal resource allocation without exchanging spectrum dynamics information between remote nodes. Considering the power masks, each node makes resource-allocation decisions based on current or past local information from neighboring nodes to satisfy the throughput requirement of each flow. Parameters of these proposed schemes are configured to maintain the network stability. The performance of the proposed scheme for both asynchronous and synchronous scenarios is analyzed comparatively. Both cases of sufficient and insufficient network capacity are considered. [ABSTRACT FROM PUBLISHER]

A major problem in wireless networks is coping with limited resources, such as bandwidth and energy. These issues become a major algorithmic challenge in view of the dynamic nature of the wireless domain. We consider in this paper the single-transmitter power assignment problem under time-varying channels, with the objective of maximizing the data throughput. It is assumed that the transmitter has a limited power budget, to be sequentially divided during the lifetime of the battery. We deviate from the classic work in this area, which leads to explicit “water-filling” solutions, by considering a realistic scenario where the channel state quality changes arbitrarily from one transmission to the other. The problem is accordingly tackled within the framework of competitive analysis, which allows for worst-case performance guarantees in setups with arbitrarily varying channel conditions. We address both a “discrete” case, where the transmitter can transmit only at a fixed power level, and a “continuous” case, where the transmitter can choose any power level out of a bounded interval. For both cases, we propose online power-allocation algorithms with proven worst-case performance bounds. In addition, we establish lower bounds on the worst-case performance of any online algorithm and show that our proposed algorithms are optimal. [ABSTRACT FROM PUBLISHER]

Aggregate traffic loads and topology in multihop wireless networks may vary slowly, permitting MAC protocols to “learn” how to spatially coordinate and adapt contention patterns. Such an approach could reduce contention, leading to better throughput. To that end, we propose a family of MAC scheduling algorithms and demonstrate general conditions, which, if satisfied, ensure lattice rate optimality (i.e., achieving any rate-point on a uniform discrete lattice within the throughput region). This general framework enables the design of MAC protocols that meet various objectives and conditions. In this paper, as instances of such a lattice-rate-optimal family, we propose distributed, synchronous contention-based scheduling algorithms that: 1) are lattice-rate-optimal under both the signal-to-interference-plus-noise ratio (SINR)-based and graph-based interference models; 2) do not require node location information; and 3) only require three-stage RTS/CTS message exchanges for contention signaling. Thus, the protocols are amenable to simple implementation and may be robust to network dynamics such as topology and load changes. Finally, we propose a heuristic, which also belongs to the proposed lattice-rate-optimal family of protocols and achieves faster convergence, leading to a better transient throughput. [ABSTRACT FROM PUBLISHER]

This paper considers the downlink traffic from a base station to two different clients. When assuming infinite backlog, it is known that inter-session network coding (INC) can significantly increase the throughput. However, the corresponding scheduling solution (when assuming dynamic arrivals instead and requiring bounded delay) is still nascent. For the two-flow downlink scenario, we propose the first opportunistic INC + scheduling solution that is provably optimal for time-varying channels, i.e., the corresponding stability region matches the optimal Shannon capacity. In particular, we first introduce a new binary INC operation, which is distinctly different from the traditional wisdom of XORing two overheard packets. We then develop a queue-length-based scheduling scheme and prove that it, with the help of the new INC operation, achieves the optimal stability region with time-varying channel quality. The proposed algorithm is later generalized to include the capability of rate adaptation. Simulation results show that it again achieves the optimal throughput with rate adaptation. A byproduct of our results is a scheduling scheme for stochastic processing networks with random departure, which relaxes the assumption of deterministic departure in the existing results. [ABSTRACT FROM PUBLISHER]

We consider the problem of resource allocation and control of multihop networks in which multiple source-destination pairs communicate confidential messages, to be kept confidential from the intermediate nodes. We pose the problem as that of network utility maximization, into which confidentiality is incorporated as an additional quality of service constraint. We develop a simple, and yet provably optimal dynamic control algorithm that combines flow control, routing and end-to-end secrecy-encoding. In order to achieve confidentiality, our scheme exploits multipath diversity and temporal diversity due to channel variability. Our end-to-end dynamic encoding scheme encodes confidential messages across multiple packets, to be combined at the ultimate destination for recovery. We first develop an optimal dynamic policy for the case in which the number of blocks across which secrecy encoding is performed is asymptotically large. Next, we consider encoding across a finite number of packets, which eliminates the possibility of achieving perfect secrecy. For this case, we develop a dynamic policy to choose the encoding rates for each message, based on the instantaneous channel state information, queue states and secrecy outage requirements. By numerical analysis, we observe that the proposed scheme approaches the optimal rates asymptotically with increasing block size. Finally, we address the consequences of practical implementation issues such as infrequent queue updates and de-centralized scheduling. We demonstrate the efficacy of our policies by numerical studies under various network conditions. [ABSTRACT FROM AUTHOR]

Internet performance is tightly related to the properties of TCP and UDP protocols, jointly responsible for the delivery of the great majority of Internet traffic. It is well understood how these protocols behave under first-in–first-out (FIFO) queuing and what are the network congestion effects. However, no comprehensive analysis is available when flow-aware mechanisms such as per-flow scheduling and dropping policies are deployed. Previous simulation and experimental results leave a number of unanswered questions. In this paper, we tackle this issue by modeling via a set of fluid nonlinear ordinary differential equations (ODEs) the instantaneous throughput and the buffer occupancy of N long-lived TCP sources under three per-flow scheduling disciplines (Fair Queuing, Longest Queue First, Shortest Queue First) and with longest queue drop buffer management. We study the system evolution and analytically characterize the stationary regime: Closed-form expressions are derived for the stationary throughput/sending rate and buffer occupancy, which give a thorough understanding of short/long-term fairness for TCP traffic. Similarly, we provide the characterization of the loss rate experienced by UDP flows in the presence of TCP traffic. As a result, the analysis allows to quantify benefits and drawbacks related to the deployment of flow-aware scheduling mechanisms in different networking contexts. The model accuracy is confirmed by a set of ns2 simulations and by the evaluation of the three scheduling disciplines in a real implementation in the Linux kernel. [ABSTRACT FROM PUBLISHER]

We consider multiuser scheduling in wireless networks with channel variations and flow-level dynamics. Recently, it has been shown that the MaxWeight algorithm, which is throughput-optimal in networks with a fixed number of users, fails to achieve the maximum throughput in the presence of flow-level dynamics. In this paper, we propose a new algorithm, called Workload-based Scheduling with Learning, which is provably throughput-optimal, requires no prior knowledge of channels and user demands, and performs significantly better than previously suggested algorithms. [ABSTRACT FROM PUBLISHER]

The problem of dynamic resource allocation for service provisioning in multi-tier distributed clouds is particularly challenging due to the coexistence of several factors: the need for joint allocation of cloud and network resources, the need for online decision-making under time-varying service demands and resource prices, and the reconfiguration cost associated with changing resource allocation decisions. We study this problem from an online optimization perspective to address all these challenges. We design an online algorithm that decouples the original offline problem over time by constructing a series of regularized subproblems, solvable at each corresponding time slot using the output of the previous time slot. We prove that, without prediction beyond the current time slot, our algorithm achieves a parameterized competitive ratio for arbitrarily dynamic workloads and resource prices. If prediction is available, we demonstrate that existing prediction-based control algorithms lack worst case performance guarantees for our problem, and we design two novel predictive control algorithms that inherit the theoretical guarantees of our online algorithm, while exhibiting improved practical performance. We conduct evaluations in a variety of settings based on real-world dynamic inputs and show that, without prediction, our online algorithm achieves up to nine times total cost reduction compared with the sequence of greedy one-shot optimizations and at most three times the offline optimum; with moderate predictions, our control algorithms can achieve two times total cost reduction compared with existing prediction-based algorithms. [ABSTRACT FROM AUTHOR]

By sharing resources among different cloud providers, the paradigm of federated clouds exploits temporal availability of resources and geographical diversity of operational costs for efficient job service. While interoperability issues across different cloud platforms in a cloud federation have been extensively studied, fundamental questions on cloud economics remain: When and how should a cloud trade resources (e.g., virtual machines) with others, such that its net profit is maximized over the long run, while a close-to-optimal social welfare in the entire federation can also be guaranteed? To answer this question, a number of important, interrelated decisions, including job scheduling, server provisioning, and resource pricing, should be dynamically and jointly made, while the long-term profit optimality is pursued. In this work, we design efficient algorithms for intercloud virtual machine (VM) trading and scheduling in a cloud federation. For VM transactions among clouds, we design a double-auction-based mechanism that is strategy-proof, individual-rational, ex-post budget-balanced, and efficient to execute over time. Closely combined with the auction mechanism is a dynamic VM trading and scheduling algorithm, which carefully decides the true valuations of VMs in the auction, optimally schedules stochastic job arrivals with different service level agreements (SLAs) onto the VMs, and judiciously turns on and off servers based on the current electricity prices. Through rigorous analysis, we show that each individual cloud, by carrying out the dynamic algorithm in the online double auction, can achieve a time-averaged profit arbitrarily close to the offline optimum. Asymptotic optimality in social welfare is also achieved under homogeneous cloud settings. We carry out simulations to verify the effectiveness of our algorithms, and examine the achievable social welfare under heterogeneous cloud settings, as driven by the real-world Google cluster usage traces. [ABSTRACT FROM PUBLISHER]

We consider the problem of optimal rate allocation and admission control for adaptive video streaming sessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the number of users supported. We derive sample path upper bounds for the long-term net utility rate in terms of either a linear program or a concave optimization problem, depending on whether the admissible rate set is discrete or continuous. We then show that the upper bounds are asymptotically achievable in large-scale systems by policies which either deny access to a user or assign it a fixed rate for its entire session, without relying on any advance knowledge of the duration. Moreover, the asymptotically optimal policies exhibit a specific structure, which allow them to be characterized through just a single variable, and have the further property that the induced offered load is unity. We exploit the latter insights to devise parsimonious online algorithms for learning and tracking the optimal rate assignments and establish the convergence of these algorithms. Extensive simulation experiments demonstrate that the proposed algorithms perform well, even in relatively small-scale systems. [ABSTRACT FROM AUTHOR]

Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users’ service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation. [ABSTRACT FROM AUTHOR]

Natural disasters are time-varying in nature. They adversely affect backbone datacenter (DC) networks, thereby resulting in huge loss of data within a short span of time. Maximizing last-minute data backup in an endangered DC network hit by a progressive disaster is, therefore, of utmost importance to ensure data protection and service continuation. Thus, in this article, we propose a novel Mixed-Integer Linear Program (MILP) and a heuristic (D-RADDAR) with reduced time-complexity to address risk-aware emergency backup in endangered DC networks hit by dynamic disasters. The mathematical framework for maximizing the overall last-minute backup percentage is developed. A quantitative analysis of the risk associated with such last-minute backups is carried out. The proposed algorithms are evaluated in terms of the DC backup success percentage and compared to existing dynamic backup algorithms in the literature. Extensive simulations indicate that among all existing heuristics, the D-RADDAR achieves maximum data evacuation while maintaining minimal risk and having a polynomial time-complexity. [ABSTRACT FROM AUTHOR]

There is a trade-off in network slicing between the twin goals of providing tailored performance and increasing resource utilisation through increased opportunities for sharing. To balance this trade-off, we propose a system consisting of assured resources, which are available if needed over the lifetime of a slice, and auxiliary resources, which are offered on a probabilistic basis in the short-term based on forecasted resource demand. We employ the practice of overbooking, which is widely used in many industries such as airlines and hotels, to increase resource utilisation when offering auxiliary resources. After deriving probabilistic results relating to the availability of auxiliary resources, we then design an algorithm to determine how much to overbook by. We also propose several ways of distributing auxiliary resource offers among slices, as well as providing an approach for dealing with overbookings. Finally, we highlight the conditions that are conducive to effective overbooking by examining the effects of varying several key system parameters, providing both analytical and numerical results. [ABSTRACT FROM AUTHOR]

We consider elastic resource provisioning in the cloud, focusing on in-memory key-value stores used as caches. Our goal is to dynamically scale resources to the traffic pattern minimizing the overall cost, which includes not only the storage cost, but also the cost due to misses. In fact, a small variation of the cache miss ratio may have a significant impact on user perceived performance in modern web services, which in turn has an impact on the overall revenues for the content provider using such services. We propose and study a dynamic algorithm for TTL caches, which is able to obtain close-to-minimal costs. Since high-throughput caches require low complexity operations, we discuss a practical implementation of such a scheme requiring constant overhead per request independently from the cache size. We evaluate our solution with real-world traces collected from Akamai, and show that the TTL approach is able to track the optimal cache configuration and achieve significant cost savings specially in highly dynamic settings that are likely to require elastic cloud services. [ABSTRACT FROM AUTHOR]

Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS’s scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and latency) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads. [ABSTRACT FROM AUTHOR]

Reducing latency in distributed computing and data storage systems is gaining increasing importance. Several empirical works have reported on the efficacy of scheduling redundant requests in such systems. That is, one may reduce job latency by: 1) scheduling the same job at more than one server and 2) waiting only until the fastest of them responds. Several theoretical models have been proposed to explain the power of using redundant requests, and all of the existing results rely heavily on a common assumption: all redundant requests of a job can be immediately cancelled as soon as one of them is completed. We study how one should schedule redundant requests when such assumption does not hold. This is of great importance in practice, since cancellation of running jobs typically incurs non-negligible delays. In order to bridge the gap between the existing models and practice, we propose a new queueing model that captures such cancellation delays. We then find how one can schedule redundant requests to achieve the optimal average job latency under the new model. Our results show that even with a small cancellation overhead, the actual optimal scheduling policy differs significantly from the optimal scheduling policy when the overhead is zero. Furthermore, we study optimal dynamic scheduling policies, which appropriately schedule redundant requests based on the number of jobs in the system. Our analysis reveals that for the two-server case, the optimal dynamic scheduler can achieve 7%–16% lower average job latency, compared with the optimal static scheduler. [ABSTRACT FROM AUTHOR]

The advent and continued growth of large data centers has led to much interest in switch architectures that can economically meet the high capacities needed for interconnecting the thousands of servers in these data centers. Various multilayer architectures employing thousands of switches have been proposed in the literature. We make use of the observation that the traffic in a data center is a mixture of relatively static and rapidly fluctuating components, and develop a combined scheduler for both these components using a generalization of the load-balanced scheduler. The presence of the known static component introduces asymmetries in the ingress-egress capacities, which preclude the use of a load-balanced scheduler as is. We generalize the load-balanced scheduler and also incorporate an opportunistic scheduler that sends traffic on a direct path when feasible to enhance the overall switch throughput. Our evaluations show that this scheduler works very well despite avoiding the use of a central scheduler for making packet-by-packet scheduling decisions. [ABSTRACT FROM PUBLISHER]