With the rapid proliferation of streaming services, network load exhibits highly time-varying and bursty behavior, posing serious challenges for maintaining Quality of Service (QoS) in Crowdsourced Cloud-Edge Platforms (CCPs). While CCPs leverage Predict-then-Schedule architecture to improve QoS and profitability, accurate load forecasting remains challenging under traffic surges. Existing methods either minimize mean absolute error, resulting in underprovisioning and potential Service Level Agreement (SLA) violations during peak periods, or adopt conservative overprovisioning strategies, which mitigate SLA risks at the expense of increased resource expenditure. To address this dilemma, we propose HRS, a Hybrid Representation framework with Scheduling awareness that integrates numerical and image-based representations to better capture extreme load dynamics. We further introduce a Scheduling-Aware Loss (SAL) that captures the asymmetric impact of prediction errors, guiding predictions that better support scheduling decisions. Extensive experiments on four real-world datasets demonstrate that HRS consistently outperforms ten baselines and achieves state-of-the-art performance, reducing SLA violation rates by 63.1% and total profit loss by 32.3%. Our code is available at [29].

As the rapid development of artificial intelligence (AI) and large language models (LLM), how to deploy related applications onto computing nodes has become a hot topic, and containerized service provides an excellent approach for this. The most time-consuming step of this approach is image extraction, the procedure of decompressing all layers of image package downloaded from remote image registry. Therefore, achieving fast image extracting is crucial for the efficient deployment of AI services. In this paper, we introduce a collaborative orchestration and extraction strategy, CORES. Firstly, we eliminate the dependencies among image layers, which impede unordered extraction of image layers. Based on this, we model the image extraction as a mixed integer linear programming (MILP) problem, aiming to minimize total extraction time. Then we use improved Benders decomposition to iteratively obtain a near-optimal solution with lower time complexity. Extensive experiments conducted on the real system validate the superior performance of our strategy. Compared with our closest baseline LOPO, CORES reduces the average image extracting time by 19.60%, significantly enhancing the efficiency of AI service deployment.

Efficient workload forecasting is pivotal for both service orchestration and request dispatching in quality of service (QoS)-oriented multi-tenant edge cloud platforms (MT-ECPs) with a native tiered architecture. However, the spatial-temporal heterogeneity and structural constraints of native tiered architecture present significant challenges for the forecasting in sophisticated MT-ECPs. To tackle these challenges, we propose SCOUT, which is a novel Self-supervised learning-enhanced Cloud-edge collabOrative Unified workload forecasTing framework. First, we design a cross-granularity collaborative mechanism that enables SCOUT to balance accuracy and efficiency in forecasting within the tiered architecture of MT-ECPs. Notably, we employ an auxiliary self-supervised learning method at the cloud that enhances workload pattern representations, making them reflective of both spatial and temporal heterogeneity. Extensive experiments on two real-world workload datasets show that SCOUT outperforms state-of-the-art methods for MT-ECP's workload forecasting, decreases time consumption and reduces communication costs.

With the rise of AI-Generated Content (AIGC) services, deployments within edge-cloud environments are becoming increasingly prevalent. Containerization offers resource isolation, lightweight deployment, and portability, making it a suitable technology for AIGC services. However, deploying AIGC services often requires large container images, leading to high deployment latency and bandwidth consumption. Based on real-world trace analysis showing the long-tail effect, where a few popular images account for the majority of requests, there is strong potential for optimizing caching mechanisms. This pattern can result in frequent cache misses and increased bandwidth consumption, especially under heavy load. In this paper, we propose a ValueOptimized Distributed Caching Mechanism (VODCM), which dynamically optimizes caching policies through a value-driven framework combined with deep reinforcement learning (DRL). VODCM prioritizes high-value images based on access frequency, layer size, and network latency, significantly improving cache hit rates and reducing network overhead. Preliminary evaluations show that VODCM enhances cache efficiency and reduces network and resource demands, offering an effective solution for AIGC image management in edge-cloud environments.

Responding to the escalating interest in long-term forecasting within the industry, we introduce EasyTS, a comprehensive toolkit engineered to streamline data collection, analysis, and model creation procedures. EasyTS acts as a unified solution, driving progress in long-term time series forecasting. The platform provides effortless access to various time series datasets, including a newly open-sourced multi-scenario dataset in the electricity domain. Integrated visualization and analysis tools help unveil inherent data features and relationships. EasyTS facilitates a user-friendly model validation approach with versatile evaluation criteria. This toolkit allows researchers to compare their models proficiently against renowned benchmarks. With our ongoing commitment to expanding our dataset collection and enhancing toolkit functionalities, we aspire to contribute significantly to the time series forecasting domain. Code is available at this repository: https://github.com/EdgeBigBang/EasyTS.git.

With the rapid increase in user scale and the explosive rise of emerging applications, the contradiction between heavy load pressure and excellent network performance is becoming increasingly prominent, and task processing is gradually shifting towards the edge of the network. However, the resources of edge networks are limited, making it difficult to meet the huge computing and storage needs, and managing and allocating edge nodes is also a huge challenge. The Kubernetes (K8S) framework for deploying and orchestrating containerized applications provides a solution for this. How to improve the adaptability of K8S in edge networks, meet the demand of services for heterogeneous resources, and train decision models with better performance using limited datasets has become an urgent problem to be solved. Based on the above issues, we propose a distributed service migration architecture for multi-user access, and design a service migration algorithm based on imitation learning to achieve resource combination optimization and reduce the impact of insufficient data on model training. Design agent models based on diffusion models to accelerate model convergence and avoid the increase in training costs caused by constantly updating agent models. Our results show that the efficiency of the expert model is 92.0%, and the learning process of the agent model can converge within 100 training cycles with an accuracy of 97.89%. The service processing delay, throughput rate, and model convergence are all significantly better than those of classical algorithms.

To improve parallel I/O performance, it is imperative to optimize the adjustable parameters across the different layers of the I/O software stack. Finding an optimal configuration for different scenarios is hampered by the complex interaction dynamics between these parameters and the large parameter space. Previous research efforts have focused on tuning these parameters using independent algorithms; however, these approaches exhibit certain shortcomings such as unstable performance results and delayed convergence rates.This paper introduces OPRAEL, an auto-tuning approach on parallel I/O tasks by ensembles and performance modeling using regression analysis. To test its effectiveness, we applied this approach on the Tianhe-II supercomputer using one well-known I/O benchmark(IOR) and two I/O kernels(S3D-I/O, BT-I/O). Leveraging our experience in predictive modeling, we optimized the tuning of the I/O stack parameters. Our experimental results show a remarkable 10.2X improvement in write performance speedup for the optimization task with BT-I/O and a 500x500x500 input. We also compared the potential of using a single search algorithm versus using reinforcement learning search in the I/O parameter auto-optimization task. Our results show that OPRAEL outperforms the traditional approach, resulting in a maximum 8.4X improvement in write performance for the 128-process IOR optimization.

Nowadays, real-time interactive content services have been the most dazzling sector of next-generation Internet. The high-quality perceptions of virtual scenes have given rise to the strict requirements of high bandwidth and low latency, where the edge-cloud system promises several benefits. However, there still remain prominent challenges, when taking the economical efficiency into consideration, including location-heterogeneity, context-directability, and capacity-exploitation. In this paper, we propose a location-context-capacity aware bandwidth cost economizing strategy in the edge-cloud system, i.e., LoCoCa. LoCoCa adopts server pools partition mechanism, then achieving the optimal burstable billing in each pool. Here, a location-aware graph construction and partition algorithm is designed to solve the server pools partition problem. Then an improved burstable billing optimization mechanism, with a context index and an adaptive bandwidth capacity, is also proposed to economize bandwidth costs. Finally, the realistic edge-cloud company's trace-based experimental results verify LoCoCa reduces bandwidth costs by 81.83 %, compared with the baselines.

Deep neural networks are deemed to be the cornerstone of a series of mobile intelligent systems, and their inference processes bring about a mass of computation-intensive tasks. To migrate the burden of inference computation from resource-constrained mobile devices, device-edge cooperative inference in mobile edge computing provides a fine-grained processing method. However, the geographical dispersion of resources and the mobility pattern of devices pose technical issues in the scheduling of co-inference systems, which have not been fully considered. In this paper, we propose a learning-based scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider a resource provisioning strategy based on the number of devices and a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Next, we propose an algorithm based on proximal policy optimization for each device to make the decision independently. Further, we adopt long short-term memory in the algorithm to capture the temporal characteristics of the system state. Experiments using a real-world network and computing trace demonstrate that the proposed algorithm can efficiently sense the mobile system to make decisions at various system scales and two mobility scenes. The average pipeline time of the proposed algorithm is only 67.63% of that of local processing, which is 97.50% of that of the omniscient algorithm.

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: (1) First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, and what are the major abnormal behaviors. (2) Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. (3) Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users.

Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate and efficient workload prediction. Clustering-based methods for dynamic MT-ECP modeling often incur excessive costs due to the need to maintain numerous data clusters and models, which leads to excessive costs. Existing end-to-end time series prediction methods are challenging to provide consistent prediction performance in dynamic MT-ECP. In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP. Meticulously designed global pooling and information merging mechanisms can effectively identify and utilize global application patterns to drive local workload predictions. The integration of static content-aware mechanisms enhances model robustness in real-world scenarios. Through experiments on five real-world datasets, DynEformer achieved state-of-the-art in the dynamic scene of MT-ECP and provided a unified end-to-end prediction scheme for MT-ECP.

To distribute contents through Information-centric networks (ICN) was proposed as an important future Internet architecture, in which contents was considered as the main part. In ICN, contents name are the routing locator for efficient contents distribution. Contents are dynamically cached in network routers of Internet service providers (ISPs), which is different from traditional content distribution networks that still use IP address as routing locator. Caching has been identified as a critical component. Many cache mechanisms determine contents' cache time by the popularity of the contents, i.e., popular contents can be kept in cache for much longer time. Instead, our previous work has proposed a time-to-live based caching mechanism for ISPs to monetize their cache resource, in which the contents' cache time is determined by contents provider's (CP's) payment, and the CP was charged by an affine price function of contents cache time in ISPs' cache. In this paper, a concave pricing mechanism is considered, and CP is charged by an concave price function of contents cache time in ISPs' cache. We theoretically analyze the proposed concave pricing mechanism and provide solution for CP to optimally choose the time length stay in ISP's cache.

Reinforcement learning (RL) is emerging as a powerful technique for solving complex code optimization tasks with an ample search space. While promising, existing solutions require a painstaking manual process to tune the right task-specific RL architecture, for which compiler developers need to determine the composition of the RL exploration algorithm, its supporting components like state, reward, and transition functions, and the hyperparameters of these models. This paper introduces SuperSonic, a new open-source framework to allow compiler developers to integrate RL into compilers easily, regardless of their RL expertise. SuperSonic supports customizable RL architecture compositions to target a wide range of optimization tasks. A key feature of SuperSonic is the use of deep RL and multi-task learning techniques to develop a meta-optimizer to automatically find and tune the right RL architecture from training benchmarks. The tuned RL can then be deployed to optimize new programs. We demonstrate the efficacy and generality of SuperSonic by applying it to four code optimization problems and comparing it against eight auto-tuning frameworks. Experimental results show that SuperSonic consistently improves hand-tuned methods by delivering better overall performance, accelerating the deployment-stage search by 1.75x on average (up to 100x).

introduction Share on Introduction to the Special Section on Energy-efficient and Secure Computing for Artificial Intelligence and Beyond Authors: Meikang Qiu Dakota State University, USA Dakota State University, USASearch about this author , Ke Xu Tsinghua University, China Tsinghua University, ChinaSearch about this author , Cheng Zhang Ibaraki University, Japan Ibaraki University, JapanSearch about this author , Tianwei Zhang Nanyang Technological University, Singapore Nanyang Technological University, SingaporeSearch about this author Authors Info & Claims ACM Transactions on Sensor NetworksVolume 18Issue 4November 2022 Article No.: 51epp 1–3https://doi.org/10.1145/3558553Published:09 March 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Researches on cognitive networks and heterogeneous networks with different communication technologies, such as 5G/4G cellular networks, wireless LANs, are very popular nowadays. However, existing researches do not consider users' satisfaction with social cooperative behaviours. Therefore, SHaaS (Sharing framework for Heterogeneous network services as a Service) has been proposed in our previous work. In the SHaaS framework, cooperation among users and service providers is considered to improve users' satisfaction and service providers' profitability. In this paper, an evaluation model of SHaaS is proposed in the case of normal situation and network failure situation. The evaluation results show that SHaaS can improve users' satisfaction and service providers' profitability.

In order to prevent potential accidents between pedestrians and vehicles, it will be effective to introduce a vehicle-to-pedestrian warning system. The system detects potential accidents between pedestrians and vehicles in advance and sends warning to pedestrians and vehicles ahead of time so that potential accidents can be avoided. The time ahead of the potential accidents is named prior notification time and it is set in advance in the system. However, there is a difference between the prior notification time and the actual notification time due to system delay and inaccuracy of GPS localization. This difference is named warning error. The location estimation error and the speed of pedestrians and vehicles will affect the appropriate estimation of prior notification time. In this paper, how warning error is changed with vehicle speed is investigated through simulation. The simulation results show that the warning error increases as the vehicle speed increases. The maximum warning error is 2 seconds regardless of the vehicle speed. Thus, notification time delay can be reduced by estimating the maximum value of the warning error and increasing the prior notification time in advance.

With the increase of smart devices, mobile crowdsensing, in which a crowdsensing Internet of Things (IoT) platform collects data from smart devices (such as smartphone) users, has become a popular paradigm. Various incentive mechanisms are widely employed for the IoT platform to incentivize smart device users to provide sensing data. Traditional works concentrated on rewarding smart device users for their short term effort to provide data, without considering smart device users’ long term factors and the quality of data. In this paper, smart device users’ quality of data is considered by incorporating smart device users’ long term factor reputation. A quality maximization problem with budget constraints is formulated for IoT platform, and the optimal pricing solution is obtained through theoretical analysis. Our proposed optimal pricing based incentive mechanism is validated by extensive numerical simulations.

The telecommunication services market has been greatly changed because of the significantly changing of telecommunication network technologies and corresponding users' environment. The application services on the network have become more important. As the application service platform, the telecommunication network with suitable pricing and high utility are required from users. In this paper, we aim to provide telecommunication network service with quality of service that satisfies users and social welfare. The impact of reliability of telecommunication service on users' utility is studied. The failure rate of service is important, but few studies have focused on this point. The relation between the failure rate and willingness to pay (WTP) as well as willingness to accept (WTA) are clarified through questionnaire survey. The survey results show the differences regarding the failure rate between mass and business users. In addition, we propose the method to improve the sum and average of users' utility with the auction based trading among the users when the failure occurred, and show the effectiveness of the method by the simulation.

Video caching is one of the most important research issues in Content-Centric Network (CCN) and greatly affects its overall performance. The computational complexity of state-of-the-art optimal caching schemes is high, due to the arbitrary network topologies. In this paper, the popularity-aware video caching in topology-known CCN is studied. The complex arbitrary network typology is mapped into a virtual cascade network topology and a caching scheme is designed in accordance with the transformed virtual network rather than the original network. This scheme is proved optimal, and is with polynomial computational complexity. Simulations are conducted and the results show that the proposed scheme outperforms the existing schemes.

Since errors are very common in the scientific and engineering data and/or evaluation processes. A reliable computation outsourcing scheme should not introduce extra error gains. However, we observed the existing matrix computation outsourcing schemes could not reconcile the reliability and security. In this paper, we introduce a new fully homomorphic matrix disguising scheme, based on sparse unitary disguising matrices, which take into account reliability and security. Our scheme can be regarded as a variant of matrix fully homomorphic applications in Symmetric key cryptosystem also.

Software-defined networking (SDN) enables network subscribers to negotiate QoS parameters in a on-demand basis. On the other hand, peak-time congestion accompanying the fast-growing traffic in recent years forces Internet service provider (ISP) to put forward a new pricing scheme by taking into account when a user uses Internet in addition to how much a user uses Internet. In this paper, we study the payoff optimization problem of ISP and network subscribers in SDN-enabled networks. A self-interested network subscriber always tries to obtain network resources as much as possible even if the network is congested; on the other hand, rational ISP tends to charge a higher price without providing subscribers guaranteed Quality of Service (QoS). A Stackelberg game is hence constructed to analyze the competitive interactions between ISP and home network subscribers. Specifically, ISP decides its pricing strategy for each time slot by solving a payoff optimization problem. Given the pricing strategy, network subscribers then decide the bandwidth to be reserved in a on-demand basis aiming to optimize their own payoff as well. We analyze the Nash equilibrium solution of the game. Simulation results confirm that the proposed pricing scheme can largely improve the payoff of network subscribers and ISP, compared to the usage-based pricing (UBP) scheme. Furthermore, the portion of surplus obtained by ISP increases with the increase of traffic load.

The past few years have witnessed a huge acceleration in global Internet traffic. Users' demand for contents is also rising accordingly. Therefore, content providers (CPs) that provide contents for users get high revenue from the traffic growth. There are generally two ways for CPs to get revenue: (i) charge users for the contents they view or download; (ii) get revenue from advertisers. On the other hand, Internet service providers (ISPs) are investing in network infrastructure to provide better quality of service (QoS), but they do not benefit directly from the content traffic. One option for ISPs to compensate their investment cost is sharing CPs' revenue by side payment from CPs to ISPs. Then ISPs will be motivated to keep on investing in developing new network technology and enlarging the capacity to improve QoS. However, it is important to evaluate how each player is affected by this kind of side payment. Our previous work has studied this problem by assuming that CPs charged users for the contents they view or download, in this paper it is considered that CP does not directly charge end users, but charges advertisers for revenue. Stackelberg game is utilized to study the interactions among ISP, CP, end users and advertisers. A unique Nash equilibrium is established and numerical analysis has validated our theoretic results. It shows that side payment from CP to ISP impairs the CP's investment of contents, and ISP can benefit from charging CP, while CP's payoff is impaired.

In this paper, we consider the problem of spectrum sharing in a Cognitive Radio Network (CRN) with spectrum holder, two secondary operators and secondary users (SUs). In the system model under consideration, the spectrum allocated to the two secondary operators can be shared by SUs, which means that secondary operators buy spectrum from spectrum holder and then sell spectrum access service to SUs. We model the relationship between secondary operators and SUs as a two-stage stackelberg game, where secondary operators make spectrum channel quality and price decisions in the first stage, and then the SUs make their spectrum demands decisions. The backward induction method is employed to solve the stackelberg game. Numerical results are performed to evaluate our analysis.

Nowadays, mobile network operators (MNOs) are trying to deploy wireless local area network (LAN) to offload mobile data from their cellular networks to complementary wireless LAN for congestion relief and cost savings. However, these network-centric methods do not take into consideration mobile user's (MU's) interests of monetary cost, energy consumption, and applications' deadlines. How the MU decides whether to offload their traffic to a complementary wireless LAN is non-trivial and important issue. Previous studies assume that MNO adopts usage-based pricing for mobile data, which only cares about how much a MU consumes data but not when a MU consumes data. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption under time-dependent pricing (TDP). We formulate MU's wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Extensive simulations are conducted to validate our proposed offloading algorithm.

In order to improve the quality of service (QoS) for mobile users (MUs) and save investment cost for deploying new cellular base station, mobile network operators (MNOs) are deploying wireless local area network (LAN) access points (APs) to offload MU's traffic from cellular network to wireless LAN. However, offloading too much traffic from cellular network may impair MNO's profit since the cellular network price is higher than that of wireless LAN, whose price is low or even zero. Therefore, how to deploy wireless LAN APs to offload traffic without impairing MNO's profit is a critical problem for MNOs. As far as the authors understand, existing studies about deployment of wireless LAN APs do not consider MNO's profit and are usually in a heuristic manner. In this paper, we study the location-based advertising (LBA) leveraged wireless LAN deployment, where MNO may also collect revenue by selling LBA service in different locations to advertisers. We formulate MNO's profit maximization problem by considering different MU's demand in different locations, wireless LAN price for MUs, and revenue from LBA service. Extensive simulations are conducted to validate our analytical results.

The ongoing evolution of personal mobile devices capabilities and wireless technologies result in a huge growth of traffic on mobile networks (3G/4G). One of the most promising approaches to handle this data crisis is to offload the fast growing traffic onto femtocell networks. Since both the 3G/4G macrocell and femtocells operate on the same licensed spectrum, the cross-tier interference should be well managed. In this paper, we propose a utility-based transmission power allocation policy for the uplink transmission in femtocell networks. Our main motivation is to design the transmission power allocation policy aiming at optimizing the joint utility of femtocell users (FUs) subject to a interference temperature constraint at the macrocell base station (MBS) side. We provide a novel floating-ceiling water-filling (FCWF) algorithm with little computational overhead to obtain the optimal solution for the joint utility optimization problem. Numerical results confirm that the joint utility and average SINR can be significant improved with the proposed method.

Content Centric Network (CCN) has become a heated research topic recently, as it is proposed as an alternative of the future network. The routers in CCN have the caching abilities and the caching strategies affect the system performance greatly. Each content in CCN is associated with a popularity, which is determined by the corresponding requested times. Popularity-aware caching scheme caches the popular content close to users and can lead to better caching performance in terms of smaller average transmission hops traveled. Content popularity significantly affects the overall system performance, and the content size is not considered during the content level popularity (i.e. item popularity) calculation. In this paper, we study the impact of the item popularity and chunk popularity in CCN, where the chunk popularity is the normalized item popularity considering the content size. Extensive simulations are conducted and the simulation results show the advantages and disadvantages of each scheme. A new popularity calculation method is proposed to perform the tradeoff between the item popularity and chunk popularity.

With rapid increases in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying WiFi hotspots to offload their mobile traffic. However, these network-centric methods usually do not fulfill interests of mobile users (MUs). MUs consider many problems to decide whether to offload their traffic to a complementary WiFi network. In this paper, we study the WiFi offloading problem from MU's perspective by considering delay-tolerance of traffic, monetary cost, energy consumption as well as the availability of MU's mobility pattern. We first formulate the WiFi offloading problem as a finite-horizon discrete-time Markov decision process (FDTMDP) with known MU's mobility pattern and propose a dynamic programming based offloading algorithm. Since MU's mobility pattern may not be known in advance, we then propose a reinforcement learning based offloading algorithm, which can work well with unknown MU's mobility pattern. Extensive simulations are conducted to validate our proposed offloading algorithms.

Network traffic load usually differs significantly at different times of a day due to users' different time preference. Network congestion may happen in traffic peak times. In order to prevent this from happening, network service providers (NSPs) can either over-provision capacity for demand at peak times of the day, or use dynamic time-dependent pricing (TDP) scheme to reduce the demand at traffic peak times. Since over-provisioning network capacity is costly, many researchers have proposed TDP schemes to control congestion as well as to improve the revenue of NSPs. To the best of our knowledge, all these studies consider only the monopoly NSP case. In our previous work, the duopoly and oligopoly NSP cases have been studied. NSPs try to maximize their overall revenue by setting time-dependent prices, while users choose NSPs by considering their own time preference, congestion statuses in the networks and the prices set by the NSPs. One assumption that has been made is that Quality of Service (QoS) function of each NSP is linear, which means that the level of QoS degradation is proportional to the number of users in the network. However, in reality, the level of QoS may degrade rapidly after a certain point, which is not reflected through linear QoS functions. Therefore, concave QoS function is a better choice. In this paper, the case of concave QoS function is considered. TDP is evaluated under different QoS functions. The results shows that TDP is also effective under concave QoS functions.

Pricing cloud resources plays an important role in leading to the success of cloud computing. Cloud services are priced at different levels in infrastructure-as-a-service (IaaS) cloud market. For example, Amazon EC2 offers its cloud resources with three pricing schemes, the subscription model, pay-as-you-go model and spot pricing model. With more and more IaaS cloud service providers (CSPs) beginning to provide cloud services, they form a competitive market to compete for cloud users. Therefore, how to set optimal prices in order to maximize their revenue in a competitive IaaS cloud computing market while at the same time meeting the cloud users' demand satisfaction is a problem that CSPs should consider. Towards this end, in this paper, we study subscription pricing competition in a duopoly IaaS cloud computing market. First, we analyze whether or not the cloud users choose to use cloud service. Then, we present a game theoretic analysis of a cloud market with two CSPs competing non-cooperatively for cloud users.

In this paper, we consider a price-based connection admission control (CAC) for wireless random access networks. In particular, a network operator determines sequential prices to dynamically maintain the traffic admitted into the network below the channel capacity. The CAC tries to ensure quality of service (QoS) guarantees to users and hence maximize the overall revenue. We find that the revenue maximization problem over all sequential prices is NP-hard. Therefore, a greedy algorithm is employed for obtaining a simple, easy-to-implement solution.