Bandwidth Pricing Research Articles

Monetizing Spare Bandwidth

Residential Internet speeds have been rapidly increasing, reaching averages of ~100 Mbps in most developed countries. Several studies have shown that users have way more bandwidth than they need, only using about 20-30% on a regular day. Several systems exploit this trend by enabling users to monetize their spare bandwidth, e.g., by sharing their WiFi connection or by participating in distributed proxy or VPN (dVPN) services. Despite the proliferation of such systems, little is known on how such marketplaces operate, what are the key factors that determine the price of the spare bandwidth, and how such prices differ worldwide. In this work, we shed some light on this topic using dVPNs as a use-case. We start by formalizing the problem of bandwidth monetization as an optimization between a buyer's cost and seller's income. Next, we explore three popular dVPNs (Mysterium, Sentinel, and Tachyon) using both active and passive measurements. We find that dVPNs have a large and growing footprint, and offer comparable performance to their centralized counterpart. We identify Mysterium (in the US) as the most concrete realization of a bandwidth marketplace, for which we derive a value of spare Internet bandwidth ranging between 11 and 14 cents per GB. We also show that both buyers and sellers utilize ad-hoc "rules-of-thumb" when choosing their prices, which results in a sub-optimal marketplace. By applying our optimization, a seller's income can be tripled by setting a price lower than the default one which allows to attract more buyers. These observations motivate us to create RING, a first and concrete system which helps sellers to automatically adjust their prices and traffic volumes across multiple marketplaces.

Firefly algorithm and learning-based geographical task scheduling for operational cost minimization in distributed green data centers

Green Data Centers (GDCs) are more and more deployed world-wide. They integrate many renewable sources to provide clean power and decrease their operating cost. GDCs are typically deployed over multiple locations where renewable energy availability, bandwidth prices and grid electricity cost have high geographical diversity. This paper focuses on delay-bounded applications in distributed GDCs (DGDCs) and performs cost and energy-effective scheduling of multiple heterogeneous applications verifying delay bound constraints of different tasks. DGDCs’ operational cost minimization problem is formulated and successfully optimized using an innovative modified Firefly Algorithm (mFA). Real-life data trace-driven experiments are conducted to evaluate the effectiveness of the proposed mFA in solving this problem. High performance task scheduling results are obtained. The operational cost of each GDC is minimized, the utilization of solar and wind renewable energy from the different geographical locations is maximized while delay bound constraints of all tasks are strictly met. Compared to Bat Algorithm, Simulated-annealing Bat Algorithm and basic firefly algorithm, mFA can produce a schedule that outperforms its peers’ drastically in terms of operational cost of DGDCs. Moreover, mFA finds more rapidly both global or local optima than its peers. It succeeds to meet all equality and inequality constraints at all time slots while its peers may sometimes fail to find satisfactory solutions at some particular time slots.

Cost-Efficient Scheduling of Multicast Transfers with Deadline Guarantees Across Edge Datacenters

Moderate-scale datacenters are increasingly deployed at the network edge to support low-latency and high-bandwidth internet of Things (IoT) and 5G applications. These applications usually have bulk data to transfer from one datacenter to several datacenters, which can cause a significant amount of bandwidth costs. Traffic engineering (TE) systems at the network edge must minimize the bandwidth costs of multicast transfers across edge datacenters. To avoid service quality degradation, TE should also guarantee transfer deadlines, which have received little attention in existing work. Due to the dynamic bandwidth pricing schemes, it is challenging to strike a good balance between minimizing costs and guaranteeing transfer deadlines. In this paper, we present CDScheduler a cost-efficient scheduling solution for multicast transfers with deadline guarantees. To reduce bandwidth costs, CDScheduler uses Steiner trees for forwarding and adaptive routing that considers bandwidth price variation and transfer demands. We formulate the cost-efficient multicast transfer scheduling problem and propose an algorithm based on linear program relaxation and randomized rounding to find a solution that guarantees timely completion and reduces cost. Extensive evaluations show that CDScheduler can reduce bandwidth cost significantly and outperforms the state-of-the-art solutions by cutting down up to 78% bandwidth cost.

Less is More: Service Profit Maximization in Geo-Distributed Clouds

Nowadays cloud providers purchase a good deal of bandwidth from Internet service providers to satisfy the growing requests from corporate customers for the exclusive use of inter-datacenter bandwidth. For exclusive bandwidth services, neither maximizing the revenue nor minimizing the cost can bring the maximal profit to cloud providers. The diversity of bandwidth prices and the random arrival time of user requests further increase the difficulty in economically scheduling the services to meet user requests from cloud providers. In this article, we propose to help cloud providers maximize their service profits by properly selecting user requests to serve rather than satisfying them all. We formulate the problem of service profit maximization and prove its NP-hardness. To handle offline request submission, we propose a solution that maximizes the service profit by alternately maximizing the service revenue and minimizing the service cost. To maximize service profit under online request submission, we propose an online scheduling algorithm that carefully handles the risk of not being able to pay off the incremental service cost and makes scheduling decisions in real time. Our extensive evaluations demonstrate that our solutions can achieve more than 1.6x the service profits of existing solutions.

Pricing in Abundance

Small island nations that have relied on satellites for international connectivity are now being connected by submarine cables that have infinitely more capacity. The hope is that these cables will lift the social and economic development of the economies connected. This hope is more likely to be realized with the adoption of wholesale traffic pricing based on the capacity abundance brought by the cable system rather than historical wholesale bandwidth pricing, which assumes capacity scarcity. Reductions in the wholesale cost of international connectivity are more likely to be passed on to end users if there is retail competition. The proposed wholesale pricing model facilitates increased retail competition. These ideas are explored in a case study of the Cook Islands, which is a member of the Manatua Cable Project.

Geography-Aware Task Scheduling for Profit Maximization in Distributed Green Data Centers

Infrastructure in Distributed Green Data Centers (DGDCs) is concurrently shared by multiple different applications to flexibly provide a growing number of services to global users in a cost-effective way. A highly challenging problem is how to maximize the total profit of the DGDC provider in a market where Internet Service Provider (ISP) bandwidth price, availability of green energy, price of power grid, and revenue brought by the execution of tasks all vary with geographical locations. Unlike existing studies, this article proposes a Geography-Aware Task Scheduling (GATS) approach by considering spatial variations in DGDCs to maximize the total profit of the DGDC provider by intelligently scheduling tasks of all applications. In each time slot, the formulated profit maximization problem is solved as a convex optimization one via the interior point method. Trace-driven simulations show that GATS achieves larger total profit and higher throughput than two typical task scheduling approaches.

Biobjective Task Scheduling for Distributed Green Data Centers

The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners-This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.

Focus on software, data acquisition, and instrumentation

Focus on software, data acquisition, and instrumentation

Cost-Efficient Scheduling of Bulk Transfers in Inter-Datacenter WANs

With the quick growth of traffic between data centers, inefficient transfer scheduling in inter-datacenter networks can lead to a huge waste of bandwidth thus significant bandwidth cost. Previous work have explored different ways, such as software-defined WANs and dynamic pricing mechanisms, to overcome the inefficiency of inter-datacenter networks. However, there is a big challenge in addressing the fundamental conflicts between the deadline-aware transfer scheduling and minimizing the bandwidth cost. Unlike existing efforts that schedule inter-datacenter transfers under fixed link capacities, wherein some deadlines are violated and the service quality is degraded, we aim to finish all the transfers on time with as little bandwidth as possible to minimize the bandwidth cost. We take into account the variation of bandwidth price and the deadline requirements of services, and formulate the problem of cost-efficient scheduling of bulk transfers with deadline guarantee, which is shown to be NP-hard. Benefitting from the relax-and-round method, we propose a progressively-descending algorithm (PDA) to schedule bulk transfers and meet the above goals with a guaranteed approximation ratio. We apply our algorithm in a bulk transfer scheduler, Butler, and build a small-scale testbed to evaluate its efficiency. Both large-scale simulation and testbed experiment results validate the ability of our scheme on cutting down the bandwidth cost. Compared with existing approaches, it reduces up to 60% bandwidth cost and increases the network utilization by up to 140%.

Internet Infrastructure in Africa: Status and Opportunities

There is a major information gap in Africa, where access to information is structurally disabled.This study was conducted taking the University of Dodoma in Tanzania as a case study. The causes for poor internet access were identified as: Lack of alignment between last-mile, middle-mile, and long-range network infrastructure; Tyranny of bad on-premises network design; Up to 15 network hops just to leave on-premises network architecture; Lack of best-practice templates and benchmarks for on-premises, middle-mile and national backbone network architecture in emerging and developing markets; and local operators charging exorbitant bandwidth prices. The US Federal Communication Commission’s (FCC) definition of broadband is 25 Mbps per host. The World Bank defines broadband as 12 Mbps per hundred consumers.According to the United Nations, broadband is a basic human right and an absolute necessity for productivity and sustainable growth

Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G

Telco content delivery networks (CDNs) have envisioned building highly distributed and cloudified sites to provide a high-quality CDN service in the 5G era. However, there are still two open problems to be addressed. First, telco CDNs are operated upon the underlay network evolving towards information-centric networking (ICN). Different from CDNs that perform on the application layer, ICN enables information-centric forwarding to the network layer. Thus, it is challenging to take advantage of the benefits of both ICN and CDN to provide a high-quality content delivery service in the context of ICN-based telco CDNs. Second, bandwidth pricing and request mapping issues in ICN-based telco CDNs have not been thoroughly studied. In this paper, we first propose an ICN-based telco CDN framework that integrates the information-centric forwarding enabled by ICN and the powerful edge caching enabled by telco CDNs. Then, we propose a location-dependent pricing (LDP) strategy, taking into consideration the congestion level of different sites. Furthermore, on the basis of LDP, we formulate a price-aware request mapping (PARM) problem, which can be solved by existing linear programming solvers. Finally, we conduct extensive simulations to evaluate the effectiveness of our design.

Spatial Task Scheduling for Cost Minimization in Distributed Green Cloud Data Centers

The infrastructure resources in distributed green cloud data centers (DGCDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks’ delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods. Note to Practitioners —This paper investigates the cost minimization problem for DGCDCs while meeting delay-bound constraints for all arriving tasks. Previous task scheduling methods do not jointly investigate the spatial diversity in bandwidth prices of ISPs, electricity prices, and the availability of renewable green energy. Therefore, they fail to cost-effectively schedule all arriving tasks of heterogeneous applications during their delay-bound constraints. In this paper, a new method that overcomes the shortcomings of the existing methods is proposed. It is obtained by using the proposed SBA that solves a constrained optimization problem. Simulation results demonstrate that compared with two typical scheduling methods, it increases the throughput and decreases the cost. It can be readily implemented and integrated into real-world industrial DGCDCs. The future work needs to investigate the indeterminacy of renewable energy and the uncertainty in arriving tasks with rough deep neural network approaches on STSRO.

IDaaS: Inter-Datacenter Network as a Service

Increasing number of Internet-scale applications, such as video streaming, incur huge amount of wide area traffic. Such traffic over the unreliable Internet without bandwidth guarantee suffers unpredictable network performance. This result, however, is unappealing to the application providers. Fortunately, Internet giants like Google and Microsoft are increasingly deploying their private wide area networks (WANs) to connect their global datacenters. Such high-speed private WANs are reliable, and can provide predictable network performance. In this paper, we propose a new type of service—inter-datacenter network as a service (iDaaS), where traditional application providers can reserve bandwidth from those Internet giants to guarantee their wide area traffic. Specifically, we design a bandwidth trading market among multiple iDaaS providers and application providers, and concentrate on the essential bandwidth pricing problem. The involved challenging issue is that the bandwidth price of each iDaaS provider is not only influenced by other iDaaS providers, but also affected by the application providers. To address this issue, we characterize the interaction between iDaaS providers and application providers using a Stackelberg game model, and analyze the existence and uniqueness of the equilibrium. We further present an efficient bandwidth pricing algorithm by blending the advantage of a geometrical Nash bargaining solution and the demand segmentation method. For comparison, we present two bandwidth reservation algorithms, where each iDaaS provider's bandwidth is reserved in a weighted fair manner and a max-min fair manner, respectively. Finally, we conduct comprehensive trace-driven experiments. The evaluation results show that our proposed algorithms not only ensure the revenue of iDaaS providers, but also provide bandwidth guarantee for application providers with lower bandwidth price per unit.

Bandwidth Price Estimation for Scalable and Responsive Rate Control

This paper concerns the analysis of important algorithmic attributes, namely, the rate of convergence and scalability, and their impact on Network Utility Maximization (NUM). The contribution of the paper is a novel distributed rate control mechanism with strong convergence and scalability properties. The proposed algorithm employs a distinctive distributed framework, where rate control is derived as a Sequential Quadratic Programming (SQP) mechanism incorporated with interior-point and trust-region methods. The NUM problem is solved by a barrier method that penalizes any violation of constraints. Lagrangian is applied to the barrier objective function, where multipliers are estimated using Least-square method to iteratively solve the quadratic approximation of the Lagrangian function at the current point to generate a search direction. The uniqueness of the algorithm is that it allows sources to estimate bandwidth prices and thereby enforces a scalable network core by pushing algorithmic complexity to the edges. The fast convergence of the algorithm, in turn, improves the responsiveness of rate control and enables reduced buffer occupancy. The convergence of the proposed algorithm is proved theoretically and is evaluated via simulations. The results demonstrate reasonable reduction of computation-time in tracking the optimal rates and validate the strong convergence properties of the proposed algorithm.

Negotiating Premium Peering Prices

We have developed a novel methodology for deriving bandwidth prices for premium direct peering between Access ISPs (A-ISPs) and Content and Service Providers (CSPs) that want to deliver content and services in premium quality. Our methodology establishes a direct link between service profitability, for example, from advertising, user and subscriber loyalty, interconnection costs, and finally bandwidth price for peering. Unlike existing work in both the networking and economics literature, our resulting computational model, built around Nash bargaining, can be used for deriving quantitative results comparable to actual market prices. We analyze the U.S. market and derive prices for video, that compare favorably with existing prices for transit and paid peering. We also observe that the fair prices returned by the model for high-profit/low-volume services such as search, are orders of magnitude higher than current bandwidth prices. This implies that resolving existing (fierce) interconnection tussles may require per service, instead of wholesale, peering between A-ISPs and CSPs. Our model can be used for deriving initial benchmark prices for such negotiations.

Timely Delivery Versus Bandwidth Allocation for DASH-Based Video Streaming Over LTE

We study HTTP video streaming over a wireless access cell, such as LTE. We derive a closed form expression for the probability of timely delivery of the streamed video packets as a function of the bandwidth allocated to the user. The probability expression uses application, transport, and physical layer parameters relating to the employed video stream rate and to the quality of the communications channel. We validate the analytical results via numerical simulations based on the execution of real video traces and with LTE channel quality index trace modeling. The attained results allow the system designer, scheduler, and manager to calculate the bandwidth level that should be allocated to maintain acceptable system performance, expressed by two quality-of-experience (QoE) metrics, namely the video rate and the probability of timely delivery of video packets. The model provides a rationale and tools for performing admission procedures, bandwidth pricing policies, and cell dimensioning designs.

A numerical strategy for telecommunications networks capacity planning under demand and price uncertainty

The massive use of Internet in the last twenty years has created a huge demand for telecommunications networks capacity. In this work, financial option pricing methods are applied to the problem of network investment decision timing. The main innovative aspect is the consideration of two uncertain factors: the capacity demand and the bandwidth price, the evolution of which is modeled by suitable stochastic processes. Thus, we consider the optimal decision problem of upgrading a line in terms of the (highly volatile) uncertain demand for capacity and the price. By using real options pricing methodology, a set of partial differential equation problems are posed and appropriate numerical methods based on characteristics methods combined with finite elements to approximate the solution are proposed. The combination with a dynamic programming strategy gives rise to a global algorithm to help in the decision of optimizing the value of the line.

Utility Maximized Two-level Game-Theoretic Approach for Bandwidth Allocation in Heterogeneous Radio Access Networks

To solve the optimal bandwidth allocation problem in heterogeneous radio access networks (H-RANs), a two-level game-theoretic approach is proposed to maximize the utility of network providers and users by considering the network resource distributions and service demands. In the area-level game, the Nash equilibrium is achieved by using a noncooperative game for the bandwidth allocation in different areas to maximize network utility. Moreover, in each service area, the traffic-level bandwidth allocation and pricing approach is applied to allocate the appropriate portion of bandwidth for different networks and users using the Stackelberg game, with the network as the leader and the user as the follower. Furthermore, the Stackelberg equilibrium is achieved by an iterative algorithm in this paper. Simulation results prove that the proposed game-theoretic approaches can efficiently and significantly maximize the utility of networks and users.

Interdatacenter Job Routing and Scheduling With Variable Costs and Deadlines

To reduce their operational costs, datacenter (DC) operators can schedule large jobs at DCs in different geographical locations with time- and location-varying electricity and bandwidth prices. We introduce a framework and algorithms to do so that minimize electricity and bandwidth cost subject to job indivisibility, deadlines, priorities, and DC resource constraints. In doing so, we provide a way for DC operators to predict their operational costs for different DC placements and capacities, and thus make informed decisions about how to expand their DC network. Our distributed algorithm uses estimated job arrivals and day-ahead electricity prices to optimize over sliding time windows. We demonstrate its effectiveness on a Google DC trace and investigate the effects of different cost and performance criteria. The algorithm leverages heterogeneous job resource requirements and routing and scheduling flexibility: even deadline and indivisibility constraints yield little cost increase, though they significantly improve job completion times and localization at only one DC, respectively. We show that our algorithm reduces the cost much more than optimizing only electricity, only bandwidth, or a combination of resource costs and job completion times.

Cost-Aware Capacity Provisioning for Internet Video Streaming CDNs

With the increasing popularity of the Internet video streaming services (e.g. YouTube and Netflix), content delivery networks (CDNs) are heavily used to stream video contents to users, and consume more and more power and bandwidths in recent years. In this paper, we investigate the problem of saving a video streaming CDN’s operating expense, including both its energy cost and the traffic cost. From our measurement study on the CDN infrastructure of Youku, which is the largest video site in China, we find that there exists an inherent conflict between improving a video streaming CDN’s energy efficiency for power saving, and maintaining the CDN’s ISP-friendly server selection policy. To address this problem, we propose a cost-aware capacity provisioning algorithm, which dynamically plans the service capacities of a CDN’s server clusters in numerous ISPs, and optimizes its overall operating cost regarding both the energy consumptions and the cross-ISP traffics. By using the workload derived from real-world measurement and applying actual power and bandwidth price parameters, we show with experiments that our approach can significantly reduce a video streaming CDN’s overall operating cost, and avoid frequent server switches effectively. To our best knowledge, this work is the first one that identifies and resolves the inherent conflict between a CDN’s energy efficiency and its ISP-friendly policy.