Artificial Intelligence Generated Content (AIGC) models typically have hundreds of billions of parameters, and developers experience prohibitively long pulling time from a central model registry to their local environments. Peer-to-peer (P2P)-enabled model distribution that pulls models from local peers within a cluster instead of the central model registry is emerging as a promising technique to reduce model pulling time. Nonetheless, such model distribution systems have to handle bursty concurrent pulling tasks. This may occupy the network bandwidth of some peers for a long time, thereby making the peers unable to respond to further pulling tasks. We thus aim to design a lightweight and scalable model distribution system to balance the network resource usage of peers, by proposing learning-driven algorithms to accurately predict network status between peers and implementing the design in real production environments. Specifically, we first propose a lightweight network measurement mechanism that combines active delay probing and passive bandwidth inference with low resource overhead. We also propose a learning-driven task scheduling algorithm based on a structural graph representation with a varied-multi-hop attention mechanism, to predict bursty patterns of concurrent pulling tasks. We then design an asynchronous model training and inference method to enable seamless incremental learning based on the dynamic network status data. We finally implement our system design and the learning-driven algorithm in a Cloud Native Computing Foundation (CNCF) projectDragonflythat has already been publicly released since its version$v2.1.0$. Real experiments in the Ant Group’s production environment show that our system reduces the total completion time by at least 10% and increases the average bandwidth utilization of peers by 20%, compared with mainstream systems and algorithms.

Digital twin is emerging as a key technology to monitor the status of complex industry systems. Valuable insights, such as running statuses and anomalies, can be analyzed from the collected system status timely. Considering that the data updating from each system component (known as a physical object) to its digital twin is performed continuously, timely and accurate streaming analytics based on machine learning models is a key technology to analyze such data efficiently. In this paper, we focus on enabling low-delay yet highly-accurate streaming analytics for digital twin applications in mobile edge computing (MEC) networks. Specifically, we formulate a fundamental optimization problem of digital twin placements and model selections for streaming analytics, with the aim of minimizing both the analytic loss and the processing delay. To this end, we first consider the problem with a single query, for which, we propose an approximation algorithm with provable approximation ratio for a special case, and then devise an efficient algorithm for the original problem with a single query. We then study the online digital twin placement and model selection problem for streaming analytics with multiple queries under real scenarios, where resource demands of arrival queries and resource availability of MEC network are uncertain. We propose an online learning algorithm with a bounded regret to make admission policies. We finally evaluate the performance of the proposed algorithms by extensive simulations. Results show that the weighted sums of the total processing delay and the cumulative loss in the solution delivered by the proposed algorithms outperform their counterparts by 12.5% with a single query and 13.3% with multiple queries, respectively.

Big data analytics for Earth observation has been playing an increasingly important role in supporting environmental monitoring, disaster early warning, and sustainable development through timely analysis of massive multi-source data collected by satellites. With the growing need for such timely Big Data analysis, Space Edge Computing (SEC) networks have been proposed to provide in-orbit analytic services for users worldwide, by integrating computing capability through Low-Earth-Orbit (LEO) satellites. However, the monitoring frequency of LEO satellites over specific target areas remains limited due to orbital constraints, making it difficult to meet the high-frequency data acquisition demands of Big Data analytics. Satellite inclination maneuver is a promising method to cover a wide range of target areas and enhance monitoring frequency by adjusting orbital inclination of satellites. Although such maneuvering enables satellites to timely process datasets, reducing energy wastage caused by inter-satellite data transmission, it consumes propulsion fuel, which is limited and difficult to replenish in a timely manner. Therefore, balancing the energy consumed for data processing and the fuel consumed for maneuvering is essential for efficient and sustainable Big Data analytics in SEC networks. In this paper, we aim to optimize Big Data query evaluation problem with satellite maneuver in an SEC network, focusing on minimizing the weighted sum of energy consumed for processing and fuel consumed for maneuvering. Specifically, we consider that each Earth observation service needs to guarantee a certain level of monitoring frequency, which may not always be satisfied by the original orbital coverage of LEO satellites. In such cases, some satellites will be selected to perform inclination maneuvers for additional monitoring and processing to guarantee the quality of Earth observation services. To this end, we first propose an approximation algorithm with a provable approximation ratio for the offline query evaluation problem, which leverages a customized auxiliary graph to jointly minimize energy and fuel consumption. We then devise an online learning algorithm, referred to as the customized Lipschitz bandit learning algorithm, with a bounded regret for the online Big Data query evaluation problem in an SEC network. We finally evaluate the performance of the proposed algorithms in a real SEC network topology. Experiment results show that the performance of the proposed algorithms achieve 11% lower energy consumption and 10.6% lower fuel consumption than those of their comparison counterparts.

Data stream processing is a functionality of various AI applications to obtain continuous insights from data streams. Serverless edge computing (SEC) is a key solution for implementing data stream processing requests by deploying serverless functions into cloudlets. However, existing data stream processing methods focus more on processing delay, ignoring fault tolerance and complex dependencies among functions, resulting in critical events being missed in the event of any fault and processing inefficiency. Besides, due to the uncertainty of data streams, existing function deployment methods may not be suitable for their newly changed data rates, causing resource waste or shortages. To address these problems, we first propose an optimization framework to enable efficient and fault tolerant function deployment, such that the delay of data stream processing is minimized while meeting its fault tolerant requirements and resource capacity constraints of cloudlets in an SEC network. We then design an online learning algorithm that predicts data rate changes through a multi-timescale machine learning method and proactively adjusts instance locations and numbers to absorb data rate uncertainty. Experimental results in a real test-bed show that our proposed algorithms outperform their counterparts by 13.5% on the average delay and 26.3% on the average fault tolerance.

Serverless computing are invented to free developers of analytic services from the management of cloud resources. Developers only need to submit their code to a serverless edge cloud (SEC) and the cloud platform matches the submitted code to itsserverless functions, enabled by the paradigm of Function as a Service (FaaS). However, serverless functions usually are short-lived with limited resources. In this paper, we aim to address the challenge of how to fully utilize the short-lived serverless functions to enable continuous analysis of the most freshly-generated big data. We first formulate an optimization problem of age-aware big data query evaluation in an SEC network so that theage of datais minimized, where the age of data is the duration between the generation time of the data and the current time. We then propose approximation algorithms for the age-aware big data query evaluation problem of a single query, by proposing a parameterized virtualization technique that smartly handles the large resource demands of big data queries in short-lived and resource-constraint serverless functions. We further consider the scenario where big data queries arrive into the system one-by-one and their resource demands are uncertain. To this end, we devise an online learning algorithm with bounded regret by leveraging a memory repacking mechanism to improve resource utilization, for the problem of online age-aware big data query evaluation. We evaluate the performance of the proposed algorithms through extensive simulations to testify their performances in large scales. We also validate the effectiveness of the proposed mechanism in a real test-bed built on Kubernetes and OpenWhisk with TPC-DS benchmarks. Experimental results show that the proposed algorithms outperform the state-of-the-arts, by reducing the age of data by$8.82\%$on average.

Metaverse, as the cyberspace against the real world, offers various immersive services enabling users to entertain, learn and work. The digital twin (DT) technology acts as a fundamental enabler of Metaverse services by mapping user devices to DTs and timely analyzing the status of user devices. Mobile edge computing (MEC) significantly improves the QoS of DT-empowered Metaverse services because it can deploy DTs in cloudlets closer to user devices. However, each user device often has a complex structure with multiple interdependent subsystems that frequently communicate. Further, the resources in MEC network are highly distributed and limited. Thus, provisioning DT-empowered Metaverse services in MEC networks faces the challenge of efficiently mapping intertwining subsystems in user devices to DTs, maximizing admitted service requests and resource utilization. In this paper, we first formulate throughput maximization problems for DT-empowered Metaverse services in an MEC network, with the aim to maximize the total data rate of service requests of Metaverse services while meeting resource capacity constraints of the MEC network. We then propose an approximation algorithm with a provable approximation ratio for the problem with a given set of service requests if both monitoring and analytical DTs are consolidated into a single edge server. Otherwise, we devise an efficient heuristic for the problem with monitoring and analytical DTs possibly being placed into different edge servers. We also consider a dynamic throughput maximization problem of Metaverse service provisioning for a given monitoring period, in which service requests arrive into the system dynamically without the knowledge of future arrivals, service resource demands, and service delay requirements, for which we devise an online learning algorithm. We finally evaluate the performance of the proposed algorithms by extensive simulations. Simulation results show that the throughputs of the proposed algorithms outperform their comparison counterparts by at least 50 %.

Pretrained Foundation Models (PFMs) are regarded as a promising accelerator for the development of various Artificial Intelligence (AI) applications, and have recently been widely fine-tuned to satisfy users' personalized inference demands. As many users are attracted to PFM-based AI applications, remote data centers are increasingly unable to solely bear the enormous computational demands and meet the delay requirements of inference requests. Mobile edge computing (MEC) offers a viable solution for delivering low-latency inference services by pulling fine-tuned PFMs from the remote data center to cloudlets in the proximity of users. However, a fine-tuned PFM typically comprises billions of model parameters, which are highly resource-intensive, time-consuming, and cost-prohibitive to execute at the edge. To address this, we investigate a novel joint large model selection and pulling problem in MEC networks. The novelty of our study lies in exploring parameter sharing among fine-tuned PFMs based on their common knowledge. Specifically, we first formulate a Non-Linear Integer Programming (NLIP) for the problem to minimize the total delay of implementing all inference requests. We then transform the NLIP into an equivalent Integer Linear Program (ILP) that is much simpler to solve. We further propose a randomized algorithm with a provable approximation ratio for the problem. We also consider the online version of the problem with uncertain request demand, and develop an online learning algorithm with a bounded regret. The crux of the online algorithm is the adoption of the multi-armed bandit technique with restricted context for dynamic admissions of inference requests. We finally conduct extensive experiments based on real datasets. Experimental results demonstrate that our algorithms reduce at least 38% in total delays and average costs, while achieving a 5% improvement in average accuracies.

Federated continual learning (FCL) is emerging as a key technology for time-sensitive applications in highly adaptive environments including autonomous driving and industrial digital twin. Each FCL trains machine learning models using newly-generated datasets as soon as possible, to obtain a highly accurate machine learning model for new event predictions. Theage of data, defined as the time difference between the generation time of a dataset and the current time, is widely adopted as a key criterion to evaluate both timeline and quality of training. In this paper, we study the problem of age-aware FCL in a mobile edge computing (MEC) network. We not only investigate optimization techniques that optimize the data selection and aggregator placement for FCL but also implement a real system as a prototype for age-aware FCL. Specifically, we first propose an approximation algorithm with a provable approximation ratio for the age-aware data selection and aggregator placement problem for FCL with a single request. In real application scenarios, there are usually multiple FCL requests that require to train models, and delays in the MEC network are usually uncertain. We then study the problem of age-aware data selection and aggregator placement problem for FCL with uncertain delays and multiple requests, by devising an online learning algorithm with a bounded regret based on contextual bandits. We finally implement a prototype for FCL in an MEC network, with various heterogeneous user equipments (UEs) and cloudlets with different computing capabilities in the network. Experiment results show that the performance of the proposed algorithms outperform existing studies, by achieving 47% lower age of data and 12% higher model accuracy.

Satellite-terrestrial networks are emerging as the next-generation networking paradigm for Beyond-5 G (B5G) and 6 G networks. Meanwhile, Mobile Edge Computing (MEC) is envisioned as the key technology to provide network services within the proximity of users, by deploying computing resource in ground locations that are close to users. With the fast deployment of Low-Earth-Oribt (LEO) satellites, a new paradigm of MEC is emerging by enabling LEO satellites serving as edge servers in lower orbits that are close to ground users. In this way, the ground users can be further served by LEO satellites in lower orbits instead of conventional high-orbit satellites. Also, since LEO satellites provide shorter paths from users to services, the performance is enhanced compared with ground MEC networks. In this paper, we aim to enable low-latency network services in a Satellite Edge Computing (SEC) network that integrates the MEC and satellite-terrestrial networks. In particular, we consider that each network service is composed of a sequence of Virtualized Network Functions (VNFs), where the traffic of user requests has to be processed by the VNFs in a service chain in the specified order before reaching its destination. To this end, we first formulate a delay-aware service chaining problem in an SEC network to minimize the average delay of implementing a user request, by jointly placing VNFs to LEO satellites in the SEC network and routing the traffic of each user request from its source to destination. We then devise an approximation algorithm with an approximation ratio for the problem in an SEC network with a single user request, by devising a novel concept ofchaining orbitand auxiliary graph construction technique. We also design an online algorithm for the online delay-aware service chaining problem in an SEC network, if user requests arrive into the system without the knowledge of their arrivals and the network delays are uncertain. We finally evaluate the performance of the proposed algorithms using real satellite network topologies, and results show that the proposed algorithms achieve 28.5% lower delay than their counterparts.

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We third deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.

In the coming era of Metaverse, Augmented Reality (AR) has become a key enabler of diverse applications including healthcare, education, smart cities, and entertainments. To provide users with interactive and immersive experience, most AR applications require extremely high responsiveness and ultra-low processing latency. Mobile edge computing (MEC) has demonstrated great potentials in meeting such stringent latency requirements and resource demands of AR applications, by implementing AR requests in edge servers within the proximity of users. In this paper, we investigate the reward maximization problem for AR applications with uncertain resource demands in an MEC network, such that the accumulative reward of services provided for AR applications is maximized, while ensuring that the responsiveness of AR applications is enhanced, subject to network resource capacity. To this end, we formulate an exact solution when the problem size is small, otherwise we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We also develop an online learning algorithm with a bounded regret for the dynamic reward maximization problem without the knowledge of future arrivals of AR requests, by adopting the Multi-Armed Bandits (MAB) technique. Considering maximizing the reward may defer the implementations of some urgent yet low-award requests, we propose a fairness-aware online learning algorithm for the dynamic reward maximization problem, through a data rate prediction mechanism that adopts a multi-task and multi-timescale Long Short-Term Memory (MT2-LSTM) method. Finally, we evaluate the performance of the proposed algorithms for AR applications by building a real test bed. Experimental results show that the proposed algorithms outperform existing studies by improving the award by 13%.

Unmanned Aerial Vehicles (UAVs) have gained increasing attention by both academic and industrial communities, due to their flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this article, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow time, where the flow time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrate that the proposed algorithms outperform their comparison counterparts, by reducing the flow time no less than 19% on average.

With the emergence of various short video platforms such as TikTok and Instagram, coupled with the accelerated pace of people's lives, people are spending more time sharing and watching online videos than ever before, and they gradually turn their attention to short videos with short duration and novel content. Browsing and watching short videos by users with their energy-capacitated devices, such as phones and tablets, have become one of the main ways for users to entertain online. Timely response and high quality online video delivery are of the utmost importance to guarantee the quality of service (QoS) experienced by users. Caching videos at locations close to the video demanders can significantly improve the QoS by reducing the access delay of high quality videos. Together with an explosive growth of mobile devices and great demand for bandwidth, Device-to-Device (D2D) network is emerging as a promising technology to enable ultra-low latency communications, by allowing mobile devices to communicate with each other with or without the involvement of network infrastructures. Caching videos in D2D networks can further reduce video response delays of high quality videos, thereby improving the QoS experienced by users when watching short videos. However, how to cache the most appropriate videos at strategic mobile devices is crucial to satisfy the QoS requirements of users. Specifically, the QoS experienced by users depends on many intertwining factors from both users and the D2D network, such as videos characteristics, various users’ demands for different videos, different communities of users, and energy levels of devices. Motivated by these facts, we investigate the video caching problems in a D2D network. The novelty of our study is to jointly consider the intertwining factors from both users and the D2D network when users access short videos. Specifically, we first formulate an optimization problem of video caching with the objective to minimize the average delay experienced by mobile devices, subject to the cache storage capacity and energy budget of each mobile device. We then propose an approximation algorithm with an approximation ratio for the offline video caching problem. We further devise an online learning algorithm for the online context-aware video caching problem. We finally conduct extensive experiments based on real datasets compared with existing similar studies. Experimental results demonstrate that our algorithms can achieve better average performance with confidence levels. For instance, our algorithms achieve 86% lower average delay experienced by users and 20% average energy consumption of each device, as well as 7% higher average hit ratio and 1.3 times more residual cache storage resource, than their counterparts.

Internet-of-Things (IoT) applications from many industries, such as transportation (maritime, road, rail, air) and fleet management, offshore monitoring, and farming are located in remote areas without cellular connectivity. Such IoT applications continuously generate stream data with hidden values that need to unveiled in real time. Streaming analytics is emerging as a popular type of Big Data analytics to process large volume of stream data for IoT applications in remote regions. Built upon terrestrial-satellite integrated networks, Satellite Edge Computing (SEC) equipped with computing resource in satellites has been envisioning as a key enabling technology to timely analyze stream data of IoT applications in remote regions on the Earth. Considering the dynamically-moving property of satellites in an SEC network, it is vital to optimize the responsiveness of each Big Data analytical query, such that none of such queries takes much longer time to wait for available satellites with sufficient computing resource. Furthermore, the uncertain data volumes of Big Data queries in SEC networks make the resources in satellites fragmented, particularly when the collaboration among satellites is intermittent. Therefore, directly application of existing methods may not guarantee the timelineness of streaming analytics in an SEC network. To address the afore-mentioned unique challenges of streaming analytics in SEC networks, it is urgent to design new algorithms and methods for timely Big Data processing. Specifically, we use theflow timeto capture the responsiveness of streaming analytics in satellite edge computing, which is the time between the generation of the first unit of a dataset and the finish time of the data processing. We consider the flow time minimization problem for query evaluation of Big Data analytics in an SEC network with the aim of minimizing the average flow time of Big Data analytical queries, under an assumption of uncertain volumes of datasets. To this end, we first propose an approximation algorithm with a provable approximation ratio for the offline version of the flow time minimization problem. We then devise an online learning algorithm, referred to the customized Lipschitz bandit learning algorithm, with a bounded regret for the online version of the problem. We finally evaluate the performance of the proposed algorithms in a real SEC network topology. Experiment results show that the performance of the proposed algorithm outperforms its counterparts by at least 13% in terms of flow time.

As data-driven applications proliferate, satellite edge clouds (SEC) consisting of low-earth-orbit (LEO) satellites have demonstrated great potentials to achieve global connectivity. However, communication cost between satellites and ground stations is still high compared to terrestrial mobile networks, and it is difficult to preserve data privacy. Federated learning (FL) thus is emerging as a promising technique to enable distributed machine learning on various FL participants and global model sharing among the participants, which significantly reduces the communication cost without data leakage. As FL advocates training global models using large-scale numbers of distributed participants, it is quite crucial to design incentive mechanisms to inspire participants to contribute their data. In this paper, we study incentive mechanism design issues by formulating a utility-aware FL problem in an SEC network. We then design a double-auction mechanism for the problem with a fixed number of participating satellites for FL jobs. We further devise a repeated double auction mechanism with various numbers of participating satellites for FL jobs. We finally evaluate the performance of the proposed mechanisms against existing methods by simulations, and simulation results show that the proposed mechanisms can achieve higher admission ratio and utility than their counterparts.

Serverless computing is emerging as an enabling technology for elastic and low-cost AI applications in the edge of core networks. It allows AI developers to decompose a complex training and time-sensitive inference task into multiple functions with dependency, and upload the task to a Multi-access Edge Computing platform (MEC) for execution. Serverless computing adopts a popular design principle: the disaggregation of storage and computation, making the functions ‘stateless’. However, most AI applications are ‘stateful’ and rely on an external storage service to manage their states (ephemeral data). This will incur a prohibitively long delay for delay-sensitive AI applications if external services storing the states are far from the serverless functions. Motivated by this critical issue, in this paper we investigate a fundamental problem in serverless computing – the stateful serverless application placement problem, for which, we first propose an efficient heuristic algorithm, and then devise an approximation algorithm with a provable approximation ratio for one of its special cases. We also consider the online version of the problem, and develop an online learning-driven algorithm with a bounded regret. The crux of the online algorithm is the adoption of the multi-armed bandits technique for dynamic admissions of inference requests, under the uncertainty of both data volumes of requests and network delays. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results show that the proposed algorithms outperform their counterparts, reducing at least 32% in the total cost and 27% of the average delay.

Multi-access edge computing (MEC) enables extreme low-latency AI services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. Meanwhile, a 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing for both computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network in a hierarchical service market. For the service caching problem, we first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with a provable approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency loss of the game due to selfishness of network service providers. Considering that the request rate of each service may not be given in advance, we study the service caching problem with the uncertainlity of request rates, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.

With the development of 5G technology, mobile edge computing is emerging as an enabling technique to reduce the response latency of network services by deploying cloudlets at 5G base stations to form mobile edge cloud (MEC) networks. Network service providers now shift their services from remote clouds to cloudlets of MEC networks in the proximity of users. However, the permanent placement of network services into an MEC network is not economic due to limited computing and bandwidth resources imposed on its cloudlets. A smart way is to cache frequently demanded services from remote clouds to cloudlets of the MEC network. In this paper, we study the problem of service caching in an MEC network under a service market with multiple network service providers competing for both computation and bandwidth resources in terms of Virtual Machines (VMs) in the MEC network. We first propose an Integer Linear Program (ILP) solution and a randomized rounding algorithm, for the problem without VM sharing among different network service providers. We then devise a distributed and stable game-theoretical mechanism for the problem with VM sharing among network service providers, with the aim to minimize the social cost of all network service providers, through introducing a novel cost sharing model and a coalition formation game. We also analyze the performance guarantee of the proposed mechanism, Strong Price of Anarchy (SPoA). We third consider the cost- and delay-sensitive service caching problem with temporal VM sharing, and propose a mechanism with provable SPoA. We finally evaluate the performance through extensive simulations and a real world test-bed implementation. Experimental results demonstrate that the proposed algorithms outperform existing approaches by achieving at least$40\%$lower social cost via service caching and resource sharing among different network service providers.

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues – especially when multiple users issue concurrentFL requests. Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.

The age of data (AoD) is identified as one of the most novel and important metrics to measure the quality of big data analytics for Internet-of-Things (IoT) applications. Meanwhile, mobile edge computing (MEC) is envisioned as an enabling technology to minimize the AoD of IoT applications by processing the data in edge servers close to IoT devices. In this paper, we study the AoD minimization problem for IoT big data processing in MEC networks. We first propose an exact solution for the problem by formulating it as an Integer Linear Program (ILP). We then propose an efficient heuristic for the offline AoD minimization problem. We also devise an approximation algorithm with a provable approximation ratio for a special case of the problem, by leveraging the parametric rounding technique. We thirdly develop an online learning algorithm with a bounded regret for the online AoD minimization problem under dynamic arrivals of IoT requests and uncertain network delay assumptions, by adopting the Multi-Armed Bandit (MAB) technique. We finally evaluate the performance of the proposed algorithms by extensive simulations and implementations in a real test-bed. Results show that the proposed algorithms outperform existing approaches by reducing the AoD around 10%.

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a lightweight base station device and thus can act as an aerial base station for users. Unlike most existing studies that assumed that a UAV can serve all users in its communication range, we observe that both computation and communication capabilities of a single lightweight UAV are very limited, due to various constraints on its size, weight, and power supply. Thus, a single UAV can only provide communication services to a limited number of users. We study a novel problem of deploying$K$UAVs in the top of a disaster area such that the sum of the data rates of users served by the UAVs is maximized, subject to that (i) the number of users served by each UAV is no greater than its service capacity; and (ii) the communication network induced by the$K$UAVs is connected. We then propose a$\frac {1-1/e}{\lfloor \sqrt {K} \rfloor }$-approximation algorithm for the problem, improving the current best result of the problem by five times (the best approximation ratio so far is$\frac {1-1/e}{5(\sqrt {K} +1)}$), where$e$is the base of the natural logarithm. We finally evaluate the algorithm performance via simulation experiments. Experimental results show that the proposed algorithm is very promising. Especially, the solution delivered by the proposed algorithm is up to 12% better than those by existing algorithms.

More and more artificial intelligence (AI) applications, such as virtual reality (VR) and video analytics, are rapidly progressing towards enterprise and end-users with the promise of bringing immersive experience. Driven by the desire to improve users’ experience and promote business scenarios, such AI applications have unprecedented requirements for ultra-low latency as well as abundant computing resource in networks. Data centers in the core network can meet these demands by deploying various AI services and providing abundant resources. However, data transmission delay from data centers to end-users is too time-consuming because of traffic congestion in the core network, which compromises the performance of the AI applications. 5G and edge computing are emerging technologies to guarantee the timeliness for the delay-sensitive applications. The delay experienced by AI users can be significantly reduced, by ‘caching’ various services that are initially deployed at data centers to cloudlets in edge networks. Although ubiquitous edge service caching is always preferable for improving user experiences, it is impractical to cache all services from data centers to edge cloudlets, due to often limited caching budget of service providers and resource capacity constraints of cloudlets. Therefore, a service provider has to cautiously decide how many instances of a service can be cached, and where to cache the service instances. In this article, we investigate a fundamental problem ofservice cachingfrom remote data centers to edge cloudlets in a multi-tiered edge cloud network. We first develop two approximation algorithms with approximation ratios to solve the problem for users demanding a single type of service. We then devise an efficient heuristic to solve the problem that users require different types of services. We finally conduct extensive experiments on a real test-bed to evaluate the performance of the proposed algorithms, and experimental results demonstrate that our algorithms can outperform some existing algorithms significantly.

Augmented Reality (AR) has various practical applications in healthcare, education, and entertainment. To provide a fully interactive and immersive experience, AR applications require extremely high responsiveness and ultra-low processing latency. Mobile edge computing (MEC) has shown great potential in meeting such stringent requirements and demands of AR applications by implementing AR requests in edge servers within the close proximity of these applications. In this paper, we investigate the problem of reward maximization for AR applications with uncertain demands in an MEC network, such that the reward of provisioning services for AR applications is maximized and the responsiveness of AR applications is enhanced, subject to both network resource capacity. We devise an exact solution for the problem if the problem size is small, otherwise we develop an efficient approximation algorithm with a provable approximation ratio for the problem. We also devise an online learning algorithm with a bounded regret for the dynamic reward maximization problem without the knowledge of the future arrivals of AR requests, by adopting the technique of Multi-Armed Bandits (MAB). We evaluate the performance of the proposed algorithms through simulations. Experimental results show that the proposed algorithms outperform existing studies by 17 % higher reward.

With the fast development of next-generation networking techniques, a Network Function Virtualization (NFV) market is emerging as a major market that allows network service providers to trade various network services among consumers. Therefore, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players, so they have incentives to participate in activities of the market. In this paper, we first formulate the social welfare maximization problem, with an aim to maximize the total revenue of all players in the NFV market. For the social welfare maximization problem, we design an efficient incentive-compatible mechanism and analyze the existence of a Nash equilibrium of the mechanism. We also consider an online social welfare maximization problem without the knowledge of future request arrivals. We devise an online learning algorithm based on Multi-Armed Bandits (MAB) to allow both customers and network service providers to make decisions with uncertainty of customers' strategy. We evaluate the performance of the proposed mechanisms by both simulations and test-bed implementations, and the results show that the proposed mechanisms obtain at most 23% higher social welfare than existing studies.

Mobile edge computing becomes a promising technology to mitigate the latency of various cloud services. In addition, network function virtualization (NFV) has been shown a great potential in reducing the operational cost of cloud services while enhancing the flexibility of virtual network function deployments, by implementing dedicated hardware network functions as pieces of software in generic servers. Recently, the GPU acceleration has been investigated to speed up flow processing in virtual network functions (VNFs), by leveraging the parallelism of GPUs. VNFs that need accelerations prefer to stay at cloudlets (locations) equipped with GPUs. However, little attention has been paid for the VNF placement that takes into account GPU-affinity in cloudlets of mobile edge clouds. In this paper, we consider the affinity-aware throughput maximization problem in a mobile edge cloud via leveraging the parallelism on GPUs for user requests with VNF requirements. We consider two types of affinities in the VNF placement: Thesoft-affinitythat allows VNFs to be executed by either CPUs or GPUs in cloudlets; and thehard-affinitythat only allows VNFs to be placed to the GPUs of a specified set of cloudlets. We formulate two corresponding VNF placement problems in a mobile edge cloud. Specifically, we first propose an exact solution to the soft-affinity throughput maximization problem by formulating an Integer Linear Program (ILP). We then propose an efficient algorithm for the problem, by proposing a randomized algorithm with a provable approximation ratio for the hard-affinity-aware throughput maximization problem and extending the proposed approximation algorithm to the soft-affinity throughput maximization problem. Furthermore, assuming that user requests arrive into the mobile edge cloud one by one without the knowledge of future arrivals, we devise an online algorithm with a good competitive ratio for this dynamic hard-affinity-aware throughput maximization problem. Finally, we evaluate the performance of the proposed algorithms, through simulations and implementations in a real test-bed. Experimental results show that the performance of the proposed algorithms outperform their existing counterparts and achieve higher throughput.

Conventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., autonomous vehicles) require a much lower response latency and more secure guarantees than those offered by remote clouds today. Mobile edge clouds (MEC) supported by the network function virtualization (NFV) technique have been envisioned as an ideal platform for supporting such IoT applications. Specifically, MECs enable to handle IoT applications in edge networks to shorten network latency, and NFV enables agile and low-cost network functions to run in low-cost commodity servers as virtual machines (VMs). One fundamental problem for the provisioning of IoT applications in an NFV-enabled MEC is where to place virtualized network functions (VNFs) for IoT applications in the MEC, such that the operational cost of provisioning IoT applications is minimized. In this paper, we first address this fundamental problem, by considering a special case of the IoT application placement problem, where the IoT application and VNFs of each service request are consolidated into a single location (gateway or cloudlet), for which we propose an exact solution and an approximation algorithm with a provable approximation ratio. We then develop a heuristic algorithm that controls the resource violation ratios of edge clouds in the network. For the IoT application placement problem for IoT applications where their VNFs can be placed to multiple locations, we propose an efficient heuristic that jointly places the IoT application and its VNFs. We finally study the performance of the proposed algorithms by simulations and implementations in a real test-bed, Experimental results show that the performance of the proposed algorithms outperform their counterparts by at least 10 percent.

With increasing focus on Artificial Intelligence (AI) applications, Deep Neural Networks (DNNs) have been successfully used in a number of application areas. As the number of layers and neurons in DNNs increases rapidly, significant computational resources are needed to execute a learned DNN model. This ever-increasing resource demand of DNNs is currently met by large-scale data centers with state-of-the-art GPUs. However, increasing availability of mobile edge computing and 5G technologies provide new possibilities for DNN-driven AI applications, especially where these application make use of data sets that are distributed in different locations. One fundamental process of a DNN-driven application in mobile edge clouds is the adoption of “inferencing” - the process of executing a pre-trained DNN based on newly generated image and video data from mobile devices. We investigate offloading DNN inference requests in a 5G-enabled mobile edge cloud (MEC), with the aim to admit as many inference requests as possible. We propose exact and approximate solutions to the problem of inference offloading in MECs. We also consider dynamic task offloading for inference requests, and devise an online algorithm that can be adapted in real time. The proposed algorithms are evaluated through large-scale simulations and using a real world test-bed implementation. The experimental results demonstrate that the empirical performance of the proposed algorithms outperform their theoretical counterparts and other similar heuristics reported in literature.

The rise of big data brings extraordinary benefits and opportunities to businesses and governments. Enterprise users can analyze their consumers' data and infer the business value obtained, such as purchasing goods correlations, customer preferences, and hidden patterns. Meanwhile, with the emerge of big data processing frameworks, such as Hadoop and Tensor-flow, more and more mobile users are embracing big data analytics by issuing queries to analyze their data. In this paper, we investigate the problem of Quality-of-Service (QoS) aware query evaluation for big data analytics in a mobile edge cloud to maximize the system throughput while minimizing the query evaluation time of each admitted query, by exploring the materialization of intermediate query results. We consider dynamic big-data query evaluations where user queries arrive one by one without the knowledge of future arrivals, and the system needs to respond to each query by accepting or rejecting the query immediately. We propose an online algorithm for query admissions within a finite time horizon, the proposed algorithm can intelligently determine whether some immediate results during a query evaluation need to be materialized for later use of other queries, by making use of the Reinforcement Learning (RL) method with predictions. We finally investigate the performance of the proposed algorithm by simulations, and results show that the performance of the proposed algorithm is promising, by achieving a higher system throughput while reducing the average evaluation cost per query by from 20% to 52% compared to the comparison benchmarks.

Mobile edge computing (MEC) is emerging as an enabling technology of low-latency network services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. In MEC networks, telcooperators can place their services to cloudlets, such that the service accessing delay of users is minimized. In this paper, we investigate a fundamental problem of caching services that are originally deployed in remote clouds to cloudlets in an MEC network within the proximity of users. Specifically, we focus on the service caching problem in a two-tiered MEC network with both remote clouds and cloudlets that are close to users, in which multiple network service providers competing computing and bandwidth resources. This setting is significantly different from existing studies that focused on offloading user tasks from mobile devices to cloudlets in MEC networks that typically do not consider a service market with multiple network service providers. For the service caching problem in a two-tiered MEC network, we propose a novel approximation-restricted framework that guarantees the stableness of the service market. Under the proposed framework, an approximation algorithm with an approximation ratio for the problem with non-selfish players and an efficient, stable Stackelberg congestion game with selfish players have been proposed. We also analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency of the proposed game degrades due to selfish behavior of network service providers. We finally evaluate the performance of our mechanism on both simulated environments and a real test-bed. Results show that the performance of our proposed mechanism is promising.

Mobile edge computing (MEC) is envisioned as an enabling technology for extreme low-latency services in the next generation 5G access networks. In a 5G-enabled MEC, computing resources are attached to base stations. In this way, network service providers can cache their services from remote data centers to base stations in the MEC to serve user tasks in their close proximity, thereby reducing the service latency. However, mobile users usually have various dynamic hidden features, such as their locations, user group tags, and mobility patterns. Such hidden features normally lead to uncertainties of the 5G-enabled MEC, such as user demand and processing delay. This poses significant challenges for the service caching and task offloading in a 5G-enabled MEC. In this paper, we investigate the problem of dynamic service caching and task offloading in a 5G-enabled MEC with user demand and processing delay uncertainties. We first propose an online learning algorithm for the problem with given user demands by utilizing the technique of Multi-Armed Bandits (MAB), and theoretically analyze the regret bound of the algorithm. We also propose a novel architecture of Generative Adversarial Networks (GAN) to accurately predict the user demands based on small samples of hidden features of mobile users. Based on the proposed GAN model, we then devise an efficient heuristic for the problem with the uncertainties of both user demand and processing delay. We finally evaluate the performance of the proposed algorithms by simulations based on a realistic dataset of user data. Experiment results show that the performance of the proposed algorithms outperform existing algorithms by around 15%.

With the development of 5G technology, mobile edge computing is emerging as an enabling technique to promote Quality of Service (QoS) of network services. In particular, the response latency of network services can be significantly reduced by deploying cloudlets at 5G base stations in mobile edge clouds. Network service providers that usually deploy their services in remote clouds now shift their services from the remote clouds to the network edge in the proximity of users. However, the permanent placement of their services into edge clouds may not be economic, since computing and bandwidth resources in edge clouds are limited and relatively expensive. A smart way is to cache the services that are frequently requested by mobile users in edge clouds. In this paper, we study the problem of service caching in mobile edge network under a mobile service market with multiple network service providers completing for both computation and bandwidth resources of the edge cloud. We propose an Integer Linear Program (ILP) and a randomized rounding algorithm, for the problem without resource sharing among the network service providers. We also devise a distributed and stable game-theoretical mechanism for the problem with resource sharing among the network service providers, with the objective to minimize the social cost of all network service providers, by introducing a novel cost sharing model and a coalition formation game. We analyze the performance of the mechanism by showing a good guaranteed gap between the solution obtained and the optimal one, i.e., Strong Price of Anarchy (SPoA). We finally evaluate the performance of our algorithms by extensive simulations, and the obtained results show that the social cost of all players can be reduced significantly via allowing cooperation among network service providers in service caching.

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference.

Machine learning (ML) has been penetrating into our daily life by facilitating many daily applications, e.g., self-driving, cloud gaming, product fault detection and drones. Meanwhile, there is an emerging trend that adopts ML methods into network optimization problems, such as flow classification, traffic engineering, routing, and etc. Conventional ML methods need careful training for a specific application of a given network structure, and the trained model normally cannot be applied to other applications and network structures. In this paper, we aim to design adaptive ML methods for network optimization problems, with the trained models having the ability of being deployed to any similar problems. In particular, we consider the virtualized network function (VNF) provisioning problem as our target optimization problem. We first propose a deep Q-learning-based optimization framework for VNF provisioning in a mobile edge network with network capacity constraints, by devising an adaptive graph feature embedding method. We then propose a series of deep Q-learning based learning algorithms for the problems of service chaining and the throughput maximization, based on the proposed learning-based optimization framework. We also propose a novel design of master-slave dual neural network that enables the decisions on both cloudlet selections and routing path finding. To stabilize and accelerate the convergence of the proposed methods, we devise a novel environment generation and termination strategy and a new structure for the replay buffer. We also evaluate the performance of the proposed framework and algorithms by extensive simulations. Results show that the proposed algorithms outperform existing methods by around 12%, and the trained model in a network can be directly adapted to other network structures and settings.

Telecommunication networks are undergoing a disruptive transition toward distributed mobile edge networks with virtualized network functions (VNFs) [e.g., firewalls, intrusion detection systems (IDSs), and transcoders] within the proximity of users. This transition will enable network services, especially Internet-of-Things (IoT) applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this article, we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into integer linear programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a nontrivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on reinforcement learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real testbed. The experimental results demonstrate that the proposed algorithms are promising.

Mobile edge computing and network function virtualization (NFV) paradigms enable new flexibility and possibilities of the deployment of extreme low-latency services for Internet-of-Things (IoT) applications within the proximity of their users. However, this poses great challenges to find optimal placements of virtualized network functions (VNFs) for data processing requests of IoT applications in a multi-tier cloud network, which consists of many small- or medium-scale servers, clusters, or cloudlets deployed within the proximity of IoT nodes and a few large-scale remote data centers with abundant computing and storage resources. In particular, it is challenging to jointly consider VNF instance placement and routing traffic path planning for user requests, as they are not only delay sensitive but also resource hungry. In this article, we consider admissions of NFV-enabled requests of IoT applications in a multi-tier cloud network, where users request network services by issuing service requests with service chain requirements, and the service chain enforces the data traffic of the request to pass through the VNFs in the chain one by one until it reaches its destination. To this end, we first formulate the throughput maximization problem with the aim to maximize the system throughput. We then propose an integer linear program solution if the problem size is small; otherwise, we devise an efficient heuristic that jointly takes into account VNF placements to both cloudlets and data centers and routing path finding for each request. For a special case of the problem with a set of service chains, we propose an approximation algorithm with a provable approximation ratio. Next, we also devise efficient learning-based heuristics for VNF provisioning for IoT applications by incorporating the mobility and energy conservation features of IoT devices. We finally evaluate the performance of the proposed algorithms by simulations. The simulation results show that the performance of the proposed algorithms is promising.

Stringent delay requirements of many mobile applications have led to the development of mobile edge clouds, to offer low latency network services at the network edges. Most conventional network services are implemented via hardware-based network functions, including firewalls and load balancers, to guarantee service security and performance. However, implementing hardware-based network functions usually incurs both a high capital expenditure (CAPEX) and operating expenditure (OPEX). Network Function Virtualization (NFV) exhibits a potential to reduce CAPEX and OPEX significantly, by deploying software-based network functions in virtual machines (VMs) on edge-clouds. We consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request has both service function chain and end-to-end delay requirements. Specifically, each multicast request requires chaining of a sequence of network functions (referred to as a service function chain) from a source to a set of destinations within specified end-to-end delay requirements. We devise an approximation algorithm with a provable approximation ratio for a single multicast request admission if its delay requirement is negligible; otherwise, we propose an efficient heuristic. Furthermore, we also consider admissions of a given set of the delay-aware NFV-enabled multicast requests, for which we devise an efficient heuristic such that the system throughput is maximized, while the implementation cost of admitted requests is minimized. We finally evaluate the performance of the proposed algorithms in a real test-bed, and experimental results show that our algorithms outperform other similar approaches reported in literature.

Driven by stringent delay requirements of mobile applications, the mobile edge cloud has emerged as a major platform to offer low latency network services from the edge of networks. Most conventional network services are implemented via hardware-based network functions, such as firewalls and load balancers, to guarantee service security and performance. However, implementing such hardware-based network functions incurs high purchase and maintenance costs. Network function virtualization (NFV) as a promising technology exhibits great potential to reduce the purchase and maintenance costs by implementing network functions as software in virtual machines (VMs). In this paper, we consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request requires to process its traffic in a specified sequence of network functions (referred to as a service chain) before the traffic from a source to a set of destinations. We devise a provable approximation algorithm with an approximation ratio for the problem if requests do not have delay requirements; otherwise, we propose an efficient heuristic for it. We also evaluate the performance of the proposed algorithms against the state-of-the-art NFV-enabled multicasting algorithms, and results show that our algorithms outperform their counterparts.

Vehicular Content Centric Network (VCCN) is proposed to cope with mobility and intermittent connectivity issues of vehicular ad hoc networks by enabling the Content Centric Network (CCN) model in vehicular networks. The ubiquitous in-network caching of VCCN allows nodes to cache contents frequently accessed data items, improving the hit ratio of content retrieval and reducing the data access delay. Furthermore, it can significantly mitigate bandwidth pressure. Therefore, it is crucial to cache more popular contents at various caching nodes. In this paper, we propose a novel cache replacement scheme named Popularity-based Content Caching (PopCC), which incorporates the future popularity of contents into our decision making. We adopt Hidden Markov Model (HMM) to predict the content popularity based on the inherent characters of the received interests, request ratio, request frequency and content priority. To evaluate the performance of our proposed scheme PopCC, we compare it with some state-of-the-art schemes in terms of cache hit, average access delay, average hop count and average storage usage. Simulations demonstrate that the proposed scheme possesses a better performance.

Enterprise users at different geographic locations generate large-volume data that is stored at different geographic datacenters. These users may also perform big data analytics on the stored data to identify valuable information in order to make strategic decisions. However, it is well known that performing big data analytics on data in geographical-located datacenters usually is time-consuming and costly. In some delay-sensitive applications, the query result may become useless if answering a query takes too long time. Instead, sometimes users may only be interested in timely approximate rather than exact query results. When such approximate query evaluation is the case, applications must sacrifice timeliness to get more accurate evaluation results or tolerate evaluation result with a guaranteed error bound obtained from analyzing the samples of the data to meet their stringent timeline. In this paper, we study quality-of-service (QoS)-aware data replication and placement for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on the problems of placing data samples of the source data at some strategic datacenters to meet stringent query delay requirements of users, by exploring a non-trivial trade-off between the cost of query evaluation and the error bound of the evaluation result. We first propose an approximation algorithm with a provable approximation ratio for a single approximate query. We then develop an efficient heuristic algorithm for evaluating a set of approximate queries with the aim to minimize the evaluation cost while meeting the delay requirements of these queries. We finally demonstrate the effectiveness and efficiency of the proposed algorithms through both experimental simulations and implementations in a real test-bed, real datasets are employed. Experimental results show that the proposed algorithms are promising.

Cloud computing built on virtualization technologies promises provisioning elastic computing and bandwidth resource services for enterprises that outsource their IT services as virtual networks. To share the cloud resources efficiently among different enterprise IT services, embedding their virtual networks into a distributed cloud that consists of multiple data centers, poses great challenges. Motivated by the fact that most virtual networks operate on long-term basis and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem of embedding as many virtual networks as possible to a distributed cloud such that the revenue collected by the cloud service provider is maximized, while the service level agreements (SLAs) between enterprises and the cloud service provider are met. We first propose an efficient embedding algorithm for the problem, by incorporating a novel embedding metric that accurately models the dynamic workloads on both data centers and inter-data center links, provided that the periodic resource demands of each virtual network are given and all virtual networks have identical resource demand periods. We then show how to extend this algorithm for the problem when different virtual networks may have different resource demand periods. Furthermore, we also develop a prediction mechanism to predict the periodic resource demands of each virtual network if its resource demands are not given in advance. We finally evaluate the performance of the proposed algorithms through experimental simulation based on both synthetic and real network topologies. Experimental results demonstrate that the proposed algorithms outperform existing algorithms from 10 to 31 percent in terms of performance improvement.

Enterprise users at different geographic locations generate large-volume data and store their data at different geographic datacenters. These users may also issue ad hoc queries of big data analytics on the stored data to identify valuable information in order to help them make strategic decisions. However, it is well known that querying such large-volume big data usually is time-consuming and costly. Sometimes, users are only interested in timely approximate rather than exact query results. When this approximation is the case, applications must sacrifice either timeliness or accuracy by allowing either the latency of delivering more accurate results or the accuracy error of delivered results based on the samples of the data, rather than the entire set of data itself. In this paper, we study the QoSaware data replications and placements for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on placing the samples of the source data with certain error bounds at some strategic datacenters to meet users' stringent query response time. We propose an efficient algorithm for evaluating a set of big data analytic queries with the aim to minimize the evaluation cost of the queries while meeting their response time requirements. We demonstrate the effectiveness of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.

With more and more businesses and organizations outsourcing their IT services to distributed clouds for cost savings, historical and operational data generated by the services have been growing exponentially. The generated data that are referred to as big data, stored at different geographic datacenters, now become an invaluable asset to these businesses and organizations, as they can make use of the data through analysis to identify business advantages and make strategic decisions. Big data analytics thus has been emerged as a main research topic in cloud computing. To efficiently evaluate a big data analytic query in a distributed cloud consisting of multiple datacenters at different geographic locations interconnected by the Internet, it poses great challenges: (i) the source data of the query typically are located at different datacenters; and (ii) the resource demands of the query may be beyond the supplies of any single datacenter at that moment. In this paper, we formulate an online query evaluation problem for big data analytic queries in distributed clouds, with an objective to maximize the query acceptance ratio while minimizing the accumulative query evaluation cost, for which we first propose a novel metric to model the usages of different resources in the distributed cloud, by incorporating the capacities and workloads of different datacenters and links, as well as resource demands of different queries. We then devise efficient online algorithms for query evaluations under both unsplittable and splittable source data assumptions. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are promising, and outperform other heuristics at 95% confidence intervals.

With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.

Distributed clouds, consisting of multiple data centers located at different geographical locations, provide a plethora of services to users. They however consume enormous amounts of electricity to power their data centers. The electricity bill is almost 30%-50% of their operational costs. Minimizing the electricity cost of distributed clouds thus is crucial to reduce the operational cost of their cloud service providers. In this paper, we study the problem of minimizing the electricity cost of a distributed cloud, by exploring the heterogeneities of cloud resources and user demands, and time-varying electricity prices, for which we first propose a two-stage optimization framework: dispatching user task requests to different data centers by incorporating the resource demands of the task requests, the workload, and the electricity price in each data center, and energy consumption profiles of different servers in each data center; followed by further energy optimization within each data center through consolidating Virtual Machines (VMs) to different servers to improve the resource utilization ratio. One critical constraint on such task dispatch and VM consolidation is to meet various user Service Level Agreements (SLAs), which include average task scheduling delays and resource demand violation limitations. Under the proposed framework, we then devise efficient scheduling algorithms for task dispatching and VM consolidations, while keeping both the average scheduling delay and resource demand violation limitation of each admitted task met. We finally evaluate the performance of the proposed algorithms through experimental simulations, using real data sets - the real electricity prices and task traces. Experimental simulation results demonstrate that the proposed algorithms are promising.

Cloud computing built on virtualization technologies promises provisioning elastic computing and communication resources to enterprise users. To share cloud resources efficiently, embedding virtual networks of different users to a distributed cloud consisting of multiple data centers (a substrate network) poses great challenges. Motivated by the fact that most enterprise virtual networks usually operate on long-term basics and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem by embedding as many virtual networks as possible to a substrate network such that the revenue of the service provider of the substrate network is maximized, while meeting various Service Level Agreements (SLAs) between enterprise users and the cloud service provider. For this problem, we propose an efficient embedding algorithm by exploring periodic resource demands of virtual networks, and employing a novel embedding metric that models the workloads on both substrate nodes and communication links if the periodic resource demands of virtual networks are given; otherwise, we propose a prediction model to predict the periodic resource demands of these virtual networks based on their historic resource demands. We also evaluate the performance of the proposed algorithms by experimental simulation. Experimental results demonstrate that the proposed algorithms outperform existing algorithms, improving the revenue from 10% to 31%.

In mobile cloud computing (MCC) paradigm, cloud service providers not only offer powerful cloud data centers but also provide small-scale cloudlets in some strategic locations for mobile users to access their rich resources. Due to the flexibility and locality of cloudlets, most requests of mobile users can be processed locally. However, the cloudlets usually have limited resources and processing abilities, which implies that they may not be capable to process every incoming request. Instead, some resource-intensive requests need to be sent to remote data centers for processing and such a processing is transparent to users. In this paper, we address the online request admission issue in a cloudlet with an objective to maximize the system throughput, for which we first propose a novel admission cost model to model critical resource consumptions. We then devise efficient control algorithms for online request admissions. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results indicate that the proposed algorithms are promising and outperform other heuristics.