Opportunity networks provide a chance to offload the tremendous cellular traffic generated by sharing popular content on mobile networks. Analyzing the content spread characteristics in real opportunity environments can discover important clues for traffic offloading decision making. However, relevant published work is very limited since it is not easy to collect data from real environments. In this study, we elaborate the analysis on the dataset collected from a real opportunity environment formed by the users of Xender, which is one of the leading mobile applications for content sharing. To discover content transmission characteristics, scale, speed, and type analyses are implemented on the dataset. The analysis results show that file transmission has obvious periodicity, that only a very small fraction of files spread widely, and that application files have much higher probability to be popular than other files. We also propose a solution to maximize file spread scales, which is very helpful for forecasting popular files. The experimental results verify the effectiveness and usefulness of our solution.

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and actuators are connected to the network. As one of the most sophisticated IoE applications, real-time video analytics is promising to significantly improve public safety, business intelligence, and healthcare & life science, among others. However, cloud-centric video analytics requires that all video data must be preloaded to a centralized cluster or the cloud, which suffers from high response latency and high cost of data transmission, given the scale of zettabytes of video data generated by IoE devices. Moreover, video data is rarely shared among multiple stakeholders due to various concerns, which restricts the practical deployment of video analytics that takes advantages of many data sources to make smart decisions. Furthermore, there is no efficient programming interface for developers and users to easily program and deploy IoE applications across geographically distributed computation resources. In this paper, we present a new computing framework, Firework , which facilitates distributed data processing and sharing for IoE applications via a virtual shared data view and service composition. We designed an easy-to-use programming interface for Firework to allow developers to program on Firework . This paper describes the system design, implementation, and programming interface of Firework. The experimental results of a video analytics application demonstrate that Firework reduces up to 19.52 percent of response latency and at least 72.77 percent of network bandwidth cost, compared to a cloud-centric solution.

Now we are entering the era of the Internet of Everything (IoE) moreover, billions of sensors and actuators are connected to the network.

This SpringerBrief introduces Edge computing, and several key techniques that enable the practical application of Edge computing techniques. Case studies that demonstrate the benefits of Edge computing, ranging from smart home, public safety, to medical services are featured.

In this chapter, we will summarize the challenges in Edge Computing and bring forward some potential solutions and opportunities worth further research, including programmability, naming, data abstraction, service management, privacy and security and optimization metrics.

In the Internet of Everything era, billions of geographically distributed things will connect to the Internet and generate hundreds of zettabytes of data per year. Pushing that data to the cloud requires tremendous network bandwidth cost and latency. This is too onerous for some latency-sensitive applications, such as vehicle tracking using city-wide cameras. One application currently limited by such obstacles is the America's Missing Broadcast Emergency Response (AMBER) Alert system-but edge computing could transform this system's capabilities. Edge computing is a new computing paradigm that greatly diminishes data transmission and response latency by processing data at the proximity of data sources. However, most vision-based analytics are compute-intensive, and an edge device might be overwhelmed given tens of frames each second for real-time analysis. Also, the system needs a customized and flexible interface to implement efficient tracking strategies. To meet these needs, here we extend a big data processing framework, called Firework, to support collaboration between multiple edge devices and customizable task-scheduling strategies. Based on this extended version of Firework, we implement the AMBER alert assistant (A3), which efficiently tracks and locates a vehicle by analyzing city cameras' data in real time. We also propose two kinds of customized task-scheduling algorithms for vehicle tracking in A3. Comprehensive evaluation results show that A3 achieves real-time video analytics by collaborating among multiple edge devices; and the proposed location-direction-related diffusion strategy effectively controls the searching area for vehicle tracking by smartly selecting candidate cameras.

In this chapter, we will introduce a few essential tools and software enabling edge computing. What the tools and software appear in this chapter is a tip of the thousands of open-sourced or production-ready tools and software available in the community. Thus, this chapter serves as a high-level literature review of representatives of the most popular tools and software.

With the proliferation of Internet of things (IoT) and the burgeoning of 4G/5G network,we have seen the dawning of the IoE (Internet of everything) era,where there will be a huge volume of data generated by things that are immersed in our daily life,and hundreds of applications will be deployed at the edge to consume these data.Cloud computing as the de facto centralized big data processing platform is not efficient enough to support these applications emerging in IoE era,i.e.,1)the computing capacity available in the centralized cloud cannot keep up with the explosive growing computational needs of massive data generated at the edge of the network;2) longer user-perceived latency caused by the data movement between the edge and the cloud;3) privacy and security concerns from data owners in the edge;4) energy constraints of edge devices.These issues in the centralized big data processing era have pushed the horizon of a new computing paradigm,edge computing,which calls for processing the data at the edge of the network.Leveraging the power of cloud computing,edge computing has the potential to address the limitation of computing capability,the concerns of response time requirement,bandwidth cost saving,data safety and privacy,as well as battery life constraint."Edge" in edge computing is defined as any computing and network resources along the path between data sources and cloud data centers.In this paper,we introduce the definition of edge computing,followed by several case studies,ranging from cloud offloading to smart home and city,as well as collaborative edge to materialize the concept of edge computing.Finally,we present several challenges and opportunities in the field of edge computing,and hope this paper will gain attention from the community and inspire more research in this direction.

We present LAVEA, a system built for edge computing, which offloads computation tasks between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-edge collaboration. Our results reveal that the client-edge configuration has task speedup against local or client-cloud configurations.

AMBER alert systems are inefficient since object searching heavily relies on reports of witnesses, who might miss alerts and cannot search enough areas of city. Using automatic license plate recognition (ALPR) technique, city-wide video surveillance is of great improvement for vehicle searching. However, analyzing huge amount of video data in the cloud leads to vast cost of data transmission and high response latency. Edge computing as an emerging computing paradigm can significantly reduce the cost of data transmission and response latency for latency-sensitive applications due to the data processing at the proximity of data sources. In this poster, we propose an enhanced AMBER alert system using collaborative edges, called AMBER Alert Assistant (A3 in short), which can search the suspect vehicle by analyzing static and mobile cameras' data in real time fashion. We propose location-direction-related diffusion that effectively optimizes the searching area for vehicle searching. The evaluation results show that real-time video analytics can be achieved by collaboratively leveraging multiple edge nodes.

Data protection is the process of backing up data in case of a data loss event. It is one of the most critical routine activities for every organization. Detecting abnormal backup jobs is important to prevent data protection failures and ensure the service quality. Given the large scale backup endpoints and the variety of backup jobs, from a backup-as-a-service provider viewpoint, we need a scalable and flexible outlier detection method that can model a huge number of objects and well capture their diverse patterns. In this paper, we introduce H 2 O, a novel hybrid and hierarchical method to detect outliers from millions of backup jobs for large scale data protection. Our method automatically selects an ensemble of outlier detection models for each multivariate time series composed by the backup metrics collected for each backup endpoint by learning their exhibited characteristics. Interactions among multiple variables are considered to better detect true outliers and reduce false positives. In particular, a new seasonal-trend decomposition based outlier detection method is developed, considering the interactions among variables in the form of common trends, which is robust to the presence of outliers in the training data. The model selection process is hierarchical, following a global to local fashion. The final outlier is determined through an ensemble learning by multiple models. Built on top of Apache Spark, H2O has been deployed to detect outliers in a large and complex data protection environment with more than 600,000 backup endpoints and 3 million daily backup jobs. To the best of our knowledge, this is the first work that selects and constructs large scale outlier detection models for multivariate time series on Big Data platforms.

Cloud computing, arguably, has become the de facto computing platform for the big data processing by researchers and practitioners for the last decade, and enabled different stakeholders to discover valuable information from large scale data. At the same time, in the decade, we have witnessed the fast growing deployment of billions of sensors and actuators in multiple applications domains, such as transportation, manufacturing, connected/wearable health care, smart city and so on, stimulating the emerging of Edge Computing (a.k.a., fog computing, cloudlet). However, data, as the core of both cloud computing and edge computing, is still owned by each stakeholder and rarely shared due to privacy concern and formidable cost of data transportation, which significantly limits Internet of Things (IoT) applications that need data input from multiple stakeholders (e.g., video analytics collects data from cameras owned by police department, transportation department, retailer stores, etc.).In this paper, we envision that in the era of IoT the demand of distributed big data sharing and processing applications will dramatically increase since the data producing and consuming are pushed to the edge of the network. Data processing in collaborative edge environment needs to fuse data owned by multiple stakeholders, while keeping the computation within stakeholders' data facilities. To attack this challenge, we propose a new computing paradigm, Firework, which is designed for big data processing in collaborative edge environment (CEE). Firework fuses geographically distributed data by creating virtual shared data views that are exposed to end users via predefined interfaces by data owners. The interfaces are provided in the form of a set of datasets and a set of functions, where the functions are privacy preserved and bound to the datasets. Firework targets to share data while ensuring data privacy and integrity for stakeholders. By pushing the data processing as close as to data sources, Firework also aims to avoid data movement from the edge of the network to the cloud and improve the response latency.

The need for real-time and large-scale data processing has led to the development of frameworks for distributed stream processing in the cloud. To provide fast, scalable, and fault tolerant stream processing, recent Distributed Stream Processing Systems (DSPS) treat streaming workloads as a series of batch jobs, instead of a series of records. Batch-based stream processing systems could process data at high rate but lead to large end-to-end latency. In this paper we concentrate on minimizing the end-to-end latency of batched streaming system by leveraging adaptive batch sizing and execution parallelism tuning. We propose, DyBBS, a heuristic algorithm integrated with isotonic regression to automatically learn and adjust batch size and execution parallelism according to workloads and operating conditions. Our approach does not require workload specific knowledge. The experimental results show that our algorithm significantly reduces end-to-end latency compared to state-of-the-art : i) for Reduce workload, the latency can be reduced by 34.97% and 48.02% for sinusoidal and Markov chain data input rates, respectively; and ii) for Join workload, the latency reductions are 63.28% and 67.51% for sinusoidal and Markov chain data input rates, respectively.

Power loss from an uninterruptible power supply can account for 15 percent of a datacenter's energy. A rack-level power model that relates IT workload and its power dissipation allows optimized workload placement that can save a datacenter roughly $1.4 million in annual energy costs.

The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.

With the advent of cloud computing and online services, large enterprises rely heavily on their data centers to serve end users. Among different server components, hard disk drives are known to contribute significantly to server failures. Disk failures as well as their impact on the performance of storage systems and operating costs are becoming an increasingly important concern for data center designers and operators. However, there is very little understanding on the characteristics of disk failures in data centers. Effective disk failure management and data recovery also requires a deep understanding of the nature of disk failures. In this paper, we present a systematic approach to provide a holistic and insightful view of disk failures. We study a large-scale storage system from a production data center. We categorize disk failures based on their distinctive manifestations and properties. Then we characterize the degradation of disk errors to failures by deriving the degradation signatures for each failure category. The influence of disk health attributes on failure degradation is also quantified. We discuss leveraging the derived degradation signatures to forecast disk failures even in their early stages. To the best of our knowledge, this is the first work that shows how to discover the categories of disk failures and characterize their degradation processes on a production data center.

The majority of large-scale data intensive applications executed by data centers are based on MapReduce or its open-source implementation, Hadoop. Such applications are executed on large clusters requiring large amounts of energy, making the energy costs a considerable fraction of the data center's overall costs. Therefore minimizing the energy consumption when executing each MapReduce job is a critical concern for data centers. In this paper, we propose a framework for improving the energy efficiency of MapReduce applications, while satisfying the service level agreement (SLA). We first model the problem of energy-aware scheduling of a single MapReduce job as an Integer Program. We then propose two heuristic algorithms, called energy-aware MapReduce scheduling algorithms (EMRSA-I and EMRSA-II), that find the assignments of map and reduce tasks to the machine slots in orderto minimize the energy consumed when executing the application. We perform extensive experiments on a Hadoop cluster to determine the energy consumption and execution time for several workloads from the HiBench benchmark suite including TeraSort, PageRank, and K-means clustering, and then use this data in an extensive simulation study to analyze the performance of the proposed algorithms. The results show that EMRSA-I and EMRSA-II are able to find near optimal job schedules consuming approximately 40 percent less energy on average than the schedules obtained by a common practice scheduler that minimizes the makespan.

Energy efficiency has become a very important concern for enterprise data centers due to the significant cost on electricity. To improve the energy efficiency of data centers, researchers and practitioners have proposed enormous work to reduce the energy consumption of data centers. Most of previous work focus on reducing the energy consumption of IT equipment; however, the power losses caused by uninterruptible power supply (UPS) is not considered, which could account for 15% of the total energy cost of a data center. Recent trend shows that rack level UPSes are getting popular. In this work, we focus on power minimization for both IT equipment and power losses of rack level UPS in an enterprise data center. We propose a rack level power model that builds a direct relationship between IT workload and its power dissipation. Based on this rack level power model, we formulate a mathematical formula for the workload placement optimization problem. The experimental results show that the rack level power model precisely matches the measured power, and the error rate is within ±2.5%. The simulation also indicates up to 5.2% power saving compared to uniform workload allocation, which means $1.425 millions/yr energy cost savings for a 76 megawatts data center with PUE 1.7.

Cloud computing has become an attractive platform, offering on-demand computing resources and storage capacity for both personal and commercial use. However, the data centers hosting these clouds use a staggering amount of energy, making energy consumption a major expense. For both homogeneous and heterogeneous clouds, energy consumption varies significantly, owing to the heterogeneity of hardware and software. Therefore, to reduce a data center's energy consumption, our approach leverages knowledge of the power consumption behavior of the underlying hardware and the characteristics of workloads in order to increase their overall energy efficiency. Our ultimate goal is to provide a scheduling mechanism that allows cloud providers to reduce the energy consumption of their data centers without needing to replace the underlying hardware, and to do so seamlessly, without impacting clients' performance requirements.In this paper, we introduce RESCUE, an energy-aware scheduler for heterogeneous cloud environments. RESCUE ranks nodes within the cloud based on their application-specific energy efficiency (ASEE), which implies the correlation between the various hardware and software and the energy efficiency. According to ASEE, RESCUE can assign the whole workload to the most energy-efficient machine while keeping the same performance. We evaluated RESCUE on a private cloud using three benchmarks: BS Seeker, Matrix Stressmark, and TPC-W. We implemented RESCUE with Eucalyptus and compared the energy consumed using RESCUE as the scheduling policy to the other Eucalyptus built-in policies: Round Robin and Greedy. The results show that with a nonaggressive control policy where the machine remains in active mode (ACPI CO state), RESCUE reduces the overall energy consumption by up to 16.7% on average for BS Seeker and Matrix Stressmark, while for TPC-W, RESCUE saves up to 6.6% of the total energy. Furthermore, with an aggressive control policy that set the idle machines in "sleep" mode (ACPI S3 state), RESCUE-with-sleep can further reduce the total energy consumption by 51.5% for BS Seeker, 48.8% for Matrix Stressmark, and 21.3% for TPC-W compared to RESCUE. (C) 2014 Elsevier Inc. All rights reserved.

BACKGROUND:The increasing cost in terms of money and healthcare resources is driving healthcare providers to provide home-based telecare instead of institutionalized healthcare. Falling is one of the most common and dangerous accidents for elderly individuals and a significant factor affecting the living quality of the elderly. Many efforts have been put toward providing a robust method to detect falls accurately and in a timely manner. This study facilitated a reliable, safe, and real-time home-based healthcare environment, which we have termed the Home Healthcare Sentinel System (HONEY), to detect falls for elderly people in the home telecare environment. The basic idea of HONEY is a three-step detection scheme that consists of multimodality signal sources, including an accelerometer sensor, audio, images, and video clips via speech recognition and on-demand video techniques. MATERIALS AND METHODS:The magnitude of acceleration, corresponding to a user's movements, triggers fall detection combining speech recognition and on-demand video. If a fall occurs, an alarm e-mail is delivered to medical staff or caregivers at once, containing the fall information, so that caregivers could make a primary diagnosis based on it. This article also describes the implementation of the prototype of HONEY. RESULTS:A comprehensive evaluation with 10 volunteers shows that HONEY has high accuracy of 94% for fall detection, 18% higher than the Advanced Magnitude Algorithm (AMA), which is a wearable sensor-based method, and the false-positive and false-negative rates are 3% and 10%, respectively, 19% and 16% lower than AMA, respectively. The average response time for a detected fall is 46.2 s, which is also short enough for first aid. CONCLUSIONS:In summary, HONEY provides a highly reliable and convenient fall detection solution for the home-based environment.