Weisong Shi

Edge Computing: Vision and Challenges

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.

Preserving source location privacy in monitoring-based wireless sensor networks

While a wireless sensor network is deployed to monitor certain events and pinpoint their locations, the location information is intended only for legitimate users. However, an eavesdropper can monitor the traffic and deduce the approximate location of monitored objects in certain situations. We first describe a successful attack against the flooding-based phantom routing, proposed in the seminal work by Celal Ozturk, Yanyong Zhang, and Wade Trappe. Then, we propose GROW (Greedy Random Walk), a two-way random walk, i.e., from both source and sink, to reduce the chance an eavesdropper can collect the location information. We improve the delivery rate by using local broadcasting and greedy forwarding. Privacy protection is verified under a backtracking attack model. The message delivery time is a little longer than that of the broadcasting-based approach, but it is still acceptable if we consider the enhanced privacy preserving capability of this new approach. At the same time, the energy consumption is less than half the energy consumption of flooding-base phantom routing, which is preferred in a low duty cycle, environmental monitoring sensor network

PET: A PErsonalized Trust Model with Reputation and Risk Evaluation for P2P Resource Sharing

Building a good cooperation in the P2P resource sharing is a fundamental and challenging research topic because of peer anonymity, peer independence, high dynamics of peer behaviors and network conditions, and the absence of an effective security mechanism. In this paper, we propose PET, a personalized trust model, to help the construction of a good cooperation, especially in the context of economic-based solutions for the P2P resource sharing. The trust model consists of two parts: reputation evaluation and risk evaluation. Reputation is the accumulative assessment of the long-term behavior, while the risk evaluation is the opinion of the short-term behavior. The risk part is employed to deal with the dramatic spoiling of peers, which makes PET differ from other trust models that based on the reputation only. This paper contributes to first modeling the risk as the opinion of short-term trustworthiness and combining with traditional reputation evaluation to derive the trustworthiness in this field.

Design and Implementation of TARF: A Trust-Aware Routing Framework for WSNs

The multihop routing in wireless sensor networks (WSNs) offers little protection against identity deception through replaying routing information. An adversary can exploit this defect to launch various harmful or even devastating attacks against the routing protocols, including sinkhole attacks, wormhole attacks, and Sybil attacks. The situation is further aggravated by mobile and harsh network conditions. Traditional cryptographic techniques or efforts at developing trust-aware routing protocols do not effectively address this severe problem. To secure the WSNs against adversaries misdirecting the multihop routing, we have designed and implemented TARF, a robust trust-aware routing framework for dynamic WSNs. Without tight time synchronization or known geographic information, TARF provides trustworthy and energy-efficient route. Most importantly, TARF proves effective against those harmful attacks developed out of identity deception; the resilience of TARF is verified through extensive evaluation with both simulation and empirical experiments on large-scale WSNs under various scenarios including mobile and RF-shielding network conditions. Further, we have implemented a low-overhead TARF module in TinyOS; as demonstrated, this implementation can be incorporated into existing routing protocols with the least effort. Based on TARF, we also demonstrated a proof-of-concept mobile target detection application that functions well against an antidetection mechanism.

In Cloud, Can Scientific Communities Benefit from the Economies of Scale?

The basic idea behind cloud computing is that resource providers offer elastic resources to end users. In this paper, we intend to answer one key question to the success of cloud computing: in cloud, can small-to-medium scale scientific communities benefit from the economies of scale? Our research contributions are threefold: first, we propose an innovative public cloud usage model for small-to-medium scale scientific communities to utilize elastic resources on a public cloud site while maintaining their flexible system controls, i.e., create, activate, suspend, resume, deactivate, and destroy their high-level management entities-service management layers without knowing the details of management. Second, we design and implement an innovative system-DawningCloud, at the core of which are lightweight service management layers running on top of a common management service framework. The common management service framework of DawningCloud not only facilitates building lightweight service management layers for heterogeneous workloads, but also makes their management tasks simple. Third, we evaluate the systems comprehensively using both emulation and real experiments. We found that for four traces of two typical scientific workloads: High-Throughput Computing (HTC) and Many-Task Computing (MTC), DawningCloud saves the resource consumption maximally by 59.5 and 72.6 percent for HTC and MTC service providers, respectively, and saves the total resource consumption maximally by 54 percent for the resource provider with respect to the previous two public cloud solutions. To this end, we conclude that small-to-medium scale scientific communities indeed can benefit from the economies of scale of public clouds with the support of the enabling system.

Analysis of ratings on trust inference in open environments

Ratings (also known as recommendations) provide an efficient and effective way to build trust relationship in the human society, by making use of the information from others rather than exclusively relying on ones own direct observations. However, it is uncertain that whether the rating can play the same positive effect in the open computing environment because of differences between the computing world and human society. We envisage that there are two kinds of uncertainties: the uncertainty resulting from rating aggregation algorithms and the uncertainty resulting from other algorithm-independent design factors, which are coined as algorithm uncertainty and factor uncertainty in this paper. The algorithm uncertainty is related to such a problem: are the complex aggregating algorithms necessary? The factor uncertainty refers to how the performance of ratings is affected by all kinds of factors, including trust model design related factors and trust model design independent factors. In this paper, we take an initial step to answer these two uncertainties. First, we study the effect of all factors based on a simple averaging rating algorithm in terms of several proposed performance metrics. Then we compare different rating aggregation algorithms in the same context and platform, focusing on several relevant metrics. The simulation results show that ratings are not always as helpful as what we expected, especially when the system is facing malicious raters and highly dynamic peer behaviors. In certain circumstances, the simple average aggregation algorithm performs better than the complex ones, especially when there are considerable number of bad raters in the system. Considering the system dynamics, the cost of the algorithm design, and the system overhead, we argue that it is not worth putting too much energy on the design of complex rating aggregation schemes for trust inference in open computing environments.

An Adaptive Rescheduling Strategy for Grid Workflow Applications

Scheduling is the key to the performance of grid workflow applications. Various strategies are proposed, including static scheduling strategies which map jobs to resources before execution time, or dynamic alternatives which schedule individual job only when it is ready to execute. While sizable work supports the claim that the static scheduling performs better for workflow applications than the dynamic one, it is questioned how a static schedule works effectively in a grid environment which changes constantly. This paper proposes a novel adaptive rescheduling concept, which allows the workflow planner works collaboratively with the run time executor and reschedule in a proactive way had the grid environment changes significantly. An HEFT-based adaptive rescheduling algorithm is presented, evaluated and compared with traditional static and dynamic strategies respectively. The experiment results show that the proposed strategy not only outperforms the dynamic one but also improves over the traditional static one. Furthermore we observed that it performs more efficiently with data intensive application of higher degree of parallelism.

Using Wireless Sensor Networks for Fire Rescue Applications: Requirements and Challenges

Research in wireless sensor networks has attracted a lot of attention in recent years. Real applications, such as habitat monitoring, environmental and structural monitoring, start to work in practical. In this paper, we argue that wireless sensor network is a very promising technology for fire rescue applications. First, we abstract four specific requirements of this application, including accountability of firefighters, real-time monitoring, intelligent scheduling and resource allocation, and Web-enabled service and integration. To meet these requirements, we propose FireNet, a wireless sensor network architecture for this specific type of application. Based on these requirements and the characteristics of wireless sensor networks, several research challenges in terms of new protocols as well as hardware and software support are examined. Finally, we conclude that wireless sensor network is a very powerful and suitable tool to be applied in this application

CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping

BackgroundResearch in genetics has developed rapidly recently due to the aid of next generation sequencing (NGS). However, massively-parallel NGS produces enormous amounts of data, which leads to storage, compatibility, scalability, and performance issues. The Cloud Computing and MapReduce framework, which utilizes hundreds or thousands of shared computers to map sequencing reads quickly and efficiently to reference genome sequences, appears to be a very promising solution for these issues. Consequently, it has been adopted by many organizations recently, and the initial results are very promising. However, since these are only initial steps toward this trend, the developed software does not provide adequate primary functions like bisulfite, pair-end mapping, etc., in on-site software such as RMAP or BS Seeker. In addition, existing MapReduce-based applications were not designed to process the long reads produced by the most recent second-generation and third-generation NGS instruments and, therefore, are inefficient. Last, it is difficult for a majority of biologists untrained in programming skills to use these tools because most were developed on Linux with a command line interface.ResultsTo urge the trend of using Cloud technologies in genomics and prepare for advances in second- and third-generation DNA sequencing, we have built a Hadoop MapReduce-based application, CloudAligner, which achieves higher performance, covers most primary features, is more accurate, and has a user-friendly interface. It was also designed to be able to deal with long sequences. The performance gain of CloudAligner over Cloud-based counterparts (35 to 80%) mainly comes from the omission of the reduce phase. In comparison to local-based approaches, the performance gain of CloudAligner is from the partition and parallel processing of the huge reference genome as well as the reads. The source code of CloudAligner is available at http://cloudaligner.sourceforge.net/ and its web version is at http://mine.cs.wayne.edu:8080/CloudAligner/ConclusionsOur results show that CloudAligner is faster than CloudBurst, provides more accurate results than RMAP, and supports various input as well as output formats. In addition, with the web-based interface, it is easier to use than its counterparts.

Workload characterization on a production Hadoop cluster: A case study on Taobao

MapReduce is becoming the state-of-the-art computing paradigm for processing large-scale datasets on a large cluster with tens or thousands of nodes. It has been widely used in various fields such as e-commerce, Web search, social networks, and scientific computation. Understanding the characteristics of MapReduce workloads is the key to achieving better configuration decisions and improving the system throughput. However, workload characterization of MapReduce, especially in a large-scale production environment, has not been well studied yet. To gain insight on MapReduce workloads, we collected a two-week workload trace from a 2,000-node Hadoop cluster at Taobao, which is the biggest online e-commerce enterprise in Asia, ranked 14th in the world as reported by Alexa. The workload trace covered 912,157 jobs, logged from Dec. 4 to Dec. 20, 2011. We characterized the workload at the granularity of job and task, respectively and concluded with a set of interesting observations. The results of workload characterization are representative and generally consistent with data platforms for e-commerce websites, which can help other researchers and engineers understand the performance and job characteristics of Hadoop in their production environments. In addition, we use these job analysis statistics to derive several implications for potential performance optimization solutions.