An Optimistic Checkpoint Mechanism Based on Job Characteristics and Resource Availability for Dynamic Grids

In the paper,based on the job characteristics and resources availability,an optimistic checkpoint mechanism for dynamic grids(OCM4G) is proposed.It can determine whether to checkpoint a given job running on a given resource node and establish optimal aperiodic checkpoint intervals by applying the knowledge of job characteristics and resource availability.We evaluate OCM4G over a real grid environment(ChinaGrid) and the results show that OCM4G achieves better performance than the periodic checkpoint and the analytical method of calculating aperiodic checkpoint intervals.

Sponge:An Oversubscription Strategy Supporting Performance Interference Management in Cloud

Resource oversubscription optimizes the utilization of the computing resources. Many well-known virtual machine monitors(VMMs)such as Xen and KVM,adopt this approach to help maximize the yield of the cloud datacenters That is,with proper resource oversubscription strategies,more virtual machines(VMs) can be supported by limited resources. However performance interference among VMs hosting in the same physical machines(PMs) exists in cloud environment,and probably aggravated by resource oversubscription strategies,which aims to put more VMs into the same PM. In this paper,we present a resource oversubscription strategy called Sponge targeting cloud platforms Sponge mitigates the issue of performance interference among the oversubscribed co-hosting VMs. Sponge also provides a VM association strategy for each PM to handle with its besteffort. We performed our evaluation on a virtua datacenter simulated by Xen. Our evaluation results show that Sponge improves the resources utilization and manages to make each VM mee its performance requirement even hosting with other VMs in the same PM.

Dependability Analysis for Fault-Tolerant Computer Systems Using Dynamic Fault Graphs

Dependability analysis is an important step in designing and analyzing safety computer systems and protection systems.Introducing multi-processor and virtual machine increases the system faults' complexity,diversity and dynamic,in particular for software-induced failures,with an impact on the overall dependability.Moreover,it is very different for safety system to operate successfully at any active phase,since there is a huge difference in failure rate between hardware-induced and softwareinduced failures.To handle these difficulties and achieve accurate dependability evaluation,consistently reflecting the construct it measures,a new formalism derived from dynamic fault graphs(DFG) is developed in this paper.DFG exploits the concept of system event as fault state sequences to represent dynamic behaviors,which allows us to execute probabilistic measures at each timestamp when change occurs.The approach automatically combines the reliability analysis with the system dynamics.In this paper,we describe how to use the proposed methodology drives to the overall system dependability analysis through the phases of modeling,structural discovery and probability analysis,which is also discussed using an example of a virtual computing system.

Ethereum smart contract security research:survey and future research opportunities

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and context.One particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and arresting.Hence,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and 2019.Specifically,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution environment.We also identify potential research opportunities and future research agenda.

Demystifying Ethereum account diversity:observations,models and analysis

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts.These massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this paper.The benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal activities.Unfortunately,the exploration of blockchain account diversity remains scarce.Even the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are different.To this end,we present the first attempt to demystify the account diversity on Ethereum.The key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the premise.We then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its effectiveness.Furthermore,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage actions.As for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total transactions.The latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

Optimizing non-coalesced memory access for irregular applications with GPU computing

General purpose graphics processing units(GPGPUs)can be used to improve computing performance considerably for regular applications.However,irregular memory access exists in many applications,and the benefits of graphics processing units(GPUs)are less substantial for irregular applications.In recent years,several studies have presented some solutions to remove static irregular memory access.However,eliminating dynamic irregular memory access with software remains a serious challenge.A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access,especially for indirect memory access.Data reordering and index redirection are suggested to reduce the number of memory transactions,thereby improving the performance of GPU kernels.To improve the efficiency of data reordering,an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data.Through concurrently executing the compute unified device architecture(CUDA)streams of data reordering and the data processing kernel,the overhead of data reordering can be reduced.After these optimizations,the volume of memory transactions can be reduced by 16.7%-50%compared with CUSPARSE-based benchmarks,and the performance of irregular kernels can be improved by 9.64%-34.9%using an NVIDIA Tesla P4 GPU.

UCat: heterogeneous memory management for unikernels

Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-based VMs in a single physical server. In such a cloud computing platform, main memory is the primary bottleneck resource for high-density application deployment. Recently, non-volatile memory (NVM) technologies has become increasingly popular in cloud data centers because they can offer extremely large memory capacity at a low expense. However, there still remain many challenges to utilize NVMs for unikernel-based VMs, such as the difficulty of heterogeneous memory allocation and high performance overhead of address translations.In this paper, we present UCat, a heterogeneous memory management mechanism that support multi-grained memory allocation for unikernels. We propose front-end/back-end cooperative address space mapping to expose the host memory heterogeneity to unikernels. UCat exploits large pages to reduce the cost of two-layer address translation in virtualization environments, and leverages slab allocation to reduce memory waste due to internal memory fragmentation. We implement UCat based on a popular unikernel--OSv and conduct extensive experiments to evaluate its efficiency. Experimental results show that UCat can reduce the memory consumption of unikernels by 50% and TLB miss rate by 41%, and improve the throughput of real-world benchmarks such as memslap and YCSB by up to 18.5% and 14.8%, respectively.

ReCSA:a dedicated sort accelerator using ReRAM-based content addressable memory

With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data sets.Hardware sorting algorithms have attracted much attention because they can take advantage of different hardware's parallelism.But the traditional hardware sort accelerators suffer“memory wall”problems since their multiple rounds of data transmission between the memory and the processor.In this paper,we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array simultaneously.Using this designed ReCAM array,we present ReCSA,which is the first dedicated ReCAM-based sort accelerator.Besides hardware designs,we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting performance.The sorting algorithm in ReCSA can process various data types,such as integer,float,double,and strings.We also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort accelerators.The experimental results show that ReCSA has 90.92×,46.13×,27.38×,84.57×,and 3.36×speedups against CPU-,GPU-,FPGA-,NDP-,and PIM-based platforms when processing numeric data sets.ReCSA also has 24.82×,32.94×,and 18.22×performance improvement when processing string data sets compared with CPU-,GPU-,and FPGA-based platforms.

Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

Computer vision(CV)algorithms have been extensively used for a myriad of applications nowadays.As the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV algorithms.Single Instruction Multiple Data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV algorithms.In this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge Detection.By our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input images.Whereas,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V co-processor.In our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

On the Security of Smart Home Systems:A Survey

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the fastest-growing.However,the rapid development of the smart home has also made smart home systems a target for attackers.Recently,researchers have made many efforts to investigate and enhance the security of smart home systems.Toward a more secure smart home ecosystem,we present a detailed literature review on the security of smart home systems.Specifically,we categorize smart home systems’security issues into the platform,device,and communication issues.After exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security standards.Finally,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

基于污点和概率的逃逸恶意软件多路径探索

Static analysis is often impeded by malware obfuscation techniques,such as encryption and packing,whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution information.Unfortunately,malware can employ evasive techniques to detect the analysis environment and alter its behavior accordingly.While known evasive techniques can be explicitly dismantled,the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations,such as logic bombs that rely on uncertain conditions,let alone unsupported evasive techniques,which contain evasions without corresponding dismantling strategies and those leveraging unknown implementations.In this paper,we present Antitoxin,a prototype for automatically exploring evasive malware.Antitoxin utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive techniques.The probabilities of branch execution are derived from dynamic coverage,while taint analysis helps identify paths associated with evasive techniques that rely on uncertain conditions.Subsequently,Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path exploration.This is achieved through forced execution,which forcefully sets the outcomes of branches on selected paths.Additionally,Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques,thereby reducing exploration overhead.Furthermore,Antitoxin provides valuable insights into sensitive behaviors,facilitating deeper manual analysis.Our experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic manner.The probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations,enabling the dismantling of unsupported techniques such as C2 and significantly improving efficiency compared to linear exploration when dealing with complex control flows.Additionally,taint analysis can accurately identify branches related to logic bombs,facilitating preferential exploration.

URMG: Enhanced CBMG-Based Method for Automatically Testing Web Applications in the Cloud

To satisfy the rapid growth of cloud technologies, a large number of web applications have been developed and deployed, and these applications are being run in clouds. Due to the scalability provided by clouds, a single web application may be concurrently visited by several millions or billions of users. Thus, the testing and performance evaluations of these applications are increasingly important. User model based evaluations can significantly reduce the manual work required, and can enable us to determine the performance of applications under real runtime environments. Hence, it has become one of the most popular evaluation methods in both industry and academia. Significant efforts have focused on building different kinds of models using mining web access logs, such as Markov models and Customer Behavior Model Graph(CBMG). This paper proposes a new kind of model, named the User Representation Model Graph(URMG), which is built based on CBMG. It uses an algorithm to refine CBMG and optimizes the evaluations execution process. Based on this model, an automatic testing and evaluation system for web applications is designed, implemented, and deployed in our test cloud, which is able to execute all of the analysis and testing operations using only web access logs. In our system, the error rate caused by random access to applications in the execution phase is also reduced, and the results show that the error rate of the evaluation that depends on URMG is 50% less than that which depends on CBMG.

PSG： a two-layer graph model for document summarization

图模型被作为边把句子用作图节点，和句子的类似广泛地在文件摘要使用了。在这份报纸，为文件摘要的一个新奇的图模型被介绍，在句子包括的那个不仅句子关联而且短语关联信息被利用。简言之，我们构造一个短语句子二层的图结构模型(PSG ) 到总结文件。我们为通用文件摘要和集中质问的摘要使用这个模型。试验性的结果证明我们的模型极大地超过存在工作。

An effective framework for asynchronous incremental graph processing

Although many graph processing systems have been proposed, graphs in the real-world are often dynamic. It is important to keep the results of graph computation up-todate. Incremental computation is demonstrated to be an efficient solution to update calculated results. Recently, many incremental graph processing systems have been proposed to handle dynamic graphs in an asynchronous way and are able to achieve better performance than those processed in a synchronous way. However, these solutions still suffer from sub-optimal convergence speed due to their slow propagation of important vertex state (important to convergence speed) and poor locality. In order to solve these problems, we propose a novel graph processing framework. It introduces a dynamic partition method to gather the important vertices for high locality, and then uses a priority-based scheduling algorithm to assign them with a higher priority for an effective processing order. By such means, it is able to reduce the number of updates and increase the locality, thereby reducing the convergence time. Experimental results show that our method reduces the number of updates by 30%, and reduces the total execution time by 35%, compared with state-of-the-art systems.

Efficient FPGA-based graph processing with hybrid pull-push computational model

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world graphs.Programmability and pipeline parallelism of FPGAs make it potential to process different stages of graph iterations.Nevertheless,considering the limited on-chip resources and streamline pipeline computation,the efficiency of hybrid model on FPGAs often suffers due to well-known random access feature of graph processing.In this paper,we present a hybrid graph processing system on FPGAs,which can achieve the best of both worlds.Our approach on FPGAs is unique and novel as follow.First,we propose to use edge block(consisting of edges with the same destination vertex set),which allows to sequentially access edges at block granularity for locality while still preserving the precision.Due to the independence of blocks in the sense that all edges in an inactive block are associated with inactive vertices,this also enables to skip invalid blocks for reducing redundant computation.Second,we consider a large number of vertices and their associated edge-blocks to maintain a predictable execution history.We also present to switch models in advance with few stalls using their state statistics.Our evaluation on a wide variety of graph algorithms for many real-world graphs shows that our approach achieves up to 3.69x speedup over state-of-the-art FPGA-based graph processing systems.

China in the eyes of news media:a case study under COVID-19 epidemic

As one of the early COVID-19 epidemic outbreak areas,China attracted the global news media’s attention at the beginning of 2020.During the epidemic period,Chinese people united and actively fought against the epidemic.However,in the eyes of the international public,the situation reported about China is not optimistic.To better understand how the international public portrays China,especially during the epidemic,we present a case study with big data technology.We aim to answer three questions:(1)What has the international media focused on during the COVID-19 epidemic period?(2)What is the media’s tone when they report China?(3)What is the media’s attitude when talking about China?In detail,we crawled more than 280000 pieces of news from 57 mainstream media agencies in 22 countries and made some interesting observations.For example,international media paid more attention to Chinese livelihood during the COVID-19 epidemic period.In March and April,“progress of Chinese vaccines,”“specific drugs and treatments,”and“virus outbreak in U.S.”became the media’s most common topics.In terms of news attitude,Cuba,Malaysia,and Venezuela had a positive attitude toward China,while France,Canada,and the United Kingdom had a negative attitude.Our study can help understand China’s image in the eyes of the international media and provide a sound basis for image analysis.

Superpage-Friendly Page Table Design for Hybrid Memory Systems

Page migration has long been adopted in hybrid memory systems comprising dynamic random access memory(DRAM)and non-volatile memories(NVMs),to improve the system performance and energy efficiency.However,page migration introduces some side effects,such as more translation lookaside buffer(TLB)misses,breaking memory contiguity,and extra memory accesses due to page table updating.In this paper,we propose superpagefriendly page table called SuperPT to reduce the performance overhead of serving TLB misses.By leveraging a virtual hashed page table and a hybrid DRAM allocator,SuperPT performs address translations in a flexible and efficient way while still remaining the contiguity within the migrated pages.

Media Power Measuring via Emotional Contagion

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/regions.However,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media power.To more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between media.We use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media power.We propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional levels.MMPEC also interprets experimental results through correlation and causality analyses,ensuring explainability.Case analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.