Nowadays massive log streams are generated from many Internet and cloud services. Storing log streams consumes a large amount of disk space and incurs high cost. Traditional compression methods can be applied to reduce storage cost, but are inefficient for log analysis, because fetching relevant log entries from compressed data often requires retrieval and decompression of large blocks of data. We propose a column-wise compression approach for well-formatted log streams, where each log entry can be independently compressed or decompressed for analysis. Specifically, we separate a log entry into several columns and compress each column with different models. We have implemented our approach as a library and integrated it into two applications, a log search system and a log joining system. Experimental results show that our compression scheme outperforms traditional compression methods for decompression times and has a competitive compression ratio. For log search, our approach achieves better query times than using traditional compression algorithms for both in-core and out-of-core cases. For joining log streams, our approach achieves the same join quality with only 30% memory of uncompressed streams.
{"title":"Cowic: A Column-Wise Independent Compression for Log Stream Analysis","authors":"Hao Lin, Jingyu Zhou, Bin Yao, M. Guo, Jie Li","doi":"10.1109/CCGrid.2015.45","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.45","url":null,"abstract":"Nowadays massive log streams are generated from many Internet and cloud services. Storing log streams consumes a large amount of disk space and incurs high cost. Traditional compression methods can be applied to reduce storage cost, but are inefficient for log analysis, because fetching relevant log entries from compressed data often requires retrieval and decompression of large blocks of data. We propose a column-wise compression approach for well-formatted log streams, where each log entry can be independently compressed or decompressed for analysis. Specifically, we separate a log entry into several columns and compress each column with different models. We have implemented our approach as a library and integrated it into two applications, a log search system and a log joining system. Experimental results show that our compression scheme outperforms traditional compression methods for decompression times and has a competitive compression ratio. For log search, our approach achieves better query times than using traditional compression algorithms for both in-core and out-of-core cases. For joining log streams, our approach achieves the same join quality with only 30% memory of uncompressed streams.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"26 1","pages":"21-30"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89419407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtualization technology as the foundation of cloud computing provides many benefits in cost, security, and management, but all of them rely on the reliability of the underlying virtualization software - the hypervisor (or virtual machine monitor). Cloud data centers are built upon 10Ks to 100Ks commodity servers. Hardware errors in these large scale computer systems are not rare events. When hardware errors occur during the hypervisor execution, they may cause failures or data corruptions in co-located VMs, undermining the whole system reliability. In this paper, we propose DualVisor, that uses a software redundancy based fault tolerance technique to protect the hypervisor from hardware errors. DualVisor replicates hypervisor executions and data structures for error detection and recovery. In this work, we first study the need for a hardware error-resilient hypervisor. Then, we discuss the design considerations in detail. We implement a prototype in the hypervisor to demonstrate the feasibility and evaluate the performance overhead. Our preliminary results show that the performance overhead of DualVisor is fairly small (less than 6%) for tested applications.
{"title":"DualVisor: Redundant Hypervisor Execution for Achieving Hardware Error Resilience in Datacenters","authors":"Xin Xu, H. H. Huang","doi":"10.1109/CCGrid.2015.30","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.30","url":null,"abstract":"Virtualization technology as the foundation of cloud computing provides many benefits in cost, security, and management, but all of them rely on the reliability of the underlying virtualization software - the hypervisor (or virtual machine monitor). Cloud data centers are built upon 10Ks to 100Ks commodity servers. Hardware errors in these large scale computer systems are not rare events. When hardware errors occur during the hypervisor execution, they may cause failures or data corruptions in co-located VMs, undermining the whole system reliability. In this paper, we propose DualVisor, that uses a software redundancy based fault tolerance technique to protect the hypervisor from hardware errors. DualVisor replicates hypervisor executions and data structures for error detection and recovery. In this work, we first study the need for a hardware error-resilient hypervisor. Then, we discuss the design considerations in detail. We implement a prototype in the hypervisor to demonstrate the feasibility and evaluate the performance overhead. Our preliminary results show that the performance overhead of DualVisor is fairly small (less than 6%) for tested applications.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"16 1","pages":"485-494"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87555920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Fox, J. Qiu, Supun Kamburugamuve, S. Jha, André Luckow
We review the High Performance Computing Enhanced Apache Big Data Stack HPC-ABDS and summarize the capabilities in 21 identified architecture layers. These cover Message and Data Protocols, Distributed Coordination, Security & Privacy, Monitoring, Infrastructure Management, DevOps, Interoperability, File Systems, Cluster & Resource management, Data Transport, File management, NoSQL, SQL (NewSQL), Extraction Tools, Object-relational mapping, In-memory caching and databases, Inter-process Communication, Batch Programming model and Runtime, Stream Processing, High-level Programming, Application Hosting and PaaS, Libraries and Applications, Workflow and Orchestration. We summarize status of these layers focusing on issues of importance for data analytics. We highlight areas where HPC and ABDS have good opportunities for integration.
{"title":"HPC-ABDS High Performance Computing Enhanced Apache Big Data Stack","authors":"G. Fox, J. Qiu, Supun Kamburugamuve, S. Jha, André Luckow","doi":"10.1109/CCGrid.2015.122","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.122","url":null,"abstract":"We review the High Performance Computing Enhanced Apache Big Data Stack HPC-ABDS and summarize the capabilities in 21 identified architecture layers. These cover Message and Data Protocols, Distributed Coordination, Security & Privacy, Monitoring, Infrastructure Management, DevOps, Interoperability, File Systems, Cluster & Resource management, Data Transport, File management, NoSQL, SQL (NewSQL), Extraction Tools, Object-relational mapping, In-memory caching and databases, Inter-process Communication, Batch Programming model and Runtime, Stream Processing, High-level Programming, Application Hosting and PaaS, Libraries and Applications, Workflow and Orchestration. We summarize status of these layers focusing on issues of importance for data analytics. We highlight areas where HPC and ABDS have good opportunities for integration.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"27 1 1","pages":"1057-1066"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75150524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cache memories have been introduced in recent generations of Graphics Processing Units (GPUs) to benefit general-purpose computing on GPUs (GPGPUs). In this work, we analyze the memory access patterns of GPGPU applications and propose a cost-effective profiling-based method to identify the data accesses that should bypass the L1 data cache to improve performance. The evaluation indicates that the proposed L1 cache bypassing can improve the GPU performance by 13.8% on average.
{"title":"Boosting GPU Performance by Profiling-Based L1 Data Cache Bypassing","authors":"Y. Huangfu, Wei Zhang","doi":"10.1109/CCGrid.2015.67","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.67","url":null,"abstract":"Cache memories have been introduced in recent generations of Graphics Processing Units (GPUs) to benefit general-purpose computing on GPUs (GPGPUs). In this work, we analyze the memory access patterns of GPGPU applications and propose a cost-effective profiling-based method to identify the data accesses that should bypass the L1 data cache to improve performance. The evaluation indicates that the proposed L1 cache bypassing can improve the GPU performance by 13.8% on average.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"9 1","pages":"1119-1122"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73890974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many modern advanced biology experiments are carried on by Electron Microscope(EM) image analysis. Segmentation is one of the most important and complex steps in the process of image analysis. Previous ISBI contest results and related research show that Convolution Neural Network(CNN)has high classification accuracy in EM image segmentation. Besides it eliminates the pain of extracting complex features which's indispensable for traditional classification algorithms. However CNN's extremely time-consuming and fault vulnerability due to long time execution prevent it from being widely used in practice. In this paper, we try to address these problems by providing reliable high performance CNN framework for medial image segmentation. Our CNN has light weighted user level checkpoint, which costs seconds when doing one checkpoint and restart. On the fact of lacking in platform diversity in current parallel CNN framework, our CNN system tries to make it general by providing distributed cross-platform parallelism implementation. Currently we have integrated Theano's GPU implementation in our CNNsystem, and we explore parallelism potential on multi-core CPUs and many-core Intel Phi by testing performance of main kernel functions of CNN. In the future, we will integrate implementation son other two platforms into our CNN framework.
{"title":"A Reliable Distributed Convolutional Neural Network for Biology Image Segmentation","authors":"Xiuxia Zhang, Guangming Tan, Mingyu Chen","doi":"10.1109/CCGrid.2015.108","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.108","url":null,"abstract":"Many modern advanced biology experiments are carried on by Electron Microscope(EM) image analysis. Segmentation is one of the most important and complex steps in the process of image analysis. Previous ISBI contest results and related research show that Convolution Neural Network(CNN)has high classification accuracy in EM image segmentation. Besides it eliminates the pain of extracting complex features which's indispensable for traditional classification algorithms. However CNN's extremely time-consuming and fault vulnerability due to long time execution prevent it from being widely used in practice. In this paper, we try to address these problems by providing reliable high performance CNN framework for medial image segmentation. Our CNN has light weighted user level checkpoint, which costs seconds when doing one checkpoint and restart. On the fact of lacking in platform diversity in current parallel CNN framework, our CNN system tries to make it general by providing distributed cross-platform parallelism implementation. Currently we have integrated Theano's GPU implementation in our CNNsystem, and we explore parallelism potential on multi-core CPUs and many-core Intel Phi by testing performance of main kernel functions of CNN. In the future, we will integrate implementation son other two platforms into our CNN framework.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"55 1","pages":"777-780"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80429155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing data enters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions, all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.
{"title":"A Virtual Machine Placement Taxonomy","authors":"Fabio Lopez Pires, B. Barán","doi":"10.1109/CCGrid.2015.15","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.15","url":null,"abstract":"Cloud computing data enters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions, all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"40 1","pages":"159-168"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76158062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jungmin Son, A. V. Dastjerdi, R. Calheiros, Xiaohui Ji, Young Yoon, R. Buyya
Software-Defined Networking not only addresses the shortcoming of traditional network technologies in dealing with frequent and immediate changes in cloud data centers but also made network resource management open and innovation-friendly. To further accelerate the innovation pace, accessible and easy-to-learn testbeds are required which estimate and measure the performance of network and host capacity provisioning approaches simultaneously within a data center. This is a challenging task and is often costly if accomplished in a physical environment. Thus, a lightweight and scalable simulation environment is necessary to evaluate the network allocation capacity policies while avoiding such a complicated and expensive facility. This paper introduces CloudSimSDN, a simulation framework for SDN-enabled cloud environments based on CloudSim. This paper develops and presents the overall architecture and features of the framework and provides several use cases. Moreover, we empirically validate the accuracy and effectiveness of CloudSimSDN through a number of simulations of a cloud-based three-tier web application.
{"title":"CloudSimSDN: Modeling and Simulation of Software-Defined Cloud Data Centers","authors":"Jungmin Son, A. V. Dastjerdi, R. Calheiros, Xiaohui Ji, Young Yoon, R. Buyya","doi":"10.1109/CCGrid.2015.87","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.87","url":null,"abstract":"Software-Defined Networking not only addresses the shortcoming of traditional network technologies in dealing with frequent and immediate changes in cloud data centers but also made network resource management open and innovation-friendly. To further accelerate the innovation pace, accessible and easy-to-learn testbeds are required which estimate and measure the performance of network and host capacity provisioning approaches simultaneously within a data center. This is a challenging task and is often costly if accomplished in a physical environment. Thus, a lightweight and scalable simulation environment is necessary to evaluate the network allocation capacity policies while avoiding such a complicated and expensive facility. This paper introduces CloudSimSDN, a simulation framework for SDN-enabled cloud environments based on CloudSim. This paper develops and presents the overall architecture and features of the framework and provides several use cases. Moreover, we empirically validate the accuracy and effectiveness of CloudSimSDN through a number of simulations of a cloud-based three-tier web application.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"25 1","pages":"475-484"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76311009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Naskos, Emmanouela Stachtiari, A. Gounaris, P. Katsaros, Dimitrios Tsoumakos, I. Konstantinou, S. Sioutas
The focus of this work is the on-demand resource provisioning in cloud computing, which is commonly referredto as cloud elasticity. Although a lot of effort has been invested in developing systems and mechanisms that enable elasticity, the elasticity decision policies tend to be designed without quantifying or guaranteeing the quality of their operation. We present an approach towards the development of more formalized and dependable elasticity policies. We make two distinct contributions. First, we propose an extensible approach to enforcing elasticity through the dynamic instantiation and online quantitative verification of Markov Decision Processes(MDP) using probabilistic model checking. Second, various concrete elasticity models and elasticity policies are studied. We evaluate the decision policies using traces from a realNoSQL database cluster under constantly evolving externalload. We reason about the behaviour of different modelling and elasticity policy options and we show that our proposal can improve upon the state-of-the-art in significantly decreasing under-provisioning while avoiding over-provisioning.
{"title":"Dependable Horizontal Scaling Based on Probabilistic Model Checking","authors":"A. Naskos, Emmanouela Stachtiari, A. Gounaris, P. Katsaros, Dimitrios Tsoumakos, I. Konstantinou, S. Sioutas","doi":"10.1109/CCGrid.2015.91","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.91","url":null,"abstract":"The focus of this work is the on-demand resource provisioning in cloud computing, which is commonly referredto as cloud elasticity. Although a lot of effort has been invested in developing systems and mechanisms that enable elasticity, the elasticity decision policies tend to be designed without quantifying or guaranteeing the quality of their operation. We present an approach towards the development of more formalized and dependable elasticity policies. We make two distinct contributions. First, we propose an extensible approach to enforcing elasticity through the dynamic instantiation and online quantitative verification of Markov Decision Processes(MDP) using probabilistic model checking. Second, various concrete elasticity models and elasticity policies are studied. We evaluate the decision policies using traces from a realNoSQL database cluster under constantly evolving externalload. We reason about the behaviour of different modelling and elasticity policy options and we show that our proposal can improve upon the state-of-the-art in significantly decreasing under-provisioning while avoiding over-provisioning.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"21 1","pages":"31-40"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80782389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the last 10 years, experts in critical infrastructure security have been increasingly directing their focus and attention to the security of control structures such as Supervisory Control and Data Acquisition (SCADA) systems in the light of the move toward Internet-connected architectures. However, this more open architecture has resulted in an increasing level of risk being faced by these systems, especially as they became offered as services and utilised via Service Oriented Architectures (SOA). For example, the SOA-based SCADA architecture proposed by the AESOP project concentrated on facilitating the integration of SCADA systems with distributed services on the application layer of a cloud network. However, whilst each service specified various security goals, such as authorisation and authentication, the current AESOP model does not attempt to encompass all the necessary security requirements and features of the integrated services. This paper presents a concept for an innovative integrated cloud platform to reinforce the integrity and security of SOA-based SCADA systems that will apply in the context of Critical Infrastructures to identify the core requirements, components and features of these types of system. The paper uses the SmartGrid to highlight the applicability and importance of the proposed platform in a real world scenario.
{"title":"Security-Oriented Cloud Platform for SOA-Based SCADA","authors":"T. Baker, M. Mackay, A. Shaheed, Bandar Aldawsari","doi":"10.1109/CCGrid.2015.37","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.37","url":null,"abstract":"During the last 10 years, experts in critical infrastructure security have been increasingly directing their focus and attention to the security of control structures such as Supervisory Control and Data Acquisition (SCADA) systems in the light of the move toward Internet-connected architectures. However, this more open architecture has resulted in an increasing level of risk being faced by these systems, especially as they became offered as services and utilised via Service Oriented Architectures (SOA). For example, the SOA-based SCADA architecture proposed by the AESOP project concentrated on facilitating the integration of SCADA systems with distributed services on the application layer of a cloud network. However, whilst each service specified various security goals, such as authorisation and authentication, the current AESOP model does not attempt to encompass all the necessary security requirements and features of the integrated services. This paper presents a concept for an innovative integrated cloud platform to reinforce the integrity and security of SOA-based SCADA systems that will apply in the context of Critical Infrastructures to identify the core requirements, components and features of these types of system. The paper uses the SmartGrid to highlight the applicability and importance of the proposed platform in a real world scenario.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"1 1","pages":"961-970"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80827887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flash memory based solid state drives (SSD) are increasingly common in portable and mobile computing devices such as laptops, mobile phones, and tablets. Due to space, weight, and power constraints, portable devices are often restricted to a single storage device, which makes them susceptible to data loss from internal errors. On the other hand, mission-critical mobile applications like wireless healthcare always demand a high level of data reliability. This is mainly because data sampled from mobile and dynamic environments are most likely irreproducible. An effective approach to improving storage and data reliability is the RAID (redundant arrays of inexpensive disks) organization. However, the multiple disks required to implement RAID make it incompatible with the aforementioned restrictions of many portable devices. In this paper, we propose a SIRF (single internally redundant flash) architecture that leverages the internal hierarchical structure and parallelism of SSDs to provide redundancy similar to RAID in a single drive configuration. The initial effort focuses on implementing SIRF-1 (mirroring), which is the corollary to its RAID-1 counterpart. In SIRF-1, data is mirrored across SSD channels to optimally exploit parallelism for both read and write operations. Simulation results show that for read-dominant workloads SIRF-1 significantly outperforms a non-mirrored SSD by up to 39.5% in terms of mean response time. For write-intensive workloads, SIRF-1 pays a performance penalty no more than 5.5%.
{"title":"SIRF-1: Enhancing Reliability of Single Flash SSD through Internal Mirroring for Mission-Critical Mobile Applications","authors":"Michael S. MacFadden, Richard Shelby, T. Xie","doi":"10.1109/CCGrid.2015.28","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.28","url":null,"abstract":"Flash memory based solid state drives (SSD) are increasingly common in portable and mobile computing devices such as laptops, mobile phones, and tablets. Due to space, weight, and power constraints, portable devices are often restricted to a single storage device, which makes them susceptible to data loss from internal errors. On the other hand, mission-critical mobile applications like wireless healthcare always demand a high level of data reliability. This is mainly because data sampled from mobile and dynamic environments are most likely irreproducible. An effective approach to improving storage and data reliability is the RAID (redundant arrays of inexpensive disks) organization. However, the multiple disks required to implement RAID make it incompatible with the aforementioned restrictions of many portable devices. In this paper, we propose a SIRF (single internally redundant flash) architecture that leverages the internal hierarchical structure and parallelism of SSDs to provide redundancy similar to RAID in a single drive configuration. The initial effort focuses on implementing SIRF-1 (mirroring), which is the corollary to its RAID-1 counterpart. In SIRF-1, data is mirrored across SSD channels to optimally exploit parallelism for both read and write operations. Simulation results show that for read-dominant workloads SIRF-1 significantly outperforms a non-mirrored SSD by up to 39.5% in terms of mean response time. For write-intensive workloads, SIRF-1 pays a performance penalty no more than 5.5%.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"87 1","pages":"343-351"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84122547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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