In the Bitcoin system, a peer-to-peer electronic currency system, the delay overhead in transaction verification prevents the Bitcoin from gaining increasing popularity nowadays as it makes the system vulnerable to double spend attacks. This paper introduces a proximity-aware extension to the current Bitcoin protocol, named Bitcoin Clustering Based Ping Time protocol (BCBPT). The ultimate purpose of the proposed protocol, that is based on how the clusters are formulated and the nodes define their membership, is to improve the transaction propagation delay in the Bitcoin network. In BCBPT, the proximity of connectivity in the Bitcoin network is increased by grouping Bitcoin nodes based on ping latencies between nodes. We show, through simulations, that the proximity base ping latency defines better clustering structures that optimize the performance of the transaction propagation delay. The reduction of the communication link cost measured by the information propagation time between nodes is mainly considered as a key reason for this improvement. Bitcoin Clustering Based Ping Time protocol is more effective at reducing the transaction propagation delay compared to the existing clustering protocol (LBC) that we proposed in our previous work.
{"title":"Proximity Awareness Approach to Enhance Propagation Delay on the Bitcoin Peer-to-Peer Network","authors":"M. Sallal, Gareth Owenson, M. Adda","doi":"10.1109/ICDCS.2017.53","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.53","url":null,"abstract":"In the Bitcoin system, a peer-to-peer electronic currency system, the delay overhead in transaction verification prevents the Bitcoin from gaining increasing popularity nowadays as it makes the system vulnerable to double spend attacks. This paper introduces a proximity-aware extension to the current Bitcoin protocol, named Bitcoin Clustering Based Ping Time protocol (BCBPT). The ultimate purpose of the proposed protocol, that is based on how the clusters are formulated and the nodes define their membership, is to improve the transaction propagation delay in the Bitcoin network. In BCBPT, the proximity of connectivity in the Bitcoin network is increased by grouping Bitcoin nodes based on ping latencies between nodes. We show, through simulations, that the proximity base ping latency defines better clustering structures that optimize the performance of the transaction propagation delay. The reduction of the communication link cost measured by the information propagation time between nodes is mainly considered as a key reason for this improvement. Bitcoin Clustering Based Ping Time protocol is more effective at reducing the transaction propagation delay compared to the existing clustering protocol (LBC) that we proposed in our previous work.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122194500","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}
M. Ferdous, Andrea Margheri, F. Paci, Mu Yang, V. Sassone
Cloud federation is an emergent cloud-computing paradigm where partner organisations share data and services hosted on their own cloud platforms. In this context, it is crucial to enforce access control policies that satisfy data protection and privacy requirements of partner organisations. However, due to the distributed nature of cloud federations, the access control system alone does not guarantee that its deployed components cannot be circumvented while processing access requests. In order to promote accountability and reliability of a distributed access control system, we present a decentralised runtime monitoring architecture based on blockchain technology.
{"title":"Decentralised Runtime Monitoring for Access Control Systems in Cloud Federations","authors":"M. Ferdous, Andrea Margheri, F. Paci, Mu Yang, V. Sassone","doi":"10.1109/ICDCS.2017.178","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.178","url":null,"abstract":"Cloud federation is an emergent cloud-computing paradigm where partner organisations share data and services hosted on their own cloud platforms. In this context, it is crucial to enforce access control policies that satisfy data protection and privacy requirements of partner organisations. However, due to the distributed nature of cloud federations, the access control system alone does not guarantee that its deployed components cannot be circumvented while processing access requests. In order to promote accountability and reliability of a distributed access control system, we present a decentralised runtime monitoring architecture based on blockchain technology.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130208612","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}
Traditionally, distributed computing concentrates on computation understood at the level of information exchange and sets aside human and organizational concerns as largely to be handled in an ad hoc manner. Increasingly, however, distributed applications involve multiple loci of autonomy. Research in multiagent systems (MAS) addresses autonomy by drawing on concepts and techniques from artificial intelligence. However, MAS research generally lacks an adequate understanding of modern distributed computing. In this Blue Sky paper, we envision decentralized multiagent systems as a way to place decentralized intelligence in distributed computing, specifically, by supporting computation at the level of social meanings. We motivate our proposals for research in the context of the Internet of Things (IoT), which has become a major thrust in distributed computing. From the IoT's representative applications, we abstract out the major challenges of relevance to decentralized intelligence. These include the heterogeneity of IoT components; asynchronous and delay-tolerant communication and decoupled enactment; and multiple stakeholders with subtle requirements for governance, incorporating resource usage, cooperation, and privacy. The IoT yields high-impact problems that require solutions that go beyond traditional ways of thinking. We conclude with highlights of some possible research directions in decentralized MAS, including programming models; interaction-oriented software engineering; and what we term enlightened governance.
{"title":"The Internet of Things and Multiagent Systems: Decentralized Intelligence in Distributed Computing","authors":"Munindar P. Singh, A. Chopra","doi":"10.1109/ICDCS.2017.304","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.304","url":null,"abstract":"Traditionally, distributed computing concentrates on computation understood at the level of information exchange and sets aside human and organizational concerns as largely to be handled in an ad hoc manner. Increasingly, however, distributed applications involve multiple loci of autonomy. Research in multiagent systems (MAS) addresses autonomy by drawing on concepts and techniques from artificial intelligence. However, MAS research generally lacks an adequate understanding of modern distributed computing. In this Blue Sky paper, we envision decentralized multiagent systems as a way to place decentralized intelligence in distributed computing, specifically, by supporting computation at the level of social meanings. We motivate our proposals for research in the context of the Internet of Things (IoT), which has become a major thrust in distributed computing. From the IoT's representative applications, we abstract out the major challenges of relevance to decentralized intelligence. These include the heterogeneity of IoT components; asynchronous and delay-tolerant communication and decoupled enactment; and multiple stakeholders with subtle requirements for governance, incorporating resource usage, cooperation, and privacy. The IoT yields high-impact problems that require solutions that go beyond traditional ways of thinking. We conclude with highlights of some possible research directions in decentralized MAS, including programming models; interaction-oriented software engineering; and what we term enlightened governance.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128734112","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. Goumas, K. Nikas, Ewnetu Bayuh Lakew, Christos Kotselidis, Andrew Attwood, E. Elmroth, Michail Flouris, N. Foutris, J. Goodacre, D. Grohmann, Vasileios Karakostas, P. Koutsourakis, M. Kersten, M. Luján, E. Rustad, John Thomson, Luis Tomás, Atle Vesterkjaer, J. Webber, Y. Zhang, N. Koziris
Despite their proliferation as a dominant computing paradigm, cloud computing systems lack effective mechanisms to manage their vast amounts of resources efficiently. Resources are stranded and fragmented, ultimately limiting cloud systems' applicability to large classes of critical applications that pose non-moderate resource demands. Eliminating current technological barriers of actual fluidity and scalability of cloud resources is essential to strengthen cloud computing's role as a critical cornerstone for the digital economy. ACTiCLOUD proposes a novel cloud architecture that breaks the existing scale-up and share-nothing barriers and enables the holistic management of physical resources both at the local cloud site and at distributed levels. Specifically, it makes advancements in the cloud resource management stacks by extending state-of-the-art hypervisor technology beyond the physical server boundary and localized cloud management system to provide a holistic resource management within a rack, within a site, and across distributed cloud sites. On top of this, ACTiCLOUD will adapt and optimize system libraries and runtimes (e.g., JVM) as well as ACTiCLOUD-native applications, which are extremely demanding, and critical classes of applications that currently face severe difficulties in matching their resource requirements to state-of-the-art cloud offerings.
{"title":"ACTiCLOUD: Enabling the Next Generation of Cloud Applications","authors":"G. Goumas, K. Nikas, Ewnetu Bayuh Lakew, Christos Kotselidis, Andrew Attwood, E. Elmroth, Michail Flouris, N. Foutris, J. Goodacre, D. Grohmann, Vasileios Karakostas, P. Koutsourakis, M. Kersten, M. Luján, E. Rustad, John Thomson, Luis Tomás, Atle Vesterkjaer, J. Webber, Y. Zhang, N. Koziris","doi":"10.1109/ICDCS.2017.252","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.252","url":null,"abstract":"Despite their proliferation as a dominant computing paradigm, cloud computing systems lack effective mechanisms to manage their vast amounts of resources efficiently. Resources are stranded and fragmented, ultimately limiting cloud systems' applicability to large classes of critical applications that pose non-moderate resource demands. Eliminating current technological barriers of actual fluidity and scalability of cloud resources is essential to strengthen cloud computing's role as a critical cornerstone for the digital economy. ACTiCLOUD proposes a novel cloud architecture that breaks the existing scale-up and share-nothing barriers and enables the holistic management of physical resources both at the local cloud site and at distributed levels. Specifically, it makes advancements in the cloud resource management stacks by extending state-of-the-art hypervisor technology beyond the physical server boundary and localized cloud management system to provide a holistic resource management within a rack, within a site, and across distributed cloud sites. On top of this, ACTiCLOUD will adapt and optimize system libraries and runtimes (e.g., JVM) as well as ACTiCLOUD-native applications, which are extremely demanding, and critical classes of applications that currently face severe difficulties in matching their resource requirements to state-of-the-art cloud offerings.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124817015","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}
Releasing private data to the future is a challenging problem. Making private data accessible at a future point in time requires mechanisms to keep data secure and undiscovered so that protected data is not available prior to the legitimate release time and the data appears automatically at the expected release time. In this paper, we develop new mechanisms to support self-emerging data storage that securely hide keys of encrypted data in a Distributed Hash Table (DHT) network that makes the encryption keys automatically appear at the predetermined release time so that the protected encrypted private data can be decrypted at the release time. We show that a straight-forward approach of privately storing keys in a DHT is prone to a number of attacks that could either make the hidden data appear before the prescribed release time (release-ahead attack) or destroy the hidden data altogether (drop attack). We develop a suite of self-emerging key routing mechanisms for securely storing and routing encryption keys in the DHT. We show that the proposed scheme is resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks. Our experimental evaluation demonstrates the performance of the proposed schemes in terms of attack resilience and churn resilience.
{"title":"Timed-Release of Self-Emerging Data Using Distributed Hash Tables","authors":"C. Li, Balaji Palanisamy","doi":"10.1109/ICDCS.2017.109","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.109","url":null,"abstract":"Releasing private data to the future is a challenging problem. Making private data accessible at a future point in time requires mechanisms to keep data secure and undiscovered so that protected data is not available prior to the legitimate release time and the data appears automatically at the expected release time. In this paper, we develop new mechanisms to support self-emerging data storage that securely hide keys of encrypted data in a Distributed Hash Table (DHT) network that makes the encryption keys automatically appear at the predetermined release time so that the protected encrypted private data can be decrypted at the release time. We show that a straight-forward approach of privately storing keys in a DHT is prone to a number of attacks that could either make the hidden data appear before the prescribed release time (release-ahead attack) or destroy the hidden data altogether (drop attack). We develop a suite of self-emerging key routing mechanisms for securely storing and routing encryption keys in the DHT. We show that the proposed scheme is resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks. Our experimental evaluation demonstrates the performance of the proposed schemes in terms of attack resilience and churn resilience.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125402915","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}
M. Alhowaidi, B. Ramamurthy, B. Bockelman, D. Swanson
Named Data Networking (NDN) is one of the promising future internet architectures, which focuses on the data rather than its location (IP/host-based system). NDN has several characteristics which facilitate addressing and routing the data: fail-over, in-network caching and load balancing. This makes it useful in areas such as managing scientific data. The CMS experiment on the Large Hadron Collider (LHC) has a data access problem amenable to content-centric networking. CERN Virtual Machine File System (CVMFS) is used by High Energy Physics (HEP) community for worldwide software distribution. CVMFS maintain its data by using content-addressable storage, which makes it suitable for NDN. n this paper, we investigate the possibilities of using a content-centric networking architecture such as NDN on distributing CMS software.
{"title":"The Case for Using Content-Centric Networking for Distributing High-Energy Physics Software","authors":"M. Alhowaidi, B. Ramamurthy, B. Bockelman, D. Swanson","doi":"10.1109/ICDCS.2017.295","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.295","url":null,"abstract":"Named Data Networking (NDN) is one of the promising future internet architectures, which focuses on the data rather than its location (IP/host-based system). NDN has several characteristics which facilitate addressing and routing the data: fail-over, in-network caching and load balancing. This makes it useful in areas such as managing scientific data. The CMS experiment on the Large Hadron Collider (LHC) has a data access problem amenable to content-centric networking. CERN Virtual Machine File System (CVMFS) is used by High Energy Physics (HEP) community for worldwide software distribution. CVMFS maintain its data by using content-addressable storage, which makes it suitable for NDN. n this paper, we investigate the possibilities of using a content-centric networking architecture such as NDN on distributing CMS software.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115469695","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}
Juan F. Pérez, R. Birke, Mathias Björkqvist, L. Chen
Wimpy virtual instances equipped with small numbers of cores and RAM are popular public and private cloud offerings because of their low cost for hosting applications. The challenge is how to run latency-sensitive applications using such instances, which trade off performance for cost. In this study, we analytically and experimentally show that simultaneously scaling resources at coarse granularity and workloads, i.e., submitting multiple query clones to different servers, at fine granularity can overcome the performance disadvantages of wimpy VM instances and achieve stringent latency targets that are even lower than the average execution times of wimpy servers. To such an end, we first derive a closed-form analysis for the latency under any given VM provisioning and query replication level, considering cloning policies that can (not) terminate outstanding clones with (without) an overhead. Validated on trace-driven simulations, our analysis is able to accurately predict the latency and efficiently search for the optimal number of VMs and clones. Secondly, we develop a dual elastic scaler, DuoScale, that dynamically scales VMs and clones according to the workload dynamics so as to achieve the target latency in a cost-effective manner. The effectiveness of DuoScale lies on the observation that the application performance only scales sub-linearly with increasing vertical or horizontal resource provisioning, i.e., resources per VM or number of VMs. We evaluate DuoScale against VM-only scaling strategies via extensive trace-driven simulations as well as experimental results on a cloud test-bed. Our results show that DuoScale is able to achieve the stringent target latency by using clones on wimpy VMs with cost savings up to 50%, compared to scaling brawny VMs that have better performance at a higher unit cost.
{"title":"Dual Scaling VMs and Queries: Cost-Effective Latency Curtailment","authors":"Juan F. Pérez, R. Birke, Mathias Björkqvist, L. Chen","doi":"10.1109/ICDCS.2017.231","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.231","url":null,"abstract":"Wimpy virtual instances equipped with small numbers of cores and RAM are popular public and private cloud offerings because of their low cost for hosting applications. The challenge is how to run latency-sensitive applications using such instances, which trade off performance for cost. In this study, we analytically and experimentally show that simultaneously scaling resources at coarse granularity and workloads, i.e., submitting multiple query clones to different servers, at fine granularity can overcome the performance disadvantages of wimpy VM instances and achieve stringent latency targets that are even lower than the average execution times of wimpy servers. To such an end, we first derive a closed-form analysis for the latency under any given VM provisioning and query replication level, considering cloning policies that can (not) terminate outstanding clones with (without) an overhead. Validated on trace-driven simulations, our analysis is able to accurately predict the latency and efficiently search for the optimal number of VMs and clones. Secondly, we develop a dual elastic scaler, DuoScale, that dynamically scales VMs and clones according to the workload dynamics so as to achieve the target latency in a cost-effective manner. The effectiveness of DuoScale lies on the observation that the application performance only scales sub-linearly with increasing vertical or horizontal resource provisioning, i.e., resources per VM or number of VMs. We evaluate DuoScale against VM-only scaling strategies via extensive trace-driven simulations as well as experimental results on a cloud test-bed. Our results show that DuoScale is able to achieve the stringent target latency by using clones on wimpy VMs with cost savings up to 50%, compared to scaling brawny VMs that have better performance at a higher unit cost.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123354002","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}
H. Ding, Chen Qian, Jinsong Han, Ge Wang, Wei Xi, K. Zhao, Jizhong Zhao
An important function of smart environments is the ubiquitous access of computing devices. In public areas such as hospitals, libraries, and airports, people may want to interact with nearby computing systems to get information, such as directions to a hospital room, locations of books, and flight departure/arrival information. Touch screen based displays and kiosks, which are commonly used today, may incur extra hardware cost or even possible germ and bacteria infection. This work provides a new solution: users can make queries and inputs by performing in-air handwriting to an array of passive RFID tags, named RFIPad. This input method does not require human hands to carry any device and hence is convenient for applications in public areas. Besides the mobile and contactless property, this system is a cost-efficient extension to current RFID systems: an existing reader can monitor multiple RFIPads while performing its regular applications such as identification and tracking. We implement a prototype of RFIPad using commercial off-the-shelf UHF RFID devices. Experimental results show that RFIPad achieves >91% accuracy in recognizing basic touch-screen operations and English letters.
{"title":"RFIPad: Enabling Cost-Efficient and Device-Free In-air Handwriting Using Passive Tags","authors":"H. Ding, Chen Qian, Jinsong Han, Ge Wang, Wei Xi, K. Zhao, Jizhong Zhao","doi":"10.1109/ICDCS.2017.141","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.141","url":null,"abstract":"An important function of smart environments is the ubiquitous access of computing devices. In public areas such as hospitals, libraries, and airports, people may want to interact with nearby computing systems to get information, such as directions to a hospital room, locations of books, and flight departure/arrival information. Touch screen based displays and kiosks, which are commonly used today, may incur extra hardware cost or even possible germ and bacteria infection. This work provides a new solution: users can make queries and inputs by performing in-air handwriting to an array of passive RFID tags, named RFIPad. This input method does not require human hands to carry any device and hence is convenient for applications in public areas. Besides the mobile and contactless property, this system is a cost-efficient extension to current RFID systems: an existing reader can monitor multiple RFIPads while performing its regular applications such as identification and tracking. We implement a prototype of RFIPad using commercial off-the-shelf UHF RFID devices. Experimental results show that RFIPad achieves >91% accuracy in recognizing basic touch-screen operations and English letters.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122970487","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}
Yaodong Huang, Xintong Song, Fan Ye, Yuanyuan Yang, Xiaoming Li
Edge devices (e.g., smartphones, tablets, connected vehicles, IoT nodes) with sensing, storage and communication resources are increasingly penetrating our environments. Many novel applications can be created when nearby peer edge devices share data. Caching can greatly improve the data availability, retrieval robustness and latency. In this paper, we study the unique issue of caching fairness in edge environment. Due to distinct ownership of peer devices, caching load balance is critical. We consider fairness metrics and formulate an integer linear programming problem, which is shown as summation of multiple Connected Facility Location (ConFL) problems. We propose an approximation algorithm leveraging an existing ConFL approximation algorithm, and prove that it preserves a 6.55 approximation ratio. We further develop a distributed algorithm where devices exchange data reachability and identify popular candidates as caching nodes. Extensive evaluation shows that compared with existing wireless network caching algorithms, our algorithms significantly improve data caching fairness, while keeping the contention induced latency similar to the best existing algorithms.
{"title":"Fair Caching Algorithms for Peer Data Sharing in Pervasive Edge Computing Environments","authors":"Yaodong Huang, Xintong Song, Fan Ye, Yuanyuan Yang, Xiaoming Li","doi":"10.1109/ICDCS.2017.151","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.151","url":null,"abstract":"Edge devices (e.g., smartphones, tablets, connected vehicles, IoT nodes) with sensing, storage and communication resources are increasingly penetrating our environments. Many novel applications can be created when nearby peer edge devices share data. Caching can greatly improve the data availability, retrieval robustness and latency. In this paper, we study the unique issue of caching fairness in edge environment. Due to distinct ownership of peer devices, caching load balance is critical. We consider fairness metrics and formulate an integer linear programming problem, which is shown as summation of multiple Connected Facility Location (ConFL) problems. We propose an approximation algorithm leveraging an existing ConFL approximation algorithm, and prove that it preserves a 6.55 approximation ratio. We further develop a distributed algorithm where devices exchange data reachability and identify popular candidates as caching nodes. Extensive evaluation shows that compared with existing wireless network caching algorithms, our algorithms significantly improve data caching fairness, while keeping the contention induced latency similar to the best existing algorithms.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124714529","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}
Graph partitioning is important for optimizing the performance and communication cost of large graph processing jobs. Recently, many graph applications such as social networks store their data on geo-distributed datacenters (DCs) to provide services worldwide with low latency. This raises new challenges to existing graph partitioning methods, due to the costly Wide Area Network (WAN) usage and the multi-levels of network heterogeneities in geo-distributed DCs. In this paper, we propose a geo-aware graph partitioning method named G-Cut, which aims at minimizing the inter-DC data transfer time of graph processing jobs in geo-distributed DCs while satisfying the WAN usage budget. G-Cut adopts two novel optimization phases which address the two challenges in WAN usage and network heterogeneities separately. G-Cut can be also applied to partition dynamic graphs thanks to its light-weight runtime overhead. We evaluate the effectiveness and efficiency of G-Cut using realworld graphs with both real geo-distributed DCs and simulations. Evaluation results show that G-Cut can reduce the inter-DC data transfer time by up to 58% and reduce the WAN usage by up to 70% compared to state-of-the-art graph partitioning methods with a low runtime overhead.
{"title":"On Achieving Efficient Data Transfer for Graph Processing in Geo-Distributed Datacenters","authors":"Amelie Chi Zhou, Shadi Ibrahim, Bingsheng He","doi":"10.1109/ICDCS.2017.98","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.98","url":null,"abstract":"Graph partitioning is important for optimizing the performance and communication cost of large graph processing jobs. Recently, many graph applications such as social networks store their data on geo-distributed datacenters (DCs) to provide services worldwide with low latency. This raises new challenges to existing graph partitioning methods, due to the costly Wide Area Network (WAN) usage and the multi-levels of network heterogeneities in geo-distributed DCs. In this paper, we propose a geo-aware graph partitioning method named G-Cut, which aims at minimizing the inter-DC data transfer time of graph processing jobs in geo-distributed DCs while satisfying the WAN usage budget. G-Cut adopts two novel optimization phases which address the two challenges in WAN usage and network heterogeneities separately. G-Cut can be also applied to partition dynamic graphs thanks to its light-weight runtime overhead. We evaluate the effectiveness and efficiency of G-Cut using realworld graphs with both real geo-distributed DCs and simulations. Evaluation results show that G-Cut can reduce the inter-DC data transfer time by up to 58% and reduce the WAN usage by up to 70% compared to state-of-the-art graph partitioning methods with a low runtime overhead.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121290706","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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