In this paper, we propose a novel Multi-keyword Fuzzy Symmetric Searchable Encryption (SSE) with patterns hidden, namely MFSSE. In MFSSE, the search trapdoor can be modified differently each time even if the keywords are the same when performing multi-keyword search to prevent the leakage of search patterns. Moreover, MFSSE modifies the search trapdoor by introducing random false negative and false positive errors to resist access pattern leakage. Furthermore, MFSSE utilizes efficient cryptographic algorithms (e.g., Locality-Sensitive Hashing) and lightweight operations (such as, integer addition, matrix multiplication, etc.) to minimize computational and communication, and storage overheads on mobile devices while meeting security and functional requirements. Specifically, its query process requires only a single round of communication, in which, the communication cost is linearly related to the number of the documents in the database, and is independent of the total number of keywords and the number of queried keywords; its computational complexity for matching a document is $O(1)$ ; and it requires only a small amount of fixed local storage (i.e., secret key) to be suitable for mobile scenarios. The experimental results demonstrate that MFSSE can prevent the leakage of access patterns and search patterns, while keeping a low communication and computation overheads.
{"title":"MFSSE: Multi-Keyword Fuzzy Ranked Symmetric Searchable Encryption With Pattern Hidden in Mobile Cloud Computing","authors":"Dajiang Chen;Zeyu Liao;Zhidong Xie;Ruidong Chen;Zhen Qin;Mingsheng Cao;Hong-Ning Dai;Kuan Zhang","doi":"10.1109/TCC.2024.3430237","DOIUrl":"10.1109/TCC.2024.3430237","url":null,"abstract":"In this paper, we propose a novel Multi-keyword Fuzzy Symmetric Searchable Encryption (SSE) with patterns hidden, namely MFSSE. In MFSSE, the search trapdoor can be modified differently each time even if the keywords are the same when performing multi-keyword search to prevent the leakage of search patterns. Moreover, MFSSE modifies the search trapdoor by introducing random false negative and false positive errors to resist access pattern leakage. Furthermore, MFSSE utilizes efficient cryptographic algorithms (e.g., Locality-Sensitive Hashing) and lightweight operations (such as, integer addition, matrix multiplication, etc.) to minimize computational and communication, and storage overheads on mobile devices while meeting security and functional requirements. Specifically, its query process requires only a single round of communication, in which, the communication cost is linearly related to the number of the documents in the database, and is independent of the total number of keywords and the number of queried keywords; its computational complexity for matching a document is \u0000<inline-formula><tex-math>$O(1)$</tex-math></inline-formula>\u0000; and it requires only a small amount of fixed local storage (i.e., secret key) to be suitable for mobile scenarios. The experimental results demonstrate that MFSSE can prevent the leakage of access patterns and search patterns, while keeping a low communication and computation overheads.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1042-1057"},"PeriodicalIF":5.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-10DOI: 10.1109/TCC.2024.3426282
Ahmad Taghinezhad-Niar;Javid Taheri
Emerging computing paradigms like mist, edge, and fog computing address challenges in the real-time processing of vast Internet of Things (IoT) applications. Alongside, cloud computing offers a suitable platform for executing services. Together, they form a multi-tier computing environment known as compute-continuum to efficiently enhance data management and task execution of real-time tasks. The primary considerations for compute-continuum include variations in resource configuration and network architecture, rental cost model, application security needs, energy consumption, transmission latency, and system reliability. To address these problems, we propose two scheduling algorithms (RCSECH and RSECH) for real-time multi-workflow scheduling frameworks. Both algorithms optimize for rental cost, energy consumption, and task reliability when scheduling real-time workflows while considering deadlines and security requirements as constraints. RCSECH also factors in reliability alongside these constraints. The environment under investigation consists of a compute-continuum architecture consisting of mist, edge, fog, and cloud layers, each potentially composed of heterogeneous resources. The framework undergoes evaluation via simulation experiments, revealing promising results. Specifically, the framework exhibits the capability to enhance reliability by up to 7%, reduce energy consumption by 8%, surpass reliability constraints by more than 25%, and generate cost savings by at least 15%.
{"title":"Security, Reliability, Cost, and Energy-Aware Scheduling of Real-Time Workflows in Compute-Continuum Environments","authors":"Ahmad Taghinezhad-Niar;Javid Taheri","doi":"10.1109/TCC.2024.3426282","DOIUrl":"10.1109/TCC.2024.3426282","url":null,"abstract":"Emerging computing paradigms like mist, edge, and fog computing address challenges in the real-time processing of vast Internet of Things (IoT) applications. Alongside, cloud computing offers a suitable platform for executing services. Together, they form a multi-tier computing environment known as compute-continuum to efficiently enhance data management and task execution of real-time tasks. The primary considerations for compute-continuum include variations in resource configuration and network architecture, rental cost model, application security needs, energy consumption, transmission latency, and system reliability. To address these problems, we propose two scheduling algorithms (RCSECH and RSECH) for real-time multi-workflow scheduling frameworks. Both algorithms optimize for rental cost, energy consumption, and task reliability when scheduling real-time workflows while considering deadlines and security requirements as constraints. RCSECH also factors in reliability alongside these constraints. The environment under investigation consists of a compute-continuum architecture consisting of mist, edge, fog, and cloud layers, each potentially composed of heterogeneous resources. The framework undergoes evaluation via simulation experiments, revealing promising results. Specifically, the framework exhibits the capability to enhance reliability by up to 7%, reduce energy consumption by 8%, surpass reliability constraints by more than 25%, and generate cost savings by at least 15%.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 3","pages":"954-965"},"PeriodicalIF":5.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As dependence on Content Delivery Networks (CDNs) increases, there is a growing need for innovative solutions to optimize cache performance amid increasing traffic and complicated cache-sharing workloads. Allocating exclusive resources to applications in CDNs boosts the overall cache hit ratio (OHR), enhancing efficiency. However, the traditional method of creating the miss ratio curve (MRC) is unsuitable for CDNs due to the diverse sizes of items and the vast number of applications, leading to high computational overhead and performance inconsistency. To tackle this issue, we propose a l ightweight and a daptive cache p artitioning scheme called $varepsilon$ -LAP. This scheme uses a corresponding shadow cache for each partition and sorts them based on the average hit numbers on the granularity unit in the shadow caches. During partition resizing, $varepsilon$ -LAP transfers storage capacity, measured in units of granularity, from the $(N-k+1)$ -th ( $kleq frac{N}{2}$ ) partition to the $k$ -th partition. A learning threshold parameter, i.e., $varepsilon$ , is also introduced to prudently determine when to resize partitions, improving caching efficiency. This can eliminate about 96.8% of unnecessary partition resizing without compromising performance. $varepsilon$ -LAP, when deployed in PicCloud at Tencent , improved OHR by 9.34% and reduced the average user access latency by 12.5 ms. Experimental results show that $varepsilon$ -LAP outperforms other cache partitioning schemes in terms of both OHR and access latency, and it effectively adapts to workload variations.
{"title":"$varepsilon$ɛ-LAP: A Lightweight and Adaptive Cache Partitioning Scheme With Prudent Resizing Decisions for Content Delivery Networks","authors":"Peng Wang;Yu Liu;Ziqi Liu;Zhelong Zhao;Ke Liu;Ke Zhou;Zhihai Huang","doi":"10.1109/TCC.2024.3420454","DOIUrl":"10.1109/TCC.2024.3420454","url":null,"abstract":"As dependence on Content Delivery Networks (CDNs) increases, there is a growing need for innovative solutions to optimize cache performance amid increasing traffic and complicated cache-sharing workloads. Allocating exclusive resources to applications in CDNs boosts the overall cache hit ratio (OHR), enhancing efficiency. However, the traditional method of creating the miss ratio curve (MRC) is unsuitable for CDNs due to the diverse sizes of items and the vast number of applications, leading to high computational overhead and performance inconsistency. To tackle this issue, we propose a \u0000<u>l</u>\u0000ightweight and \u0000<u>a</u>\u0000daptive cache \u0000<u>p</u>\u0000artitioning scheme called \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP. This scheme uses a corresponding shadow cache for each partition and sorts them based on the average hit numbers on the granularity unit in the shadow caches. During partition resizing, \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP transfers storage capacity, measured in units of granularity, from the \u0000<inline-formula><tex-math>$(N-k+1)$</tex-math></inline-formula>\u0000-th (\u0000<inline-formula><tex-math>$kleq frac{N}{2}$</tex-math></inline-formula>\u0000) partition to the \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-th partition. A learning threshold parameter, i.e., \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000, is also introduced to prudently determine when to resize partitions, improving caching efficiency. This can eliminate about 96.8% of unnecessary partition resizing without compromising performance. \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP, when deployed in \u0000<i>PicCloud</i>\u0000 at \u0000<i>Tencent</i>\u0000, improved OHR by 9.34% and reduced the average user access latency by 12.5 ms. Experimental results show that \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-LAP outperforms other cache partitioning schemes in terms of both OHR and access latency, and it effectively adapts to workload variations.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 3","pages":"942-953"},"PeriodicalIF":5.3,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-18DOI: 10.1109/TCC.2024.3415165
Houming Qiu;Kun Zhu;Dusit Niyato
The Industrial Metaverse is driving a new revolution wave for smart manufacturing domain by reproducing the real industrial environment in a virtual space. Real-time synchronization and rendering of all industrial factors result in numerous time-sensitive and computation-intensive tasks, especially matrix multiplication. Distributed edge computing (DEC) can be exploited to handle these tasks due to its low-latency and powerful computing. In this paper, we propose an efficient and reliable coded DEC framework to compute large-scale matrix multiplication tasks. However, an existence of stragglers causes high computation latency that seriously limits the application of DEC in the Industrial Metaverse. To mitigate the impact of stragglers, we design a secure and flexible PolyDot (SFPD) code, which enables information theoretic security (ITS) protection. Several improvements can be achieved with the proposed SFPD. First, it can achieve a smaller recovery threshold than that of the existing codes in almost all settings. And compared with the original PolyDot codes, our SFPD code considers the extra workers required to add ITS protection. It also provides a flexible tradeoff between recovery threshold and communication & computation loads by simply adjusting two given storage parameters $p$ and $t$ . Furthermore, as an important application scenario, the SFPD code is employed to secure model training in machine learning, which can alleviate the straggler effects and protect ITS of raw data. The experiments demonstrate that the SFPD code can significantly speed up the training process while providing ITS of data. Finally, we provide comprehensive performance analysis which shows the superiority of the SFPD code.
{"title":"Secure and Flexible Coded Distributed Matrix Multiplication Based on Edge Computing for Industrial Metaverse","authors":"Houming Qiu;Kun Zhu;Dusit Niyato","doi":"10.1109/TCC.2024.3415165","DOIUrl":"10.1109/TCC.2024.3415165","url":null,"abstract":"The Industrial Metaverse is driving a new revolution wave for smart manufacturing domain by reproducing the real industrial environment in a virtual space. Real-time synchronization and rendering of all industrial factors result in numerous time-sensitive and computation-intensive tasks, especially matrix multiplication. Distributed edge computing (DEC) can be exploited to handle these tasks due to its low-latency and powerful computing. In this paper, we propose an efficient and reliable coded DEC framework to compute large-scale matrix multiplication tasks. However, an existence of stragglers causes high computation latency that seriously limits the application of DEC in the Industrial Metaverse. To mitigate the impact of stragglers, we design a secure and flexible PolyDot (SFPD) code, which enables information theoretic security (ITS) protection. Several improvements can be achieved with the proposed SFPD. First, it can achieve a smaller recovery threshold than that of the existing codes in almost all settings. And compared with the original PolyDot codes, our SFPD code considers the extra workers required to add ITS protection. It also provides a flexible tradeoff between recovery threshold and communication & computation loads by simply adjusting two given storage parameters \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$t$</tex-math></inline-formula>\u0000. Furthermore, as an important application scenario, the SFPD code is employed to secure model training in machine learning, which can alleviate the straggler effects and protect ITS of raw data. The experiments demonstrate that the SFPD code can significantly speed up the training process while providing ITS of data. Finally, we provide comprehensive performance analysis which shows the superiority of the SFPD code.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1026-1041"},"PeriodicalIF":5.3,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1109/TCC.2024.3414440
Fahao Chen;Peng Li;Celimuge Wu;Song Guo
Distributed machine learning (DML) has shown great promise in accelerating model training on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning jobs share GPU clusters, where the most fundamental and critical challenge is how to efficiently schedule these jobs on GPUs. However, existing works about DML job scheduling are constrained to settings with homogeneous GPUs. GPU heterogeneity is common in practice, but its influence on multiple DML job scheduling has been seldom studied. Moreover, DML jobs have internal structures that contain great parallelism potentials, which have not yet been fully exploited in the heterogeneous computing environment. In this paper, we propose Hare , a DML job scheduler that exploits both inter-job and intra-job parallelism in a heterogeneous GPU cluster. Hare adopts a relaxed fixed-scale synchronization scheme that allows independent tasks to be flexibly scheduled within a training round. Given full knowledge of job arrival time and sizes, we propose a fast heuristic algorithm to minimize the average job completion time and derive its theoretical bound is derived. Without prior knowledge of jobs, we propose an online algorithm based on the Heterogeneity-aware Least-Attained Service (HLAS) policy. We evaluate Hare using a small-scale testbed and a trace-driven simulator. The results show that it can outperform the state-of-the-art, achieving a performance improvement of about 2.94×.
{"title":"Non-Clairvoyant Scheduling of Distributed Machine Learning With Inter-Job and Intra-Job Parallelism on Heterogeneous GPUs","authors":"Fahao Chen;Peng Li;Celimuge Wu;Song Guo","doi":"10.1109/TCC.2024.3414440","DOIUrl":"10.1109/TCC.2024.3414440","url":null,"abstract":"Distributed machine learning (DML) has shown great promise in accelerating model training on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning jobs share GPU clusters, where the most fundamental and critical challenge is how to efficiently schedule these jobs on GPUs. However, existing works about DML job scheduling are constrained to settings with homogeneous GPUs. GPU heterogeneity is common in practice, but its influence on multiple DML job scheduling has been seldom studied. Moreover, DML jobs have internal structures that contain great parallelism potentials, which have not yet been fully exploited in the heterogeneous computing environment. In this paper, we propose \u0000<italic>Hare</i>\u0000, a DML job scheduler that exploits both inter-job and intra-job parallelism in a heterogeneous GPU cluster. \u0000<italic>Hare</i>\u0000 adopts a relaxed fixed-scale synchronization scheme that allows independent tasks to be flexibly scheduled within a training round. Given full knowledge of job arrival time and sizes, we propose a fast heuristic algorithm to minimize the average job completion time and derive its theoretical bound is derived. Without prior knowledge of jobs, we propose an online algorithm based on the Heterogeneity-aware Least-Attained Service (HLAS) policy. We evaluate \u0000<italic>Hare</i>\u0000 using a small-scale testbed and a trace-driven simulator. The results show that it can outperform the state-of-the-art, achieving a performance improvement of about 2.94×.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"1011-1025"},"PeriodicalIF":5.3,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.1109/TCC.2024.3410390
Zuodong Jin;Dan Tao;Peng Qi;Ruipeng Gao
Due to the unrestricted location of cloud resources, an increasing number of users are opting to apply for them. However, determining the appropriate resource quota has always been a challenge for applicants. Excessive quotas can result in resource wastage, while insufficient quotas can pose stability risks. Therefore, it's necessary to propose an adaptive quota scheme for cloud resource. Most existing researches have designed fixed quota schemes for all users, without considering the differences among users. To solve this, we propose an adaptive cloud quota scheme through dynamic portraits and task-resource optimal matching. Specifically, we first aggregate information from text, statistical, and fractal three dimensions to establish dynamic portraits. On this basis, the bidirectional mixture of experts (Bi-MoE) model is designed to match the most suitable resource combinations for tasks. Moreover, we define the time-varying rewards and utilize portrait-based reinforcement learning (PRL) to obtain the optimal quotas, which ensures stability and reduces waste. Extensive simulation results demonstrate that the proposed scheme achieves a memory utilization rate of around 70%. Additionally, it shows improvements in task execution stability, throughput, and the percentage of effective execution time.
{"title":"An Adaptive Cloud Resource Quota Scheme Based on Dynamic Portraits and Task-Resource Matching","authors":"Zuodong Jin;Dan Tao;Peng Qi;Ruipeng Gao","doi":"10.1109/TCC.2024.3410390","DOIUrl":"10.1109/TCC.2024.3410390","url":null,"abstract":"Due to the unrestricted location of cloud resources, an increasing number of users are opting to apply for them. However, determining the appropriate resource quota has always been a challenge for applicants. Excessive quotas can result in resource wastage, while insufficient quotas can pose stability risks. Therefore, it's necessary to propose an adaptive quota scheme for cloud resource. Most existing researches have designed fixed quota schemes for all users, without considering the differences among users. To solve this, we propose an adaptive cloud quota scheme through dynamic portraits and task-resource optimal matching. Specifically, we first aggregate information from text, statistical, and fractal three dimensions to establish dynamic portraits. On this basis, the bidirectional mixture of experts (Bi-MoE) model is designed to match the most suitable resource combinations for tasks. Moreover, we define the time-varying rewards and utilize portrait-based reinforcement learning (PRL) to obtain the optimal quotas, which ensures stability and reduces waste. Extensive simulation results demonstrate that the proposed scheme achieves a memory utilization rate of around 70%. Additionally, it shows improvements in task execution stability, throughput, and the percentage of effective execution time.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"996-1010"},"PeriodicalIF":5.3,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geographically distributed data centers (DCs) have emerged as significant energy consumers, which has led to the integration of renewable energy sources (RES) into DC power provisioning systems. However, the intermittent nature of RES and the randomness of user requests can cause significant fluctuations in DC operating power. It can be detrimental to the operation of IT equipment and lead to instability in the power grid. In this paper, aiming for tightly coupled interconnection scenarios with multi-data centers in varying regions, a multi-data center tie-line power smoothing method based on demand response is proposed. By modulating the power load of server clusters with workload scheduling, we establish a control model combined with intra-DC temporal task migration and inter-DC spatial task migration to deal with high-frequency power fluctuations. The uninterruptible power supply (UPS) battery control model is established to tackle low-frequency fluctuations. Furthermore, we design the two-stage heuristic power regulation algorithm to achieve the best practice of smoothing effect by real-time tracking of power targets after two-layer filtering. Finally, this paper performs a detailed performance simulation evaluation based on tracking data from a real DC and wind and photovoltaic (PV) new energy generation data, using four interconnected DC parks of different sizes across different regions as examples. The simulation results demonstrate that the proposed method effectively smoothing the multi-data center's tie-line power. Additionally, inter-DC temporal task migration serves as a viable solution to overcome the limitations of task migration response within a single DC, reducing the frequency of UPS battery bank charges and discharges, which in turn prolongs their service life. This approach facilitates the utilization of RES while maintaining power quality, and it also aids in reducing the escalating operation and maintenance expenses of DCs.
{"title":"Multi-Data Center Tie-Line Power Smoothing Method Based on Demand Response","authors":"Ting Yang;Yuxing Hou;Shaotang Cai;Jie Yu;Haibo Pen","doi":"10.1109/TCC.2024.3410377","DOIUrl":"10.1109/TCC.2024.3410377","url":null,"abstract":"Geographically distributed data centers (DCs) have emerged as significant energy consumers, which has led to the integration of renewable energy sources (RES) into DC power provisioning systems. However, the intermittent nature of RES and the randomness of user requests can cause significant fluctuations in DC operating power. It can be detrimental to the operation of IT equipment and lead to instability in the power grid. In this paper, aiming for tightly coupled interconnection scenarios with multi-data centers in varying regions, a multi-data center tie-line power smoothing method based on demand response is proposed. By modulating the power load of server clusters with workload scheduling, we establish a control model combined with intra-DC temporal task migration and inter-DC spatial task migration to deal with high-frequency power fluctuations. The uninterruptible power supply (UPS) battery control model is established to tackle low-frequency fluctuations. Furthermore, we design the two-stage heuristic power regulation algorithm to achieve the best practice of smoothing effect by real-time tracking of power targets after two-layer filtering. Finally, this paper performs a detailed performance simulation evaluation based on tracking data from a real DC and wind and photovoltaic (PV) new energy generation data, using four interconnected DC parks of different sizes across different regions as examples. The simulation results demonstrate that the proposed method effectively smoothing the multi-data center's tie-line power. Additionally, inter-DC temporal task migration serves as a viable solution to overcome the limitations of task migration response within a single DC, reducing the frequency of UPS battery bank charges and discharges, which in turn prolongs their service life. This approach facilitates the utilization of RES while maintaining power quality, and it also aids in reducing the escalating operation and maintenance expenses of DCs.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"983-995"},"PeriodicalIF":5.3,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-04DOI: 10.1109/TCC.2024.3408905
Sabrina De Capitani di Vimercati;Dario Facchinetti;Sara Foresti;Gianluca Oldani;Stefano Paraboschi;Matthew Rossi;Pierangela Samarati
We address the problem of indexing encrypted data outsourced to an external cloud server to support server-side execution of multi-attribute queries. Our approach partitions the dataset in groups with the same number of tuples, and associates all tuples in a group with the same combination of index values, so to guarantee protection against static inferences. Our indexing approach does not require any modifications to the server-side software stack, and requires limited storage at the client for query support. The experimental evaluation considers, for the storage of the encrypted and indexed dataset, both a relational database (PostgreSQL) and a key-value database (Redis). We carried out extensive experiments evaluating client-storage requirements and query performance. The experimental results confirm the efficiency of our solution. The proposal is supported by an open source implementation.
{"title":"Multi-Dimensional Flat Indexing for Encrypted Data","authors":"Sabrina De Capitani di Vimercati;Dario Facchinetti;Sara Foresti;Gianluca Oldani;Stefano Paraboschi;Matthew Rossi;Pierangela Samarati","doi":"10.1109/TCC.2024.3408905","DOIUrl":"10.1109/TCC.2024.3408905","url":null,"abstract":"We address the problem of indexing encrypted data outsourced to an external cloud server to support server-side execution of multi-attribute queries. Our approach partitions the dataset in groups with the same number of tuples, and associates all tuples in a group with the same combination of index values, so to guarantee protection against static inferences. Our indexing approach does not require any modifications to the server-side software stack, and requires limited storage at the client for query support. The experimental evaluation considers, for the storage of the encrypted and indexed dataset, both a relational database (PostgreSQL) and a key-value database (Redis). We carried out extensive experiments evaluating client-storage requirements and query performance. The experimental results confirm the efficiency of our solution. The proposal is supported by an open source implementation.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 3","pages":"928-941"},"PeriodicalIF":5.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud-assisted computation empowers resource-constrained clients to efficiently tackle computationally intensive tasks by outsourcing them to resource-rich cloud servers. In the current era of Big Data, the widespread need to solve large-scale modular linear systems of equations ( $mathcal {LMLSE}$ ) of the form $mathbf {A}mathbf {x}equiv mathbf {b};{rm mod};{q}$ poses a significant challenge, particularly for lightweight devices. This paper delves into the secure outsourcing of $mathcal {LMLSE}$ under a malicious single-server model and, to the best of our knowledge, introduces the inaugural protocol tailored to this specific context. The cornerstone of our protocol lies in the innovation of a novel matrix encryption method based on sparse unimodular matrix transformations. This novel technique bestows our protocol with several key advantages. First and foremost, it ensures robust privacy for all computation inputs, encompassing $mathbf {A},mathbf {b}, q$ , and the output $mathbf {x}$ , as validated by thorough theoretical analysis. Second, the protocol delivers optimal verifiability, enabling clients to detect cloud server misbehavior with an unparalleled probability of 1. Furthermore, it boasts high efficiency, requiring only a single interaction between the client and the cloud server, significantly reducing local-client time costs. For an $m$ -by- $n$ matrix $mathbf {A}$ , a given parameter $lambda =omega (log q)$ , and $rho =2.371552$ , the time complexity is diminished from $O(max lbrace m n^{rho -1}, m^{rho -2} n^{2}rbrace cdot (log q)^{2})$ to $O((mn+m^{2})lambda log q+mn(log q)^{2})$ . The comprehensive results of our experimental performance evaluations substantiate the protocol's practical efficiency and effectiveness.
{"title":"How to Securely and Efficiently Solve the Large-Scale Modular System of Linear Equations on the Cloud","authors":"Chengliang Tian;Jia Yu;Panpan Meng;Guoyan Zhang;Weizhong Tian;Yan Zhang","doi":"10.1109/TCC.2024.3408240","DOIUrl":"10.1109/TCC.2024.3408240","url":null,"abstract":"Cloud-assisted computation empowers resource-constrained clients to efficiently tackle computationally intensive tasks by outsourcing them to resource-rich cloud servers. In the current era of Big Data, the widespread need to solve large-scale modular linear systems of equations (\u0000<inline-formula><tex-math>$mathcal {LMLSE}$</tex-math></inline-formula>\u0000) of the form \u0000<inline-formula><tex-math>$mathbf {A}mathbf {x}equiv mathbf {b};{rm mod};{q}$</tex-math></inline-formula>\u0000 poses a significant challenge, particularly for lightweight devices. This paper delves into the secure outsourcing of \u0000<inline-formula><tex-math>$mathcal {LMLSE}$</tex-math></inline-formula>\u0000 under a malicious single-server model and, to the best of our knowledge, introduces the inaugural protocol tailored to this specific context. The cornerstone of our protocol lies in the innovation of a novel matrix encryption method based on sparse unimodular matrix transformations. This novel technique bestows our protocol with several key advantages. First and foremost, it ensures robust privacy for all computation inputs, encompassing \u0000<inline-formula><tex-math>$mathbf {A},mathbf {b}, q$</tex-math></inline-formula>\u0000, and the output \u0000<inline-formula><tex-math>$mathbf {x}$</tex-math></inline-formula>\u0000, as validated by thorough theoretical analysis. Second, the protocol delivers optimal verifiability, enabling clients to detect cloud server misbehavior with an unparalleled probability of 1. Furthermore, it boasts high efficiency, requiring only a single interaction between the client and the cloud server, significantly reducing local-client time costs. For an \u0000<inline-formula><tex-math>$m$</tex-math></inline-formula>\u0000-by-\u0000<inline-formula><tex-math>$n$</tex-math></inline-formula>\u0000 matrix \u0000<inline-formula><tex-math>$mathbf {A}$</tex-math></inline-formula>\u0000, a given parameter \u0000<inline-formula><tex-math>$lambda =omega (log q)$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$rho =2.371552$</tex-math></inline-formula>\u0000, the time complexity is diminished from \u0000<inline-formula><tex-math>$O(max lbrace m n^{rho -1}, m^{rho -2} n^{2}rbrace cdot (log q)^{2})$</tex-math></inline-formula>\u0000 to \u0000<inline-formula><tex-math>$O((mn+m^{2})lambda log q+mn(log q)^{2})$</tex-math></inline-formula>\u0000. The comprehensive results of our experimental performance evaluations substantiate the protocol's practical efficiency and effectiveness.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 3","pages":"913-927"},"PeriodicalIF":5.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight.
{"title":"Decentralized Funding of Public Goods in Blockchain System: Leveraging Expert Advice","authors":"Jichen Li;Yukun Cheng;Wenhan Huang;Mengqian Zhang;Jiarui Fan;Xiaotie Deng;Jan Xie;Jie Zhang","doi":"10.1109/TCC.2024.3394973","DOIUrl":"10.1109/TCC.2024.3394973","url":null,"abstract":"Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"725-736"},"PeriodicalIF":6.5,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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