Distributed storage allocation for multi-class data

Roshandeh, Koosha Pourtahmasi; Noori, Moslem; Ardakani, Masoud; Tellambura, Chintha

doi:10.1109/isit.2017.8006924

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Several papers dealt with the problem of finding allocations in DSSs with fixed code redundancy where either reliabilities or capacities of SN are not homogeneous, e.g., [16], [17], [18], [19], [20], [21], [22]. All these papers seek to find optimal allocations but do not consider attacks to the DSSs brought by possibly colluding malicious SNs.…”

Section: Related Workmentioning

confidence: 99%

On the Impact of Pollution Attacks on Coding-Based Distributed Storage Systems

Gaeta

2022

IEEE Trans.Inform.Forensic Secur.

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Coding-based distributed storage systems (DSS) are employed in many diverse heterogeneous settings, e.g., cloud storage data centers, peer-to-peer systems, wireless sensor networks, fog/edge computing system, to provide better throughput, latency, reliability, scalability, load adaptation, geographical migration and fault tolerance with respect to traditional monolithic enterprise storage systems. Despite the undoubted advantages offered by coding, reliability and security are jeopardized by a pollution attack that can easily disrupt the entire system and degrade performance. In this paper we take an abstract view of a DSS and we investigate by means of mathematical modeling what are the availability, robustness, and timeliness of heterogeneous, coding-based DSS when storage nodes (SN) are unreliable and can be malicious. To this end, we focus on a class of allocations of coded fragments to SNs that we call feasible allocations; the model takes into account both reliability and reactivity of SNs. We define robust availability and timeliness of feasible allocations that we use to characterize the overall performance and robustness of the DSS in a reference scenario. Our analysis reveals that code redundancy is a double-edged sword in a DSS where malicious SNs come into play and that there exists an optimal value of code redundancy regardless all system parameters that maximizes the number of malicious SNs that can be tolerated to achieve maximum DSS performance. We also found that larger codes are preferred over short ones as they yield superior DSS performance in the presence of malicious SNs. Furthermore, when multiple feasible allocations yield the highest DSS performance timeliness can be used as a guide for the choice. Finally, heterogeneity plays a role in determining the timeliness of the maximally spread allocations in the case of targeted attacks.

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Section: Related Workmentioning

confidence: 99%

On the Impact of Pollution Attacks on Coding-Based Distributed Storage Systems

Gaeta

2022

IEEE Trans.Inform.Forensic Secur.

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Data allocation for multi-class distributed storage systems

Roshandeh

Noori

Ardakani

et al. 2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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Distributed storage systems (DSSs) provide a scalable solution for reliably storing massive amounts of data coming from various sources. Heterogeneity of these data sources often means different data classes (types) exist in a DSS, each needing a different level of quality of service (QoS). As a result, efficient data storage and retrieval processes that satisfy various QoS requirements are needed. This paper studies storage allocation, meaning how data of different classes must be spread over the set of storage nodes of a DSS. More specifically, assuming a probabilistic access to the storage nodes, we aim at maximizing the weighted sum of the probability of successful data recovery of data classes, when for each class a minimum QoS (probability of successful recovery) is guaranteed. Solving this optimization problem for a general setup is intractable. Thus, we find the optimal storage allocation when the data of each class is spread minimally over the storage nodes, i.e. minimal spreading allocation (MSA). Using upper bounds on the performance of the optimal storage allocation, we show that the optimal MSA allocation approaches the optimal performance in many practical cases. Computer simulations are also presented to better illustrate the results.

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Distributed storage allocation for multi-class data

Cited by 2 publications

References 12 publications

On the Impact of Pollution Attacks on Coding-Based Distributed Storage Systems

On the Impact of Pollution Attacks on Coding-Based Distributed Storage Systems

Data allocation for multi-class distributed storage systems

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