Multi-objective data placement for multi-cloud socially aware services

Abstract

Abstract-Socially aware services often have a large user base and data of users have to be partitioned and replicated over multiple geographically distributed clouds. Choosing in which cloud to place data, however, is difficult. Effective data placements entail meeting multiple system objectives, including reducing the usage of cloud resources, providing good service quality to users, and even minimizing the carbon footprint, while facing critical challenges such as the interconnection of social data, the conflicting requirements of different objectives, and the customized multi-cloud data access policies.In this paper, we study multi-objective optimization for placing users' data over multiple clouds for socially aware services. We build a model framework that can accommodate a range of different objectives, and based on this model we formulate the optimization problem. Leveraging graph cuts, we propose an optimization approach that decomposes our original problem into two simpler subproblems and solves them alternately in multiple rounds. We carry out evaluations using a large group of realworld geographically distributed users with realistic interactions, and place users' data over 10 clouds all across the US. We demonstrate results that are significantly superior to standard and de facto methods in all objectives, and also show that our approach is capable of exploring trade-offs among objectives, converges fast and scales to a huge user base.

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

“…fl a g = 0 (10) else = + 1 (11) end if (12) end while (13) end if (14) end for (15) Classify the VMs in V as several special VMs (16) end for (17) Return SV Algorithm 1: Special VM identification for data access.…”

“…Data placement has a direct impact on the data access efficiency and the cost for data storage, as the locations of big data could affect the overhead for the service renting and the access time for data extraction. Therefore, reasonable and efficient data placement methods are essential to the performance of big data processing [13][14][15]. For the data placement in hybrid clouds, the payment of public cloud services and the energy consumption generated in the private cloud are key factors to determine the locations of the datasets for the execution of the privacy-aware applications.…”

Adaptive Data Placement for Improving Performance of Online Social Network Services in a Multicloud Environment

“…technique to place the data of user and their friends as close as possible [3]. This is because if the user accesses their friends' data without relaying the user's request to another cloud, 269 418 316 281 240 105 22 22 7 5 33 120 111 287 276 285 277 276 296 309 250 179 312 300 6166 6761 7135 6891 5058 3254 2090 1381 1262 1218 1381 1934 3051 3810 4871 5616 5574 5350 5404 5477 4668 4780 4755 5309 127 144 152 108 127 120 77 54 48 51 56 85 90 107 137 152 145 112 76 62 86 66 81 115 35 22 36 26 18 11 5 4 13 18 109 90 61 58 41 37 56 44 33 22 48 58 57 43 468 600 545 419 276 196 158 103 112 152 184 209 311 450 491 573 563 549 483 487 495 389 411 410 354 411 382 244 192 209 132 100 85 78 77 128 183 274 318 334 277 241 228 310 244 227 236 the additional latency delay is avoided.…”

“…Some studies have addressed this issue by continuously monitoring the service's performance and provisioning resources additionally as needed. Other studies have addressed this issue by applying a more efficient replication technique [1][2][3][4][5][6].…”

“…First, in most Multicloud studies and projects, user data are redundantly stored in several clouds using the master-slave replications technique [3]. In particular, where there are more than 20 clouds, such as the United States [7][8][9], the users benefit in terms of latency delay by accessing the closest cloud to use the OSN service [1,2].…”