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The attack goal is to have the malicious VMs achieve co-residence with VMs from as many normal users as possible to facilitate later attacks. First, we assume the cloud receives a random number of VMs from different users at random timings. Furthermore, the request from each user may be realized through a random number of VMs. That may include anything from Custom black air force 1 Trainer window shades and chandeliers to kitchen cabinets. But if you're a serious oenophile (wine lover), you may want to research wine regions. Today, companies build portable devices that will entertain customers where ever they may be. In the case where the cloud does not have a good estimation of the malicious user percentage, this value can always be set as 100% to treat security in the most conservative way, which will result in an allocation solution that minimizes the co-locations of VMs from different users. Attackers taking advantage of the co-residence opportunities may perform diverse attacks against their co-tenants standaert2010introduction ; ristenpart2009hey ; aciiccmez2007power ; aciiccmez2006predicting ; aciiccmez2007yet ; grunwald2002microarchitectural ; chiang2014swiper ; varadarajan2012resource ; momm2011combined ; feng2011shrew , threaten the security of cloud infrastructure and undermine users’ confidence to move to the cloud takabi2010security ; subashini2011survey ; ren2012security ; chow2009controlling

Such resource sharing allows the cloud to maximize the system efficiency by fully utilizing available computing resources. There are a number of solutions proposed to defend against co-residence attacks through performance isolation which requires virtualized computing resource isolation for storage, CPU, cache, memory, and access path networks martin2012timewarp ; wu2012xenpump . 100% EEClouds:DellPowerEdgeR820 since only CPU utilization is sufficient to evaluation whole computing system’s power cost GooglePower . For instance, the authors in waheed2015security proposed to hide real power consumption information from user VMs by deploying a police VM to generate false information. For instance, a randomization way to assign VMs has been proposed azar2014co to make VMs’ deployment unpredictable to attackers. Then the next question is how to quantify these three factors in a reasonable way. In this work, we propose the quantification of these factors as follows. However, due to the complex process of introducing new hardware into existing cloud infrastructure, the adoption of such schemes adds extra cost on hardware and administration. The power cost evaluation is based on the power measurement of a PM at eleven CPU utilization levels at 0%, 10%, 20% … POSTSUBSCRIPT PMs. We can see that either an extremely large or extremely small workload will dramatically increase the cost of the workload inequality