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The zombie node and ii a given percentage of ghost nodes that consistency level is set to one and the time scale is made changes for each test e. The most evident result is that the completion one node out of four is maliciously signaled as dead. The decrease of the throughput. This is due to the very few re- most evident result is that the attack becomes more effec- quests for which the coordinator asks the data to the zombie tive as the consistency gets stricter.
This was reasonable node see Section 2 for the read path. Indeed, when a data to expect because the tighter the consistency is, the more request is sent from the coordinator to the zombie node, the the required alive replicas are, and the practical effect of the read remains blocked at the client side until a timeout of attack is indeed to lower the number of nodes that seem to 10 seconds expires at the coordinator because no reply has be running.
The impact of the number of ghosts when the been received yet. In the case of consistency level set to consistency is fixed is trickier to understand.
We run three quorum, the completion time of the test batch is two order batches for each distinct consistency level and number of of magnitude larger when the attack takes place see Fig. Using the same notation, Fig.
The figures show a similar behav- those sent by the other nodes. This is a non determinis- ior. There is no extension of the completion time, because tic process because there is no synchronization at all among a write only fails when all the required replicas are seen as the scheduling of gossiping operations of distinct nodes. Fur- dead by the coordinator, and this can be detected immedi- thermore, which replicas are required to fulfill a read request ately without having to wait for any timeout to expire.
The is determined by the key specified at runtime in the request only effect of the attack is that some writes fail, which again Figure 5: Comparison between safe and attacked scenario on Figure 7: Comparison between original and robust Cassan- successful reads count over time when consistency level is quorum. Figure 8: Comparison between original and robust Cassan- Figure 6: Comparison between safe and attacked scenario on suc- cessful writes count over time when consistency level is quorum.
Let us notice that the proposed mechanism This section proposes a possible solution to obviate to the also works in the presence of a more powerful attacker able absence of an authentication mechanism within Cassandra. The basic idea is to modify the original membership proto- Evaluations We evaluated the impact of this enhanced ver- col, forcing the nodes to sign critical information through sion of the membership protocol on the performances deliv- asymmetric cryptography. Using the same setting described in Section 4, we ran a set of batches for different levels of con- sistency, where each batch consists in writes followed by as many reads.
We then evaluated the average request 5. At each gossip round, a node crease is mainly due to the encryption and decryption op- i: i updates its heartbeat, ii generates the signature, iii erations performed on the signatures and they involve that creates its digest and iv sends it out in the list of gossip small overhead thanks to the very low frequency they get digests.
When node j receives the digest, it checks which executed. If average latency at steady state is about 0. When node j gets the content of the signature, it compares the IP address 6. If j has to store newer informa- can be divided into two categories: replicated state machines  and byzantine quorum systems [11, 3, 1]. Both the ap- making some read and write requests fail randomly, with proaches are based on the idea that the state of the storage a consequent relevant degradation of delivered performance is replicated among processes and the main difference is in to underlying applications.
This attack degrades expected the number of replicas involved simultaneously in the state performance in terms of throughput and latency of read maintenance protocol. These solutions work considering a operations of one or two orders of magnitude according to stable membership, while our attacks inject uncertainty into the selected consistency level while write operations increase the local view of each node, which creates troubles to tradi- their failure rate.
Additionally the attack is very difficult to tional BFT protocols to ensure correctness along the time. The latter minimal overhead as shown by the experiments. Baldoni, S. Bonomi, and A. An the validity of the above invariant along the time. The at- algorithm for implementing bft registers in distributed tack shown in this paper is actually an intrusion where the systems with bounded churn.
Baldoni, M. Platania, L. Querzoni, and S.
Note that the byzantine node is not Practical uniform peer sampling under churn. Software reju-  R. Synchronous byzantine quorum systems. Distributed Computing, 13 1 —52, Jan. However, not all the fake information injected by the byzan-  E.
Bortnikov, M. Gurevich, I. Keidar, G. Kliot, and tine node before being stopped by the intrusion tolerant sys- A. Brahms: byzantine resilient random tem is removed by the system itself i.
Castro and B. Practical byzantine fault of zombie nodes and the byzantine node could exploit the tolerance and proactive recovery. ACM Trans.
Cisco Annual Security Report, Mechanisms used to spread fake information in this paper  DataStax. Case Study: Adobe, Other papers provides practical solution to the accuracy problem of the  J. The sybil attack. In First International membership in unstructured peer-to-peer including [2, 9]. Jesi, A. Montresor, and M. Secure deploy than Sybil attack, because a malicious node does not peer sampling. Computer Networks, Addition-  A.
Lakshman and P. Cassandra: structured ally, the attack shown in our work is also more difficult to storage system on a p2p network. Malkhi and M. Byzantine quorum systems. Nev- Distributed Computing, 11 4 —, Oct. The single partition will be slowed down.
So try to choose a balanced number of partitions. Here is the table MusicPlaylist. Only one partition will be created with the SongId. There will not be any other partition in the table MusicPlaylist. Data retrieval will be slow by this data model due to the bad primary key. Here is another table MusicPlaylist. Data will be clustered on the basis of SongName.
In this table, each year, a new partition will be created. All the songs of the year will be on the same node. This primary key will be very useful for the data.
Our data retrieval will be fast by this data model. Model Your Data in Cassandra Following things should be kept in mind while modelling your queries. Determine what queries you want to support First of all, determine what queries you want.
For example, do you need? Joins Filtering on which column etc.
Create table according to your queries Create table according to your queries. Create a table that will satisfy your queries.
Try to create a table in such a way that a minimum number of partitions needs to be read. Handling One to One Relationship One to one relationship means two tables have one to one correspondence. For example, the student can register only one course, and I want to search on a student that in which course a particular student is registered in.
So in this case, your table schema should encompass all the details of the student in corresponding to that particular course like the name of the course, roll no of the student, student name, etc.
For example, a course can be studied by many students. I want to search all the students that are studying a particular course.