Summary of “A Novel Approach to Prevent Cache-Based Side-Channel Attack in the Cloud”
Read more at https://mrg-goel.medium.com/summary-of-a-novel-approach-to-prevent-cache-based-side-channel-attack-in-the-cloud-2bd802e20155
Double checked locking is used to lazily initialize a field to avoid the synchronization cost on every access. It ensures that after the first check the field is initialized, subsequent checks will see the initialized value without needing to synchronize again. CPU caches can cause problems like false sharing when multiple threads read and write nearby but independent variables in memory. Tools like Java Object Layout and memory barriers can help address issues with caches. Concurrency is difficult and even experts are still learning due to mysterious data races that can occur.
Efficient and Fast Time Series Storage - The missing link in dynamic software...Florian Lautenschlager
(1) Chronix is a time series database that aims to provide both fast queries and efficient storage of operational data through semantic compression, chunking of time series, and multi-dimensional storage of records and their attributes.
(2) Benchmarks on real-world operational data showed that Chronix outperforms related time series databases in write throughput, storage efficiency, and access times for typical analysis queries.
(3) While still a proof-of-concept, Chronix shows potential for fast and efficient operational time series storage and analysis needed for dynamic software monitoring and diagnostics.
DAX: A Widely Distributed Multi-tenant Storage Service for DBMS HostingRui Liu
DAX is a distributed, multi-tenant storage service for hosting DBMS in the cloud. It replicates data across multiple data centers for disaster tolerance. DAX uses optimistic I/O and client-managed durability to reduce read and write latencies. It provides consistency guarantees appropriate for DBMS workloads, ensuring the latest data is visible and a single version survives failures.
Sharding: Past, Present and Future with Krutika DhananjayGluster.org
- Sharding is a client-side translator that splits files into equally sized chunks or shards to improve performance and utilization of storage resources. It sits above the distributed hash table (DHT) in Gluster.
- Sharding benefits virtual machine image storage by allowing data healing and replication at the shard level for better scalability. It also distributes load more evenly across bricks.
- For general purpose use, sharding aims to maximize parallelism during writes while maintaining consistency through atomic operations and locking frameworks. Key challenges include updating file metadata without locking and handling operations like truncates and appends correctly across shards.
This document discusses distributed storage technologies and challenges. It covers persistent memory, distributed storage case studies like GlusterFS and Ceph, and challenges such as network latency. It also addresses how distributed storage systems scale performance and capacity through techniques like horizontal scaling across commodity nodes and maintaining a single namespace.
Decoupling Provenance Capture and Analysis from ExecutionPaul Groth
Presentation for the paper:
Manolis Stamatogiannakis, Paul Groth and Herbert Bos. Decoupling Provenance Capture and Analysis from Execution
Presented at Theory and Practice of Provenance 2015 (TaPP'15)
http://workshops.inf.ed.ac.uk/tapp2015/
Life as a GlusterFS Consultant with Ivan RossiGluster.org
This document describes the experiences of Ivan Rossi working as a Gluster consultant listed on gluster.org. It outlines the types of clients that contact him, including small businesses and those looking for help troubleshooting Gluster issues. It also shares some case studies, such as helping a company that had millions of unorganized files slowing down operations and advising businesses on using Gluster in private and public clouds. The document aims to convey what it is like to be a Gluster consultant and provide advice based on lessons learned from previous work.
IEEE Paper Presentation by Chandan KumarChandan Kumar
This document proposes using time-series forecasting techniques to predict server load in cloud data centers. This would allow for detecting overloaded hosts and migrating virtual machines (VMs) to balance load and reduce energy consumption. Key steps include using exponential smoothing to predict future loads, detecting overloaded hosts when loads exceed thresholds, selecting the least utilized VM to migrate, and choosing destination hosts with minimum increased energy. Simulation results show the proposed Smoothed Exponential Smoothing technique reduces energy consumption, number of overloaded nodes, VM migrations, and SLA violations compared to other algorithms.
Double checked locking is used to lazily initialize a field to avoid the synchronization cost on every access. It ensures that after the first check the field is initialized, subsequent checks will see the initialized value without needing to synchronize again. CPU caches can cause problems like false sharing when multiple threads read and write nearby but independent variables in memory. Tools like Java Object Layout and memory barriers can help address issues with caches. Concurrency is difficult and even experts are still learning due to mysterious data races that can occur.
Efficient and Fast Time Series Storage - The missing link in dynamic software...Florian Lautenschlager
(1) Chronix is a time series database that aims to provide both fast queries and efficient storage of operational data through semantic compression, chunking of time series, and multi-dimensional storage of records and their attributes.
(2) Benchmarks on real-world operational data showed that Chronix outperforms related time series databases in write throughput, storage efficiency, and access times for typical analysis queries.
(3) While still a proof-of-concept, Chronix shows potential for fast and efficient operational time series storage and analysis needed for dynamic software monitoring and diagnostics.
DAX: A Widely Distributed Multi-tenant Storage Service for DBMS HostingRui Liu
DAX is a distributed, multi-tenant storage service for hosting DBMS in the cloud. It replicates data across multiple data centers for disaster tolerance. DAX uses optimistic I/O and client-managed durability to reduce read and write latencies. It provides consistency guarantees appropriate for DBMS workloads, ensuring the latest data is visible and a single version survives failures.
Sharding: Past, Present and Future with Krutika DhananjayGluster.org
- Sharding is a client-side translator that splits files into equally sized chunks or shards to improve performance and utilization of storage resources. It sits above the distributed hash table (DHT) in Gluster.
- Sharding benefits virtual machine image storage by allowing data healing and replication at the shard level for better scalability. It also distributes load more evenly across bricks.
- For general purpose use, sharding aims to maximize parallelism during writes while maintaining consistency through atomic operations and locking frameworks. Key challenges include updating file metadata without locking and handling operations like truncates and appends correctly across shards.
This document discusses distributed storage technologies and challenges. It covers persistent memory, distributed storage case studies like GlusterFS and Ceph, and challenges such as network latency. It also addresses how distributed storage systems scale performance and capacity through techniques like horizontal scaling across commodity nodes and maintaining a single namespace.
Decoupling Provenance Capture and Analysis from ExecutionPaul Groth
Presentation for the paper:
Manolis Stamatogiannakis, Paul Groth and Herbert Bos. Decoupling Provenance Capture and Analysis from Execution
Presented at Theory and Practice of Provenance 2015 (TaPP'15)
http://workshops.inf.ed.ac.uk/tapp2015/
Life as a GlusterFS Consultant with Ivan RossiGluster.org
This document describes the experiences of Ivan Rossi working as a Gluster consultant listed on gluster.org. It outlines the types of clients that contact him, including small businesses and those looking for help troubleshooting Gluster issues. It also shares some case studies, such as helping a company that had millions of unorganized files slowing down operations and advising businesses on using Gluster in private and public clouds. The document aims to convey what it is like to be a Gluster consultant and provide advice based on lessons learned from previous work.
IEEE Paper Presentation by Chandan KumarChandan Kumar
This document proposes using time-series forecasting techniques to predict server load in cloud data centers. This would allow for detecting overloaded hosts and migrating virtual machines (VMs) to balance load and reduce energy consumption. Key steps include using exponential smoothing to predict future loads, detecting overloaded hosts when loads exceed thresholds, selecting the least utilized VM to migrate, and choosing destination hosts with minimum increased energy. Simulation results show the proposed Smoothed Exponential Smoothing technique reduces energy consumption, number of overloaded nodes, VM migrations, and SLA violations compared to other algorithms.
Apache Solr as a compressed, scalable, and high performance time series databaseFlorian Lautenschlager
This document summarizes Florian Lautenschlager's presentation at FOSDEM 2015 about using Apache Solr as a scalable time series database. The presentation discusses how to efficiently store billions of time-correlated data objects using data compression techniques and metadata in Solr documents. This allows fast retrieval of data points within milliseconds while using only 37GB of disk space for 68 billion objects. The document also outlines Solr's query capabilities and how custom functions can perform server-side decompression and aggregation for efficient querying of time series data stored in Solr.
The talk presents fomr of the nic.at R&D work in the fields of Encrypted DNS. The main topics are standardization efforts (and reasoning) about EDNS(0) padding, and a cost estimation of DNS over TLS.
This talk was giving in July 2017 at JSCA, an event of the .fr ccTLD operator AFNIC:
DSD-INT 2017 The use of big data for dredging - De BoerDeltares
Presentation by Gerben de Boer (van Oord) at the Symposium Earth Observation and Data Science, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.
Chronix is a time series database that can efficiently store billions of time series data points in a small amount of disk space and retrieve data within milliseconds. It works by splitting time series into fixed-size chunks, compressing the chunks, and storing the compressed chunks and associated metadata in Solr/Lucene records. Chronix provides common time series aggregations, transformations, and analyses through its API. The developers tuned Chronix's performance by evaluating different compression techniques and chunk sizes on real-world datasets. Chronix outperformed other time series databases in storage needs, query speed, and memory usage in their tests.
This document discusses data management in cloud platforms. It begins with an introduction to cloud computing, noting its benefits like reduced costs and ability to scale resources. It then covers cloud characteristics like elasticity and security risks. The document discusses data analysis challenges in clouds like fault tolerance and heterogeneous environments. It focuses on data replication techniques between master and slave nodes and different replication types. It also covers master-slave election processes to select a primary node and considers factors like priority, network partitions, and quick detection of a failed primary.
FPGAs are proposed as a solution to speed up DNA variation identification while lowering power consumption compared to GPUs. FPGAs can handle both the large amounts of genomic data involved in analysis as well as the sequential and parallel processes like Hidden Markov Models 6 times faster than GPUs. FPGAs are also more power efficient than GPUs for algorithms like k-Nearest Neighbors that are 8 times less power consuming on FPGAs. Based on these factors, FPGAs are concluded to be the best solution for algorithms like XHMM and CLAMMS used in genomics analysis.
Load Balancing In Cloud Computing newpptUtshab Saha
The document discusses various load balancing algorithms for cloud computing including round robin, first come first serve (FCFS), and simulated annealing. It provides implementations of each algorithm in CloudSim and compares the results. Round robin and FCFS showed similar overall response times, data center processing times, and maximum/minimum values. Simulated annealing had slightly lower average overall response time. The document proposes using a genetic algorithm for host-side optimization to select the best host for virtual machine requests.
cStor is a resilient storage engine built with proven building blocks of storage components. The storage block layer is derived from the user space ZFS inherited from the proven OpenSolaris stack. The volumes can be accessed via iSCSI Target which is derived from Linux to BSD port. Both of these core components of cStor have been field tested at thousands of installations for many years.
How Teads scale with Apache Cassandra.
Internet scale means tons of data, read heavy workload, massive data ingestion and low latency.
The French AdTech company Teads uses Cassandra massively, a reliable and performant Open Source database.
Spawning Cassandra nodes in AWS is a piece of cake with Terraform and Chef.
GlusterFS is an open-source clustered file system that aggregates disk resources from multiple servers into a single global namespace. It scales to several petabytes and thousands of clients. GlusterFS clusters storage over RDMA or TCP/IP, managing data through a unified namespace. Its stackable userspace design delivers high performance for diverse workloads.
OSDC 2013 | Neues in DRBD9 by Philipp ReisnerNETWAYS
DRBD is a block based replication solution, known and available for years under Linux. DRBD allows the implementation of high available systems without SAN. Further use cases are the implementation of storage heads for IP based SANs and long-distance replications over the internet.
At present DRBD has a general limitation on two nodes. With DRBD 9 a new release (beta) is under way, allowing for the first time a replication up to 30 nodes.
With these features many application possibilities open up for cluster file systems, storage for virtual machines “cloud storage”, and much more.
As service providers and primary code contributors in the Islandora Community, discoverygarden encounters customers who are ingesting, accessing, and storing high volumes of data. For example, a customer who had 150,000 objects in 2012 now has three million objects and expectations to grow to five million in the very short term. This is increasingly common.
As repositories grow in size they can encounter poor performance, particularly during large ingests and derivative generation. To accommodate growing repositories caching mechanisms, infrastructure changes, and code updates are necessary.
The presentation will explore customer case studies that demonstrate interim solutions and the extensive, ongoing research and development to find long-term solutions.
Provenance for Data Munging EnvironmentsPaul Groth
Data munging is a crucial task across domains ranging from drug discovery and policy studies to data science. Indeed, it has been reported that data munging accounts for 60% of the time spent in data analysis. Because data munging involves a wide variety of tasks using data from multiple sources, it often becomes difficult to understand how a cleaned dataset was actually produced (i.e. its provenance). In this talk, I discuss our recent work on tracking data provenance within desktop systems, which addresses problems of efficient and fine grained capture. I also describe our work on scalable provence tracking within a triple store/graph database that supports messy web data. Finally, I briefly touch on whether we will move from adhoc data munging approaches to more declarative knowledge representation languages such as Probabilistic Soft Logic.
Presented at Information Sciences Institute - August 13, 2015
A Fast and Efficient Time Series Storage Based on Apache SolrQAware GmbH
OSDC 2016, Berlin: Talk by Florian Lautenschlager (@flolaut, Senior Software Engineer at QAware)
Abstract: How to store billions of time series points and access them within a few milliseconds? Chronix! Chronix is a young but mature open source project that allows one for example to store about 15 GB (csv) of time series in 238 MB with average query times of 21 ms. Chronix is built on top of Apache Solr a bulletproof distributed NoSQL database with impressive search capabilities. In this code-intense session we show how Chronix achieves its efficiency in both respects by means of an ideal chunking, by selecting the best compression technique, by enhancing the stored data with (pre-computed) attributes, and by specialized query functions.
As enterprises adopt cloud native infrastructure to run their applications, data security and compliance is becoming a crucial area of interest. When you run your containers in a public cloud, you want to make sure that the data being accessed is secure and that there are no bread crumbs left behind once the container exits. A common mistake many people make is to host-mount a volume directly inside a container, which leaves the container's data behind (directly on the host.)
In this session, we focus on the best practices for ensuring the security and compliance of your applications’ persistent volumes. But ensuring security is an on-going exercise. Ideally you would deploy intelligent software that can constantly monitor and audit the application environment for security holes and breaches.
Autopilot is an automated application runtime management engine built for Kubernetes, and is an open source project sponsored by Portworx: https://github.com/libopenstorage/autopilot
Presented by Gunjan Patel, Gou Rao, and Aditya Dani, January 2019. More details here: https://www.meetup.com/openstack/events/258284618/
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)Deeksha Arya
The document proposes a multi-cloud data hosting scheme called CHARM that aims to store data across multiple clouds in a cost-efficient manner while maintaining high availability. CHARM uses both replication and erasure coding to redundantly store data blocks. It selects appropriate clouds and redundancy strategies to minimize monetary costs based on clouds' heterogeneous pricing policies and guarantee data availability. CHARM also rebalances data distribution in response to changes in data access patterns and cloud pricing.
Presentation for the St.Louis IOT meetup on building a modern data architecture tailored to handle IOT data. The focus of the presentation is the serving layer featuring Apache Cassandra.
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...Data Con LA
While the last few years have seen great advancements in computing paradigms for big data stores, there remains one critical bottleneck in this architecture - the ingestion process. Instead of immediate insights into the data, a poor ingestion process can cause headaches and problems to no end. On the other hand, a well-designed ingestion infrastructure should give you real-time visibility into how your systems are functioning at any given time. This can significantly increase the overall effectiveness of your ad-campaigns, fraud-detection systems, preventive-maintenance systems, or other critical applications underpinning your business.
In this session we will explore various modes of ingest including pipelining, pub-sub, and micro-batching, and identify the use-cases where these can be applied. We will present this in the context of open source frameworks such as Apache Flume, Kafka, among others that can be used to build related solutions. We will also present when and how to use multiple modes and frameworks together to form hybrid solutions that can address non-trivial ingest requirements with little or no extra overhead. Through this discussion we will drill-down into details of configuration and sizing for these frameworks to ensure optimal operations and utilization for long-running deployments.
Deduplication is a technique that eliminates redundant data by storing only a single copy of identical files or blocks to reduce storage needs. It can provide major savings, especially in backup environments where it saves more than 90% of storage space in common scenarios. There are two main deduplication strategies - file level deduplication stores a single copy of each file, while block level deduplication divides files into blocks and stores only unique blocks. Deduplication can occur at different locations like the client or server, and can be done in-line during data transfer or later in batches, with different throughput impacts. It is widely used in backup systems and disaster recovery due to its storage-saving nature.
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block AlgorithmIJERA Editor
In cloud computing, data generated in electronic form are large in amount. To maintain this data efficiently, there is a necessity of data recovery services. To cater this, we propose a smart remote data backup algorithm, Seed Block Algorithm. The objective of proposed algorithm is twofold; first it help the users to collect information from any remote location in the absence of network connectivity and second to recover the files in case of the file deletion or if the cloud gets destroyed due to any reason. The time related issues are also being solved by proposed seed block algorithm such that it will take minimum time for the recovery process. Proposed seed block algorithm also focuses on the security concept for the back-up files stored at remote server, without using any of the existing encryption techniques.
The document provides an overview of parallel computing concepts including:
1) Implicit parallelism in microprocessor architectures has led to techniques like pipelining and superscalar execution to better utilize increasing transistor budgets, though dependencies limit parallelism.
2) Memory latency and bandwidth bottlenecks have shifted performance limitations to the memory system, though caches can improve effective latency through higher hit rates.
3) Communication costs, including startup time, per-hop latency, and per-word transfer time, are a major overhead in parallel programs that use techniques like message passing, packet routing, and cut-through routing to reduce communication costs.
Apache Solr as a compressed, scalable, and high performance time series databaseFlorian Lautenschlager
This document summarizes Florian Lautenschlager's presentation at FOSDEM 2015 about using Apache Solr as a scalable time series database. The presentation discusses how to efficiently store billions of time-correlated data objects using data compression techniques and metadata in Solr documents. This allows fast retrieval of data points within milliseconds while using only 37GB of disk space for 68 billion objects. The document also outlines Solr's query capabilities and how custom functions can perform server-side decompression and aggregation for efficient querying of time series data stored in Solr.
The talk presents fomr of the nic.at R&D work in the fields of Encrypted DNS. The main topics are standardization efforts (and reasoning) about EDNS(0) padding, and a cost estimation of DNS over TLS.
This talk was giving in July 2017 at JSCA, an event of the .fr ccTLD operator AFNIC:
DSD-INT 2017 The use of big data for dredging - De BoerDeltares
Presentation by Gerben de Boer (van Oord) at the Symposium Earth Observation and Data Science, during Delft Software Days - Edition 2017. Thursday, 2 November 2017, Delft.
Chronix is a time series database that can efficiently store billions of time series data points in a small amount of disk space and retrieve data within milliseconds. It works by splitting time series into fixed-size chunks, compressing the chunks, and storing the compressed chunks and associated metadata in Solr/Lucene records. Chronix provides common time series aggregations, transformations, and analyses through its API. The developers tuned Chronix's performance by evaluating different compression techniques and chunk sizes on real-world datasets. Chronix outperformed other time series databases in storage needs, query speed, and memory usage in their tests.
This document discusses data management in cloud platforms. It begins with an introduction to cloud computing, noting its benefits like reduced costs and ability to scale resources. It then covers cloud characteristics like elasticity and security risks. The document discusses data analysis challenges in clouds like fault tolerance and heterogeneous environments. It focuses on data replication techniques between master and slave nodes and different replication types. It also covers master-slave election processes to select a primary node and considers factors like priority, network partitions, and quick detection of a failed primary.
FPGAs are proposed as a solution to speed up DNA variation identification while lowering power consumption compared to GPUs. FPGAs can handle both the large amounts of genomic data involved in analysis as well as the sequential and parallel processes like Hidden Markov Models 6 times faster than GPUs. FPGAs are also more power efficient than GPUs for algorithms like k-Nearest Neighbors that are 8 times less power consuming on FPGAs. Based on these factors, FPGAs are concluded to be the best solution for algorithms like XHMM and CLAMMS used in genomics analysis.
Load Balancing In Cloud Computing newpptUtshab Saha
The document discusses various load balancing algorithms for cloud computing including round robin, first come first serve (FCFS), and simulated annealing. It provides implementations of each algorithm in CloudSim and compares the results. Round robin and FCFS showed similar overall response times, data center processing times, and maximum/minimum values. Simulated annealing had slightly lower average overall response time. The document proposes using a genetic algorithm for host-side optimization to select the best host for virtual machine requests.
cStor is a resilient storage engine built with proven building blocks of storage components. The storage block layer is derived from the user space ZFS inherited from the proven OpenSolaris stack. The volumes can be accessed via iSCSI Target which is derived from Linux to BSD port. Both of these core components of cStor have been field tested at thousands of installations for many years.
How Teads scale with Apache Cassandra.
Internet scale means tons of data, read heavy workload, massive data ingestion and low latency.
The French AdTech company Teads uses Cassandra massively, a reliable and performant Open Source database.
Spawning Cassandra nodes in AWS is a piece of cake with Terraform and Chef.
GlusterFS is an open-source clustered file system that aggregates disk resources from multiple servers into a single global namespace. It scales to several petabytes and thousands of clients. GlusterFS clusters storage over RDMA or TCP/IP, managing data through a unified namespace. Its stackable userspace design delivers high performance for diverse workloads.
OSDC 2013 | Neues in DRBD9 by Philipp ReisnerNETWAYS
DRBD is a block based replication solution, known and available for years under Linux. DRBD allows the implementation of high available systems without SAN. Further use cases are the implementation of storage heads for IP based SANs and long-distance replications over the internet.
At present DRBD has a general limitation on two nodes. With DRBD 9 a new release (beta) is under way, allowing for the first time a replication up to 30 nodes.
With these features many application possibilities open up for cluster file systems, storage for virtual machines “cloud storage”, and much more.
As service providers and primary code contributors in the Islandora Community, discoverygarden encounters customers who are ingesting, accessing, and storing high volumes of data. For example, a customer who had 150,000 objects in 2012 now has three million objects and expectations to grow to five million in the very short term. This is increasingly common.
As repositories grow in size they can encounter poor performance, particularly during large ingests and derivative generation. To accommodate growing repositories caching mechanisms, infrastructure changes, and code updates are necessary.
The presentation will explore customer case studies that demonstrate interim solutions and the extensive, ongoing research and development to find long-term solutions.
Provenance for Data Munging EnvironmentsPaul Groth
Data munging is a crucial task across domains ranging from drug discovery and policy studies to data science. Indeed, it has been reported that data munging accounts for 60% of the time spent in data analysis. Because data munging involves a wide variety of tasks using data from multiple sources, it often becomes difficult to understand how a cleaned dataset was actually produced (i.e. its provenance). In this talk, I discuss our recent work on tracking data provenance within desktop systems, which addresses problems of efficient and fine grained capture. I also describe our work on scalable provence tracking within a triple store/graph database that supports messy web data. Finally, I briefly touch on whether we will move from adhoc data munging approaches to more declarative knowledge representation languages such as Probabilistic Soft Logic.
Presented at Information Sciences Institute - August 13, 2015
A Fast and Efficient Time Series Storage Based on Apache SolrQAware GmbH
OSDC 2016, Berlin: Talk by Florian Lautenschlager (@flolaut, Senior Software Engineer at QAware)
Abstract: How to store billions of time series points and access them within a few milliseconds? Chronix! Chronix is a young but mature open source project that allows one for example to store about 15 GB (csv) of time series in 238 MB with average query times of 21 ms. Chronix is built on top of Apache Solr a bulletproof distributed NoSQL database with impressive search capabilities. In this code-intense session we show how Chronix achieves its efficiency in both respects by means of an ideal chunking, by selecting the best compression technique, by enhancing the stored data with (pre-computed) attributes, and by specialized query functions.
As enterprises adopt cloud native infrastructure to run their applications, data security and compliance is becoming a crucial area of interest. When you run your containers in a public cloud, you want to make sure that the data being accessed is secure and that there are no bread crumbs left behind once the container exits. A common mistake many people make is to host-mount a volume directly inside a container, which leaves the container's data behind (directly on the host.)
In this session, we focus on the best practices for ensuring the security and compliance of your applications’ persistent volumes. But ensuring security is an on-going exercise. Ideally you would deploy intelligent software that can constantly monitor and audit the application environment for security holes and breaches.
Autopilot is an automated application runtime management engine built for Kubernetes, and is an open source project sponsored by Portworx: https://github.com/libopenstorage/autopilot
Presented by Gunjan Patel, Gou Rao, and Aditya Dani, January 2019. More details here: https://www.meetup.com/openstack/events/258284618/
CHARM(A Cost-Efficient Multi-Cloud Data Hosting Scheme with High Availability)Deeksha Arya
The document proposes a multi-cloud data hosting scheme called CHARM that aims to store data across multiple clouds in a cost-efficient manner while maintaining high availability. CHARM uses both replication and erasure coding to redundantly store data blocks. It selects appropriate clouds and redundancy strategies to minimize monetary costs based on clouds' heterogeneous pricing policies and guarantee data availability. CHARM also rebalances data distribution in response to changes in data access patterns and cloud pricing.
Presentation for the St.Louis IOT meetup on building a modern data architecture tailored to handle IOT data. The focus of the presentation is the serving layer featuring Apache Cassandra.
Big Data Day LA 2015 - Always-on Ingestion for Data at Scale by Arvind Prabha...Data Con LA
While the last few years have seen great advancements in computing paradigms for big data stores, there remains one critical bottleneck in this architecture - the ingestion process. Instead of immediate insights into the data, a poor ingestion process can cause headaches and problems to no end. On the other hand, a well-designed ingestion infrastructure should give you real-time visibility into how your systems are functioning at any given time. This can significantly increase the overall effectiveness of your ad-campaigns, fraud-detection systems, preventive-maintenance systems, or other critical applications underpinning your business.
In this session we will explore various modes of ingest including pipelining, pub-sub, and micro-batching, and identify the use-cases where these can be applied. We will present this in the context of open source frameworks such as Apache Flume, Kafka, among others that can be used to build related solutions. We will also present when and how to use multiple modes and frameworks together to form hybrid solutions that can address non-trivial ingest requirements with little or no extra overhead. Through this discussion we will drill-down into details of configuration and sizing for these frameworks to ensure optimal operations and utilization for long-running deployments.
Deduplication is a technique that eliminates redundant data by storing only a single copy of identical files or blocks to reduce storage needs. It can provide major savings, especially in backup environments where it saves more than 90% of storage space in common scenarios. There are two main deduplication strategies - file level deduplication stores a single copy of each file, while block level deduplication divides files into blocks and stores only unique blocks. Deduplication can occur at different locations like the client or server, and can be done in-line during data transfer or later in batches, with different throughput impacts. It is widely used in backup systems and disaster recovery due to its storage-saving nature.
Data Back-Up and Recovery Techniques for Cloud Server Using Seed Block AlgorithmIJERA Editor
In cloud computing, data generated in electronic form are large in amount. To maintain this data efficiently, there is a necessity of data recovery services. To cater this, we propose a smart remote data backup algorithm, Seed Block Algorithm. The objective of proposed algorithm is twofold; first it help the users to collect information from any remote location in the absence of network connectivity and second to recover the files in case of the file deletion or if the cloud gets destroyed due to any reason. The time related issues are also being solved by proposed seed block algorithm such that it will take minimum time for the recovery process. Proposed seed block algorithm also focuses on the security concept for the back-up files stored at remote server, without using any of the existing encryption techniques.
The document provides an overview of parallel computing concepts including:
1) Implicit parallelism in microprocessor architectures has led to techniques like pipelining and superscalar execution to better utilize increasing transistor budgets, though dependencies limit parallelism.
2) Memory latency and bandwidth bottlenecks have shifted performance limitations to the memory system, though caches can improve effective latency through higher hit rates.
3) Communication costs, including startup time, per-hop latency, and per-word transfer time, are a major overhead in parallel programs that use techniques like message passing, packet routing, and cut-through routing to reduce communication costs.
The document discusses cloud computing and how to properly size cloud resources for applications. It defines cloud computing as on-demand access to configurable computing resources over a network. Key aspects are ubiquitous access, rapid provisioning and release of resources with minimal management, and sharing resources in a configurable pool. Properly sizing resources involves understanding both the supply of offerings from cloud providers as well as the demand requirements of the specific application. Factors like CPU, storage, memory, and network needs must be assessed based on the application's usage patterns to ensure optimal performance.
Towards the extinction of mega data centres? To which extent should the Clou...Thierry Coupaye
Keynote by Thierry Coupaye at the IEEE International Conference on Cloud Networking, Niagara Falls, Canada, October 2015.
Summary: Cloud computing emerged, a decade or so ago, from underused computing and storage ressources in Internet players mega data centres that were thought to be provided "as a service". As a result of this inception, Cloud is often considered as a synonym for massive data center, which somehow fuels a very centralised vision of (cloud) computing and storage provision. However, we might be at a time in which the pendulum begins to swing back. Indeed, several initiatives are emerging around a vision of more geographically distributed clouds where computing and storage resources are made available at the edge of the network, close to users, in complement or replacement of massive remote data centres. This presentation discusses, through some examples, the evolution of cloud architectures towards more distribution, the signs and stakes of these mutations.
This document discusses cloud computing concepts including origins, definitions, characteristics, deployment models, service models, virtualization benefits, and economic principles. Specifically:
- Cloud computing originated from telecommunications networks and focuses on abstracting internal components while defining overall architecture and interfaces.
- Key characteristics include on-demand access to pooled resources over the network, rapid elasticity, and metered usage-based billing.
- Virtualization provides abstraction between hardware and software for location-independent and flexible resource allocation.
- Economic principles demonstrate how cloud computing can reduce costs compared to traditional fixed infrastructure models through pay-per-use pricing and scaling to fluctuating demand.
Cloud Native Microservices - Building Blocks for Digital InnovationDiego Pacheco
This document discusses cloud-native microservices and their role in digital innovation. It notes that microservices allow for increased speed, availability, and the ability to quickly rollback changes through abstraction, decoupling, and isolation. This enables shorter lead times, reduced dependencies on people, and self-service capabilities. Cloud-native microservices can further reduce costs and increase portability by avoiding vendor lock-in. The document also outlines new practices like latency protection, automated canary analysis, and chaos testing that are needed to support cloud-native microservices at scale.
Harnessing the cloud for securely outsourcing large scale systems of linear e...JPINFOTECH JAYAPRAKASH
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A novel approach to prevent cache based side-channel attack in the cloud (1)
1. A Novel Approach to Prevent Cache-Based Side-Channel
Attack in the Cloud
Writtenby MuhammedSadiqueUK*, DivyaJamesin 2016
2. AGENDA
1. Cloud and side channels
2. Side channel attacks
3. Existing sol vs. proposed sol.
4. Decision algorithm
5. Conclusion
3. BACKGROUND
❏ THE CLOUD MODEL
❏ SIDE CHANNEL
❏ SIDE CHANNEL ATTACK
❏ CACHE BASED SIDE CHANNEL ATTACk
4. The Cloud Model
❏ Resources for more than one client
❏ Hidden details of infrastructure
❏ Always on
❏ Pay per use
❏ Servers accessed remotely
❏ Example : Amazon web services, Google cloud, Microsoft Azure
5. Side-Channel
❏ A mode of bypassing virtual machine for gaining information from the physical
implementation rather than brute force or theoretical weaknesses in the algorithm
6. Side-Channel Attack
❏ A side channel attack is any attack based on information gained from the
implementation of a computer system, rather than a weakness in the
implemented algorithm itself.
❏ The things which can be exploited in side channel attack can be timing information,
power consumption, electromagnetic leaks or even sound as all of these can
provide an extra source of information.
7. How secure is your cache against side-
channel attacks?
❏ caches are essential for the performance of modern computers
❏ Security-critical data can leak through very unexpected side channels, making side-
channel attacks very dangerous threats
8. Cache-Based Side-Channel Attack
❏ Cache side channel attacks are basically attacks based on attackers ability to
monitor cache accesses made by the victim in a shared physical system asi in
virtualized environment or a type of cloud service
❏ AIM: Extract Information
❏ Source : leakage
❏ Procedure : convert leakage into information
❏ Types: sequential and parallel
9. Purpose of the paper
❏ cache-based side-channels in a cloud environment
❏ sequential type of side channel attack.
❏ There are several server-side defences inpace to handle cache-based side
channels.
❏ Ex. cache flushing - Make cache useless
❏ prevent the side-channel’s occurrence - an algorithm designed to implement the
technique.
❏ Minimalistic fashion to help minimize resulting overhead
11. Currentscenario
❏ Focusses on flushing the cache
❏ Reduces usefulness of the cache
❏ Increased cost due to flushing the cache
Solution
❏ Focuses on disabling the difference in access time
❏ Includes two new functions in hypervisor: wait function, Algorithm
❏ Prevents time information parameter leakage in the cache of the cloud
❏ Usefulness of cache, decrease the cost and prevent the data loss
12. Cache-Wait
❏ If the time taken for the cache miss is greater than the cache hit, Cache-Wait operates.
❏ Cache-Wait will hold the cache execution process for the specific time.
❏ The specific time is determined from the difference in the accessing time required for
fetching data from the main memory and the cache memory. That is, the difference in
accessing time required between cache miss and cache hit.
❏ In general, a wait would only be necessary before the Probe step
16. Conclusion
❏ cloud’s architecture is particularly susceptible to cache-based side-channel attacks.
❏ interfering with the cloud model is necessary (
❏ sequential side-channels are taken care by their solution
❏ Focus cache-based side-channels in the Cloud and does not interfere with the
Cloud model
❏ The time information parameter leakage
❏ Efficient algorithm proposed
17. Our Opinion
❏ Great job in reducing cost when
❏ Future plan is to implement this approach in real- time environment and in the
Docker
❏ Amount of flush function execution is much more when there are five or more
virtual machines.
❏ Parallel cache-based side channel attacks or hardware based side channel attacks
are still large area to focus in security terms.
Editor's Notes
AIM: preventing the time information parameter leakage in the cache in the cloud without affecting the cache functionality