1. Presenter: Zhengyu Yang
Advisor: Ningfang Mi
Presenter: Zhengyu Yang
Electrical and Computer Engineering Department
Northeastern University

2. Data Creation Percentage
Data Capacity
Catastrophe!
* IBM and SINTEF ICT
Data Creation Percentage
Available Storage Inventory
92%
(2016~2017)
Data creation is exploding.
92% of the world’s data was created in the last two years alone.
At the current rate, the world’s data storage capacity will be overtaken by this spring.
If we do nothing, a data capacity catastrophe is no longer a joke.

3. Data Access People can’t wait!
Patient Time
People can’t wait!
Baby Boomers
~ 30 sec
Millennials
~ 5 sec
Generation Z
~ 1 sec
Generation X
~ 15 sec
Generation ? ?
Besides, capacity, people also request their files can be downloaded nighening-fast everywhere, anytime, any devices.
Researchers found that people’s patient time are different across generations.
Generation Z, the iphone generation can no longer tolerate larger than 1 sec responses.
How about the future? VR, even 1 sec is too much.
So we conclude that people can’t wait!

4. Backend Infrastructure
Framework for Applications in Big Data Era (2017)
User
Web
Virtualized Servers
It’s all about resource management!
SQL/NoSQL
Database
Real Time Batch
Streaming
Data Process
Engine
Machine Learning Analytics
Delay
Delay
Delay
Delay
Let’s take a look at the backend infrastructure of cloud computing and see what is the main bottleneck.
User applications send requests through the cloud, and the datacenter has thousands of virtualized servers hosting the backend programs to serve user requests.
Inside these VMs, data process engines needs to talk with NoSQL databases, machine learning apps, and even real time batch streaming apps.
All these apps triggers huge amount of I/Os and I/O in fact is the bottleneck of the cloud computing, and solving that can shorten the waiting time.

5. 4
Research Focus
Framework for Applications in Big Data Era (2017)
To investigate a new resource management layer between diverse applications and heterogeneous servers in a large scale cluster system.
In such a cluster system, a large variety of applications are running.: data processing or image processing applications, web applications… Different applications often show different features of their workloads: CPU-intensive, I/O intensive,… or light and stable traffic load or heavy load with high variance across time… for example, we monitored HP servers and backupservers and found very clearly usage patterns of these servers. E.g., a big spike can be found during morning for servers while the traffic load significantly increases during midnight for backup servers。
On the other hand, the backend servers are not homogeneous any more. Hterogeneous hardware platforms are found in large scale data centers because there are a large number of hardware architectures in terms of specific speeds and capacities of the processor, memory, storage and networking subsystems.
Maintaining such a large system with high efficiency and QoS at low cost is an inherently difficult problem. Co-scheduling a large set of applications can incur severe resource contention. Simultaneously launching jobs from different applications during a short time period can immediately cause a significant burst, which further aggravates resource competition and load unbalancing in data centers. This motivates us to develop new techniques for capacity planning and resource management of such cluster systems to improve system performance and resource utilization and provide high QoS, especially under temporal dependent workloads.
A large data center typically hosts tens of thousands of applications with diverse workloads each day. These applications need different performance management solutions to meet their varying resource and performance requirements

6. Efficiency Improvement for Data Processing Platforms5
Framework for Applications in Big Data Era (2017)
LsPS - Job size based scheduler
HaSTE - YARN scheduler
TuMM – Slef-adjusting slot configuration
OpRM - Idleness management
AutoPath – Spark scheduler
Scheduler
sCache– Spark RDD caching
Resource
Management 5 5

7. Flash-based Storage Management
Framework for Applications in Big Data Era (2017)
[1] SSD-HDD Caching
[2] All-Flash Tiering
[3] Deduplication
[4] Reliability
GREM
AutoTiering
ElasticDedup
AutoReplica
[5] Datacenter Cost
[6] I/O Stack
[7] Compute vs. Cache
[8] Docker vs. VM
minTCO
H-NVMe
SparkCache
DockerVMSpark
1st gen datacenter is all-HDD. Since 2008, SSD is used as cache for SSD-HDD hybrid datacenter.
With the SSD price decreasing and SSD capacity increasing, All-Flash datacenter comes earlier than pp’s expectation. For example, TLC SSD replace HDD, MLC SSD replace TLC SSD and NVMe SSD top end for high performance.
Is SSD always good?
The answer is no.
First focus on hybrid, for performance, we present GREM, a SSD dynamic partition solution to share SSD for multiple VMs.
For reliability, we present AT, replication manager for hybrid cluster to recover from disasters.

8. Big data era and popular big data platforms:
2018 REU Research Project
Framework for Applications in Big Data Era (2017)
Big data era and popular big data platforms:
1. Hadoop MapReduce: typical two-stage process
2. Spark: DAGs (directed acyclic graph) with multiple processing stages
3. Ex: Iterative machine learning algorithms (K-means)
4. Complex dependency and memory access for intermediate data
Project goals:
1. Construct a distributed, virtualized environment for extensively running a variety of data processing applications
2. Deploy emerging storage (e.g., NVMe) devices to accelerate in-memory processing required by Spark-based applications
3. Understand Spark I/O access patterns and the interference between Spark scheduling and memory management
4. Develop new resource management solutions for improving efficiency of Spark-based applications

9. NUCSRL Lab Members Current Ph.D and M.S. Students
Framework for Applications in Big Data Era (2017)
Current Ph.D and M.S. Students
Graduated Ph.D. Students
(VMware)
(Uber)
Graduated M.Sc. Students
(Motorola)
(EMC)
(Acer)
(Virgin
HealthMiles)
(Microsoft)
(Seagate)
(Amazon)
(Amazon)